code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
def a( A : int , A : int ) -> str:
"""simple docstring"""
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(__UpperCamelCase , int(b / 2 ) ) * actual_power(__UpperCamelCase , int(b / 2 ) )
... | 227 |
'''simple docstring'''
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorT... | 251 | 0 |
'''simple docstring'''
import math
import sys
def lowercase_ ( lowerCAmelCase__ : int ):
"""simple docstring"""
if number != int(__UpperCamelCase ):
raise ValueError("""the value of input must be a natural number""" )
if number < 0:
... | 254 |
'''simple docstring'''
from __future__ import annotations
from scipy.special import comb # type: ignore
class _a :
'''simple docstring'''
def __init__( self, A ):
'''simple docstring'''
... | 251 | 0 |
"""simple docstring"""
import numpy as np
import qiskit
def __lowerCamelCase ( __UpperCamelCase = 8 , __UpperCamelCase = None ) -> List[Any]:
"""simple docstring"""
lowerCAmelCase_ : Any = np.random.default_rng(seed=__UpperCamelCase )
# Roughly... | 241 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
class _a ... | 251 | 0 |
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusi... | 59 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ = {
"configuration_funnel": ["FUNNEL_PRETRAINED_... | 251 | 0 |
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def a__ ( A_ = True, *A_, **A_ ):
'''simple docstring'''
if not is_tqdm_available():
raise ImportError("""Accelerate... | 88 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
UpperCamelCase_ = {"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}
try:
if not is_torch_a... | 251 | 0 |
'''simple docstring'''
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
A__ : List[Any] =50_00_00
A__ , A__ : Optional[Any] =os.path.split(__file__)
A__ : str ... | 70 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
... | 251 | 0 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
__lowerCamelCase : Any = 5_00_00
__lowerCamelCase : Tuple = 50_00
__lowerCamelCase , __lowerCamelCase : List[str] = os.path.split(__file__)
__lowerCamelCase : Li... | 18 |
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
UpperCamelCase_ = 2_0_0
# Number of elements selected in every generation of evolution. The selection takes
# place from b... | 251 | 0 |
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch i... | 8 |
'''simple docstring'''
from __future__ import annotations
def lowercase__( __UpperCamelCase: int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = [True] * limit
SCREAMING_SNAKE_CASE : List[str] = False
SCREAMING_SNAKE_CA... | 251 | 0 |
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,
)
from ... | 303 |
'''simple docstring'''
from pathlib import Path
import numpy as np
from PIL import Image
def lowercase__( __UpperCamelCase: np.ndarray ):
"""simple docstring"""
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[Any] ... | 251 | 0 |
'''simple docstring'''
import torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = '''facebook/bart-large-mnli'''
SCREAMI... | 104 |
'''simple docstring'''
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'files' ,[
['full:README.md', 'dataset_infos.json'],
['empty:README... | 251 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
"""studio-ousia/luke-base""": """https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json""",
"""studio-ousia/luke-large""... | 259 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase_ = {
"configuration_maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"],
... | 251 | 0 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _lowercase ( lowerC... | 227 |
'''simple docstring'''
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class _a ( unittest.TestCase ... | 251 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common impo... | 254 |
'''simple docstring'''
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def lowercase__( __UpperCamelCase: Union[d... | 251 | 0 |
"""simple docstring"""
from __future__ import annotations
def __lowerCamelCase ( __UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
lowerCAmelCase_ : List[Any] = 0.00
lowerCAmelCase_ : Tuple = 0
for resistor in resistors:
if re... | 241 |
'''simple docstring'''
from itertools import product
def lowercase__( __UpperCamelCase: int ,__UpperCamelCase: int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = sides_number
SCREAMING_SNAKE_CASE : str = max_face_number * dic... | 251 | 0 |
import math
class UpperCAmelCase :
def _SCREAMING_SNAKE_CASE (self : List[str] , snake_case__ : List[str] , snake_case__ : Union[str, Any] ) -> str:
'''simple docstring'''
snake_case : Any = 0.0
... | 59 |
'''simple docstring'''
class _a :
'''simple docstring'''
def __init__( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = 0
SCREAMING_SNAKE_CASE : str = 0
... | 251 | 0 |
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
__lowerCAmelCase : str = 'facebook/wmt19-en-de'
__lowerCAmelCase : Dict = FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__lowerCAmelCase : i... | 88 |
'''simple docstring'''
def lowercase__( __UpperCamelCase: str ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = [int(__UpperCamelCase ) for i in ip_va_address.split('.' ) if i.isdigit()]
return len(__UpperCamelCase ) == 4 and all(0 <... | 251 | 0 |
'''simple docstring'''
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class UpperCAmelCase ( snake_case_ ):
@require_torch
def ... | 70 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"microsoft/git-base": "https://huggingface... | 251 | 0 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
__lowerCamelCase : int = '''\\n@misc{chen2021evaluating,\n title={Evaluating Large Langua... | 18 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase_ = {
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"toke... | 251 | 0 |
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
lowerCAmelCase_ = logging.get_logger(__name__)
l... | 8 |
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
UpperCamelCase_ = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
UpperCamelCase_ = [ord(letter)... | 251 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""alibaba-damo/mgp-str-base""": """https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json""",
}
class __UpperCa... | 303 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
UpperCamelCase_ ... | 251 | 0 |
'''simple docstring'''
from collections.abc import Iterable
from typing import Generic, TypeVar
lowerCAmelCase__ = TypeVar('''_T''')
class lowercase_ (Generic[_T] ):
"""simple docstring"""
def __init__( self : Optional[int] ,lowercase__ : List[str] = Non... | 104 |
'''simple docstring'''
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
UpperCamelCase_ = argparse.ArgumentParser(
description=(
"Extraction some layers of the full BertForMaskedLM or RObertaForMaske... | 251 | 0 |
import warnings
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 ):
... | 259 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}
class _... | 251 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResamplin... | 227 |
'''simple docstring'''
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorT... | 251 | 0 |
'''simple docstring'''
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def lowercase_ (... | 254 |
'''simple docstring'''
from __future__ import annotations
from scipy.special import comb # type: ignore
class _a :
'''simple docstring'''
def __init__( self, A ):
'''simple docstring'''
... | 251 | 0 |
"""simple docstring"""
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concaten... | 241 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
class _a ... | 251 | 0 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from t... | 59 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ = {
"configuration_funnel": ["FUNNEL_PRETRAINED_... | 251 | 0 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class UpperCAmelCase_ ( unittest.Test... | 88 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
UpperCamelCase_ = {"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}
try:
if not is_torch_a... | 251 | 0 |
'''simple docstring'''
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
... | 70 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
... | 251 | 0 |
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 TaTokeniz... | 18 |
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
UpperCamelCase_ = 2_0_0
# Number of elements selected in every generation of evolution. The selection takes
# place from b... | 251 | 0 |
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
lowerCAmelCase_ = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2),... | 8 |
'''simple docstring'''
from __future__ import annotations
def lowercase__( __UpperCamelCase: int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = [True] * limit
SCREAMING_SNAKE_CASE : List[str] = False
SCREAMING_SNAKE_CA... | 251 | 0 |
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate
... | 303 |
'''simple docstring'''
from pathlib import Path
import numpy as np
from PIL import Image
def lowercase__( __UpperCamelCase: np.ndarray ):
"""simple docstring"""
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[Any] ... | 251 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''facebook/timesformer''': '''https://huggingface.co/facebook/timesformer/resolve/main/config.json''',
}
... | 104 |
'''simple docstring'''
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'files' ,[
['full:README.md', 'dataset_infos.json'],
['empty:README... | 251 | 0 |
def _A ( SCREAMING_SNAKE_CASE__ : List[Any] ):
UpperCamelCase :Union[str, Any] = 1
UpperCamelCase :Any = 2
while i * i <= n:
UpperCamelCase :str = 0
while n % i == 0:
n //= i
multiplicity += 1
n... | 259 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase_ = {
"configuration_maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"],
... | 251 | 0 |
from __future__ import annotations
def a( A : tuple[int, int] , A : int ) -> Optional[int]:
"""simple docstring"""
a = position
a = [
(y + 1, x + 2),
(y - 1, x + 2),
(y + 1, x - 2),
(y - 1... | 227 |
'''simple docstring'''
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class _a ( unittest.TestCase ... | 251 | 0 |
'''simple docstring'''
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
_UpperCamelCase = '... | 254 |
'''simple docstring'''
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def lowercase__( __UpperCamelCase: Union[d... | 251 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import Realm... | 241 |
'''simple docstring'''
from itertools import product
def lowercase__( __UpperCamelCase: int ,__UpperCamelCase: int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = sides_number
SCREAMING_SNAKE_CASE : str = max_face_number * dic... | 251 | 0 |
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_N... | 59 |
'''simple docstring'''
class _a :
'''simple docstring'''
def __init__( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = 0
SCREAMING_SNAKE_CASE : str = 0
... | 251 | 0 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available
from ...tes... | 88 |
'''simple docstring'''
def lowercase__( __UpperCamelCase: str ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = [int(__UpperCamelCase ) for i in ip_va_address.split('.' ) if i.isdigit()]
return len(__UpperCamelCase ) == 4 and all(0 <... | 251 | 0 |
'''simple docstring'''
from collections import deque
from .hash_table import HashTable
class UpperCAmelCase ( snake_case_ ):
def __init__( self : str , *__snake_case : Optional[Any] , **__snake_case : List[Any] ) -> List[Any]:
... | 70 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"microsoft/git-base": "https://huggingface... | 251 | 0 |
class a__ :
def __init__( self : List[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = 0
SCREAMING_SNAKE_CASE_ : str = 0
SCREAMING_SNAKE_CASE_ : Optional[int] = {}
def __UpperCamelCase ... | 18 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase_ = {
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"toke... | 251 | 0 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
if (
(cp >= 0x4E_00 and cp <= 0x9F_FF)
or (cp >= 0x34_0... | 8 |
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
UpperCamelCase_ = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
UpperCamelCase_ = [ord(letter)... | 251 | 0 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
... | 303 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
UpperCamelCase_ ... | 251 | 0 |
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ..... | 104 |
'''simple docstring'''
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
UpperCamelCase_ = argparse.ArgumentParser(
description=(
"Extraction some layers of the full BertForMaskedLM or RObertaForMaske... | 251 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case = {
"""configuration_autoformer""": [
"""AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AutoformerConfig""",
... | 259 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}
class _... | 251 | 0 |
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
_lowercase: str = {
"cola": 2... | 227 |
'''simple docstring'''
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorT... | 251 | 0 |
'''simple docstring'''
from __future__ import annotations
def lowercase_ ( lowerCAmelCase__ : list[int | float] , lowerCAmelCase__ : int , lowerCAmelCase__ : int ):
"""simple docstring"""
if len(__UpperCamelCase ) == 0:
raise Val... | 254 |
'''simple docstring'''
from __future__ import annotations
from scipy.special import comb # type: ignore
class _a :
'''simple docstring'''
def __init__( self, A ):
'''simple docstring'''
... | 251 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
lowercase__ = {
"""configuration_speecht5""": [
"""SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 241 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
class _a ... | 251 | 0 |
import unittest
from knapsack import knapsack as k
class UpperCAmelCase ( unittest.TestCase ):
def _SCREAMING_SNAKE_CASE (self : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case : Optional[int] = 0
... | 59 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ = {
"configuration_funnel": ["FUNNEL_PRETRAINED_... | 251 | 0 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...te... | 88 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
UpperCamelCase_ = {"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}
try:
if not is_torch_a... | 251 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
A__ : List[Any] ={
'''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig'''... | 70 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
... | 251 | 0 |
from __future__ import annotations
import math
def _snake_case ( lowerCAmelCase : list , lowerCAmelCase : list ):
"""simple docstring"""
if len(__UpperCamelCase ) != 2 or len(a[0] ) != 2 or len(__UpperCamelCase ) != 2 or len(b[0] ) != 2:
raise Exception("Matrices are not 2x... | 18 |
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
UpperCamelCase_ = 2_0_0
# Number of elements selected in every generation of evolution. The selection takes
# place from b... | 251 | 0 |
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class snake_case_ ( unittest.TestCase ):
'''simple docstring'''
def snake_case__( self :... | 8 |
'''simple docstring'''
from __future__ import annotations
def lowercase__( __UpperCamelCase: int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = [True] * limit
SCREAMING_SNAKE_CASE : List[str] = False
SCREAMING_SNAKE_CA... | 251 | 0 |
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
lowercase_ ... | 303 |
'''simple docstring'''
from pathlib import Path
import numpy as np
from PIL import Image
def lowercase__( __UpperCamelCase: np.ndarray ):
"""simple docstring"""
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[Any] ... | 251 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_token... | 104 |
'''simple docstring'''
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'files' ,[
['full:README.md', 'dataset_infos.json'],
['empty:README... | 251 | 0 |
import os
import platform
import sys
__snake_case = """3"""
print("""Python version:""", sys.version)
print("""OS platform:""", platform.platform())
print("""OS architecture:""", platform.machine())
try:
import torch
print("""Torch version:""", torch.__version__)
print("""Cuda available:""",... | 259 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase_ = {
"configuration_maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"],
... | 251 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase: str = {
"configuration_bigbird_pegasus": [
"BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BigBirdPegasusConfig",
"BigBirdPegasusOnnxConfig",
... | 227 |
'''simple docstring'''
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class _a ( unittest.TestCase ... | 251 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=__SCREAMING_SNAKE_CASE )
class _A ( __SCREAMING_SNAKE_CASE ):
_SCREAMING_SNAKE_CASE : ... | 254 |
'''simple docstring'''
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def lowercase__( __UpperCamelCase: Union[d... | 251 | 0 |
"""simple docstring"""
from __future__ import annotations
lowercase__ = tuple[int, int, int]
lowercase__ = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
lowercase__ = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
# -------------------------- defa... | 241 |
'''simple docstring'''
from itertools import product
def lowercase__( __UpperCamelCase: int ,__UpperCamelCase: int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = sides_number
SCREAMING_SNAKE_CASE : str = max_face_number * dic... | 251 | 0 |
def UpperCamelCase ( __lowerCamelCase : dict ):
snake_case : set[int] = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
snake_case : set[int] = set()
return any(
node not in visite... | 59 |
'''simple docstring'''
class _a :
'''simple docstring'''
def __init__( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = 0
SCREAMING_SNAKE_CASE : str = 0
... | 251 | 0 |
from collections.abc import Sequence
def a__ ( A_, A_ ):
'''simple docstring'''
return sum(c * (x**i) for i, c in enumerate(__UpperCamelCase ) )
def a__ ( A_, A_ ):
'''simple docstring'''
__magic_name__ = ... | 88 |
'''simple docstring'''
def lowercase__( __UpperCamelCase: str ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = [int(__UpperCamelCase ) for i in ip_va_address.split('.' ) if i.isdigit()]
return len(__UpperCamelCase ) == 4 and all(0 <... | 251 | 0 |
'''simple docstring'''
print((lambda quine: quine % quine)('''print((lambda quine: quine %% quine)(%r))'''))
| 70 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"microsoft/git-base": "https://huggingface... | 251 | 0 |
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
__lowerCamelCase : List[Any] = 6_37_81_37.0
__lowerCamelCase : Tuple = 6_35_67_52.31_42_45
__lowerCamelCase : str = 6_37_81_37
def _snake_case ( lowerCAmelCase : float ... | 18 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase_ = {
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"toke... | 251 | 0 |
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = False ):
if radian_mode:
retu... | 8 |
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
UpperCamelCase_ = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
UpperCamelCase_ = [ord(letter)... | 251 | 0 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json""",
}
class __UpperCamelCase ... | 303 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
UpperCamelCase_ ... | 251 | 0 |
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
lowerCAmelCase__ = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowerCAmelCase__ = [ord(letter) for letter in strin... | 104 |
'''simple docstring'''
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
UpperCamelCase_ = argparse.ArgumentParser(
description=(
"Extraction some layers of the full BertForMaskedLM or RObertaForMaske... | 251 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__snake_case = {
"""configuration_blip""": [
"""BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BlipCon... | 259 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}
class _... | 251 | 0 |
import sys
from pathlib import Path
_lowercase: Tuple = Path(__file__).resolve().parents[3] / "src"
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import itertools # noqa
import json # noqa
import os # noqa
import unittest # noqa
from copy import deepcopy # noqa... | 227 |
'''simple docstring'''
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorT... | 251 | 0 |
'''simple docstring'''
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
f... | 254 |
'''simple docstring'''
from __future__ import annotations
from scipy.special import comb # type: ignore
class _a :
'''simple docstring'''
def __init__( self, A ):
'''simple docstring'''
... | 251 | 0 |
"""simple docstring"""
from math import isqrt, loga
def __lowerCamelCase ( __UpperCamelCase ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase_ : Tuple = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is... | 241 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
class _a ... | 251 | 0 |
from __future__ import annotations
def UpperCamelCase ( __lowerCamelCase : int ):
snake_case : Tuple = [True] * limit
snake_case : List[str] = False
snake_case : List[Any] = False
snake_case : List[A... | 59 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ = {
"configuration_funnel": ["FUNNEL_PRETRAINED_... | 251 | 0 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetect... | 88 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
UpperCamelCase_ = {"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}
try:
if not is_torch_a... | 251 | 0 |
'''simple docstring'''
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
return np.dot(__UpperCamelCase , __UpperCamelCase )
... | 70 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
... | 251 | 0 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
__lowerCamelCase : Optional[Any] = argparse.ArgumentParser(
description=(
'''Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned... | 18 |
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
UpperCamelCase_ = 2_0_0
# Number of elements selected in every generation of evolution. The selection takes
# place from b... | 251 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''google/pegasus-large''': '''https://huggingface.co/google/pegasus-large/resolve/main/config.json''',
# See all PEGASUS models at htt... | 8 |
'''simple docstring'''
from __future__ import annotations
def lowercase__( __UpperCamelCase: int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = [True] * limit
SCREAMING_SNAKE_CASE : List[str] = False
SCREAMING_SNAKE_CA... | 251 | 0 |
def a__ ( snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = [int(__UpperCamelCase ) for i in ip_va_address.split('''.''' ) if i.isdigit()]
return len(__UpperCamelCase ) == 4 and all(0 <= int(__UpperCamelCase ) <= 254 for octet in octets )
if __... | 303 |
'''simple docstring'''
from pathlib import Path
import numpy as np
from PIL import Image
def lowercase__( __UpperCamelCase: np.ndarray ):
"""simple docstring"""
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[Any] ... | 251 | 0 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''BridgeTower/bridgetower-base''': '''https://huggingface.co/Br... | 104 |
'''simple docstring'''
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'files' ,[
['full:README.md', 'dataset_infos.json'],
['empty:README... | 251 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__snake_case = {
"""configuration_maskformer""": ["""MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MaskFormerConfig"""],
"""configuration_maskformer_... | 259 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase_ = {
"configuration_maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"],
... | 251 | 0 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase )
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = field(default="auto... | 227 |
'''simple docstring'''
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class _a ( unittest.TestCase ... | 251 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
_UpperCamelCase... | 254 |
'''simple docstring'''
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def lowercase__( __UpperCamelCase: Union[d... | 251 | 0 |
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface_hub.utils as hf_hub... | 252 |
import re
import string
import numpy as np
import datasets
UpperCAmelCase : List[str] = "\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"
UpperCAmelCase : str = "\nArgs:\n predi... | 252 | 1 |
from __future__ import annotations
def __lowerCamelCase ( lowerCamelCase__ : list[float] , lowerCamelCase__ : list[float] ):
'''simple docstring'''
lowerCamelCase = sorted(numsa + numsa )
lowerCamelCase , lowerCamelCase = divmod(len(lowerC... | 252 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def __lowerCamelCase ( lowerCamelCase__ : str , lowerCamelCase__ : str , **lowerCamelCase__ : Tuple ):
'''simple docstring'''
lowerCamelCase = AutoConfig.from_pret... | 252 | 1 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before token... | 252 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase : List[str] = {
"configuration_x_clip": [
"XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"XCLIPConfig",
"XCLIPTextConfig",
"XCLIPVisionConfig... | 252 | 1 |
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
from tokenizers import pre_t... | 252 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe ... | 252 | 1 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def __lowerCamelCase ( lowerCamelCase__ : str , lowerCamelCase__ : str , **lowerCamelCase__ : Tuple ):
'''simple docstring'''
lowerCamelCase = AutoConfig.from_pret... | 252 |
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 __lowercase ( a_ ):
"""simple docstring"""
... | 252 | 1 |
import enum
import shutil
import sys
UpperCAmelCase, UpperCAmelCase : Union[str, Any] = shutil.get_terminal_size()
UpperCAmelCase : Dict = {"UP": "A", "DOWN": "B", "RIGHT": "C", "LEFT": "D"}
class __lowercase ( enum.Enum ):
"""simple docstring"""
UpperCamel... | 252 |
def __lowerCamelCase ( lowerCamelCase__ : Any , lowerCamelCase__ : Optional[int] ):
'''simple docstring'''
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def __lowerCamelCase ( lowerCamelCase__ : List[str] , lowerCamelCase__ ... | 252 | 1 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def __lowerCamelCase ( lowerCamelCase__ : Any ):
'''simple docstring'''
lowerCamelCase = args.pruning_method
lowerCamelCase =... | 252 |
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
UpperCAmelCase : Union[str, Any] = datasets.utils.logging.get_logger(__name__)
@dataclass
class __low... | 252 | 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 Acc... | 252 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase : Optional[Any] = {
"configuration_swiftformer": [
"SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SwiftFormerConfig",
"SwiftF... | 252 | 1 |
import re
import string
import numpy as np
import datasets
UpperCAmelCase : List[str] = "\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"
UpperCAmelCase : str = "\nArgs:\n predi... | 252 |
import enum
import shutil
import sys
UpperCAmelCase, UpperCAmelCase : Union[str, Any] = shutil.get_terminal_size()
UpperCAmelCase : Dict = {"UP": "A", "DOWN": "B", "RIGHT": "C", "LEFT": "D"}
class __lowercase ( enum.Enum ):
"""simple docstring"""
UpperCamel... | 252 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : Any = logging.get_logger(__name__)
UpperCAmelCase : str = {
"facebook/nllb-moe-54B": "https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json",
}
class __lowercase... | 252 |
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __lowerCamelCase ( lowerCamelCase__ : List[str] , lowerCamelCase__ : Optional[Any... | 252 | 1 |
from ..utils import DummyObject, requires_backends
class __lowercase ( metaclass=a_ ):
"""simple docstring"""
UpperCamelCase : List[str] = ["transformers", "torch", "note_seq"]
def __init__( self , *A , **A ) -> Optional[int]:
... | 252 |
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_t... | 252 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase : Any = {
"configuration_altclip": [
"ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"AltCLIPConfig",
"AltCLIPTextConfig",
... | 252 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase : Any = {
"configuration_altclip": [
"ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"AltCLIPConfig",
"AltCLIPTextConfig",
... | 252 | 1 |
import unittest
import numpy as np
def __lowerCamelCase ( lowerCamelCase__ : np.ndarray , lowerCamelCase__ : np.ndarray , lowerCamelCase__ : np.ndarray , lowerCamelCase__ : np.ndarray | None = None , ):
'''simple docstring'''
lowerCamelCase ... | 252 |
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_rembert import R... | 252 | 1 |
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_to... | 252 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase : int = {
"configuration_autoformer": [
"AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"AutoformerConfig",
],
}... | 252 | 1 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
UpperCAmelCase : Optional[int] = Path(__file__).resolve().parents[3] / "src"
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io... | 252 |
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import SequenceFeatureExtraction... | 252 | 1 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class __lowercase :
"""simple docstring"""
def __init__( self , A ) -> str:
'''simple docstring'''
lowerCamelCase = data
lowerCamelCase ... | 252 |
# 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 a... | 252 | 1 |
from __future__ import annotations
from collections import deque
class __lowercase :
"""simple docstring"""
def __init__( self , A ) -> Dict:
'''simple docstring'''
lowerCamelCase = []
self.adlist.append(
{"""value""": """""",... | 252 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipe... | 252 | 1 |
def __lowerCamelCase ( lowerCamelCase__ : int = 1000 ):
'''simple docstring'''
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 252 |
import argparse
import gc
import json
import os
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 Accelerat... | 252 | 1 |
def __lowerCamelCase ( lowerCamelCase__ : Tuple , lowerCamelCase__ : str , lowerCamelCase__ : str , lowerCamelCase__ : str , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : Union[str, Any] ):
'''simple docstring'''
if index ... | 252 |
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --host <host> --key_path <... | 252 | 1 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
UpperCAmelCase : Optional[int] = "\\n@misc{chen2021evaluating,\n title={Evaluating La... | 252 |
from __future__ import annotations
def __lowerCamelCase ( lowerCamelCase__ : int , lowerCamelCase__ : int ):
'''simple docstring'''
if b == 0:
return (1, 0)
((lowerCamelCase) , (lowerCamelCase)) = extended_euclid(lowerCamelCase__ , a % b... | 252 | 1 |
import random
def __lowerCamelCase ( lowerCamelCase__ : list , lowerCamelCase__ : Optional[Any] ):
'''simple docstring'''
lowerCamelCase , lowerCamelCase , lowerCamelCase = [], [], []
for element in data:
if element < pivot:
... | 252 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType,... | 252 | 1 |
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