code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def lowerCamelCase_ ( _UpperCamelCase ) -> None:
"""simple docstring"""
snake_case_ , snake_case_ : Optional[Any] = analyze_text(_Up... | 60 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torcha... | 103 | 0 |
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neuroncore,
... | 61 |
"""simple docstring"""
import operator
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ = False , lowerCAmelCase_ = None ) -> list:
_snake_case = operator.lt if reverse else operator.gt
_snake_case = solution or []
if not arr:
retu... | 103 | 0 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case = logging.get_logger(__name__)
snake_case = {
"""huggingface/informer-tourism-monthly""": (
"""https://huggingface.co/huggingface/informer-touri... | 62 |
"""simple docstring"""
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.mode... | 103 | 0 |
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class a :
"""simple docstring"""
a : torch.Tensor # [batch_size x 3]
a : torch.Tensor # [batch_size x 3]
a : torch.Tensor ... | 63 |
"""simple docstring"""
from copy import deepcopy
class UpperCAmelCase :
def __init__( self : Optional[Any] , __lowerCamelCase : list[int] | None = None , __lowerCamelCase : int | None = None ):
"""simple docstring"""
... | 103 | 0 |
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def A__ ( snake_case_ ... | 64 |
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic... | 103 | 0 |
"""simple docstring"""
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
if not isinstance(__UpperCamelCase , __UpperCamelCase ):
raise ValueError("""Input must be an integer""" )
if input_num <= 0:
raise ValueError("""Input must be positive""" )... | 65 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenizatio... | 103 | 0 |
def __magic_name__ ( SCREAMING_SNAKE_CASE ) -> list:
if len(SCREAMING_SNAKE_CASE ) <= 1:
return [tuple(SCREAMING_SNAKE_CASE )]
_lowercase : List[Any] = []
def generate(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
... | 66 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
A__ : str = '''bert-generation'''
def __init__( self : Tuple , __lowerCamelCase : Optional[int]=5_0_3_5_8 ... | 103 | 0 |
import sys
from collections import defaultdict
class A_ :
"""simple docstring"""
def __init__( self : str ) -> Union[str, Any]:
_lowercase = []
def __UpperCAmelCase ( self ... | 67 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCAmelCase ( metaclass=__SCREAMING_SNAKE_CASE ):
A__ : Optional[int] = ['''torch''', '''scipy''']
def __init__( self : Any , *__lowerCamelCase : List[... | 103 | 0 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def lowercase__ ( ) -> Union[str, Any]:
"""simple docstring"""
with offline(... | 68 |
"""simple docstring"""
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ ) -> int:
return 1 if input_a == input_a else 0
def snake_case ( ) -> None:
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , ... | 103 | 0 |
'''simple docstring'''
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,
res... | 69 |
"""simple docstring"""
import os
from pathlib import Path
def snake_case ( ) -> Tuple:
from torch.utils.cpp_extension import load
_snake_case = Path(lowerCAmelCase_ ).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
_snake_case = ... | 103 | 0 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code... | 70 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixi... | 103 | 0 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : str ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ : List[Any] = [], []
while len(_SCREAMING_SNAKE_CASE ) > 1:
UpperCAmelCase_ , UpperCAmelCase_ : ... | 71 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
@staticmethod
@abstractmethod
def __UpperCAmelCase ( __lowerCamelCase : ArgumentParser ):
"... | 103 | 0 |
'''simple docstring'''
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.... | 72 |
"""simple docstring"""
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logg... | 103 | 0 |
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 _snake_case ( unittest.TestCase ):
_lowercase : Optional[int] = insp... | 73 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
snake_case = {'''tokenization_herbert''': ['''HerbertTokenizer''']}
try:
if not is_tokenizers_available():
raise Op... | 103 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""facebook/xlm-roberta-xl""": """https://huggingface.co/facebook/x... | 74 |
"""simple docstring"""
from itertools import product
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ ) -> list[int]:
_snake_case = sides_number
_snake_case = max_face_number * dice_number
_snake_case = [0] * (max_total + 1)
_snake... | 103 | 0 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ ) -> list:
if n_term == "":
return []
UpperCAmelCase__ : list = []
for temp in range(int(lowerCAmelCase__ ) ):
series.append(F"""1/{temp + 1}""" if series else '''1''' )
return series... | 75 |
"""simple docstring"""
import inspect
import unittest
from transformers import MobileNetVaConfig
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_con... | 103 | 0 |
"""simple docstring"""
a_ = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
a_ = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
a_ = {
0: 'Sunday',
1: 'Monday',
2: 'Tuesday',
3: 'Wednesday',
4: 'Thursday',
5: 'Friday',
6: 'Saturday',
}
... | 76 |
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class UpperCAmelCase ( yaml.SafeLoader ):
def __UpperCAmelCase ( self : Tuple , __lowerCamelCase : List[str] ... | 103 | 0 |
"""simple docstring"""
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
A = 6378137.0
A = 6356752.314245
A = 6_378_137
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , ... | 77 |
"""simple docstring"""
# 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.or... | 103 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE_: Union[str, Any] ={'configuration_swin': ['SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SwinConfig', 'SwinOnnxConfig']}
try:
if no... | 78 |
"""simple docstring"""
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is trul... | 103 | 0 |
from __future__ import annotations
def _lowerCamelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> None:
'''simple docstring'''
if (direction == 1 and array[indexa] > array[indexa]) or (
direction ... | 79 |
"""simple docstring"""
from math import sqrt
def snake_case ( lowerCAmelCase_ = 1000000 ) -> int:
_snake_case = 0
_snake_case = 0
_snake_case = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2... | 103 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__UpperCamelCase : Optional[Any] = {
"""configuration_convnext""": ["""CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""",... | 80 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ... | 103 | 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_in... | 81 |
"""simple docstring"""
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 FlaxX... | 103 | 0 |
"""simple docstring"""
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(lowerCAmelCase__ , n - 1 , lowerCAmelCase__ ) * a) % mod
... | 82 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torcha... | 103 | 0 |
"""simple docstring"""
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import ... | 83 |
"""simple docstring"""
import operator
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ = False , lowerCAmelCase_ = None ) -> list:
_snake_case = operator.lt if reverse else operator.gt
_snake_case = solution or []
if not arr:
retu... | 103 | 0 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
raise TypeError('only integers accepted as input' )
else:
lowercase = str(abs(__SCREAMING_SNAKE_CASE ) )
lowercase = [list(__SCREAMING_SNAK... | 84 |
"""simple docstring"""
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.mode... | 103 | 0 |
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin im... | 85 |
"""simple docstring"""
from copy import deepcopy
class UpperCAmelCase :
def __init__( self : Optional[Any] , __lowerCamelCase : list[int] | None = None , __lowerCamelCase : int | None = None ):
"""simple docstring"""
... | 103 | 0 |
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.featu... | 86 |
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic... | 103 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowerCamelCase : str = {
"""configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"""],
... | 87 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenizatio... | 103 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"""google/bigbi... | 88 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
A__ : str = '''bert-generation'''
def __init__( self : Tuple , __lowerCamelCase : Optional[int]=5_0_3_5_8 ... | 103 | 0 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import Regr... | 89 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCAmelCase ( metaclass=__SCREAMING_SNAKE_CASE ):
A__ : Optional[int] = ['''torch''', '''scipy''']
def __init__( self : Any , *__lowerCamelCase : List[... | 103 | 0 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class a__ :
'''simple docstring'''
def __init__( self , lowerCamelCase_=... | 90 |
"""simple docstring"""
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ ) -> int:
return 1 if input_a == input_a else 0
def snake_case ( ) -> None:
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , ... | 103 | 0 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=_lowercase )
class lowerCAmelCase_ ( _lowercase ):
'''simple docstring'... | 91 |
"""simple docstring"""
import os
from pathlib import Path
def snake_case ( ) -> Tuple:
from torch.utils.cpp_extension import load
_snake_case = Path(lowerCAmelCase_ ).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
_snake_case = ... | 103 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase_ = {
"""configuration_layoutlmv3""": [
... | 92 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixi... | 103 | 0 |
"""simple docstring"""
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
__A = logging.get_logger(__name__)
__A = OrderedDi... | 93 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
@staticmethod
@abstractmethod
def __UpperCAmelCase ( __lowerCamelCase : ArgumentParser ):
"... | 103 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import DistilBertConfig, 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... | 94 |
"""simple docstring"""
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logg... | 103 | 0 |
"""simple docstring"""
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
lowerCamelCase_ = object()
# For specifying empty leaf dict `{}`
lowerCamelCase_ = object()... | 95 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
snake_case = {'''tokenization_herbert''': ['''HerbertTokenizer''']}
try:
if not is_tokenizers_available():
raise Op... | 103 | 0 |
"""simple docstring"""
def a ( __UpperCAmelCase : str ) -> str:
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 96 |
"""simple docstring"""
from itertools import product
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ ) -> list[int]:
_snake_case = sides_number
_snake_case = max_face_number * dice_number
_snake_case = [0] * (max_total + 1)
_snake... | 103 | 0 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
a :Optional[Any] = 'ClapFeatureExtractor'
a :List[str] = ('RobertaTokenizer', 'RobertaToke... | 97 |
"""simple docstring"""
import inspect
import unittest
from transformers import MobileNetVaConfig
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_con... | 103 | 0 |
'''simple docstring'''
import qiskit
def a__ ( lowercase : int, lowercase : int ) -> qiskit.result.counts.Counts:
"""simple docstring"""
_UpperCamelCase = qiskit.Aer.get_backend('''aer_simulator''' )
_UpperCamelCase = qiski... | 98 |
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class UpperCAmelCase ( yaml.SafeLoader ):
def __UpperCAmelCase ( self : Tuple , __lowerCamelCase : List[str] ... | 103 | 0 |
from itertools import count
def a (lowerCAmelCase__ = 50 ):
__a = [1] * min_block_length
for n in count(lowerCAmelCase__ ):
fill_count_functions.append(1 )
for block_length in range(lowerCAmelCase__ , n + 1 ):
for block_start in range(n - block_leng... | 99 |
"""simple docstring"""
# 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.or... | 103 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A : List[str] = {"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvai... | 100 |
"""simple docstring"""
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is trul... | 103 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase__ : Any ={
'configuration_rag': ['RagConfig'],
'retrieval_rag': ['RagRetriever'],
'tokenization_rag': ['RagTokenizer'],
}
t... | 101 |
"""simple docstring"""
from math import sqrt
def snake_case ( lowerCAmelCase_ = 1000000 ) -> int:
_snake_case = 0
_snake_case = 0
_snake_case = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2... | 103 | 0 |
"""simple docstring"""
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random... | 102 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ... | 103 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
"""weiweishi/roc-bert-base-zh""": """https://huggingface.co/weiweishi/roc-bert-base-zh/resolve... | 104 |
"""simple docstring"""
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 FlaxX... | 103 | 0 |
from math import factorial, radians
def __UpperCAmelCase ( lowerCamelCase_ : float , lowerCamelCase_ : int = 18 , lowerCamelCase_ : int = 10 ) -> float:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = angle_in_degrees - (... | 105 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torcha... | 103 | 0 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
fr... | 106 |
"""simple docstring"""
import operator
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ = False , lowerCAmelCase_ = None ) -> list:
_snake_case = operator.lt if reverse else operator.gt
_snake_case = solution or []
if not arr:
retu... | 103 | 0 |
'''simple docstring'''
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerat... | 107 |
"""simple docstring"""
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.mode... | 103 | 0 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
@slow
@requ... | 108 |
"""simple docstring"""
from copy import deepcopy
class UpperCAmelCase :
def __init__( self : Optional[Any] , __lowerCamelCase : list[int] | None = None , __lowerCamelCase : int | None = None ):
"""simple docstring"""
... | 103 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
a = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig"],
... | 109 |
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic... | 103 | 0 |
"""simple docstring"""
def lowerCamelCase ( ):
UpperCAmelCase__ : Union[str, Any] = 0
for i in range(1 ,1001 ):
total += i**i
return str(_snake_case )[-10:]
if __name__ == "__main__":
print(solution())
| 110 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenizatio... | 103 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowercase = {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTConfig''', '''... | 157 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
A__ : str = '''bert-generation'''
def __init__( self : Tuple , __lowerCamelCase : Optional[int]=5_0_3_5_8 ... | 103 | 0 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Any = logging.get_logger(__name__)
__UpperCamelCase : Dict = {
'''facebook/wav2vec2-base-960h''': '''https... | 4 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCAmelCase ( metaclass=__SCREAMING_SNAKE_CASE ):
A__ : Optional[int] = ['''torch''', '''scipy''']
def __init__( self : Any , *__lowerCamelCase : List[... | 103 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_availab... | 47 |
"""simple docstring"""
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ ) -> int:
return 1 if input_a == input_a else 0
def snake_case ( ) -> None:
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , ... | 103 | 0 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class snake_case__ ( __SCREAMING_SNAKE_CASE ):
_lowerCAmelCase =['''image_processor''', '''tokenizer''']
_lowerCAmelCase ='''AutoImageProcessor'''
_lowerCAmelCase ='''... | 170 |
"""simple docstring"""
import os
from pathlib import Path
def snake_case ( ) -> Tuple:
from torch.utils.cpp_extension import load
_snake_case = Path(lowerCAmelCase_ ).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
_snake_case = ... | 103 | 0 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common... | 215 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixi... | 103 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import ... | 355 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
@staticmethod
@abstractmethod
def __UpperCAmelCase ( __lowerCamelCase : ArgumentParser ):
"... | 103 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP'... | 553 |
"""simple docstring"""
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logg... | 103 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : Any = logging.get_logger(__name__)
__lowerCamelCase : int = {
'''google/switch-base-8''': '''https://huggingface.co/google/switc... | 653 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
snake_case = {'''tokenization_herbert''': ['''HerbertTokenizer''']}
try:
if not is_tokenizers_available():
raise Op... | 103 | 0 |
'''simple docstring'''
def _lowerCAmelCase (_lowercase = 2_00 ):
"""simple docstring"""
a__ = [1, 2, 5, 10, 20, 50, 1_00, 2_00]
a__ = [0] * (pence + 1)
a__ = 1 # base case: 1 way to make 0 pence
for coin in coins:
... | 331 |
"""simple docstring"""
from itertools import product
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ ) -> list[int]:
_snake_case = sides_number
_snake_case = max_face_number * dice_number
_snake_case = [0] * (max_total + 1)
_snake... | 103 | 0 |
import operator
def __lowerCAmelCase ( __magic_name__ , __magic_name__ = False , __magic_name__ = None ):
_lowercase: List[str] = operator.lt if reverse else operator.gt
_lowercase: str = solution or []
if not arr:
return solution
_lowercase: Union[s... | 226 |
"""simple docstring"""
import inspect
import unittest
from transformers import MobileNetVaConfig
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_con... | 103 | 0 |
class _snake_case :
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_):
'''simple docstring'''
lowercase__ : Any = None
lowercase__ : int = None
lowercase__ : Tuple = graph
self._normali... | 12 |
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class UpperCAmelCase ( yaml.SafeLoader ):
def __UpperCAmelCase ( self : Tuple , __lowerCamelCase : List[str] ... | 103 | 0 |
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 ...modeling_utils import PreT... | 157 |
"""simple docstring"""
# 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.or... | 103 | 0 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : List[Any] ):
return " ".join(
''.join(word[::-1] ) if len(lowerCAmelCase_ ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_lo... | 4 |
"""simple docstring"""
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is trul... | 103 | 0 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNA... | 47 |
"""simple docstring"""
from math import sqrt
def snake_case ( lowerCAmelCase_ = 1000000 ) -> int:
_snake_case = 0
_snake_case = 0
_snake_case = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2... | 103 | 0 |
def lowercase__( A , A ):
def get_matched_characters(A , A ) -> str:
snake_case__ : Optional[int] = []
snake_case__ : Optional[Any] = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate... | 170 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ... | 103 | 0 |
'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ... | 215 |
"""simple docstring"""
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 FlaxX... | 103 | 0 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def A_ ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ ) -> np.array:
_UpperCamelCase :Optional[int] = int(np.ceil((x_end - xa)... | 355 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torcha... | 103 | 0 |
"""simple docstring"""
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class __UpperCamelCase :
def __init__( self : str , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : int , UpperCAmelCase : int ) -> Tuple... | 553 |
"""simple docstring"""
import operator
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ = False , lowerCAmelCase_ = None ) -> list:
_snake_case = operator.lt if reverse else operator.gt
_snake_case = solution or []
if not arr:
retu... | 103 | 0 |
'''simple docstring'''
import numpy as np
class A_ :
"""simple docstring"""
def __init__( self :Any ) -> List[str]:
'''simple docstring'''
snake_case_ : List[str] = (0, 0)
snake... | 653 |
"""simple docstring"""
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.mode... | 103 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
class lowerCamelCase__ ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase__ = '''bert-generation'''
def __init__( self : Tuple ... | 331 |
"""simple docstring"""
from copy import deepcopy
class UpperCAmelCase :
def __init__( self : Optional[Any] , __lowerCamelCase : list[int] | None = None , __lowerCamelCase : int | None = None ):
"""simple docstring"""
... | 103 | 0 |
_SCREAMING_SNAKE_CASE : str = {
'a': 'AAAAA',
'b': 'AAAAB',
'c': 'AAABA',
'd': 'AAABB',
'e': 'AABAA',
'f': 'AABAB',
'g': 'AABBA',
'h': 'AABBB',
'i': 'ABAAA',
'j': 'BBBAA',
'k': 'ABAAB',
'l': 'ABABA',
'm': 'ABABB',
'n': 'ABBAA',
'o': 'ABBA... | 226 |
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic... | 103 | 0 |
lowerCamelCase__ : Optional[int] = 6_5_5_2_1
def UpperCamelCase ( lowercase_ ) -> int:
'''simple docstring'''
lowercase__ : List[Any] = 1
lowercase__ : int = 0
for plain_chr in plain_text:
lowercase__ : Union[str, Any] = (a + ord(l... | 12 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenizatio... | 103 | 0 |
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 ModelTesterMixin, ids_tensor, ... | 157 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
A__ : str = '''bert-generation'''
def __init__( self : Tuple , __lowerCamelCase : Optional[int]=5_0_3_5_8 ... | 103 | 0 |
"""simple docstring"""
from copy import deepcopy
class a :
def __init__( self , _snake_case = None , _snake_case = None ):
"""simple docstring"""
if arr is None and size is not None:
lowerCAmelCase = size
lowerCAmelCase = [0] * ... | 4 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCAmelCase ( metaclass=__SCREAMING_SNAKE_CASE ):
A__ : Optional[int] = ['''torch''', '''scipy''']
def __init__( self : Any , *__lowerCamelCase : List[... | 103 | 0 |
SCREAMING_SNAKE_CASE__ = [0, 2, 4, 6, 8]
SCREAMING_SNAKE_CASE__ = [1, 3, 5, 7, 9]
def UpperCAmelCase__ ( lowerCamelCase_ : List[Any] , lowerCamelCase_ : int , lowerCamelCase_ : List[Any] , lowerCamelCase_ : str ... | 47 |
"""simple docstring"""
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ ) -> int:
return 1 if input_a == input_a else 0
def snake_case ( ) -> None:
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , ... | 103 | 0 |
import math
def lowercase__( A , A ):
snake_case__ : str = len(lowerCAmelCase_ )
snake_case__ : Dict = int(math.floor(math.sqrt(lowerCAmelCase_ ) ) )
snake_case__ : Optional[int] = 0
while arr[min(lowerCAme... | 170 |
"""simple docstring"""
import os
from pathlib import Path
def snake_case ( ) -> Tuple:
from torch.utils.cpp_extension import load
_snake_case = Path(lowerCAmelCase_ ).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
_snake_case = ... | 103 | 0 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
... | 215 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixi... | 103 | 0 |
"""simple docstring"""
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class A( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
def __init__( self , SCREAMING_S... | 355 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
@staticmethod
@abstractmethod
def __UpperCAmelCase ( __lowerCamelCase : ArgumentParser ):
"... | 103 | 0 |
"""simple docstring"""
def UpperCAmelCase ( a__ ):
'''simple docstring'''
lowerCAmelCase :List[str] = False
while is_sorted is False: # Until all the indices are traversed keep looping
lowerCAmelCase :List[str] = True
for i... | 553 |
"""simple docstring"""
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logg... | 103 | 0 |
'''simple docstring'''
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phone... | 653 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
snake_case = {'''tokenization_herbert''': ['''HerbertTokenizer''']}
try:
if not is_tokenizers_available():
raise Op... | 103 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_ava... | 331 |
"""simple docstring"""
from itertools import product
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ ) -> list[int]:
_snake_case = sides_number
_snake_case = max_face_number * dice_number
_snake_case = [0] * (max_total + 1)
_snake... | 103 | 0 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE ... | 226 |
"""simple docstring"""
import inspect
import unittest
from transformers import MobileNetVaConfig
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_con... | 103 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.... | 12 |
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class UpperCAmelCase ( yaml.SafeLoader ):
def __UpperCAmelCase ( self : Tuple , __lowerCamelCase : List[str] ... | 103 | 0 |
import re
from ..utils import cached_file
# docstyle-ignore
_lowercase = '''
Human: <<task>>
Assistant: '''
_lowercase = '''huggingface-tools/default-prompts'''
_lowercase = {'''chat''': '''chat_prompt_template.txt''', '''run''': '''run_prompt_template.txt'''}
def _A (Upper... | 157 |
"""simple docstring"""
# 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.or... | 103 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_dete... | 4 |
"""simple docstring"""
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is trul... | 103 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ = {
'''configuration_time_series_transformer''': [
'''TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''TimeSerie... | 47 |
"""simple docstring"""
from math import sqrt
def snake_case ( lowerCAmelCase_ = 1000000 ) -> int:
_snake_case = 0
_snake_case = 0
_snake_case = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2... | 103 | 0 |
from collections.abc import Generator
def lowercase__( ):
snake_case__ , snake_case__ : List[str] = 0, 1
while True:
snake_case__ , snake_case__ : int = b, a + b
yield b
def lowercase__( A = 1_0_0_0 ):
snake_c... | 170 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ... | 103 | 0 |
'''simple docstring'''
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from trans... | 215 |
"""simple docstring"""
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 FlaxX... | 103 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_availab... | 355 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torcha... | 103 | 0 |
"""simple docstring"""
def UpperCAmelCase ( a__ ):
'''simple docstring'''
if number > 0:
raise ValueError('input must be a negative integer' )
lowerCAmelCase :List[Any] = len(bin(lowerCAmelCase_ )[3:] )
lowerCAmelCase :Tuple = bin... | 553 |
"""simple docstring"""
import operator
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ = False , lowerCAmelCase_ = None ) -> list:
_snake_case = operator.lt if reverse else operator.gt
_snake_case = solution or []
if not arr:
retu... | 103 | 0 |
'''simple docstring'''
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=loggi... | 653 |
"""simple docstring"""
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.mode... | 103 | 0 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTest... | 331 |
"""simple docstring"""
from copy import deepcopy
class UpperCAmelCase :
def __init__( self : Optional[Any] , __lowerCamelCase : list[int] | None = None , __lowerCamelCase : int | None = None ):
"""simple docstring"""
... | 103 | 0 |
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_SCREAMING_SNAKE_CASE : Optional[int] = get_tests_dir('fixtures/test_sente... | 226 |
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic... | 103 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCamelCase__ : Optional[int] = {"""configuration_speech_encoder_decoder""": ["""SpeechEncoderDecoderConfig"""]}
try:
if not is_torch_available():
... | 12 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenizatio... | 103 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InformerConfig''',
],
}
try:
... | 157 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
A__ : str = '''bert-generation'''
def __init__( self : Tuple , __lowerCamelCase : Optional[int]=5_0_3_5_8 ... | 103 | 0 |
"""simple docstring"""
from pathlib import Path
import json
import tempfile
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
__UpperCamelCase : List[Any] = '''tiny-wmt19-en-ru'''
# Bui... | 4 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCAmelCase ( metaclass=__SCREAMING_SNAKE_CASE ):
A__ : Optional[int] = ['''torch''', '''scipy''']
def __init__( self : Any , *__lowerCamelCase : List[... | 103 | 0 |
from __future__ import annotations
import math
import random
from typing import Any
class _UpperCamelCase:
def __init__( self : Any ):
'''simple docstring'''
__a : List[Any] = []
__a : Dict = 0
__a... | 47 |
"""simple docstring"""
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ ) -> int:
return 1 if input_a == input_a else 0
def snake_case ( ) -> None:
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , ... | 103 | 0 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from tr... | 170 |
"""simple docstring"""
import os
from pathlib import Path
def snake_case ( ) -> Tuple:
from torch.utils.cpp_extension import load
_snake_case = Path(lowerCAmelCase_ ).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
_snake_case = ... | 103 | 0 |
'''simple docstring'''
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def __lowerCamelCase ( __snake_case : Optional[An... | 215 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixi... | 103 | 0 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_s... | 355 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
@staticmethod
@abstractmethod
def __UpperCAmelCase ( __lowerCamelCase : ArgumentParser ):
"... | 103 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.