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 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_flax_available():
import ja... | 87 |
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> list:
"""simple docstring"""
if len(lowercase_ ) <= 1:
return [tuple(lowercase_ )]
A__ = []
def generate(lowercase_ , lowercase_ ):
if k == 1:
res.append... | 87 | 1 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class A__ ( A ):
"""simple docstring"""
def __magic_name__ ( self : List[Any] , A_ : float ):
'''simple docstring'''
... | 703 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class A__ ( A ):
"""simple docstring"""
def __init__( self : Tuple , *A_ : Optional[int] , **A_ : int ... | 503 | 0 |
"""simple docstring"""
from __future__ import annotations
from math import gcd
def _lowerCamelCase( a , a = 2 , a = 1 , a = 3 , ):
# A value less than 2 can cause an infinite loop in the algorithm.
if num < 2:
raise ValueError("The input value cannot be less than ... | 528 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase = {
'configuration_clipseg': [
'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
'CLIPSegConfig',
'CLIPSegTextConfig',
'CLIPSegVisionCon... | 114 | 0 |
'''simple docstring'''
from collections import deque
class _a :
"""simple docstring"""
def __init__( self : Optional[Any] , lowercase_ : str , lowercase_ : int , lowercase_ : int ):
'''simple docstring'''
lowercase_ = ... | 603 | '''simple docstring'''
from __future__ import annotations
def A_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ->list:
lowercase_ = []
lowercase_ , lowercase_ = input_list[low:mid], input_list[mid : high + 1]
while left a... | 603 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import 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_attentio... | 107 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class lowerCAmelCase_ ( __magic_name__ ):
def __init__( self , *_lowerCAmelCase , **_lowerCAme... | 18 | 0 |
# 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... | 702 |
import re
import string
import numpy as np
import datasets
lowercase : List[str] = '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'
lowercase : List[str] = '\... | 94 | 0 |
"""simple docstring"""
import math
import unittest
def SCREAMING_SNAKE_CASE ( snake_case):
assert isinstance(snake_case, snake_case) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
retu... | 564 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
A_ : Optional[int] =logging.get_logger(__na... | 650 | 0 |
# Copyright 2022 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... | 702 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class _lowercase ( A__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = (EulerDiscreteScheduler,)
SCREAMI... | 260 | 0 |
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
_snake_case = '''src/transformers'''
_snake_cas... | 382 | import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
_snake_case = '''\
@inproceedings{popovic-2015-chrf,
title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",
author = "Popovi{\'c}, Maja",
booktitle = "Proceedings of the ... | 382 | 1 |
from torch import nn
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : Any ):
'''simple docstring'''
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
... | 708 | from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class SCREAMING_SNAKE_CASE__ ( __UpperCamelCase ):
def __lt__( self : Tuple , _lowerCAmelCase : Optional[int] ):
ret... | 390 | 0 |
"""simple docstring"""
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transform... | 420 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
UpperCAmelCase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:... | 420 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
"""configuration_clipseg""": [
"""CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""CLIPSegConfig""",
"""CLIPSegTextConfig""",
"""CLIPSegVisionConfig""... | 622 |
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 TokenizerTest... | 622 | 1 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class lowerCAmelCase_ :
def __init__( self , _lowerCAmelCase ):
_lowercase : int = data
_lowercase : Optional[int] = [0x67_452_301, 0xef_cda_... | 66 |
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_modeling_common import ModelTesterMixin, ids_tenso... | 295 | 0 |
'''simple docstring'''
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def SCREAMING_SNAKE_CASE( UpperCamelCase... | 702 |
'''simple docstring'''
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, ad... | 471 | 0 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
lowercase__ : List[str] = logging.get_logger(__name__) # pylint: disabl... | 8 |
'''simple docstring'''
def __a ( A__ , A__ ) -> Optional[int]:
_enforce_args(A__ , A__ )
if n == 0:
return 0
lowerCAmelCase = float("-inf" )
for i in range(1 , n + 1 ):
lowerCAmelCase = max(
... | 649 | 0 |
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 HeunDiscr... | 414 |
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
__a : Optional[int] = lo... | 414 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class _snake_case:
__snake_case: Optional[str] = field(
default='''codeparrot/codeparrot''' , metadata={'''help''': '''Model name or path of model to be trained.'''} )
__... | 531 |
'''simple docstring'''
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
... | 679 | 0 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class snake_case :
"""simple docstring"""
__lowerCAmelCase = 42
__lowerCAmelCase = None
__lowerCAmelCase = None
def __lowercase ( _UpperCAmelCase ) -> int:
'''s... | 714 | import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
lowerCAmelCase__ = argparse.ArgumentParser(
description=(
'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
' Distillation'
... | 576 | 0 |
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_im... | 613 | def snake_case__ ( lowercase ):
lowerCAmelCase_: Union[str, Any] = [1]
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_: int = 0, 0, 0
lowerCAmelCase_: Union[str, Any] = ugly_nums[ia] * 2
lowerCAmelCase_: str = ugly_nums[ia] * 3
lowerCAmelCase_... | 613 | 1 |
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ = 1_0 , lowerCamelCase__ = 2_2 ) -> int:
__lowerCamelCase : int = range(1 , lowerCamelCase__ )
__lowerCamelCase : List[Any] = range(1 , lowerCamelCase__ )
return sum(
1 for pow... | 337 |
import os
import pytest
from attr import dataclass
a ="""us-east-1""" # defaults region
@dataclass
class A_ :
_UpperCAmelCase : str
_UpperCAmelCase : Tuple = '''arn:aws:iam::558105141721:role/sagemaker_execution_role'''
_UpperCAmelCase : ... | 337 | 1 |
'''simple docstring'''
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def A (__lowerCamelCase :Optional[int] ):
return x + 2
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring''... | 5 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
_lowercase = logging.get_logge... | 5 | 1 |
'''simple docstring'''
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_si... | 710 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {
'''google/switch-base-8''': '''https://huggingface.co/google/switch-base-8/blob/main/config.json''',
}
class ... | 605 | 0 |
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 cached_property
from ...test_tok... | 439 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCAmelCase = {
'''configuration_pix2struct''': [
'''PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Pix2StructConfig''',
'''Pix2Struct... | 40 | 0 |
# Algorithm for the pigeonhole sorting
def _A ( _UpperCamelCase ):
_UpperCAmelCase : int = min(_UpperCamelCase ) # min() finds the minimum value
_UpperCAmelCase : Any = max(_UpperCamelCase ) # max() finds the maximum value
_UpperCAmelCase : str = max_va... | 416 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class lowerCAmelCase_ ( datasets.BeamBasedBuilder ):
def a_ ( self : Dict ... | 416 | 1 |
"""simple docstring"""
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvai... | 624 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
if not nums:
raise ValueError('''List is empty''' )
return sum(SCREAMING_SNAKE_CASE__ ) / len(SCREAMING_SNAKE_CASE__ )
if __name__ == "__main__":
import doctest
d... | 39 | 0 |
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'''facebook/encodec_24khz''': '''https://huggingface.co/facebook/encodec_24khz/resolve/main/confi... | 706 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available():
... | 376 | 0 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE__ =(IPNDMScheduler,)
SCREAMING_SNAKE_CASE__ =(("""num_inference_steps""", 50),... | 693 |
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast... | 693 | 1 |
"""simple docstring"""
def snake_case ( ) -> Tuple:
_snake_case = 0
for i in range(1 , 1001 ):
total += i**i
return str(lowerCAmelCase_ )[-10:]
if __name__ == "__main__":
print(solution())
| 404 |
"""simple docstring"""
def snake_case ( lowerCAmelCase_ = 1000 ) -> int:
return sum(e for e in range(3 , lowerCAmelCase_ ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(F"{solution() = }")
| 404 | 1 |
lowerCamelCase__ = """Alexander Joslin"""
import operator as op
from .stack import Stack
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
__a = {"""*""": op.mul, """/""": op.truediv, """+""": op.add, """-""": op.sub}
__a = Stack()... | 225 |
class SCREAMING_SNAKE_CASE :
def __init__( self : List[Any] ):
'''simple docstring'''
__a = {} # Mapping from char to TrieNode
__a = False
def UpperCamelCase_ ( self : Dict , __lowercase : list[str] ):
... | 225 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMo... | 721 |
"""simple docstring"""
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
__SCREAMING_SNAKE_CASE : Union[str, Any] = Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))
im... | 2 | 0 |
"""simple docstring"""
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
# TODO Update this
lowerCAmelCase__ = {
'facebook/esm-1b': 'h... | 621 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSeque... | 621 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase ={
"""configuration_xlm_roberta_xl""": [
"""XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""XLMRobertaXLConfig""",
... | 261 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
__UpperCAme... | 261 | 1 |
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, 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
fr... | 12 |
'''simple docstring'''
import math
lowerCamelCase :int = 1_0
lowerCamelCase :List[Any] = 7
lowerCamelCase :Union[str, Any] = BALLS_PER_COLOUR * NUM_COLOURS
def a ( lowerCamelCase__ = 20 ):
'''simple docstring'''
A_ : ... | 667 | 0 |
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_ca... | 314 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__magic_name__ = {
'''configuration_vision_text_dual_encoder''': ['''VisionTextDualEncoderConfig'''],
... | 314 | 1 |
'''simple docstring'''
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum... | 603 |
'''simple docstring'''
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase_ = {
'facebook/mask2former-swin-small-coco-instance': (
'https://huggingface.c... | 603 | 1 |
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = [
'encoder.version',
'decoder.versio... | 636 | from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _a ( lowercase__ : List[str] ):
'''simple docstring'''
if not is_accelerate_available():
return method
SCREAMING_SNAKE_CASE... | 636 | 1 |
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 to... | 25 |
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):
impor... | 311 | 0 |
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vis... | 717 |
def a (_lowerCAmelCase ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = [], []
while len(_lowerCAmelCase ) > 1:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = min(_lowerCAmelCase ), max(_lowerCAmelCase )
start.append(_lowerCAmel... | 89 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : List[Any] = {
"""MIT/ast-finetuned-audioset-10-10-0.4593""": (
"""https://huggingface.c... | 79 |
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..... | 375 | 0 |
"""simple docstring"""
def _lowerCamelCase ( __a, __a, __a ):
if exponent == 1:
return base
if exponent % 2 == 0:
SCREAMING_SNAKE_CASE_ = _modexpt(__a, exponent // 2, __a ) % modulo_value
return (x * x) % modulo_value
else:
return (base * _modexpt(_... | 628 |
"""simple docstring"""
def _lowerCamelCase ( __a ):
if not isinstance(__a, __a ):
SCREAMING_SNAKE_CASE_ = F'Input value of [number={number}] must be an integer'
raise TypeError(__a )
if number < 1:
SCREAMING_SNAKE_CASE_ = F'Input value of [number={number}] must b... | 628 | 1 |
"""simple docstring"""
from torch import nn
def _snake_case ( snake_case__ : Union[str, Any] ):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(F'Unsupported activation function: {act_f... | 91 |
'''simple docstring'''
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
a : Optional[Any] = logging.get_logger(__name__)
a :... | 69 | 0 |
from sklearn.metrics import fa_score
import datasets
SCREAMING_SNAKE_CASE : List[str] = "\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n"
SCREAMING_SNAKE_CASE : Opti... | 441 |
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, 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... | 441 | 1 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def lowercase_ ( __UpperCAmelCase ) -> int:
# This defines a "chinese character" as anything in the CJK Unicode block:
... | 299 |
"""simple docstring"""
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class _lowerCamelCase ( unittest.TestCase ):
def _lowerCAmelCase ( self : Optiona... | 299 | 1 |
"""simple docstring"""
from __future__ import annotations
class __UpperCAmelCase :
def __init__( self : str , a_ : Optional[int]=None ) -> Optional[int]:
'''simple docstring'''
a__ : str = data
a__ : Optional[int] = None
... | 251 |
"""simple docstring"""
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase = logg... | 251 | 1 |
'''simple docstring'''
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
... | 98 | 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
... | 415 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import... | 705 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase :List[Any] = {
'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
'tokenization_roc_bert': ['RoC... | 346 | 0 |
"""simple docstring"""
from typing import Dict
from .base import GenericTensor, Pipeline
class __lowercase ( __lowerCamelCase ):
def __lowercase ( self : int ,A : Dict=None ,A : Optional[Any]=None ,A : Any=None ,... | 65 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : List[str] = logging.get_logger(__name__)
_a : Dict = {
"facebook/timesformer": "https://huggingface.co/facebook/timesformer/resolve/main/config.json",
}
class ... | 56 | 0 |
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
lowerCamelCase__ = {
# 1536-bit
5: {
'''prime''': int(
'... | 717 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
lowerCamelCase__ = '''https://www.indeed.co.in/jobs?q=mobile+app+development&l='''
def lowercase_ ( SCREAMING_SNAKE_CASE : str = "mumbai" ):
... | 408 | 0 |
import torch
def lowerCAmelCase_ ( ):
if torch.cuda.is_available():
__snake_case : int = torch.cuda.device_count()
else:
__snake_case : Tuple = 0
print(F'Successfully ran on {num_gpus} GPUs' )
if __name__ == "... | 81 |
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
_snake_case : Union[str, Any] = logging.getLogger(__name__)
def lowerCAmelCase_ ( ):
__snake_case : int = argparse.ArgumentParser(
... | 81 | 1 |
"""simple docstring"""
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def _lowerCAmelCase ( *__lowerCamelCase:str , __lowerCamelCase:Optional[Union[Dict, Any]] = None , __lowerCamelCase:List[Any]=True , __lowe... | 468 |
"""simple docstring"""
import colorsys
from PIL import Image # type: ignore
def _lowerCAmelCase ( __lowerCamelCase:float , __lowerCamelCase:float , __lowerCamelCase:int ):
'''simple docstring'''
__magic_name__ = x
__magic_n... | 468 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
A__ : str = {'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig''', '''DeiTOnnxConfig... | 171 |
import argparse
import gc
import json
import os
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelera... | 171 | 1 |
import unittest
from knapsack import knapsack as k
class __lowerCAmelCase ( unittest.TestCase ):
def A__ ( self ) -> List[str]:
'''simple docstring'''
_lowercase =0
_lowercase =[0]
_lowercase ... | 702 |
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbart.modeli... | 380 | 0 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''speechbrain/m-ctc-t-large''': '''https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json'''... | 41 |
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def UpperCAmelCase__ ( __magic_name__ : float , __magic_name__ : float , __magic_name__ : bool = False ):
'''simple docstring'''
if radian_mode:
... | 348 | 0 |
import os
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : Dict = "input.txt" ):
"""simple docstring"""
with open(os.path.join(os.path.dirname(lowerCAmelCase_ ) ,lowerCAmelCase_ ) ) as input_file:
SCREAMING_SNAKE_CASE_ : Optional[... | 719 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSo... | 153 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ : Optional[Any] = {
"configuration_table_transformer": [
"TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TableTransformerConfi... | 21 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
... | 21 | 1 |
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class SCREAMING_SNAKE_CASE :
def __init__( self : Union[str, Any] , __lowercase : List[Any] , __lowercase : int , __lowercase : int ):
'''simple docstring'''
... | 547 |
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : Tuple=False ):
"""simple docstring"""
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and isinstance(_SCREAMING_... | 547 | 1 |
def _SCREAMING_SNAKE_CASE ( __lowercase : list[list[float]] ) -> list[list[float]]:
"""simple docstring"""
__A = []
for data in source_data:
for i, el in enumerate(__lowercase ):
if len(__lowercase ) < i + 1:
data_l... | 637 |
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_modeling_common import ModelTesterMixin, ids_tens... | 637 | 1 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple d... | 706 |
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
_lowerCamelCase : List[Any] = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=None, ... | 516 | 0 |
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_ber... | 317 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import Hugging... | 317 | 1 |
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from tr... | 709 |
'''simple docstring'''
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
a_ = ["small", "medium", "large"]
a_ = "lm_head.decoder.weight"
a_ = "lm_head.weight"
def UpperCamelCase_ ( __SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_C... | 92 | 0 |
'''simple docstring'''
lowercase__ = {
"joule": 1.0,
"kilojoule": 10_00,
"megajoule": 1_00_00_00,
"gigajoule": 10_00_00_00_00,
"wattsecond": 1.0,
"watthour": 36_00,
"kilowatthour": 3_60_00_00,
"newtonmeter": 1.0,
"calorie_nutr": 41_86.8,
"kilocalorie_nutr": 4... | 508 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'''microsoft/foca... | 508 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import ceil, floor, sqrt
def _A (lowerCAmelCase__ :int = 2_00_00_00 ) -> int:
'''simple docstring'''
_a = [0]
_a = 42
for idx in range(1 ... | 532 |
'''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 _A (lowerCAmelCase__ :str , lowerCAmelCase__ :int , ... | 532 | 1 |
"""simple docstring"""
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test... | 82 |
"""simple docstring"""
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import h... | 438 | 0 |
"""simple docstring"""
from sklearn.metrics import mean_squared_error
import datasets
SCREAMING_SNAKE_CASE_ = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and ... | 579 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
'''studio-ousia/luke-base''': '''https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json''',
'''stu... | 579 | 1 |
"""simple docstring"""
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def _SCREAMING_SNAKE_CASE ():
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dirname as original_dirname
... | 4 |
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import torch.nn as nn
... | 40 | 0 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
_UpperCAmelCase : List[str] = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author=... | 715 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_UpperCAmelCase : Optional[int] = {
"configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
"tokenization_biogpt": ["BioGptT... | 453 | 0 |
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the ... | 654 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def _UpperCAmelCase ( a : str ):
if "model" in orig_key:
snake_case__ = orig_key.replace("""model.""" , """""" )
if "norm1" in orig_key:
snake_case__ ... | 654 | 1 |
'''simple docstring'''
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
__UpperCAmelCase = 500_000
__UpperCAmelCase , __UpperCAmelCase = os.path.split(__file__... | 98 |
'''simple docstring'''
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def ... | 98 | 1 |
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_collato... | 300 |
from math import pi, sqrt
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: float ) -> float:
if num <= 0:
raise ValueError("math domain error" )
if num > 171.5:
raise OverflowError("math range error" )
elif num - int(lowerCAmelCase ) not in (0, 0.5):
raise Not... | 300 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case : Union[str, Any] = logging.get_logger(__name__)
_snake_case : int = {
'uclanlp/visualbert-vqa': 'https://huggingface.co/uclanlp/visualbert-vqa/re... | 707 |
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_flax_available():
impor... | 421 | 0 |
"""simple docstring"""
from math import ceil, sqrt
def UpperCamelCase (SCREAMING_SNAKE_CASE = 100_0000 ):
UpperCamelCase : int = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
Uppe... | 102 |
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class __A( a ):
def __init__( self , _snake_case="" , _snake_case="train" ) -> Union[str, Any]:
'''simple docstring'''
assert os.path.isdir(_snake_case ... | 219 | 0 |
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
UpperCamelCase__ = logging.get_logger(__name__)
class __lowercase ( a__ ):
def __init__( self : Tuple , *lowercase__ : ... | 702 |
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
UpperCamelCase__ = HUGGINGFACE_HUB_CACHE
UpperCamelCase__ = '''config.json'''
UpperCamelCase__ = '''diffusion_pytorch_model.bin'''
UpperCamelCase__ = '''diffusion... | 143 | 0 |
'''simple docstring'''
def lowercase__( __UpperCamelCase: int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = 1
for i in range(1 ,num + 1 ):
fact *= i
return fact
def lowercase__(... | 28 |
'''simple docstring'''
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class A ( _a ):
lowercase_ = (DDPMParallelScheduler,)
def __lowerCAmelCase ( self : ... | 22 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
... | 711 |
'''simple docstring'''
import math
import sys
import cva
import numpy as np
def __UpperCAmelCase ( _UpperCAmelCase : np.ndarray , _UpperCAmelCase : float ) -> np.ndarray:
# For applying gaussian function for each element in matrix.
__snake_case = math.sqrt... | 680 | 0 |
'''simple docstring'''
from collections import deque
from .hash_table import HashTable
class SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE_ ):
def __init__( self : int , *__SCREAMING_SNAKE_CASE : int , **__SCREAMING_S... | 466 |
'''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.a... | 466 | 1 |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class __A ( a ):
def __init__( self , UpperCAmelCase_ , UpperCAmelCase_ = None ... | 269 |
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,
MusicgenForConditionalGeneration,
MusicgenProcesso... | 269 | 1 |
def UpperCAmelCase_ ( __UpperCAmelCase : int = 2_00 ) -> int:
SCREAMING_SNAKE_CASE_ = [1, 2, 5, 10, 20, 50, 1_00, 2_00]
SCREAMING_SNAKE_CASE_ = [0] * (pence + 1)
SCREAMING_SNAKE_CASE_ = 1 # base case: 1 way to make 0 pence
for coin in ... | 31 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A : Optional[Any] = {
"""configuration_megatron_bert""": ["""MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegatronBertConfig"""],
}
tr... | 349 | 0 |
'''simple docstring'''
def snake_case_ ( a__ : int = 60_08_51_47_51_43 ):
"""simple docstring"""
try:
__lowercase = int(a__ )
except (TypeError, ValueError):
raise TypeError("""Parameter n must be int or castable to int.""" ... | 719 |
'''simple docstring'''
from statistics import mean, stdev
def snake_case_ ( a__ : list ,a__ : int = 3 ):
"""simple docstring"""
__lowercase = min(a__ )
__lowercase = max(a__ )
# normalize data
return [round((x - x_... | 163 | 0 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@requir... | 51 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
lowerCAmelCase : str = logging.get_logger(_... | 511 | 0 |
import math
def _SCREAMING_SNAKE_CASE ( lowercase : int = 1_00 ):
'''simple docstring'''
lowerCamelCase_ = sum(i * i for i in range(1 , n + 1 ) )
lowerCamelCase_ = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
... | 702 |
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import C... | 651 | 0 |
"""simple docstring"""
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqd... | 103 |
"""simple docstring"""
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ ) -> int:
return 1 if input_a == input_a else 0
def snake_case ( ) -> None:
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , ... | 103 | 1 |
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, Fl... | 719 |
from __future__ import annotations
def _A (UpperCamelCase : str , UpperCamelCase : list[str] | None = None ) ->list[list[str]]:
'''simple docstring'''
lowerCamelCase__ : List[str] = word_bank or []
# create a table
lowerCamelCase__ : int ... | 96 | 0 |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline | 557 |
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class lowerCamelCase__... | 557 | 1 |
"""simple docstring"""
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
class _SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
"""simple docstring"""
_a : Tuple = '''philschmid/bart-large-cnn-samsum'''
_a : Opti... | 717 |
"""simple docstring"""
from functools import lru_cache
@lru_cache
def _lowerCamelCase ( lowerCamelCase__ : int ):
if num < 0:
raise ValueError("""Number should not be negative.""" )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
impo... | 128 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
a__ : str = {'''tokenization_herbert''': ['''HerbertTokenizer''']}
try:
if not is_tokenizers_available():
raise ... | 368 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ : str = logging.get_logger(__name__)
a__ : Any = ... | 368 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
snake_case__ : List[str] ... | 706 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
snake_case__ : List[Any] = '2.13.1'
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('3.7'):
... | 592 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
UpperCAmelCase_ : Optional[int] = {
'configuration_trocr': ['TROCR_PRETRAINED_C... | 44 |
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.pipelin... | 592 | 0 |
"""simple docstring"""
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def lowercase_ ( ):
raise RuntimeError("""CUDA out of memory.""" )
class ... | 717 |
"""simple docstring"""
from math import pi, sqrt, tan
def lowercase_ ( _snake_case ):
if side_length < 0:
raise ValueError("""surface_area_cube() only accepts non-negative values""" )
return 6 * side_length**2
def lowercase_ ( _snake_case ,_snake_c... | 545 | 0 |
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import Paddi... | 659 |
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
Trai... | 659 | 1 |
"""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
from transformers.utils i... | 263 |
"""simple docstring"""
import random
from typing import Any
def UpperCAmelCase ( A__: list ) -> list[Any]:
for _ in range(len(A__ ) ):
__lowerCamelCase : List[Any] = random.randint(0 , len(A__ ) - 1 )
__lowerCamelCase : Optional[... | 263 | 1 |
def UpperCAmelCase_ ( __UpperCAmelCase : str , __UpperCAmelCase : Union[str, Any] ) -> Any:
SCREAMING_SNAKE_CASE_ = [1]
for i in range(2 , __UpperCAmelCase ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[... | 31 | """simple docstring"""
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class UpperCamelCase :
@property
def _UpperCAmelCa... | 425 | 0 |
_UpperCamelCase : Optional[int] = 6_5_5_2_1
def __UpperCamelCase ( snake_case ) -> int:
'''simple docstring'''
__A = 1
__A = 0
for plain_chr in plain_text:
__A = (a + ord(snake_case )) % MOD_ADLER
__A ... | 701 |
from __future__ import annotations
import math
from collections.abc import Callable
def __UpperCamelCase ( snake_case , snake_case , snake_case , snake_case = 1_0_0 , ) -> float:
'''simple docstring'''
__A = x_start
__A = fnc(snake_c... | 341 | 0 |
'''simple docstring'''
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPMo... | 41 |
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch... | 550 | 0 |
lowerCamelCase_ = 6_55_21
def __magic_name__ ( __a : str ):
'''simple docstring'''
UpperCamelCase__ = 1
UpperCamelCase__ = 0
for plain_chr in plain_text:
UpperCamelCase__ = (a + ord(__a )) % MOD_ADLER
UpperCamelCase__ = ... | 86 |
from __future__ import annotations
lowerCamelCase_ = '''#'''
class __A:
"""simple docstring"""
def __init__(self ):
UpperCamelCase__ = {}
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase__ = self._trie
for char in text:
... | 86 | 1 |
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
UpperCAmelCase__ : Any = [
# (stable-diffusion, HF Diffusers)
("time_embed.0.weight", "time_... | 313 |
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test... | 313 | 1 |
import math
def _A (UpperCamelCase : int ) ->str:
'''simple docstring'''
lowerCamelCase__ : Tuple = 0
lowerCamelCase__ : List[Any] = 0
while num > 0:
lowerCamelCase__ : Tuple = num % 8
lowerCamelCase__ : int = ... | 711 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def _A (UpperCamelCase : Any , UpperCamelCase : Union[str, Any] , UpperCamelCase : Optional[Any] )... | 96 | 0 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def a_ ( lowerCAmelCase_ : int, lowerCAmelCase_ : int, lowerCAmelCase_ : float = 1 / sqrt(2 ) ):
__lowerCAmelCase = tau * frequency / samplerate
__lowerCAmelCase ... | 53 |
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM... | 53 | 1 |
import collections
import os
import re
from pathlib import Path
_UpperCamelCase = "src/transformers"
# Matches is_xxx_available()
_UpperCamelCase = re.compile(r"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
_UpperCamelCase = re.compile(r"^_import_structure\s+=\s+\{([... | 583 |
from __future__ import annotations
from math import ceil, floor, sqrt
def _lowercase ( lowercase__ = 2_0_0_0_0_0_0 ):
__lowerCAmelCase : list[int] = [0]
__lowerCAmelCase : int
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ):
triangle_nu... | 583 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase__ ( self : ... | 115 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_c... | 115 | 1 |
'''simple docstring'''
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
UpperCamelCase__ = {
'... | 640 |
'''simple docstring'''
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_tor... | 640 | 1 |
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