code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a =logging.get_logger(__name__)
a ={"""vocab_file""": """spiece.mo... | 73 |
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ ) -> int:
__lowerCamelCase : Optional[int] = 0
__lowerCamelCase : Dict = len(lowerCamelCase__ ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[... | 73 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image... | 365 |
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
_A : Optional[Any] = {
'text_branch': 'text_model',
'audio_branch': 'audio_model.audio_encoder',
'attn': 'attention.self',
'self.proj': 'output.dense... | 265 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers impor... | 323 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils impo... | 323 | 1 |
"""simple docstring"""
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class _lowercase ( __UpperCAmelCase ):
lowercase_ = 'MCTCTFeatureExtractor'
lowercase_ = 'AutoTokenizer'
def __init__( self , Upp... | 205 |
"""simple docstring"""
def UpperCAmelCase ( a_ = 10 ):
'''simple docstring'''
if not isinstance(a_, a_ ) or n < 0:
raise ValueError('Invalid input' )
lowerCamelCase : Union[str, Any] = 10**n
lowerCamelCase : int = 2_8433 ... | 205 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import Heun... | 109 |
'''simple docstring'''
UpperCAmelCase_ : Dict = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
UpperCAmelCa... | 200 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMi... | 366 | """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 logging
SCREAMING_SNAKE_CASE__:Tu... | 268 | 0 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
__lowerCAmelCase = logging.get_logger(__na... | 89 |
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 ...test_tokenization_common import Toke... | 51 | 0 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=_a )
class A_ ( _a ):
lowerCAmelCase__ = field(default='image-classif... | 340 |
"""simple docstring"""
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def lowerCamelCase_( _lowerCamelCase ) -> Tuple:
'''simple docstring'''
_lowerC... | 340 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class lowerCAmelCase__ :
lowerCAmelCase : Optional[Any] = 42
lowerCAmelCase : List[st... | 234 |
'''simple docstring'''
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_... | 265 | 0 |
'''simple docstring'''
from __future__ import annotations
def _a( UpperCamelCase__ : List[str], UpperCamelCase__ : Union[str, Any], UpperCamelCase__ : int, UpperCamelCase__ : List[str] ): # noqa: E741
'''simple docstring'''
while r -... | 222 |
'''simple docstring'''
from decimal import Decimal, getcontext
from math import ceil, factorial
def _a( UpperCamelCase__ : int ):
'''simple docstring'''
if not isinstance(UpperCamelCase__, UpperCamelCase__ ):
raise TypeError(... | 222 | 1 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class __lowe... | 205 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class __lowe... | 205 | 1 |
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
SCREAMING_SNAKE_CASE_ = 2_9_9_7_9_2_4_5_8
# Symbols
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ... | 193 |
from __future__ import annotations
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> list[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = len(_SCREAMING_SNAKE_CASE ) - 1
... | 193 | 1 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#... | 53 |
"""simple docstring"""
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import V... | 268 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case_ = {'configuration_opt': ['OPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OPTConfig']}
try:
... | 356 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer
if TYPE_CHECKING:
... | 216 | 0 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=_UpperCAmelCase )
class lowercase__ ( _UpperCAmelCase ):
a_ =field(default="""image-classification""", meta... | 340 |
from collections import defaultdict
def _a ( UpperCamelCase_ : int ) -> int:
"""simple docstring"""
lowerCAmelCase__ = 1
lowerCAmelCase__ = True
for v in tree[start]:
if v not in visited:
ret += df... | 340 | 1 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
__snake_case : Optional[int] = """\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed ... | 367 |
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
| 122 | 0 |
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntimeModel... | 222 |
import requests
def A ( lowercase , lowercase ) -> None:
'''simple docstring'''
UpperCamelCase = {'Content-Type': 'application/json'}
UpperCamelCase = requests.post(lowercase , json={'text': message_body} , headers=lowercase )
if response.status_code !... | 222 | 1 |
import random
def snake_case_ ( lowerCAmelCase_ : int ):
__lowercase : List[str] = num - 1
__lowercase : List[str] = 0
while s % 2 == 0:
__lowercase : List[str] = s // 2
t += 1
for _ in... | 306 |
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_availa... | 306 | 1 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import... | 193 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
a__: Optional[int] = logging.get_logger(__name__)
a_... | 193 | 1 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeat... | 40 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_visio... | 40 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class __magic_name__ ( _UpperCamelCase ):
... | 89 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
import ... | 216 | 0 |
'''simple docstring'''
from __future__ import annotations
def UpperCAmelCase ( lowerCamelCase_ :list[int] ): # This function is recursive
'''simple docstring'''
snake_case_ : Tuple = len(lowerCamelCase_ )
# If the array contains only one element, we return... | 8 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : List[Any] = logging.get_logger(__name__)
__A : str = {
'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
# See all CANINE mode... | 8 | 1 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class UpperCamelCase_ :
'''simple docstring'''
UpperCAmelCase__ = 42
UpperCAmelCase__ = None
UpperCAmelCase__ = None
_lowerCamel... | 14 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def lowerCamelCase__ ( ) -> tuple[list[int], int]:
UpperCamelCase_ = [randint(-1000 , 1000 ) for i in range(10 )]
UpperCamelCase_ = ra... | 122 | 0 |
"""simple docstring"""
from math import ceil, sqrt
def __UpperCAmelCase ( UpperCAmelCase_ : int = 1_00_00_00 ) -> int:
'''simple docstring'''
__snake_case : int = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_widt... | 95 | """simple docstring"""
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
fro... | 95 | 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_avail... | 306 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils im... | 306 | 1 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( UpperCamelCase = 1000 ):
"""simple docstring"""
lowerCAmelCase__ : Union[str, Any] = -1
lowerCAmelCase__ : Optional[Any] = 0
for a in range(1 , n // 3 ):
# Solving the two equa... | 353 |
'''simple docstring'''
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase = True , UpperCamelCase = math.inf , UpperCamelCase = -math.inf , UpperCamelCase = math.inf ... | 184 | 0 |
"""simple docstring"""
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowercase = logging.get_logger(__name__)
... | 40 |
"""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... | 40 | 1 |
"""simple docstring"""
from __future__ import annotations
__UpperCamelCase = list[list[int]]
# assigning initial values to the grid
__UpperCamelCase = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[... | 312 | """simple docstring"""
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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_out... | 312 | 1 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ): # This function is recursive
snake_case_ = len(SCREAMING_SNAKE_CASE__ )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if ar... | 8 |
from __future__ import annotations
from math import pi, sqrt
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
if inductance <= 0:
raise ValueError('''Inductance cannot be 0 or negative''' )
elif capacitance <= 0:
r... | 8 | 1 |
"""simple docstring"""
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
... | 182 |
"""simple docstring"""
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
__lowerCAmelCase : str = {
'en': 'Machine learning is great, isn\'t it?',
'ru': 'Машинное об... | 182 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase : str = {
"""configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}
try:
if not ... | 95 |
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 : int = logging.get_logger(__name__)
... | 95 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCAmelCase = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):... | 267 |
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ ( self ):
snake_ca... | 267 | 1 |
from __future__ import annotations
import requests
def lowerCAmelCase_ ( _lowercase : str) -> Optional[int]:
"""simple docstring"""
a__ : str = F'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''
return request... | 170 |
import math
import os
import sys
def lowercase_ ( _A : str ):
"""simple docstring"""
lowerCamelCase__ : str = ""
try:
with open(_A , "rb" ) as binary_file:
lowerCamelCase__ : Union[str, Any] = binary_... | 184 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedC... | 356 |
from typing import TYPE_CHECKING
from ..utils import _LazyModule
lowercase_ = {
"""config""": [
"""EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
"""OnnxConfig""",
"""OnnxConfigWithPast""",
"""OnnxSeq2SeqConfigWithPast""",
"""PatchingSpec""",
],
... | 269 | 0 |
from __future__ import annotations
__a :List[str] = list[list[int]]
# assigning initial values to the grid
__a :Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, ... | 312 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def __snake_case ( ):
"""simple docstring"""
A_ = {
"repo_name": ["test_repo1", "test_repo2", "test_repo3"],
"... | 312 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
_A = {
'''configuration_ernie''': ['''ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieConfig''', '''ErnieOnnxConfig'''],
}
try:
if not is_to... | 369 |
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
_A = logging.get_l... | 167 | 0 |
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class lowerca... | 182 | import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('''dataset_size''' , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize('''input_in_memory_max_size''' , ['''default''', 0, 100 * 2**20, 900 * 2**20] )
def A ( _low... | 182 | 1 |
"""simple docstring"""
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelT... | 23 |
"""simple docstring"""
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
_a = (
'This metric will be removed from the library soon, met... | 23 | 1 |
'''simple docstring'''
def a__ ( a__ , a__ ):
"""simple docstring"""
return x if y == 0 else greatest_common_divisor(a__ , x % y )
def a__ ( a__ , a__ ):
"""simple docstring"""
return (x * y) // greatest_common_diviso... | 267 |
'''simple docstring'''
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... | 267 | 1 |
'''simple docstring'''
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
__snake_case : List[str] = '\\n@inproceedings{snover-etal-2006-study,\n title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",\n author = \"Snove... | 350 |
'''simple docstring'''
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
... | 136 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available():
... | 87 |
"""simple docstring"""
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeli... | 269 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Any = logging.get_logger(__name__)
lowercase : str = {
'google/realm-cc-news-pretrained-embedder': (
'https://huggingface.co/google/real... | 365 |
'''simple docstring'''
import argparse
import os
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_task_guides.py
lowercase : Any = 'src/transform... | 311 | 0 |
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... | 262 |
"""simple docstring"""
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForC... | 167 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_ut... | 274 | '''simple docstring'''
from manim import *
class __SCREAMING_SNAKE_CASE ( lowerCamelCase_ ):
'''simple docstring'''
def _UpperCamelCase ( self ):
'''simple docstring'''
UpperCAmelCase_ : Dict = Rectangle(height=0.5 , width=0.5 ... | 274 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class SCREAMING_S... | 23 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
... | 23 | 1 |
"""simple docstring"""
from decimal import Decimal, getcontext
from math import ceil, factorial
def __lowercase ( snake_case_ : int ) ->str:
'''simple docstring'''
if not isinstance(snake_case_ ,snake_case_ ):
raise TypeError('''Undefined for no... | 291 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import _LazyModule
a_ = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
a_ = _LazyModule(__name__, globals()["""... | 291 | 1 |
'''simple docstring'''
def UpperCamelCase_( snake_case : Optional[int] , snake_case : Optional[int] ):
'''simple docstring'''
snake_case_ = [0 for i in range(r + 1 )]
# nc0 = 1
snake_case_ = 1
for i in ... | 85 |
"""simple docstring"""
from __future__ import annotations
import math
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> list:
'''simple docstring'''
if len(__lowerCAmelCase ) != 2 or len(a[0] ) != 2 or len(__lowerCAmelCase ... | 136 | 0 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__SCREAMING_SNAKE_CASE = TypeVar("""KEY""")
__SCREAMING_SNAKE_CASE = TypeVar("""VAL""")
@dataclass(frozen=_A ,slots=_A )
class lowerCamelCase_ ( Gen... | 256 |
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGenerat... | 256 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
... | 81 |
'''simple docstring'''
from collections.abc import Generator
from math import sin
def lowercase ( __magic_name__ ):
'''simple docstring'''
if len(__magic_name__ ) != 32:
raise ValueError("Input must be of length 32" )
UpperCAmelCase : Un... | 311 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( UpperCAmelCase_ : int ) -> int:
'''simple docstring'''
if collection == []:
return []
# get some information about the collection
__snake_case : List[str] = len(lowercase__ )
__snake_case ... | 362 | """simple docstring"""
from __future__ import annotations
import math
def __UpperCAmelCase ( UpperCAmelCase_ : 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 == ... | 95 | 0 |
def __lowerCamelCase ( __a :Optional[Any] ) -> int:
"""simple docstring"""
A__ = 0
A__ = len(__a )
for i in range(n - 1 ):
for j in range(i + 1 , __a ):
if arr[i] > arr[j]:
num_inversions += 1
retur... | 274 |
import argparse
from collections import defaultdict
import yaml
A : str = '''docs/source/en/_toctree.yml'''
def __lowerCamelCase ( __a :str ) -> List[Any]:
"""simple docstring"""
A__ = defaultdict(__a )
A__ =... | 274 | 1 |
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipel... | 145 |
from __future__ import annotations
from collections.abc import Callable
__UpperCAmelCase = list[list[float | int]]
def lowercase__ ( __snake_case : Matrix , __snake_case : Matrix ):
'''simple docstring'''
UpperCAmelCase_ ... | 145 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, rand... | 291 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
... | 291 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a__ : int = {
'''configuration_ctrl''': ['''CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CTRLConfig'''],
'''tokenization... | 195 |
"""simple docstring"""
a__ : Optional[Any] = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
def UpperCAmelCase__ ():
'''simple docstring'''
__SCREAMING_SNAKE_CASE = input("Enter message: " )
__SCREAMING_SNAKE_CASE = input("Enter key... | 195 | 1 |
"""simple docstring"""
def lowercase ( a__ : int , a__ : int ) -> int:
return abs(a__ ) if a == 0 else greatest_common_divisor(b % a , a__ )
def lowercase ( a__ : int , a__ : int ) -> int:
while y: # --> when y=0 then loop will terminate and r... | 256 | """simple docstring"""
def lowercase ( a__ : int , a__ : int ) -> int:
return int((input_a, input_a).count(1 ) != 0 )
def lowercase ( ) -> None:
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or_gate(1 , 0 ) == ... | 256 | 1 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise Optio... | 363 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise Optio... | 318 | 0 |
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = (DDPMParallelScheduler,)
def SCREAMING_SNAKE_CASE_ (self : Any ,... | 10 |
# 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
#
# Unl... | 95 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowerCAmelCase : List[str] ={"""configuration_vit""": ["... | 32 | """simple docstring"""
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("""socket.socket""" )
@patch("""builtins.open""" )
def UpperCAmelCase__ ( lowerCAmelCase__ :Tuple , lowerCAmelCase__ :List[str] ) -> Union[str, Any]:
'... | 32 | 1 |
'''simple docstring'''
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
__a = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: ... | 145 | '''simple docstring'''
from __future__ import annotations
__a = list[tuple[int, int]]
__a = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0... | 145 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 362 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NA... | 51 | 0 |
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassi... | 195 |
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
infer_shape... | 195 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import ceil, floor, sqrt
def _snake_case ( A = 2000000 ) -> int:
lowerCAmelCase__ = [0]
lowerCAmelCase__ = 42
for idx in range(1 , ceil(sqrt(target * 2 ) *... | 228 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
class a__ ( ... | 228 | 1 |
"""simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
A = False
class __l... | 160 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : str = logging.get_logger(__name__)
__lowercase : Tuple = {'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}
class __lowercase ( _lowercas... | 318 | 0 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def lowerCamelCase ( _UpperCamelCase : Any ) -> int:
'''simple docstring'''
if "cls_token" in name:... | 320 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_... | 320 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_t... | 32 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_t... | 32 | 1 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME,... | 358 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
# See all Cvt models at https://huggingf... | 2 | 0 |
def lowerCAmelCase_ ( __a ) -> int:
"""simple docstring"""
if not isinstance(__a , __a ):
raise TypeError("only integers accepted as input" )
else:
lowerCamelCase__: Tuple =str(abs(__a ) )
lowerCamelCase__: Union[str, Any] =[list(__a ) for c... | 10 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_visi... | 51 | 0 |
def UpperCamelCase ( _a , _a ) -> str:
'''simple docstring'''
lowercase_ :int = ''''''
for word_or_phrase in separated:
if not isinstance(_a , _a ):
raise Exception('''join() accepts only strings to be ... | 252 |
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress... | 252 | 1 |
class __lowerCAmelCase :
def __init__( self :Dict , __magic_name__ :Union[str, Any] , __magic_name__ :List[str] ):
'''simple docstring'''
a = name
a = val
def __str__( ... | 228 |
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logg... | 228 | 1 |
from ...configuration_utils import PretrainedConfig
class snake_case_ ( __A ):
__A : Optional[Any] = "bert-generation"
def __init__( self : Optional[int] , lowercase_ : Any=5_03_58 , lowercase_ : List[Any]=10_24 , lowerca... | 353 | import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_sco... | 333 | 0 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def A_ ( _lowerCAmelCase : Tuple ):
"""simple docstring"""
if "cls_token" in name:
_a ... | 320 |
"""simple docstring"""
def A_ ( ):
"""simple docstring"""
_a = []
_a = 1
while len(_lowerCAmelCase ) < 1e6:
constant.append(str(_lowerCAmelCase ) )
i += 1
_a = ''''''.join(_lowerCAmelCase )
return (
int(... | 320 | 1 |
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_... | 328 |
import argparse
import json
from tqdm import tqdm
def lowerCAmelCase_ ( ) -> Tuple:
UpperCamelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--src_path" , type=UpperCamelCase_ , default="biencoder-nq-de... | 328 | 1 |
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import ... | 9 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE (A ) -> int:
"""simple docstring"""
if not isinstance(A , A ):
raise TypeError('''only integers accepted as input''' )
else:
lowercase__ = str(abs(A ) ... | 2 | 0 |
import numpy as np
from transformers import Pipeline
def snake_case__ ( SCREAMING_SNAKE_CASE_ : Optional[Any] ):
'''simple docstring'''
lowercase__ : str = np.max(SCREAMING_SNAKE_CASE_ , axis=-1 , keepdims=SCREAMING_SNAKE_CASE_ )
lowercase__... | 216 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer
if TYPE_CHECKING:
... | 216 | 1 |
from __future__ import annotations
def __lowerCamelCase ( lowerCamelCase__ : int , lowerCamelCase__ : int ):
'''simple docstring'''
if b == 0:
return (1, 0)
((lowerCamelCase) , (lowerCamelCase)) = extended_euclid(lowerCamelCase__ , a % b... | 252 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_t... | 252 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ = {'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig'''... | 34 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase_ = {
'''configuration_groupvit''': [
'''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''GroupViTConfig''',
'''GroupViTOnnxConfig''... | 34 | 1 |
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def UpperCAmelCase_ ( _A ):
'''simple docstring'''
return ... | 314 |
def __a ( ) -> list[list[int]]:
'''simple docstring'''
return [list(range(1_0_0_0 - i , -1_0_0_0 - i , -1 ) ) for i in range(1_0_0_0 )]
A_ : Union[str, Any] = generate_large_matrix()
A_ : Union[str, Any] = (
[[4,... | 333 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
_UpperCamelCase: Union[str, Any] = logging.get_logger(__name__)
class a__ ( SCREAMING_SNAKE_CASE__ ):
def __init__( self... | 53 |
"""simple docstring"""
def lowercase__ ( _UpperCAmelCase ) -> int:
'''simple docstring'''
assert isinstance(_UpperCAmelCase , _UpperCAmelCase ), f'''The input value of [n={number}] is not an integer'''
if number == 1:
ret... | 53 | 1 |
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils... | 328 |
from math import factorial
def A_ ( snake_case : int = 100 ) -> int:
'''simple docstring'''
return sum(int(snake_case ) for x in str(factorial(snake_case ) ) )
if __name__ == "__main__":
print(solution(int(input("Enter the Number: "... | 328 | 1 |
from math import factorial
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ = 1_0_0 )-> int:
"""simple docstring"""
return sum(int(SCREAMING_SNAKE_CASE_ ) for x in str(factorial(SCREAMING_SNAKE_CASE_ ) ) )
if __name__ == "__main__":
print(solution(int(input("""Enter the Nu... | 60 |
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
... | 60 | 1 |
import numpy as np
import datasets
lowercase__ ='\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced by Prof. P. C. Mahalan... | 216 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
import ... | 216 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase_ = {
'''configuration_m2m_100''': ['''M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''M2M100Config''', '''M2M100OnnxC... | 174 |
'''simple docstring'''
def __lowercase ( __lowercase ) -> int:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ):
raise ValueError("multiplicative_persistence() only accepts integral values" )
if num < 0:
raise ValueError(... | 174 | 1 |
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_u... | 34 |
'''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... | 34 | 1 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_U... | 35 | import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
lowercase = logging.get_logger(__name__)
class __lowercase ( A ):
'''simple docstring'''
def __init__( self : List[str] , *... | 35 | 1 |
'''simple docstring'''
def lowercase__ ( __lowercase : int ) -> "list[int]":
"""simple docstring"""
if upper_limit < 0:
raise ValueError('Limit for the Catalan sequence must be ≥ 0' )
__UpperCamelCase = [0] * (upper_limit + 1)
... | 53 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class snake_case ( __lowerCamelCase ):
"""simple docstring"""
pass
class snake_case :
"""simple docstring"""
def __init__( self : List[Any] , __A : ... | 53 | 1 |
'''simple docstring'''
__lowerCamelCase = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
def UpperC... | 101 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
... | 101 | 1 |
"""simple docstring"""
import math
import sys
def _snake_case ( _snake_case : int ):
if number != int(_snake_case ):
raise ValueError('''the value of input must be a natural number''' )
if number < 0:
raise ValueError('''the value of input must not be a negative number... | 60 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_fla... | 60 | 1 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class lowerCamelCase__ ( unittest.TestCase ):
def _lowerCamelCase ( self : Optional[int] ):
... | 42 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
__UpperCAmelCase = 5_00_00
__UpperCAmelCase = 50_00
__UpperCAmelCase , __UpperCAmelCase = os.path.split(__file__)
__UpperCAmelCase = os.path.jo... | 42 | 1 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def __magic_name__( lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase):
# prepare kernel
# the kernel size have... | 174 |
'''simple docstring'''
from collections.abc import Generator
def __magic_name__( ):
__lowerCAmelCase , __lowerCAmelCase = 0, 1
while True:
__lowerCAmelCase , __lowerCAmelCase = b, a + b
yield b
def __magic_name__( low... | 174 | 1 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a__ ( __snake_case ):
A__ : Dict = 'ClapFeatureExtractor'
A__ : Optional[int] = ('RobertaTokenizer', 'RobertaTokenizerFast')
def ... | 352 | from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
lowerCamelCase_ : Union[str, Any] = {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""... | 197 | 0 |
'''simple docstring'''
from typing import Any
import numpy as np
def __snake_case( _lowerCAmelCase ) -> bool:
return np.array_equal(_lowerCAmelCase , matrix.conjugate().T )
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> Any:
snake_case__ : ... | 35 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from... | 35 | 1 |
"""simple docstring"""
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
UpperCamelCase = logging.get_logger(__name__)... | 356 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import ... | 65 | 0 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
lowercase = ('''dense.weight''', '''attention.self.qu... | 101 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, Bit... | 101 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Tuple = logging.get_logger(__name__)
_lowerCamelCase : Tuple = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.j... | 365 | from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCamelCase : Any = {'... | 206 | 0 |
'''simple docstring'''
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mo... | 42 |
'''simple docstring'''
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : int = logging.get_logger(__name__)
lowercase : Union[str, Any] = {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/... | 42 | 1 |
"""simple docstring"""
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTest... | 336 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import c... | 336 | 1 |
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_confi... | 20 | """simple docstring"""
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class _A ( lowerCAmelCase , ... | 197 | 0 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_avai... | 0 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : int = 1_00 ) -> int:
__snake_case : Any = n * (n + 1) * (2 * n + 1) / 6
__snake_case : Union[str, Any] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == ... | 0 | 1 |
'''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... | 4 | from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class A ( UpperCAmelCase_ ):
__UpperCAm... | 65 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from trans... | 350 |
"""simple docstring"""
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_... | 1 | 0 |
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