code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params impo... | 458 |
from __future__ import annotations
import numpy as np
def __magic_name__ ( lowercase ) -> Tuple:
"""simple docstring"""
return np.maximum(0 , lowercase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5] | 458 | 1 |
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def lowercase ( _a ,_a ,_a ,_a ) -> Union[str, Any]:
UpperCAmelCase_: str = s.rsplit(_a ,_a )
return new.join(_a )
def lowercase ( _... | 710 |
_lowerCAmelCase = frozenset(
[
"""prompt""",
"""height""",
"""width""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
"""cross_attention_kwargs""",
]
)
_lowerCAmelCase = ... | 306 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
UpperCAmelCase__ : List[str] = logging.get_logger(__name__)
class lowerCAmelCase_ (a__ ):
"""simple docstring"""
... | 223 |
"""simple docstring"""
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
UpperCAmelCase__ : Optional[Any] = {'UserAgent': UserAgent().random}
def lowercase_ ( _snake_case ):
SCREAMIN... | 223 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ = {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}
t... | 708 | def lowerCAmelCase_ ( lowercase: float ) -> float:
'''simple docstring'''
if edge <= 0 or not isinstance(lowercase , lowercase ):
raise ValueError('''Length must be a positive.''' )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
def lowerCAmelCase_ ... | 264 | 0 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
... | 22 |
from __future__ import annotations
def _UpperCAmelCase ( a__):
'''simple docstring'''
if len(a__) == 0:
return []
a_ , a_ : List[Any] = min(a__), max(a__)
a_ : Tuple = int(max_value - min_value) + 1
a_ : list[list] = ... | 540 | 0 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
snake_case__ : Dict = """2.13.1"""
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < versi... | 707 |
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
snake_case__ : List[str] = logging.get_logger(__name__)
class _a ( UpperCAmelCase__ ):
"""simple docstring"""
def __init__( ... | 618 | 0 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
lowerCAmelCase_ = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and Mik... | 326 |
from __future__ import annotations
def A_ ( lowercase_ ) -> bool:
_snake_case : Tuple = str(lowercase_ )
return len(lowercase_ ) == 9 and set(lowercase_ ) == set('''123456789''' )
def A_ ( ) -> int | None:
for base_num in range(9999 , ... | 326 | 1 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
SCREAMING_SNAKE_CASE__ = [
"good first issue",
"good second issue",
"good difficult issue",
"enhancement",
"new pipeline/model",
"new scheduler",
"wip",
]
... | 711 | """simple docstring"""
import os
import string
import sys
SCREAMING_SNAKE_CASE__ = 1 << 8
SCREAMING_SNAKE_CASE__ = {
"tab": ord("\t"),
"newline": ord("\r"),
"esc": 27,
"up": 65 + ARROW_KEY_FLAG,
"down": 66 + ARROW_KEY_FLAG,
"right": 67 + ARROW_KEY_FLAG,
... | 104 | 0 |
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
__A =TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow('''''', '''|''', '''|'''),
datarow=DataRow(''''''... | 463 |
"""simple docstring"""
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_... | 617 | 0 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class SCREAMING_SNAKE_CASE ( ... | 218 |
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/main/config.json""",
}
class SCRE... | 218 | 1 |
from __future__ import annotations
def a ( A__ ) -> list[int]:
'''simple docstring'''
if len(A__ ) == 0:
return array
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = min(A__ ), max(A__ )
# Compute th... | 35 |
"""simple docstring"""
def __snake_case ( ) -> Union[str, Any]:
lowercase : str = 0
for i in range(1 ,1001 ):
total += i**i
return str(__A )[-10:]
if __name__ == "__main__":
print(solution())
| 607 | 0 |
'''simple docstring'''
from __future__ import annotations
def _UpperCAmelCase ( _UpperCamelCase : list[int], _UpperCamelCase : int ) -> list[int]:
A_ = 0
A_ = len(_UpperCamelCase ) - 1
while i < j:
if nums[i] + nums[j] == ... | 716 | '''simple docstring'''
from itertools import product
def _UpperCAmelCase ( _UpperCamelCase : int, _UpperCamelCase : int ) -> list[int]:
A_ = sides_number
A_ = max_face_number * dice_number
A_ = [0] * (max_total + 1)
... | 174 | 0 |
'''simple docstring'''
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_... | 26 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer... | 117 | 0 |
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attenti... | 641 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
... | 641 | 1 |
from collections.abc import Sequence
def UpperCamelCase ( snake_case__ , snake_case__):
return sum(c * (x**i) for i, c in enumerate(lowercase_))
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : Tuple = 0.0
for coeff in reversed(lowerc... | 659 |
"""simple docstring"""
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
... | 661 | 0 |
from math import factorial
def __A ( _A = 100 ):
"""simple docstring"""
return sum(int(_A ) for x in str(factorial(_A ) ) )
if __name__ == "__main__":
print(solution(int(input("""Enter the Number: """).strip())))
| 525 | def __A ( _A ):
"""simple docstring"""
__a = []
for data in source_data:
for i, el in enumerate(_A ):
if len(_A ) < i + 1:
data_lists.append([] )
data_lists[i].append(float(_A ) )
return data_lists
def __A ( _A , _A ):
... | 525 | 1 |
from __future__ import annotations
def lowerCamelCase_ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
if days_between_payments <= 0:
raise ValueError('''days_between_payments must be > 0''' )
if daily_interest_rate < 0:
raise ValueError('''daily_inter... | 141 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from t... | 141 | 1 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
__UpperCAmelCase : Optional[Any] = (3, 9, -11, 0, 7, 5, 1, -1)
__UpperCAmelCase : int = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class lowerCamelCase :
UpperCAmelCase : ... | 712 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def lowerCamelCase_ ( UpperCamelCase_ = 8 ):
_a : int = ascii_letters + digits + punctuation
return "".join(secrets.choice(UpperCamelCase_ ) fo... | 249 | 0 |
"""simple docstring"""
from random import randint, random
def _UpperCamelCase ( A , A , A , A = False , A = False , A = 5 , ):
UpperCamelCase_ =[[-1] * number_of_cells] # Create a highway without any car
UpperCamelCase_ ... | 391 |
'''simple docstring'''
from statistics import mean, stdev
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = 3 ):
__a : List[str] = min(SCREAMING_SNAKE_CASE__ )
__a : Tuple = max(SCREAMING_SNAKE_CASE... | 597 | 0 |
"""simple docstring"""
def snake_case__ ( __lowerCamelCase : str , __lowerCamelCase : str ):
"""simple docstring"""
if not (isinstance(__lowerCamelCase , __lowerCamelCase ) and isinstance(__lowerCamelCase , __lowerCamelCase )):
raise ValueError(''... | 705 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_lowercase : Optional[Any] = {
"conf... | 625 | 0 |
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('''hub/hopper-medium-v2/unet/hor32''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/unet/hor128''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/value_function''', exist_ok=True)
def _SCREAMING_SNAKE_CASE... | 239 |
from collections.abc import Iterable
from typing import Any
class _A:
"""simple docstring"""
def __init__( self , _A = None ):
__A : Any = value
__A : Node | None = None # Added in order to delete a node easier
__A :... | 239 | 1 |
def lowerCamelCase__ ( _lowerCamelCase , _lowerCamelCase ) ->str:
_UpperCAmelCase =""
for word_or_phrase in separated:
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise Exception("join() accepts only strings to be joined" )
joined += word_or_phrase + separator
ret... | 592 |
import os
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
snake_case__ : Union[str, Any] = logging.get_logger(__name__)
snake_case__ : Union[st... | 592 | 1 |
'''simple docstring'''
import datasets
UpperCAmelCase__ = '''\
@InProceedings{conneau2018xnli,
author = "Conneau, Alexis
and Rinott, Ruty
and Lample, Guillaume
and Williams, Adina
and Bowman, Samuel R.
and Schwenk, H... | 186 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
... | 186 | 1 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class lowerCAm... | 188 |
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import Fla... | 188 | 1 |
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import * | 31 |
def __magic_name__ ( __lowerCAmelCase : Any , __lowerCAmelCase : Optional[int] ) -> Optional[Any]:
__lowerCamelCase = [1]
for i in range(2 , __lowerCAmelCase ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * ... | 298 | 0 |
'''simple docstring'''
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_comm... | 427 |
'''simple docstring'''
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be check... | 427 | 1 |
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.schedul... | 14 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_enco... | 14 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase__ : Tuple = {
"configuration_poolformer": [
"POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"PoolFormerConfig",
"Po... | 719 |
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
UpperCamelCase__ : Union[str, Any] = logging.get_logger(__name__)
UpperCamelCase__ : O... | 620 | 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 transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
... | 208 |
'''simple docstring'''
def snake_case ( a_ : list ) -> list:
"""simple docstring"""
UpperCamelCase_ : List[str] = False
while is_sorted is False: # Until all the indices are traversed keep looping
UpperCamelCase_ : Tuple ... | 208 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_commo... | 489 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE ... | 489 | 1 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def lowercase__ ( snake_case_ :list[list[float]] ):
__UpperCAmelCase = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since ... | 49 |
'''simple docstring'''
import copy
import random
from transformers import CLIPTokenizer
class __SCREAMING_SNAKE_CASE ( _lowerCAmelCase ):
def __init__( self , *lowerCamelCase , **lowerCamelCase ) ->Union[str, Any]:
'''simple docstring'''
... | 448 | 0 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"""files""" , [
["""full:README.md""", """dataset_infos.json"""],
["""empty:README.md""", """data... | 83 | def snake_case ( snake_case__ :int , snake_case__ :int) -> int:
return int(input_a == input_a == 0)
def snake_case ( ) -> None:
print("""Truth Table of NOR Gate:""")
print("""| Input 1 | Input 2 | Output |""")
print(F'''| 0 ... | 83 | 1 |
def __UpperCAmelCase ( __A , __A ) -> int:
'''simple docstring'''
return abs(__A ) if a == 0 else greatest_common_divisor(b % a , __A )
def __UpperCAmelCase ( __A , __A ) -> int:
'''simple d... | 475 |
def lowerCAmelCase ( UpperCAmelCase, UpperCAmelCase ) ->int:
"""simple docstring"""
return abs(UpperCAmelCase ) if a == 0 else greatest_common_divisor(b % a, UpperCAmelCase )
def lowerCAmelCase ( UpperCAmelCase, ... | 154 | 0 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import loggin... | 557 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class SCREAMING_SNAKE_CASE_ ( _a ):
"""simple docstring... | 557 | 1 |
# flake8: noqa
# Lint as: python3
lowerCamelCase : Any = [
"VerificationMode",
"Version",
"disable_progress_bar",
"enable_progress_bar",
"is_progress_bar_enabled",
"experimental",
]
from .info_utils import VerificationMode
from .logging import disabl... | 70 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
return int((input_a, input_a).count(0 ) == 0 )
def __magic_name__ ( ) -> None:
'''simple docstring'''
assert and_... | 640 | 0 |
"""simple docstring"""
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase ) -> Any:
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
lowerCAmelCase__ : List[Any] = (boundary[1] - boundary[0]) / steps
lowerCAmelCase__ : Opt... | 705 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class _lowerCamelCase ( nn.Module ):
def __init__( self : Optional[Any] , UpperCamelCase : int = 16 , UpperCamelCase : int ... | 507 | 0 |
'''simple docstring'''
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.... | 50 |
lowerCamelCase_ : Tuple = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yottametre""": """Ym""",
}
# Expo... | 548 | 0 |
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase):
@requir... | 718 |
'''simple docstring'''
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class SCREAMING_SNAKE_CASE__ ( tf.keras.layers.Layer):
... | 432 | 0 |
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokeniz... | 663 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterM... | 663 | 1 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
return x if y == 0 else greatest_common_divisor(_UpperCamelCase , x % y )
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamel... | 721 |
'''simple docstring'''
from math import factorial
_lowerCAmelCase = {str(d): factorial(d) for d in range(10)}
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
return sum(DIGIT_FACTORIAL[d] for d in str(UpperCamelCase ) )
def _SCREAMING_SNAKE_CA... | 160 | 0 |
'''simple docstring'''
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils i... | 640 | import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class _UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self ) -> int:
'''simple docstring'''
... | 534 | 0 |
'''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
... | 666 |
'''simple docstring'''
import sys
__lowerCAmelCase = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'1254069874715852386305071569329096329522... | 666 | 1 |
'''simple docstring'''
import numpy as np
import datasets
lowerCAmelCase_ = '''
Compute the Mahalanobis Distance
Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distanc... | 531 |
'''simple docstring'''
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
fro... | 531 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ :Union[str, Any] = {
'''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
'''tokenization_luke''': ['''LukeTokenizer'''],
}... | 154 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
A_ :Dict = {
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'... | 154 | 1 |
'''simple docstring'''
def lowerCAmelCase (__A):
"""simple docstring"""
_a = 0
while len(a__) > 1:
_a = 0
# Consider two files with minimum cost to be merged
for _ in range(2):
_a = files.index(min(a__))... | 11 |
'''simple docstring'''
from collections import deque
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : int , snake_case : str , snake_case : int , snake_case : int ):
"""simple docstring"""
... | 517 | 0 |
"""simple docstring"""
import pytest
import datasets
# Import fixture modules as plugins
__lowerCAmelCase : Dict =["""tests.fixtures.files""", """tests.fixtures.hub""", """tests.fixtures.fsspec"""]
def UpperCAmelCase__ ( lowerCAmelCase__ :Dict , lowerCAmelCase__ :O... | 197 | """simple docstring"""
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy... | 197 | 1 |
'''simple docstring'''
def __UpperCamelCase( _A : str , _A : str = " " ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = []
UpperCAmelCase__ : Any = 0
for index, char in enumerate(_A ):
if char == separator:
split_words.append(... | 614 | '''simple docstring'''
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def __UpperCamelCase( _A : Any , _A : List[str]=() , _A : List[str]=None ... | 614 | 1 |
'''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 Mode... | 718 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : List[str] = {
'''google/pix2struct-textcaps-base''': ... | 581 | 0 |
from __future__ import annotations
def UpperCamelCase ( __lowercase : list[int] ,__lowercase : int ):
'''simple docstring'''
if len(__lowercase ) < k or k < 0:
raise ValueError('Invalid Input' )
A_ : Dict = sum(array[:k] )
for i ... | 558 | from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
... | 558 | 1 |
from __future__ import annotations
def _lowerCAmelCase ( __magic_name__ :Tuple , __magic_name__ :Optional[int] ):
if b == 0:
return (1, 0)
(UpperCAmelCase_) = extended_euclid(__magic_name__ , a % b )
UpperCAmelCase_ = a // ... | 719 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def _lowerCAmelCase ( __magic_name__ :list , __magic_name__ :list , __magic_name__ :list , ... | 407 | 0 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor ... | 2 |
def A__ ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ) -> str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
_UpperCAmelCase = str(bin(SCREAMING_SNAKE_C... | 32 | 0 |
'''simple docstring'''
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class __UpperCAmelCase :
'''simple docstring'''
def __init__( self , _UpperCAmelCase... | 599 |
'''simple docstring'''
def lowerCAmelCase__ ( a_ : float , a_ : list[float] ) -> float:
if discount_rate < 0:
raise ValueError('''Discount rate cannot be negative''' )
if not cash_flows:
raise ValueError('''Cash flows list cannot be emp... | 599 | 1 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_opt... | 2 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transfo... | 2 | 1 |
import argparse
import copy
def UpperCamelCase_( __magic_name__ : Tuple ):
"""simple docstring"""
_lowerCAmelCase :int = {}
with open(__magic_name__ ) as f:
for line in f:
if line.split()[0] not in dict_o... | 708 |
from __future__ import annotations
from math import pow, sqrt
def UpperCamelCase_( __magic_name__ : float , __magic_name__ : float , __magic_name__ : float ):
"""simple docstring"""
if (resistance, reactance, impedance).count(0 ) != 1:
... | 382 | 0 |
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _lower... | 590 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A_ : List[Any] = {
'configuration_lxmert': ['LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LxmertConfig'... | 303 | 0 |
class _a :
'''simple docstring'''
def __init__( self ):
__A : Union[str, Any] = ""
__A : Optional[Any] = ""
__A : Dict = []
def __UpperCAmelCase( self , __UpperCAmelCase , __UpperCAmelCase ):
... | 714 | import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
Diffusio... | 387 | 0 |
'''simple docstring'''
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def UpperCAmelCase_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_... | 310 |
'''simple docstring'''
from math import sqrt
def UpperCAmelCase_ ( lowerCAmelCase_ ):
"""simple docstring"""
lowercase = 0
for i in range(1 , int(sqrt(lowerCAmelCase_ ) + 1 ) ):
if n % i == 0 and i != sqrt(lowerCAmelCase_ ):
total... | 310 | 1 |
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ... | 105 |
def SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
_UpperCamelCase = 0
for ch in input_str:
_UpperCamelCase = ord(lowerCAmelCase )
_UpperCamelCase = pow(2 , lowerCAmelCase )
# I... | 105 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class lowerCamelCase_ ( _SC... | 31 |
from __future__ import annotations
from typing import TypedDict
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = 42
lowercase_ = 42
def UpperCAmelCase_ ( __UpperCAmelCase : str ) -> list[str]:... | 31 | 1 |
'''simple docstring'''
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 715 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE_ ( _a , unittest.TestCase ):
"""simple docstring"""
__low... | 557 | 0 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAG... | 291 |
"""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_atten... | 510 | 0 |
def UpperCamelCase_ ( a_ , a_ , a_ ) ->int:
if exponent == 1:
return base
if exponent % 2 == 0:
A =_modexpt(a_ , exponent // 2 , a_ ) % modulo_value
return (x * x) % modulo_value
else:
return (base * _modexpt(a_ , exponent - 1 ... | 689 |
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
__a = """\
@inproceedings{popovic-2015-chrf,
title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",
author = \"Popovi{\'c}, Maja\",
booktitle = \"Proceedings of the Tenth Wo... | 689 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE : Union[str, Any] = {
"""configuration_convnext""": ["""CONVNEXT_PRETRAINED_CONFI... | 141 |
'''simple docstring'''
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : int = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ... | 448 | 0 |
'''simple docstring'''
def _a ( __UpperCamelCase ):
if len(__UpperCamelCase ) <= 1:
return [tuple(__UpperCamelCase )]
a_ : List[Any] = []
def generate(__UpperCamelCase , __UpperCamelCase ):
a_ : Optional[Any] = [0] * n
... | 720 |
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
__lowerCamelCase = (
'''4S 3H 2C 7S 5H''',
'''9D 8H 2C 6S 7H''',
'''2D 6D 9D TH 7D''',
'''TC 8C 2S JH 6C''',
'''JH 8S TH AH QH''',
'''TS KS 5S 9S AC''',
'''K... | 478 | 0 |
UpperCAmelCase_ = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yottametre""": """Ym""",
}... | 2 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class A_ ( __UpperCamelCase ):
'''simple docstring'''
__snake_case = ["""image_processor""", """tokenizer"""]
__snake_case = """CLIPImageProcessor"""... | 669 | 0 |
import qiskit
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = qiskit.Aer.get_backend("aer_simulator" )
lowerCamelCase_ = qiskit.QuantumCircuit(4 , 2 )
# encode inputs in qubits 0 and 1
if bita == 1:
qc... | 313 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxT... | 313 | 1 |
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class a ( lowercase__ ):
... | 63 | '''simple docstring'''
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_image... | 78 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowerCAmelCase = {
"""configuration_mobilevit""": ["""MOBILEVIT_PRETRAINED_CONFIG_ARCHIV... | 719 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase = {
"""configuration_timesformer""": ["""TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TimesformerConfig"""],
}
try:
if not is_tor... | 319 | 0 |
"""simple docstring"""
def lowerCAmelCase__ ( __magic_name__ = 1_0 ) ->str:
if not isinstance(__magic_name__ , __magic_name__ ) or n < 0:
raise ValueError("Invalid input" )
__lowercase = 1_0**n
__lowercase = 2_8_4_3_3 * (pow(2 ,... | 118 |
"""simple docstring"""
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
_lowercase = get_logger(__name__)
class __a ( enum.Enum ):
'''simple docstring'''
... | 118 | 1 |
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
from transformers import AutoImageProcessor, Res... | 146 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTes... | 146 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_... | 70 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import... | 70 | 1 |
import os
import sys
import transformers
__lowerCAmelCase : str = '''3'''
print('''Python version:''', sys.version)
print('''transformers version:''', transformers.__version__)
try:
import torch
print('''Torch version:''', torch.__version__)
print('''Cuda available:''', torch.c... | 705 |
"""simple docstring"""
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
... | 158 | 0 |
"""simple docstring"""
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchv... | 83 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}
cla... | 609 | 0 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
requ... | 619 |
from __future__ import annotations
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , )-> tuple:
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif electron_conc < 0:
rai... | 619 | 1 |
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
A__ = logging.get_logger(__name__)
class _lowerCAmelCase ( __lowercase ):
def __init__( self : List[Any] , *__snake_case : List[Any] , ... | 166 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokenizati... | 298 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_re... | 657 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by a... | 657 | 1 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
A_ : Optional[int] = logging.get_logger(__name__)
A_ : Optional[Any] ... | 57 | from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.util... | 221 | 0 |
"""simple docstring"""
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
lowerCAmelCase_ : Dict = '''<<<<<<< This should probably be modified because it m... | 378 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchau... | 378 | 1 |
from __future__ import annotations
from math import gcd
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase = 2 , __lowerCAmelCase = 1 , __lowerCAmelCase = 3 , ) -> int | None:
"""simple docstring"""
if num < 2:
raise ValueError('''The... | 252 |
from __future__ import annotations
from typing import Any
class a :
def __init__( self :Union[str, Any] ,__lowercase :int ,__lowercase :int ,__lowercase :float = 0 ):
snake_case__ , snake_case__ : Dict = row, column
snake_case__ : Tuple ... | 252 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
lowerCAmelCase = {"""tokenization_herbert""": ["""HerbertTokenizer"""]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDe... | 675 |
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> list:
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = int(lowercase_ )
if n_element < 1:
__UpperCAmelCase : str = ValueError('''a should be a positive number''' )
... | 675 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__snake_case = {
"""configuration_efficientformer""": [
"""EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",... | 658 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__lowerCamelCase )
class UpperCAmelCase ( __lowerCamelCase ):
a__: str = fi... | 583 | 0 |
"""simple docstring"""
import gc
import threading
import time
import psutil
import torch
class __snake_case:
def __init__( self ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = psutil.Process()
_SCREAMING_SNAKE_... | 168 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def A__ ( UpperCamelCase__ ):
'''simple docstring'''
if (
(cp >= 0x4E00 and cp <= 0x... | 168 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def snake_case__ ( _A: Optional[int] , _A: Union[str, Any] , _A: Any ) ->... | 370 |
'''simple docstring'''
from collections.abc import Sequence
def a_ ( __snake_case : Sequence[float] , __snake_case : float ) -> float:
"""simple docstring"""
return sum(c * (x**i) for i, c in enumerate(__snake_case ) )
def a_ ( __snake_case : Se... | 676 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
__lowerCAmelCase = logging.get_logg... | 712 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowerCAmelCase = {
"""configuration_squeezebert""": [
"""SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Sque... | 319 | 0 |
'''simple docstring'''
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def lowerCAmelCase_ ( _lowerCamelCase: Union[dict, list, tup... | 578 |
# 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
#
# Unless required by applic... | 419 | 0 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a = logging.get_logger(__name__)
a = {
'SenseTime/deformable-detr': 'https://huggingface.co/sensetime/def... | 347 |
'''simple docstring'''
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_ ( snake_cas... | 347 | 1 |
'''simple docstring'''
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_uti... | 158 |
'''simple docstring'''
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 Mode... | 158 | 1 |
'''simple docstring'''
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class A ( a ):
__UpperCAmelCase : ... | 691 |
'''simple docstring'''
__snake_case : List[str] = "Tobias Carryer"
from time import time
class A :
def __init__( self , snake_case_ , snake_case_ , snake_case_ , snake_case_=int(time() ) ) -> str: # noqa: B008
_a = mul... | 691 | 1 |
"""simple docstring"""
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
lowerCamelCase_ = '''\
@article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks
and Collin Burns
and Saurav K... | 95 |
'''simple docstring'''
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
... | 446 | 0 |
"""simple docstring"""
from functools import lru_cache
def a__ ( __lowercase ) -> set:
_A = 2
_A = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(__lowercase )
if n > 1:
... | 718 |
"""simple docstring"""
import numpy as np
def a__ ( __lowercase , __lowercase ) -> np.ndarray:
return np.where(vector > 0 , __lowercase , (alpha * (np.exp(__lowercase ) - 1)) )
if __name__ == "__main__":
import doctest
doctest.testmod() | 621 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoCon... | 309 | '''simple docstring'''
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel... | 309 | 1 |
"""simple docstring"""
snake_case_ : List[str] = 6_5_5_2_1
def lowercase_ ( _lowercase : str ):
'''simple docstring'''
UpperCAmelCase : Dict = 1
UpperCAmelCase : Optional[int] = 0
for plain_chr in plain_text:
UpperCA... | 712 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_av... | 292 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowercase: Any = logging.get_logger(__name__)
_lowercase: List[Any] = {"""vocab_file""": """sentencepiec... | 192 |
"""simple docstring"""
import colorsys
from PIL import Image # type: ignore
def UpperCamelCase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
UpperCamelCase : Optional[int] = x
UpperCamelCase : str = ... | 102 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowerCAmelCase__ ( lowerCamelCase__ , lowerCamelCase__ ) -> list[tuple[int, int]]:
A , A = position
A = [
(y + 1, x + 2),
(y - 1, x + 2),
(y + 1,... | 109 |
"""simple docstring"""
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def lowerCAmelCase__ ( ) -> Optional[Any]:
import os as original_os
from os import path as original_path
from os import rename as original_rename
... | 109 | 1 |
'''simple docstring'''
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_uti... | 116 |
'''simple docstring'''
def lowerCamelCase__ ( __lowercase ):
if not isinstance(__lowercase , __lowercase ):
snake_case : int = F'''Input value of [number={number}] must be an integer'''
raise TypeError(__lowercase )
... | 116 | 1 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import Sequen... | 708 | import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"""microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json""",
... | 70 | 0 |
def lowerCamelCase_ ( UpperCamelCase__ : str, UpperCamelCase__ : Any ):
'''simple docstring'''
UpperCamelCase__ = (boundary[1] - boundary[0]) / steps
UpperCamelCase__ = boundary[0]
UpperCamelCase__ = boundary[1]... | 240 | 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.ut... | 240 | 1 |
"""simple docstring"""
from bisect import bisect
from itertools import accumulate
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
__A = sorted(zip(__UpperCamelCase , __UpperCamelCase ) , key... | 703 |
"""simple docstring"""
lowercase_ = 8.31_4462 # Unit - J mol-1 K-1
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError('''Invalid inputs. Enter positiv... | 215 | 0 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
lowercase : Dict = transf... | 568 |
from __future__ import annotations
def a_ ( lowerCAmelCase_ : list[float] ):
if len(lowerCAmelCase_ ) < 2:
raise ValueError('Monogons and Digons are not polygons in the Euclidean space' )
if any(i <= 0 for i in nums ):
raise ValueError('All values must be g... | 53 | 0 |
"""simple docstring"""
def lowercase__ ( lowercase_ ) -> list[list[int]]:
"""simple docstring"""
_UpperCamelCase : List[str] = []
if len(lowercase_ ) == 1:
return [nums.copy()]
for _ in range(len(lowercase_ ) ):
_Upper... | 710 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get... | 51 | 0 |
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