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 |
|---|---|---|---|---|
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAM... | 432 |
"""simple docstring"""
from statistics import mean, stdev
def _A ( _a : list , _a : int = 3 ):
"""simple docstring"""
A = min(_a )
A = max(_a )
# normalize data
return [round((x - x_min) / (x_max - x_min) ... | 617 | 0 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torc... | 260 |
"""simple docstring"""
import random
class lowerCamelCase__ :
'''simple docstring'''
@staticmethod
def UpperCamelCase__ ( lowerCamelCase_ ) -> tuple[list[int], list[int]]:
A = [ord(lowerCamelCase_ ) for i in text]
A = []... | 617 | 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 ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbo... | 623 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase ={
"configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}
try:
... | 617 | 0 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
lowerCamelCase__ = get_tests_dir() + "/... | 612 |
"""simple docstring"""
import unittest
from knapsack import greedy_knapsack as kp
class lowerCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ ( self ) -> Any:
A = [1_0, 2_0, 3_0, 4_0, 5_0, 6_0]
A... | 617 | 0 |
from __future__ import annotations
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
if len(_a ) == 0:
raise ValueError("find_max() arg is an empty sequence" )
if (
left >= len(_a )
or left... | 463 |
"""simple docstring"""
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
... | 617 | 0 |
"""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 TensorType
... | 65 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase ={
"configuration_informer": [
"INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"... | 617 | 0 |
"""simple docstring"""
class _a :
def __init__( self : Tuple ) -> Union[str, Any]:
snake_case : int = {}
def __lowercase ( self : int ) -> None:
print(self.vertex )
for i in ... | 449 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def _A ( _a : str , _a : str ):
"""simple docstring"""
A = list(_a )
A = list(_a )
A ... | 617 | 0 |
"""simple docstring"""
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def UpperCAmelCase__ (lowerCAmelCase_ = 3 ):
'''simple docstring'''
if isinstance(_a , _a ):
raise TypeError("number ... | 682 |
"""simple docstring"""
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase =get_tests_... | 617 | 0 |
'''simple docstring'''
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
'kwargs, expected' ,[
({'num_shards': 0, 'max_num_jobs': 1}, []),
({'num_shards': 10, 'max_num_jobs': 1},... | 531 |
"""simple docstring"""
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.mo... | 617 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def __A ( a_ : str ,a_ : str ):
lowerCAmelCase : Optional[Any] = list(_a )
lowerCAmelCase : str = list(_a )
... | 525 |
"""simple docstring"""
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
... | 617 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'''facebook/s2t-small-librispeech-asr''': (
'''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'''
),
... | 340 |
"""simple docstring"""
from __future__ import annotations
def _A ( _a : list[float] , _a : list[float] ):
"""simple docstring"""
A = sorted(numsa + numsa )
A , A = divmod(len(_a ) , 2 )
if mod == 1:
... | 617 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Model... | 432 |
"""simple docstring"""
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def _A ( *_a : int ):
"""simple docstring"""
if not isinstance(_a , _a ):
A = list(_a ... | 617 | 0 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from... | 260 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
UpperCAmelCase =True
except (ImportError, ModuleNotFoundError):
UpperCAmelCase =False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
... | 617 | 0 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
return "".join(sorted(_a ) )
def __lowercase( UpperCAmelCase__ ):
"""simple docstring... | 623 |
"""simple docstring"""
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available(... | 617 | 0 |
import unittest
from typing import Dict, List, Optional, Union
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_inpu... | 612 |
"""simple docstring"""
from math import pow, sqrt
def _A ( *_a : float ):
"""simple docstring"""
A = len(_a ) > 0 and all(value > 0.0 for value in values )
return result
def _A ( _a : float , _a : float ):
... | 617 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
... | 463 |
"""simple docstring"""
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_... | 617 | 0 |
"""simple docstring"""
import unittest
from knapsack import greedy_knapsack as kp
class __lowercase ( unittest.TestCase ):
def __lowercase ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = ... | 65 |
"""simple docstring"""
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self ) -> Union[str, Any]:
A = {}
def UpperCamelCase__ ( self ) -> None:
print(self.vertex )
for i in self.vertex:
... | 617 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'tanreinama/GPTSAN-2.8B-spout_is_uniform': (
'https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/ma... | 449 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
UpperCAmelCase =logging.get_logge... | 617 | 0 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.prepro... | 682 |
"""simple docstring"""
def _A ( _a : Optional[int] , _a : str ):
"""simple docstring"""
A = 0
A = len(_a ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[le... | 617 | 0 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
lowerCAmelCase_ = '''\\n\n'''
lowerCAmelCase_ = '''\nPerplexity (PPL) is one of... | 531 |
"""simple docstring"""
UpperCAmelCase =[
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def _A ( _a : Dict , _a : int , _a : Dict , _a : ... | 617 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ :Any = logging.get_logger(__name__)
lowerCAmelCase__ :Any = {
... | 618 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase__ :Tuple = {'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig''', '''DeiT... | 618 | 1 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from tran... | 618 |
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.ut... | 618 | 1 |
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
lowerCAmelCase__ :List[Any] = [
# (stable-diffusion, HF Diffusers)
('''time_embed.0.weight''', '''time_embedding.linear... | 618 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_tf,
... | 618 | 1 |
import math
import qiskit
def lowerCAmelCase__ ( a__: int = 1 , a__: int = 1 , a__: int = 1 ) -> qiskit.result.counts.Counts:
'''simple docstring'''
if (
isinstance(a__ , a__ )
or isinstance(a__ , a__ )
... | 618 |
from typing import Any
def lowerCAmelCase__ ( a__: list , a__: list , a__: dict , a__: dict , a__: dict , ) -> list:
'''simple docstring'''
_validation(
a__ , a__ , a__ , a__ , ... | 618 | 1 |
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@dataclass
class __a ( Up... | 618 |
def lowerCAmelCase__ ( a__: list ) -> list:
'''simple docstring'''
if len(a__ ) < 2:
return collection
def circle_sort_util(a__: list , a__: int , a__: int ) -> bool:
_UpperCAmelCase = Fa... | 618 | 1 |
from __future__ import annotations
class __a :
def __init__( self , _SCREAMING_SNAKE_CASE = 0 ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = key
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE , _SC... | 618 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ :Any = logging.get_logger(__name__)
lowerCAmelCase__ :Union[str, Any] = {
'''facebook/xmod-ba... | 618 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ :Any = {
'''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHI... | 618 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_availabl... | 618 | 1 |
import qiskit
def lowerCAmelCase__ ( a__: int , a__: int ) -> qiskit.result.counts.Counts:
'''simple docstring'''
_UpperCAmelCase = qiskit.Aer.get_backend('aer_simulator' )
# Create a Quantum Circuit acting on the q register
_... | 618 |
from __future__ import annotations
def lowerCAmelCase__ ( a__: str , a__: str ) -> bool:
'''simple docstring'''
_UpperCAmelCase = get_failure_array(a__ )
# 2) Step through text searching for pattern
_UpperCAmelCase , _U... | 618 | 1 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_tf,
... | 618 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class... | 618 | 1 |
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class __a ( UpperCAmelCase , unittest.... | 618 |
def lowerCAmelCase__ ( a__: int , a__: int ) -> int:
'''simple docstring'''
return x if y == 0 else greatest_common_divisor(a__ , x % y )
def lowerCAmelCase__ ( a__: int , a__: int ) -> int:
'''simple ... | 618 | 1 |
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'split_dict' , [
SplitDict(),
SplitDict({'train': SplitInfo(name='train' , num_bytes=1_3_3_7 , num_examples=4_2 , dataset_name='my_data... | 618 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
... | 618 | 1 |
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse('''3.8'''):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
lowerCAmelCase__ :Optional[int... | 618 |
def lowerCAmelCase__ ( a__: Optional[int] ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = len(a__ )
for i in range(length - 1 ):
_UpperCAmelCase = i
for k in range(i + 1 , a__ ):
... | 618 | 1 |
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ :str = logging.get_logger(__name__)
lowerCAmelCase__ :List[Any] = '''▁'''
lower... | 618 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import Inter... | 618 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ :str = logging.get_logger(__name__)
lowerCAmelCase__ :List[Any] = {
'''BridgeTower/bridgetower-base''': '''https://huggingface.co/BridgeTowe... | 618 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils... | 618 | 1 |
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
lowerCAmelCase__ :List[str] = log... | 618 |
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
lowerCAmelCase__ :int = logging.get_logger(__name__)
def lowerCAmelCase__ ( a__: Dict ) -> List[str]:
'''simple ... | 618 | 1 |
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configur... | 618 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRober... | 618 | 1 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def lowerCAmelCase__ ( a__: List[str] ... | 618 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, ge... | 618 | 1 |
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeat... | 618 |
import math
import qiskit
def lowerCAmelCase__ ( a__: int = 1 , a__: int = 1 , a__: int = 1 ) -> qiskit.result.counts.Counts:
'''simple docstring'''
if (
isinstance(a__ , a__ )
or isinstance(a__ , a__ )
... | 618 | 1 |
from __future__ import annotations
lowerCAmelCase__ :Any = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def lowerCAmelCase__ ( a__: list[list[int]] , a__: list[int] , a__: list[int] , a__: int , a__: ... | 618 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
lowerCAmelCase__ :List[Any] = {
'''sample_size''': 3_2,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''nu... | 618 | 1 |
lowerCAmelCase__ :List[str] = '''0.18.2'''
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa... | 618 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
lowerCAmelCase__ :Tuple = {
'''configuration_speecht5''': [
'''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''SPEECH... | 618 | 1 |
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __... | 618 |
import argparse
import json
import subprocess
def lowerCAmelCase__ ( a__: List[str] , a__: Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = (
F'''curl -H "Accept... | 618 | 1 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def lowerCAmelCase__ ( a__: Optional[int] , a__: List[Any] , a__: Any ) -> int:
'''simple ... | 618 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('''To use the rich extension, install rich with `pip install rich`''')
| 618 | 1 |
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def lowerCAmelCase__ ( a__: str , a__: str , a__: Optional[str] = None ) -> str:
'''simple docstring'''
if version.parse(hfh.... | 618 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase__ :Tuple = {'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig''', '''DeiT... | 618 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ :Any = {
'''configuration_electra''': ['''ELECTRA_PRETRAINED_CONFIG_ARCHI... | 618 |
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.ut... | 618 | 1 |
from __future__ import annotations
from typing import Any
class __a :
def __init__( self , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
_UpperCAmelCase = num_of_nodes
_UpperCAmelCase = []
_UpperCAmelCas... | 618 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_tf,
... | 618 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConfig,... | 618 |
from typing import Any
def lowerCAmelCase__ ( a__: list , a__: list , a__: dict , a__: dict , a__: dict , ) -> list:
'''simple docstring'''
_validation(
a__ , a__ , a__ , a__ , ... | 618 | 1 |
from __future__ import annotations
def lowerCAmelCase__ ( a__: int ) -> list[int]:
'''simple docstring'''
_UpperCAmelCase = [True] * limit
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase ... | 618 |
def lowerCAmelCase__ ( a__: list ) -> list:
'''simple docstring'''
if len(a__ ) < 2:
return collection
def circle_sort_util(a__: list , a__: int , a__: int ) -> bool:
_UpperCAmelCase = Fa... | 618 | 1 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def lowerCAmelCase__ ( a__: List[Any] , a__: Optional[Any] , a__: Union[str, Any] ) -> int:
'''simple docstring'''
_UpperCAmelCase = {
'en': 'Ma... | 618 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ :Any = logging.get_logger(__name__)
lowerCAmelCase__ :Union[str, Any] = {
'''facebook/xmod-ba... | 618 | 1 |
def lowerCAmelCase__ ( a__: Any ) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = set({'(', '[', '{'} )
_UpperCAmelCase = set({')', ']', '}'} )
_UpperCAmelCase = ... | 618 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_availabl... | 618 | 1 |
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
lowerCAm... | 618 |
from __future__ import annotations
def lowerCAmelCase__ ( a__: str , a__: str ) -> bool:
'''simple docstring'''
_UpperCAmelCase = get_failure_array(a__ )
# 2) Step through text searching for pattern
_UpperCAmelCase , _U... | 618 | 1 |
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputW... | 618 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class... | 618 | 1 |
lowerCAmelCase__ :Optional[Any] = '''Alexander Joslin'''
import operator as op
from .stack import Stack
def lowerCAmelCase__ ( a__: str ) -> int:
'''simple docstring'''
_UpperCAmelCase = {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.su... | 618 |
def lowerCAmelCase__ ( a__: int , a__: int ) -> int:
'''simple docstring'''
return x if y == 0 else greatest_common_divisor(a__ , x % y )
def lowerCAmelCase__ ( a__: int , a__: int ) -> int:
'''simple ... | 618 | 1 |
from __future__ import annotations
import math
def lowerCAmelCase__ ( a__: int ) -> list[int]:
'''simple docstring'''
if num <= 0:
_UpperCAmelCase = F'''{num}: Invalid input, please enter a positive integer.'''
raise ValueErro... | 618 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
... | 618 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.uti... | 618 |
def lowerCAmelCase__ ( a__: Optional[int] ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = len(a__ )
for i in range(length - 1 ):
_UpperCAmelCase = i
for k in range(i + 1 , a__ ):
... | 618 | 1 |
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available
... | 618 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import Inter... | 618 | 1 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class __a ( unittest.TestCase , UpperCAmelCase ):
def UpperCAmelCase__ ( self ) -> Any:
"""simple docstring"""
_UpperCAmelCase = ... | 618 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils... | 618 | 1 |
import math
def lowerCAmelCase__ ( a__: float , a__: float ) -> float:
'''simple docstring'''
if initial_intensity < 0:
raise ValueError('The value of intensity cannot be negative' )
# handling of negative values of initial int... | 618 |
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
lowerCAmelCase__ :int = logging.get_logger(__name__)
def lowerCAmelCase__ ( a__: Dict ) -> List[str]:
'''simple ... | 618 | 1 |
def lowerCAmelCase__ ( a__: int , a__: int ) -> int:
'''simple docstring'''
while a != 0:
_UpperCAmelCase , _UpperCAmelCase = b % a, a
return b
def lowerCAmelCase__ ( a__: int , a__: ... | 618 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRober... | 618 | 1 |
import argparse
import json
from tqdm import tqdm
def lowerCAmelCase__ ( ) -> str:
'''simple docstring'''
_UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--src_path' , type=a__ , de... | 618 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, ge... | 618 | 1 |
def lowerCAmelCase__ ( a__: int = 5_0 ) -> int:
'''simple docstring'''
_UpperCAmelCase = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in rang... | 618 |
import math
import qiskit
def lowerCAmelCase__ ( a__: int = 1 , a__: int = 1 , a__: int = 1 ) -> qiskit.result.counts.Counts:
'''simple docstring'''
if (
isinstance(a__ , a__ )
or isinstance(a__ , a__ )
... | 618 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
lowerCAmelCase__ :Tuple = {
'''configuration_speecht5''': [
'''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''SPEECH... | 618 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
lowerCAmelCase__ :List[Any] = {
'''sample_size''': 3_2,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''nu... | 618 | 1 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_acceler... | 618 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
lowerCAmelCase__ :Tuple = {
'''configuration_speecht5''': [
'''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''SPEECH... | 618 | 1 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __a ( UpperCAmelCase ):
_a : List[Any] = ... | 618 |
import argparse
import json
import subprocess
def lowerCAmelCase__ ( a__: List[str] , a__: Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = (
F'''curl -H "Accept... | 618 | 1 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
lowerCAmelCase__ :str = logging.getLogger(__name__)
class __a ( UpperCAmelCase ):
def __init__( self , _SCRE... | 618 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('''To use the rich extension, install rich with `pip install rich`''')
| 618 | 1 |
import inspect
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_config_docstrings.py
lowerCAmelCase__ :Any = '''src/transformers'''
# This is to make sure... | 618 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase__ :Tuple = {'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig''', '''DeiT... | 618 | 1 |
def lowerCAmelCase__ ( a__: int , a__: list ) -> str:
'''simple docstring'''
_enforce_args(a__ , a__ )
if n == 0:
return 0
_UpperCAmelCase = float('-inf' )
for i in range(1 , n + 1 ):
... | 618 |
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.ut... | 618 | 1 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
... | 618 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_tf,
... | 618 | 1 |
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def lowerCAmelCase__ ( a__: bool = True , *a__: Tuple , **a__: Optional[int] ) -> Optional[Any]:
'''simple docstring'''
... | 618 |
from typing import Any
def lowerCAmelCase__ ( a__: list , a__: list , a__: dict , a__: dict , a__: dict , ) -> list:
'''simple docstring'''
_validation(
a__ , a__ , a__ , a__ , ... | 618 | 1 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def lowerCAmelCase__ ( a__: str , a__: complex , a__: str = "x" , a__: float = 1_0**-1_0 , a__: int = 1 , ) -> complex:
'''simple docstring'''... | 618 |
def lowerCAmelCase__ ( a__: list ) -> list:
'''simple docstring'''
if len(a__ ) < 2:
return collection
def circle_sort_util(a__: list , a__: int , a__: int ) -> bool:
_UpperCAmelCase = Fa... | 618 | 1 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
lowerCAmelCase__ :Dict = get_test... | 618 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ :Any = logging.get_logger(__name__)
lowerCAmelCase__ :Union[str, Any] = {
'''facebook/xmod-ba... | 618 | 1 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TF_MODEL... | 618 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_availabl... | 618 | 1 |
def lowerCAmelCase__ ( a__: str , a__: str ) -> list:
'''simple docstring'''
_UpperCAmelCase = len(a__ )
_UpperCAmelCase = []
for i in range(len(a__ ) - pat_len + 1 ):
_UpperCAmelCase = ... | 618 |
from __future__ import annotations
def lowerCAmelCase__ ( a__: str , a__: str ) -> bool:
'''simple docstring'''
_UpperCAmelCase = get_failure_array(a__ )
# 2) Step through text searching for pattern
_UpperCAmelCase , _U... | 618 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ :Tuple = logging.get_logger(__name__)
lowerCAmelCase__ :Dict = {
'''BAAI/AltCLIP''': '''https://huggingface.co/BAAI/AltCLIP/resolve/main/con... | 618 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class... | 618 | 1 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
lowerCAmelCase__ :Dict = {
'''tiny.en''': '''https://openaipublic.azureedge.net/main/whisper/model... | 618 |
def lowerCAmelCase__ ( a__: int , a__: int ) -> int:
'''simple docstring'''
return x if y == 0 else greatest_common_divisor(a__ , x % y )
def lowerCAmelCase__ ( a__: int , a__: int ) -> int:
'''simple ... | 618 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils... | 618 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
... | 618 | 1 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTes... | 618 |
def lowerCAmelCase__ ( a__: Optional[int] ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = len(a__ )
for i in range(length - 1 ):
_UpperCAmelCase = i
for k in range(i + 1 , a__ ):
... | 618 | 1 |
import warnings
from .generation import TFGenerationMixin
class __a ( UpperCAmelCase ):
# warning at import time
warnings.warn(
'Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will '
'be removed in Transformers v5. Import as `from tran... | 618 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import Inter... | 618 | 1 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_availabl... | 618 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils... | 618 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import Inter... | 618 |
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
lowerCAmelCase__ :int = logging.get_logger(__name__)
def lowerCAmelCase__ ( a__: Dict ) -> List[str]:
'''simple ... | 618 | 1 |
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
lowerCAmelCase__ :Optional[int] ... | 618 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRober... | 618 | 1 |
def lowerCAmelCase__ ( a__: str ) -> list:
'''simple docstring'''
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(a__ ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__('''doctest''').testm... | 618 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, ge... | 618 | 1 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='session' )
def lowerCAmelCase__ ( ) -> Optional[int]:
... | 618 |
import math
import qiskit
def lowerCAmelCase__ ( a__: int = 1 , a__: int = 1 , a__: int = 1 ) -> qiskit.result.counts.Counts:
'''simple docstring'''
if (
isinstance(a__ , a__ )
or isinstance(a__ , a__ )
... | 618 | 1 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester... | 618 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
lowerCAmelCase__ :List[Any] = {
'''sample_size''': 3_2,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''nu... | 618 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ :Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase__ :Dict = {
'''uw-madison/mra-base-512-4''': '''https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json''',
}... | 618 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
lowerCAmelCase__ :Tuple = {
'''configuration_speecht5''': [
'''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''SPEECH... | 618 | 1 |
def lowerCAmelCase__ ( a__: int ) -> bool:
'''simple docstring'''
if p < 2:
raise ValueError('p should not be less than 2!' )
elif p == 2:
return True
_UpperCAmelCase = 4
_UpperCAmelCase = (... | 618 |
import argparse
import json
import subprocess
def lowerCAmelCase__ ( a__: List[str] , a__: Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = (
F'''curl -H "Accept... | 618 | 1 |
from __future__ import annotations
def lowerCAmelCase__ ( a__: str , a__: str ) -> bool:
'''simple docstring'''
_UpperCAmelCase = get_failure_array(a__ )
# 2) Step through text searching for pattern
_UpperCAmelCase , _U... | 618 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('''To use the rich extension, install rich with `pip install rich`''')
| 618 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ :Dict = logging.get_logger(__name__)
lowerCAmelCase__ :str = {
'''alibaba-damo/mgp-str-base''': '''https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json''',
}
class ... | 618 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase__ :Tuple = {'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig''', '''DeiT... | 618 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
if TYPE_CHECKING:
... | 618 |
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.ut... | 618 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by a... | 618 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_tf,
... | 618 | 1 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class... | 618 |
from typing import Any
def lowerCAmelCase__ ( a__: list , a__: list , a__: dict , a__: dict , a__: dict , ) -> list:
'''simple docstring'''
_validation(
a__ , a__ , a__ , a__ , ... | 618 | 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 TaToke... | 618 |
def lowerCAmelCase__ ( a__: list ) -> list:
'''simple docstring'''
if len(a__ ) < 2:
return collection
def circle_sort_util(a__: list , a__: int , a__: int ) -> bool:
_UpperCAmelCase = Fa... | 618 | 1 |
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
f... | 618 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ :Any = logging.get_logger(__name__)
lowerCAmelCase__ :Union[str, Any] = {
'''facebook/xmod-ba... | 618 | 1 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def lowerCAmelCase__ ( a__: int = 3 ) -> qiskit.result.counts.Counts:
'''simple docstring'''
if isinstance(a__ , a__ ):
... | 618 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_availabl... | 618 | 1 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
from ...test_tok... | 618 |
from __future__ import annotations
def lowerCAmelCase__ ( a__: str , a__: str ) -> bool:
'''simple docstring'''
_UpperCAmelCase = get_failure_array(a__ )
# 2) Step through text searching for pattern
_UpperCAmelCase , _U... | 618 | 1 |
def lowerCAmelCase__ ( a__: int , a__: list[int] , a__: int ) -> int:
'''simple docstring'''
def count_of_possible_combinations(a__: int ) -> int:
if target < 0:
return 0
if target == 0:
... | 618 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class... | 618 | 1 |
def lowerCAmelCase__ ( a__: int ) -> int:
'''simple docstring'''
if a < 0:
raise ValueError('Input value must be a positive integer' )
elif isinstance(a__ , a__ ):
raise TypeError('Input value must be a \'int\' type' )
... | 618 |
def lowerCAmelCase__ ( a__: int , a__: int ) -> int:
'''simple docstring'''
return x if y == 0 else greatest_common_divisor(a__ , x % y )
def lowerCAmelCase__ ( a__: int , a__: int ) -> int:
'''simple ... | 618 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM
@requi... | 618 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
... | 618 | 1 |
from math import sqrt
def lowerCAmelCase__ ( a__: int ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all ev... | 618 |
def lowerCAmelCase__ ( a__: Optional[int] ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = len(a__ )
for i in range(length - 1 ):
_UpperCAmelCase = i
for k in range(i + 1 , a__ ):
... | 618 | 1 |
from typing import Any
def lowerCAmelCase__ ( a__: list , a__: list , a__: dict , a__: dict , a__: dict , ) -> list:
'''simple docstring'''
_validation(
a__ , a__ , a__ , a__ , ... | 618 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import Inter... | 618 | 1 |
import os
def lowerCAmelCase__ ( ) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase = os.path.join(os.path.dirname(a__ ) , 'num.txt' )
with open(a__ ) as file_hand:
return str(sum(int(a__ ) for line in file_hand ) ... | 618 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils... | 618 | 1 |
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