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 |
|---|---|---|---|---|
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
_UpperCAmelCase = logging.get_logger(__name__)
class ... | 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 copy import deepcopy
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self : List[str] , UpperCamelCase__ : list[int] | None = None , UpperCamelCase__ : int | None = None ):
if arr is None and size is not None:
A = ... | 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 .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_collato... | 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 typing import Dict
from .base import GenericTensor, Pipeline
class _UpperCAmelCase ( __lowercase ):
'''simple docstring'''
def UpperCamelCase ( self : Any , UpperCamelCase__ : Tuple=None , UpperCamelCase__ : Dict=None , UpperCamelCase__ ... | 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 __future__ import annotations
import pandas as pd
def __UpperCamelCase (lowerCAmelCase : list[int], lowerCAmelCase : list[int], lowerCAmelCase : int ) -> list[int]:
A = [0] * no_of_processes
A = [0] * no_of_processes
# Copy the burst time i... | 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 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 |
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 |
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self : Optional[Any] , UpperCamelCase__ : list[int] ):
A = len(UpperCamelCase__ )
A = [0] * len_array
if len_array > 0:
A = array[0]
for i in ra... | 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 unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, ... | 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 |
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.path import join ... | 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 __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common im... | 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 |
def __UpperCamelCase (lowerCAmelCase : int ) -> list[int]:
if length <= 0 or not isinstance(lowerCAmelCase, lowerCAmelCase ):
raise ValueError('Length must be a positive integer.' )
return [n * (2 * n - 1) for n in range(lowerCAmelCase )]
if __name__ == "__main__":
... | 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 argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
_UpperCAmelCase = logging.getLogger(__name... | 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 argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Acce... | 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 json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def __UpperCamelCase (lowerCA... | 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 gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMS... | 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 argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
_UpperCAmelCase = {
"text_branch": "text_model",
"audio_branch": "audio_model.audio_encoder",
"attn": "attention.self",
"self.proj": "output.dens... | 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 math import factorial
_UpperCAmelCase = {str(digit): factorial(digit) for digit in range(10)}
def __UpperCamelCase (lowerCAmelCase : int ) -> int:
if not isinstance(lowerCAmelCase, lowerCAmelCase ):
raise TypeError('Parameter number must be int' )
... | 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 json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart... | 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 os
def __UpperCamelCase () -> Optional[int]:
A = os.path.join(os.path.dirname(lowerCAmelCase ), 'num.txt' )
with open(lowerCAmelCase ) as file_hand:
return str(sum(int(lowerCAmelCase ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solu... | 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 os
import jsonlines
import numpy as np
from tqdm import tqdm
_UpperCAmelCase = 2_048
_UpperCAmelCase = 4_096
_UpperCAmelCase = 42
_UpperCAmelCase = os.environ.pop("PROCESS_TRAIN", "false")
_UpperCAmelCase = {"null": 0, "short": 1, "l... | 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 |
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noqa: F401
deprecate(
... | 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 Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available(... | 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 |
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self : Tuple ):
A = {}
def UpperCamelCase ( self : List[Any] ):
print(self.vertex )
for i in self.vertex:
print(UpperCamelCase__ , ' -> ' , ... | 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 __future__ import annotations
def __UpperCamelCase (lowerCAmelCase : list, lowerCAmelCase : int, lowerCAmelCase : int, lowerCAmelCase : int ) -> list:
A = []
A , A = input_list[low:mid], input_list[mid : high + 1]
while left... | 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 List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
_UpperCAmelCase = datasets.utils.logging.get_logger(__name__)
class _UpperCAmelCase ( folder_based_builder.FolderBasedBuilderConfig ):
''... | 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 AutoTokenizer, FalconConfig, 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 import Model... | 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 argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
def __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 typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
_UpperCAmelCase = logging.get_logger(__name__)
class _UpperCAmelCase ... | 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 numpy as np
import qiskit
def __UpperCamelCase (lowerCAmelCase : int = 8, lowerCAmelCase : int | None = None ) -> str:
A = np.random.default_rng(seed=lowerCAmelCase )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.... | 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 os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import sha... | 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 collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
_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 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class _UpperCAmelCase ( __lowercase , __lowercase ):
'''simple docstring... | 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 |
# Copyright 2023 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 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 os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert import ... | 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 torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAI... | 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 __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 |
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 math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
_UpperCAmelCase = {
"gwf-440k": {
... | 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 random import random
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self : List[Any] , UpperCamelCase__ : int | None = None ):
A = value
A = random()
A = None
A =... | 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 numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
class _UpperCAmelCase ( __lowercase ):
'''simple docstring'''
... | 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 |
# Copyright 2023 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 |
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 unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
... | 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 typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_t... | 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 os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = "▁"
_UpperCAmelC... | 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 |
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 |
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 unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_UpperCAmelCase = get_tests_dir("fixtures/spiece.mod... | 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 json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _UpperCAmelCase ( __lowercase , unittest.TestCase ):
'''si... | 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 |
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self : List[str] ):
A = 0
A = 0
A = {}
def UpperCamelCase ( self : Any , UpperCamelCase__ : List[Any] ):
if vertex not in self.adj... | 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 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 |
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 json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_t... | 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 .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
require_torch_mi... | 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 : int = 50 ) -> int:
A = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2, 5 ):
for tile_start in range(row_length - tile_length + 1 ):
... | 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
def __UpperCamelCase (lowerCAmelCase : dict, lowerCAmelCase : str ) -> set[str]:
A , A = set(lowerCAmelCase ), [start]
while stack:
A = stack.pop()
explored.add(lowerCAmelCase )
# D... | 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 |
# Copyright 2023 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 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 random
from typing import Any
def __UpperCamelCase (lowerCAmelCase : list ) -> list[Any]:
for _ in range(len(lowerCAmelCase ) ):
A = random.randint(0, len(lowerCAmelCase ) - 1 )
A = random.randint(0, len(lowerCAmelCase ) - 1 )
... | 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 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 |
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 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from ... | 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 time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = R"\n Args:\n input_ids (`torch.LongTensor`... | 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 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkout... | 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 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 |
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 unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class _UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = JukeboxTokenizer
SCREAMING_SNAKE_CASE : Optional[An... | 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 operator as op
_UpperCAmelCase = "scaler.pt"
_UpperCAmelCase = "pytorch_model"
_UpperCAmelCase = "random_states"
_UpperCAmelCase = "optimizer"
_UpperCAmelCase = "scheduler"
_UpperCAmelCase = "pytorch_model.bin"
_UpperCA... | 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 TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase = {
"configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "... | 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 __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def __UpperCamelCase (lowerCAmelCase : str, lowerCAmelCase : float | Decimal, lowerCAmelCase : float = 10**-10 ) -> float:
A = a
while Tru... | 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 unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available(... | 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 typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaToken... | 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 os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import... | 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 collections.abc import Generator
from math import sin
def __UpperCamelCase (lowerCAmelCase : bytes ) -> bytes:
if len(lowerCAmelCase ) != 32:
raise ValueError('Input must be of length 32' )
A = b''
for i in [3, 2, 1, 0]:
little_endian += str... | 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 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 |
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 warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
_UpperCAmelCase = logging.get_logger(__name__)
class _UpperCAmelCase ( __lowercase ):
'''simple docstring'''
def __init__( self : Optional[int] , *Upp... | 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 json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCas... | 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 __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 |
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 unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
_UpperCAmelCase = False
class _UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
... | 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 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 |
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 |
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : int ) -> str:
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
A = str(bin(lowerCAmelCase ) )[2:] # remove the leading "0b"
A = str(bin(lowerCAmelCa... | 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 cva import destroyAllWindows, imread, imshow, waitKey
def __UpperCamelCase (lowerCAmelCase : Optional[int] ) -> Union[str, Any]:
# getting number of pixels in the image
A , A = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
fo... | 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 unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_UpperCAmelCase = "▁"
_Upper... | 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_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase = {
"configuration_distilbert": [
"DISTILBERT_PRETRAINED_CONFIG_... | 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 numpy
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self : Optional[int] , UpperCamelCase__ : numpy.ndarray , UpperCamelCase__ : numpy.ndarray ):
A = input_array
# Random initial weights are assigned where first argu... | 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 __future__ import annotations
def __UpperCamelCase (lowerCAmelCase : str, lowerCAmelCase : str ) -> bool:
A = get_failure_array(lowerCAmelCase )
# 2) Step through text searching for pattern
A , A = 0, 0 # index into text, pattern
while... | 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 csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def __UpperCamelCase (lowerCAmelCase ... | 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 torch import nn
class _UpperCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[int] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Tuple ):
super().__init__()
A = class_size
A = ... | 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 typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _UpperCAmelCase ( __l... | 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 inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils impor... | 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 Dict, Optional
import numpy as np
import datasets
_UpperCAmelCase = "\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class... | 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 collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokenization_ut... | 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 os
import re
import shutil
import sys
import tempfile
import unittest
import black
_UpperCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_copies # noqa: E402
# This is the reference ... | 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
from collections.abc import Callable
def __UpperCamelCase (lowerCAmelCase : Callable[[int | float], int | float], lowerCAmelCase : int | float, lowerCAmelCase : int | float, lowerCAmelCase : int = 100, ) -> float:
... | 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 |
def __UpperCamelCase (lowerCAmelCase : str, lowerCAmelCase : str ) -> str:
if not (isinstance(lowerCAmelCase, lowerCAmelCase ) and isinstance(lowerCAmelCase, lowerCAmelCase )):
raise ValueError('longest_common_substring() takes two strings for inputs' )
... | 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 timeit import timeit
def __UpperCamelCase (lowerCAmelCase : int ) -> int:
if number < 0:
raise ValueError('the value of input must not be negative' )
A = 0
while number:
number &= number - 1
result += 1
return result
def ... | 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 |
def __UpperCamelCase (lowerCAmelCase : int = 1_000 ) -> int:
return sum(e for e in range(3, lowerCAmelCase ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 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 |
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 |
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 ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=__lowercase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = ['''sentencepiece''']
def __init__( self : Optional[Any] , *UpperCamelCase__ : ... | 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 ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import Config... | 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 math import isqrt
def __UpperCamelCase (lowerCAmelCase : int ) -> list[int]:
A = [True] * max_number
for i in range(2, isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2, lowerCAmelCase, lowerCAmelCase ):
... | 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 sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("Googling.....")
_UpperCAmelCase = "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
_UpperCAmelCase = requests.get(url, head... | 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 |
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : list[int], lowerCAmelCase : int ) -> int:
def count_of_possible_combinations(lowerCAmelCase : int ) -> int:
if target < 0:
return 0
if target == 0:
... | 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 gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, t... | 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 |
def __UpperCamelCase (lowerCAmelCase : int ) -> list:
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError('The given input must be positive' )
# get the generated string sequence
A = gray_code_sequence_string(lowerCAmel... | 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 |
def __UpperCamelCase () -> int:
return [
a * b * (1_000 - a - b)
for a in range(1, 999 )
for b in range(lowerCAmelCase, 999 )
if (a * a + b * b == (1_000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(F'''{solution() = }''')
| 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 |
from collections.abc import Sequence
from queue import Queue
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self : Optional[Any] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Tuple , Upper... | 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 |
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