code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_to... | 699 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 699 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase = {"configuration_plbart": ["PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "PLBartConfig"]}
tr... | 699 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
def __UpperCamelCase (lowerCAmelCase : List[str] ) -> Dict:
A = r'... | 699 | 1 |
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class _UpperCAmelCase :
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = ... | 699 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self : List[Any] , UpperCamelCase__ : Collection[float] | None = None ):
if... | 699 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 699 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.uti... | 699 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
# See all Cvt models at https://huggingfa... | 699 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _UpperCAmelCase ( __lowercase ):
'''simple docstring... | 699 | 1 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
fro... | 699 |
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : int ) -> str:
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1, number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 699 | 1 |
def __UpperCamelCase (lowerCAmelCase : int = 1_000 ) -> int:
A , A = 1, 1
A = 2
while True:
A = 0
A = fa + fa
A , A = fa, f
index += 1
for _ in str(lowerCAmelCase ):
... | 699 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class _UpperCAmelCase ( __lowercase , __lowercase ):
... | 699 | 1 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
_UpperCAmelCase = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be ... | 699 |
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
_UpperCAmelCase = 0b10_11_00_11_11_10_11_00_10_01_00_00_01_11_10_11_10_11_00_01_... | 699 | 1 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
tem... | 699 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"post_extract_proj": "feature_projecti... | 699 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase = {"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}
try:
if not is_torch_available():
raise OptionalDependency... | 699 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
_UpperCAmelCase = TypeVar("T")
class _UpperCAmelCase ( Generic[T] ):
'''simple docstring'''
def __init__( self : Tuple , UpperCamelCase__ : T )... | 699 | 1 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class _UpperCAmelCase ( __lowercase )... | 699 |
from __future__ import annotations
import math
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : int, lowerCAmelCase : bool, lowerCAmelCase : list[int], lowerCAmelCase : float ) -> int:
if depth < 0:
raise ValueError('Depth can... | 699 | 1 |
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=__lowercase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = ['''torch''', '''scipy''']
def __init__( self : int , *UpperCamelCase__ : str , **Upp... | 699 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequenceCl... | 699 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
_UpperCAmelCase = {
"configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETRAINED_CON... | 699 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase = {"configuration_xglm": ["XGLM_PRETRAINED_C... | 699 | 1 |
from __future__ import annotations
def __UpperCamelCase (lowerCAmelCase : int | float | str, lowerCAmelCase : int | float | str ) -> list[str]:
if nth_term == "":
return [""]
A = int(lowerCAmelCase )
A = int(lowerCAmelCase )
A = ... | 699 |
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : int ) -> Optional[int]:
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(lowerCAmelCase, int(b / 2 ) ) * actual_power(lowerCAmelCase, int(b / 2 ) )
else:
r... | 699 | 1 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.u... | 699 |
def __UpperCamelCase (lowerCAmelCase : list[int] ) -> int:
if not numbers:
return 0
if not isinstance(lowerCAmelCase, (list, tuple) ) or not all(
isinstance(lowerCAmelCase, lowerCAmelCase ) for number in numbers ):
raise ValueError('numbers m... | 699 | 1 |
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
_UpperCAmelCase = "\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and Singh, ... | 699 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificat... | 699 | 1 |
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassification,
MobileViTVaForS... | 699 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json",
# See all BioGPT models at https://huggin... | 699 | 1 |
import random
from .binary_exp_mod import bin_exp_mod
def __UpperCamelCase (lowerCAmelCase : Union[str, Any], lowerCAmelCase : Optional[int]=1_000 ) -> List[Any]:
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
... | 699 |
import sys
def __UpperCamelCase (lowerCAmelCase : Dict ) -> Dict:
A = len(lowerCAmelCase )
A = [[0 for x in range(lowerCAmelCase )] for x in range(lowerCAmelCase )]
A = [[0 for x in range(lowerCAmelCase )] for x in range(lowerCAmelCase )]
for chai... | 699 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dime... | 699 |
from math import isqrt
def __UpperCamelCase (lowerCAmelCase : int ) -> bool:
return all(number % divisor != 0 for divisor in range(2, isqrt(lowerCAmelCase ) + 1 ) )
def __UpperCamelCase (lowerCAmelCase : int = 10**6 ) -> int:
A = 0
A = 1
... | 699 | 1 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
_UpperCAmelCase = logging.get_logger(__name__)
_Uppe... | 699 |
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
_UpperCAmelCase = logging.get_logger(__name__)
class _UpperCAmelCase ( __lowercase ):
'''simple docstring'''
def __init__( self : List[str] , *UpperCame... | 699 | 1 |
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
_UpperCAmelCase = 0b10_11_00_11_11_10_11_00_10_01_00_00_01_11_10_11_10_11_00_01_... | 699 |
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class ... | 699 | 1 |
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
_UpperCAmelCase = logging.get_logger(__name__)
class _UpperCAmelCase ( __lowercase ):
'''simple docstring'''
def __init__( self : Any , *UpperCamelCas... | 699 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"vocab_file": "vocab.json",
"tokenizer_config_file": "to... | 699 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"shi-labs/nat-mini-in1k-224": "ht... | 699 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 699 | 1 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
_UpperCAmelCase = datasets.utils.logging.get_logg... | 699 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
def __UpperCamelCase (lowerCAmelCase : List[str] ) -> Dict:
A = r'... | 699 | 1 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
_UpperCAmelCase = logging.get_logger(__name__)
def __UpperCamelCase (lowerCAmelCase : Dict, lowerCAmelCa... | 699 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self : List[Any] , UpperCamelCase__ : Collection[float] | None = None ):
if... | 699 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
Stab... | 699 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.uti... | 699 | 1 |
def __UpperCamelCase (lowerCAmelCase : int ) -> bool:
A = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 699 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _UpperCAmelCase ( __lowercase ):
'''simple docstring... | 699 | 1 |
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class _UpperCAmelCase ( yaml.SafeLoader ):
'''simple docstring'''
def UpperCamelCase ( self : Tuple , UpperCamelCase__ : Optional[int] ):
A ... | 699 |
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : int ) -> str:
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1, number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 699 | 1 |
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def __UpperCamelCase (lowerCAmelCase : Optional[int] ) -> Optional[int]:
return DownloadCommand(args.model, args.cache_dir, args.force, args.trust_remote_code )
class _UpperCAmelCase ( ... | 699 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class _UpperCAmelCase ( __lowercase , __lowercase ):
... | 699 | 1 |
import argparse
import struct
import unittest
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self : Optional[Any] , UpperCamelCase__ : bytes ):
A = data
# Initialize hash values
A = [
0X6a_09e_667,
... | 699 |
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
_UpperCAmelCase = 0b10_11_00_11_11_10_11_00_10_01_00_00_01_11_10_11_10_11_00_01_... | 699 | 1 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHybridC... | 699 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"post_extract_proj": "feature_projecti... | 699 | 1 |
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from ... | 699 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
_UpperCAmelCase = TypeVar("T")
class _UpperCAmelCase ( Generic[T] ):
'''simple docstring'''
def __init__( self : Tuple , UpperCamelCase__ : T )... | 699 | 1 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import ca... | 699 |
from __future__ import annotations
import math
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : int, lowerCAmelCase : bool, lowerCAmelCase : list[int], lowerCAmelCase : float ) -> int:
if depth < 0:
raise ValueError('Depth can... | 699 | 1 |
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class ... | 699 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequenceCl... | 699 | 1 |
from itertools import product
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : int ) -> list[int]:
A = sides_number
A = max_face_number * dice_number
A = [0] * (max_total + 1)
A = 1
A = range(lowerCAmelCase, ... | 699 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase = {"configuration_xglm": ["XGLM_PRETRAINED_C... | 699 | 1 |
def __UpperCamelCase (lowerCAmelCase : str, lowerCAmelCase : list[str] ) -> str:
A = ''
for word_or_phrase in separated:
if not isinstance(lowerCAmelCase, lowerCAmelCase ):
raise Exception('join() accepts only strings to be joined' )
... | 699 |
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : int ) -> Optional[int]:
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(lowerCAmelCase, int(b / 2 ) ) * actual_power(lowerCAmelCase, int(b / 2 ) )
else:
r... | 699 | 1 |
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test... | 699 |
def __UpperCamelCase (lowerCAmelCase : list[int] ) -> int:
if not numbers:
return 0
if not isinstance(lowerCAmelCase, (list, tuple) ) or not all(
isinstance(lowerCAmelCase, lowerCAmelCase ) for number in numbers ):
raise ValueError('numbers m... | 699 | 1 |
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
_UpperCAmelCase = logging.get_logger(__name__)
class _UpperCAmelCase ( __lowercase ):
'''simple docstring'''
def __init__( self : List[str] , *UpperCame... | 699 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificat... | 699 | 1 |
def __UpperCamelCase (lowerCAmelCase : Dict ) -> Dict: # noqa: E741
A = len(lowerCAmelCase )
A = 0
A = [0] * n
A = [False] * n
A = [False] * n
def dfs(lowerCAmelCase : Dict, lowerCAmelCase : str, lowerCAmel... | 699 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json",
# See all BioGPT models at https://huggin... | 699 | 1 |
from __future__ import annotations
import math
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : int, lowerCAmelCase : bool, lowerCAmelCase : list[int], lowerCAmelCase : float ) -> int:
if depth < 0:
raise ValueError('Depth can... | 699 |
import sys
def __UpperCamelCase (lowerCAmelCase : Dict ) -> Dict:
A = len(lowerCAmelCase )
A = [[0 for x in range(lowerCAmelCase )] for x in range(lowerCAmelCase )]
A = [[0 for x in range(lowerCAmelCase )] for x in range(lowerCAmelCase )]
for chai... | 699 | 1 |
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from tr... | 699 |
from math import isqrt
def __UpperCamelCase (lowerCAmelCase : int ) -> bool:
return all(number % divisor != 0 for divisor in range(2, isqrt(lowerCAmelCase ) + 1 ) )
def __UpperCamelCase (lowerCAmelCase : int = 10**6 ) -> int:
A = 0
A = 1
... | 699 | 1 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificat... | 699 |
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
_UpperCAmelCase = logging.get_logger(__name__)
class _UpperCAmelCase ( __lowercase ):
'''simple docstring'''
def __init__( self : List[str] , *UpperCame... | 699 | 1 |
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def __UpperCamelCase () -> None:
print('Making key files...' )
make_key_files('rsa', 1_024 )
print('Key files generation successf... | 699 |
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class ... | 699 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow... | 699 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"vocab_file": "vocab.json",
"tokenizer_config_file": "to... | 699 | 1 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
fro... | 699 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 699 | 1 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
_UpperCAmelCase = importlib.util.find_spec("s3fs") is not None
if _has_safs:
from .safilesystem import SaFileS... | 699 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
def __UpperCamelCase (lowerCAmelCase : List[str] ) -> Dict:
A = r'... | 699 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"sail/p... | 699 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self : List[Any] , UpperCamelCase__ : Collection[float] | None = None ):
if... | 699 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@requi... | 699 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.uti... | 699 | 1 |
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from transformer... | 699 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _UpperCAmelCase ( __lowercase ):
'''simple docstring... | 699 | 1 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _UpperCAmelCase ( __lowercase ):
'''simple docstring... | 699 |
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : int ) -> str:
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1, number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 699 | 1 |
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCamelCase (lowerCAmelCase : Dict, lowerCAmelCase : int, lowerCAmelCase : str ) ... | 699 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class _UpperCAmelCase ( __lowercase , __lowercase ):
... | 699 | 1 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _UpperCAmelCase ( __lowercase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = ['''image_processor''', '''tokenizer''']
SCREAMING... | 699 |
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
_UpperCAmelCase = 0b10_11_00_11_11_10_11_00_10_01_00_00_01_11_10_11_10_11_00_01_... | 699 | 1 |
import pprint
import requests
_UpperCAmelCase = "https://zenquotes.io/api"
def __UpperCamelCase () -> list:
return requests.get(API_ENDPOINT_URL + '/today' ).json()
def __UpperCamelCase () -> list:
return requests.get(API_ENDPOINT_URL + '/random' ).json()
if ... | 699 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"post_extract_proj": "feature_projecti... | 699 | 1 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_t... | 699 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
_UpperCAmelCase = TypeVar("T")
class _UpperCAmelCase ( Generic[T] ):
'''simple docstring'''
def __init__( self : Tuple , UpperCamelCase__ : T )... | 699 | 1 |
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def __UpperCamelCase (lowerCAmelCase : Union[str, Any], lowerCAmelCase : Union[str, Any], lowerCAmelCase : Dict, lowerCAmelCase : Dict ) -> Dict:
A = {
'en': 'Machine lea... | 699 |
from __future__ import annotations
import math
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : int, lowerCAmelCase : bool, lowerCAmelCase : list[int], lowerCAmelCase : float ) -> int:
if depth < 0:
raise ValueError('Depth can... | 699 | 1 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import To... | 699 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequenceCl... | 699 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase = {"configuration_xglm": ["XGLM_PRETRAINED_C... | 699 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase = {"configuration_xglm": ["XGLM_PRETRAINED_C... | 699 | 1 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCamelCase (lowerCAmelCase : Optional[Any], lowerCAmelCase : List[Any], lowerCAmelCase : int ) -... | 699 |
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : int ) -> Optional[int]:
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(lowerCAmelCase, int(b / 2 ) ) * actual_power(lowerCAmelCase, int(b / 2 ) )
else:
r... | 699 | 1 |
def __UpperCamelCase (lowerCAmelCase : list ) -> list:
if len(lowerCAmelCase ) <= 1:
return [tuple(lowerCAmelCase )]
A = []
def generate(lowerCAmelCase : int, lowerCAmelCase : list ):
if k == 1:
res.append(tuple... | 699 |
def __UpperCamelCase (lowerCAmelCase : list[int] ) -> int:
if not numbers:
return 0
if not isinstance(lowerCAmelCase, (list, tuple) ) or not all(
isinstance(lowerCAmelCase, lowerCAmelCase ) for number in numbers ):
raise ValueError('numbers m... | 699 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 699 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificat... | 699 | 1 |
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
_UpperCAmelCase = HfApi()
_UpperCAmelCase = {}
# fmt: off
_UpperCAmelCase = torch.tensor([
-0.7_5_1_5, -1.6_8_8_3, 0.2_4_2_0, 0.0_3_0_0, 0.6_3_4_7, 1.3_4_3_3, -1.1_7_4_3... | 699 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json",
# See all BioGPT models at https://huggin... | 699 | 1 |
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional imp... | 699 |
import sys
def __UpperCamelCase (lowerCAmelCase : Dict ) -> Dict:
A = len(lowerCAmelCase )
A = [[0 for x in range(lowerCAmelCase )] for x in range(lowerCAmelCase )]
A = [[0 for x in range(lowerCAmelCase )] for x in range(lowerCAmelCase )]
for chai... | 699 | 1 |
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse("3.8"):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
_UpperCAmelCase = ""
... | 699 |
from math import isqrt
def __UpperCamelCase (lowerCAmelCase : int ) -> bool:
return all(number % divisor != 0 for divisor in range(2, isqrt(lowerCAmelCase ) + 1 ) )
def __UpperCamelCase (lowerCAmelCase : int = 10**6 ) -> int:
A = 0
A = 1
... | 699 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_UpperCAmelCase = {
"configuration_blip": [
"BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BlipConfig",
... | 699 |
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
_UpperCAmelCase = logging.get_logger(__name__)
class _UpperCAmelCase ( __lowercase ):
'''simple docstring'''
def __init__( self : List[str] , *UpperCame... | 699 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase = {
"configuration_funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig"],
"conv... | 699 |
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class ... | 699 | 1 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPR... | 699 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"vocab_file": "vocab.json",
"tokenizer_config_file": "to... | 699 | 1 |
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
_UpperCAmelCase = logging.getLogger(__name__)
@dataclass
class _UpperCAmelCase ( __lowercase ):
'''simple do... | 699 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 699 | 1 |
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_... | 699 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
def __UpperCamelCase (lowerCAmelCase : List[str] ) -> Dict:
A = r'... | 699 | 1 |
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common... | 699 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self : List[Any] , UpperCamelCase__ : Collection[float] | None = None ):
if... | 699 | 1 |
from __future__ import annotations
from collections.abc import MutableSequence
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self : Any , UpperCamelCase__ : int , UpperCamelCase__ : MutableSequence[float] ):
if len(UpperCamelCase__ ) !=... | 699 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.uti... | 699 | 1 |
def __UpperCamelCase (lowerCAmelCase : str ) -> List[str]:
if not head:
return True
# split the list to two parts
A , A = head.next, head
while fast and fast.next:
A = fast.next.next
A = slow.next
A = ... | 699 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _UpperCAmelCase ( __lowercase ):
'''simple docstring... | 699 | 1 |
import torch
from transformers import AutoModel
class _UpperCAmelCase ( torch.nn.Module ):
'''simple docstring'''
def __init__( self : Union[str, Any] , UpperCamelCase__ : Any="sayef/fsner-bert-base-uncased" ):
super(UpperCamelCase__ , self ).__init__()... | 699 |
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : int ) -> str:
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1, number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 699 | 1 |
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="%(message)s")
def __UpperCamelCase (lowerCAmelCase : np.ndarray ) -> np.ndarray:
return input_array.reshape((input_array.size, 1) )
def __UpperCamelCase ... | 699 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class _UpperCAmelCase ( __lowercase , __lowercase ):
... | 699 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_... | 699 |
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
_UpperCAmelCase = 0b10_11_00_11_11_10_11_00_10_01_00_00_01_11_10_11_10_11_00_01_... | 699 | 1 |
from __future__ import annotations
import math
def __UpperCamelCase (lowerCAmelCase : float, lowerCAmelCase : int ) -> float:
A = u
for i in range(1, lowerCAmelCase ):
A = temp * (u - i)
return temp
def __UpperCamelCase () ->... | 699 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"post_extract_proj": "feature_projecti... | 699 | 1 |
import json
import sys
def __UpperCamelCase (lowerCAmelCase : Union[str, Any], lowerCAmelCase : str ) -> Dict:
with open(lowerCAmelCase, encoding='utf-8' ) as f:
A = json.load(lowerCAmelCase )
A = ['<details>', '<summary>Show updated benchmark... | 699 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
_UpperCAmelCase = TypeVar("T")
class _UpperCAmelCase ( Generic[T] ):
'''simple docstring'''
def __init__( self : Tuple , UpperCamelCase__ : T )... | 699 | 1 |
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoa... | 699 |
from __future__ import annotations
import math
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : int, lowerCAmelCase : bool, lowerCAmelCase : list[int], lowerCAmelCase : float ) -> int:
if depth < 0:
raise ValueError('Depth can... | 699 | 1 |
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
_UpperCAmelCase = Mapping[str, np.ndarray]
_UpperCAmelCase = Mapping[str, Any] # Is a nested dict.
_UpperCAm... | 699 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequenceCl... | 699 | 1 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transf... | 699 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase = {"configuration_xglm": ["XGLM_PRETRAINED_C... | 699 | 1 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_UpperCAm... | 699 |
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : int ) -> Optional[int]:
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(lowerCAmelCase, int(b / 2 ) ) * actual_power(lowerCAmelCase, int(b / 2 ) )
else:
r... | 699 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)... | 0 |
def __UpperCamelCase (lowerCAmelCase : list[int] ) -> int:
if not numbers:
return 0
if not isinstance(lowerCAmelCase, (list, tuple) ) or not all(
isinstance(lowerCAmelCase, lowerCAmelCase ) for number in numbers ):
raise ValueError('numbers m... | 699 | 0 |
from __future__ import annotations
class __lowerCamelCase :
def __init__( self: Optional[int],A_: str,A_: str ):
'''simple docstring'''
__UpperCamelCase, __UpperCamelCase = text, pattern
__UpperCamelCase, __Up... | 1 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificat... | 699 | 0 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def SCREAMING_SNAKE_CASE_ ( ) -> Tuple:
_A = 9
_A = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
... | 2 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json",
# See all BioGPT models at https://huggin... | 699 | 0 |
'''simple docstring'''
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('Googling.....')
lowerCAmelCase : List[Any] = 'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
... | 3 |
import sys
def __UpperCamelCase (lowerCAmelCase : Dict ) -> Dict:
A = len(lowerCAmelCase )
A = [[0 for x in range(lowerCAmelCase )] for x in range(lowerCAmelCase )]
A = [[0 for x in range(lowerCAmelCase )] for x in range(lowerCAmelCase )]
for chai... | 699 | 0 |
"""simple docstring"""
class a :
def __init__( self , _snake_case ):
"""simple docstring"""
lowerCAmelCase = size
lowerCAmelCase = [0] * size
lowerCAmelCase = [0] * size
@staticmethod
def UpperCamelCase__ ( _snake_case ):
... | 4 |
from math import isqrt
def __UpperCamelCase (lowerCAmelCase : int ) -> bool:
return all(number % divisor != 0 for divisor in range(2, isqrt(lowerCAmelCase ) + 1 ) )
def __UpperCamelCase (lowerCAmelCase : int = 10**6 ) -> int:
A = 0
A = 1
... | 699 | 0 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@staticmethod
@abstractmethod
def _lowercase ( _lowercase ):
"""simple do... | 5 |
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
_UpperCAmelCase = logging.get_logger(__name__)
class _UpperCAmelCase ( __lowercase ):
'''simple docstring'''
def __init__( self : List[str] , *UpperCame... | 699 | 0 |
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntimeMode... | 6 |
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class ... | 699 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a = {
'''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''V... | 7 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"vocab_file": "vocab.json",
"tokenizer_config_file": "to... | 699 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( __snake_case : int , __snake_case : int ) -> str:
if number < 0 or shift_amount < 0:
raise ValueError('both inputs must be positive integers' )
__A : Optional[Any] = str(bin(__sn... | 8 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 699 | 0 |
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self : Dict , *... | 9 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
def __UpperCamelCase (lowerCAmelCase : List[str] ) -> Dict:
A = r'... | 699 | 0 |
from __future__ import annotations
import math
_lowerCAmelCase = "2020.9.26"
_lowerCAmelCase = "xcodz-dot, cclaus, dhruvmanila"
def _snake_case ( __snake_case , __snake_case , __snake_case , __snake_case , __snake_case ):
if not all(isinstance(__snake_case , (flo... | 10 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self : List[Any] , UpperCamelCase__ : Collection[float] | None = None ):
if... | 699 | 0 |
'''simple docstring'''
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_av... | 11 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.uti... | 699 | 0 |
def UpperCamelCase ( lowercase_ , lowercase_ ) -> bool:
'''simple docstring'''
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _UpperCAmelCase ( __lowercase ):
'''simple docstring... | 699 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A__ : List[Any] = {
"""configuration_graphormer""": ["""GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GraphormerConfig"""],
}
... | 13 |
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : int ) -> str:
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1, number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 699 | 0 |
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGeneration,... | 14 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class _UpperCAmelCase ( __lowercase , __lowercase ):
... | 699 | 0 |
import doctest
from collections import deque
import numpy as np
class A :
'''simple docstring'''
def __init__(self : List[str] ) -> None:
"""simple docstring"""
lowercase__ = [2, 1, 2, -1]
lowercase__ = ... | 15 |
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
_UpperCAmelCase = 0b10_11_00_11_11_10_11_00_10_01_00_00_01_11_10_11_10_11_00_01_... | 699 | 0 |
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from dataset... | 16 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"post_extract_proj": "feature_projecti... | 699 | 0 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import jax.... | 17 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
_UpperCAmelCase = TypeVar("T")
class _UpperCAmelCase ( Generic[T] ):
'''simple docstring'''
def __init__( self : Tuple , UpperCamelCase__ : T )... | 699 | 0 |
'''simple docstring'''
from collections import namedtuple
_SCREAMING_SNAKE_CASE = namedtuple("from_to", "from_ to")
_SCREAMING_SNAKE_CASE = {
"cubicmeter": from_to(1, 1),
"litre": from_to(0.001, 10_00),
"kilolitre": from_to(1, 1),
"gallon": from_to(0.0_0454, 264.172),
... | 18 |
from __future__ import annotations
import math
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : int, lowerCAmelCase : bool, lowerCAmelCase : list[int], lowerCAmelCase : float ) -> int:
if depth < 0:
raise ValueError('Depth can... | 699 | 0 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = (DDPMScheduler,)
def UpperCAmelCase ( self , **__a) ->... | 19 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequenceCl... | 699 | 0 |
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