code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import warnings
from typing import Any, Dict, 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_uti... | 46 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
... | 249 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __a ( metaclass=A__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = ['keras_nlp']
def __init__( self : str , *lowercase_ : int , **lowercase_ : int )... | 357 |
"""simple docstring"""
import argparse
import os
import re
_SCREAMING_SNAKE_CASE : List[str] = """src/diffusers"""
# Pattern that looks at the indentation in a line.
_SCREAMING_SNAKE_CASE : Optional[int] = re.compile(r"""^(\s*)\S""")
# Pattern that matches `"key":" and puts `key` in grou... | 157 | 0 |
'''simple docstring'''
UpperCamelCase__ = '''0.21.0'''
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from ... | 181 |
"""simple docstring"""
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def snake_case_ ( A_ : Tuple, A_ : int, A_ : Dict ):
... | 72 | 0 |
'''simple docstring'''
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
fr... | 366 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, Table... | 67 | 0 |
from __future__ import annotations
_lowerCamelCase : Optional[Any] = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
_lowerCamelCase : Any = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> ... | 14 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase_ = {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}
try:
... | 309 | 0 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_g... | 360 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class UpperCAmelCase__ :
__SCREAMING_SNAKE_CASE = 42... | 243 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase_ = {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTConf... | 345 |
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def lowerCamelCase_ ( _a : List[Any] ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = [
"""decoder.version""",
... | 345 | 1 |
from ..utils import DummyObject, requires_backends
class a ( metaclass=lowercase__ ):
_lowercase = ['onnx']
def __init__( self , *A_ , **A_ ):
'''simple docstring'''
requires_backends(self , ["onnx"] )
@classmethod
def _U... | 358 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class a ( UpperCAmelCase ):
_lowercase = ["image_proc... | 189 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArgum... | 97 |
'''simple docstring'''
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , ... | 321 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleCh... | 122 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case : Union[str, Any] = {
"""configuration_convbert""": ["""CONVBERT_PRETRAINED_CONF... | 122 | 1 |
def lowercase ( SCREAMING_SNAKE_CASE__ : int = 600_851_475_143 ) -> int:
try:
_snake_case : int = int(SCREAMING_SNAKE_CASE__ )
except (TypeError, ValueError):
raise TypeError("""Parameter n must be int or castable to int.""" )
if n <= 0:
raise ValueError("""Par... | 317 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
"""xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/re... | 317 | 1 |
from bisect import bisect
from itertools import accumulate
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = sorted(zip(SCREAMING_SNAKE_CASE , SCREAMI... | 93 |
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
lowerCAmelCase = '\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Saurav Kadavath\n and Akul A... | 93 | 1 |
'''simple docstring'''
import math
lowerCamelCase :List[str] = 1_0
lowerCamelCase :List[str] = 7
lowerCamelCase :Dict = BALLS_PER_COLOUR * NUM_COLOURS
def a ( lowerCamelCase__ = 20 ):
'''simple docstring'''
A_ : int = math.comb(low... | 206 |
'''simple docstring'''
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def a ( lowerCamelCase_... | 206 | 1 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingfac... | 65 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import ... | 65 | 1 |
from random import shuffle
import tensorflow as tf
from numpy import array
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
SCREAMING_SNAKE_CASE = int(_UpperCAmelCase)
assert noofclusters < len(_UpperCAmelCase)
# Find out the dimensionality
SCREAMING_SNAKE_CASE ... | 137 |
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = test_file.split(os.path.sep)
if components[0:2] !=... | 137 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_snake_case : Any = {
'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2VecAudioConfig'],
'configuration_data2vec_text'... | 207 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class _UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowercase ( self : Any ) -> Optional[int]:
... | 207 | 1 |
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
_snake_case = [
"Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of the"
... | 26 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __A ( metaclass=A_ ):
'''simple docstring'''
lowerCAmelCase : List[str] = ["torch", "torchsde"]
def __init__( self : Tuple ,*_snake_c... | 16 | 0 |
def SCREAMING_SNAKE_CASE ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase) -> Optional[Any]:
return round(float(moles / volume) * nfactor)
def SCREAMING_SNAKE_CASE ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase) -> Union[str, Any]:
retu... | 367 |
from math import isqrt
def SCREAMING_SNAKE_CASE ( __UpperCamelCase) -> bool:
return all(number % divisor != 0 for divisor in range(2 , isqrt(__UpperCamelCase) + 1))
def SCREAMING_SNAKE_CASE ( __UpperCamelCase = 10**6) -> int:
a = 0
a = 1
... | 180 | 0 |
from math import sqrt
def lowerCamelCase_ ( _a : Union[str, Any] = 100_0000 ):
'''simple docstring'''
UpperCAmelCase_ : int = 0
UpperCAmelCase_ : int = 0
UpperCAmelCase_ : int
while num_cuboids <= limit:
max_cuboid_size += 1
... | 345 | import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'''split_dict''' , [
SplitDict(),
SplitDict({'''train''': SplitInfo(name='''train''' , num_bytes=1_337 , num_examples=42 , dataset_name='''my_dataset'''... | 182 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
def UpperCamelCase_( snake_case : int ):
'''simple docstring'''
if num <= 0:
snake_case_ = f'{num}: Invalid input, please enter a positive integer.'
raise ValueE... | 92 |
'''simple docstring'''
_SCREAMING_SNAKE_CASE : Optional[int] = "Alexander Joslin"
import operator as op
from .stack import Stack
def UpperCamelCase_( snake_case : str ):
'''simple docstring'''
snake_case_ = {"*": op.mul, "/": op.truediv, "... | 92 | 1 |
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_... | 246 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
... | 246 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCamelCase ( snake_case... | 363 |
"""simple docstring"""
from math import factorial
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if successes > trials:
raise ValueError("""successes must be lower or equal to trials"... | 324 | 0 |
"""simple docstring"""
import re
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
return [char.split() for char in re.split(R"[^ a-z A-Z 0-9 \s]" , str_ )]
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = split_... | 57 |
"""simple docstring"""
import sys
from collections import defaultdict
class _UpperCamelCase :
'''simple docstring'''
def __init__( self ):
__lowerCAmelCase = []
def snake_case ( self , __a ):
return self.node_position[vertex]
d... | 57 | 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 ... | 371 |
"""simple docstring"""
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {'vocab_file': 'vocab.json'}
_a = {
'vocab_file': {
'mgp-str': 'https:/... | 23 | 0 |
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
A__ = """."""
if __name__ == "__main__":
A__ = os.path.join(REPO_PATH, """utils/documentation_tests.txt""")
A__ = []
A__... | 82 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging... | 34 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = 0 ):
'''simple docstring'''
__UpperCamelCase , __UpperCamelCase ... | 263 |
"""simple docstring"""
import heapq as hq
import math
from collections.abc import Iterator
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase ):
'''simple docstring'''
__UpperCamelCase = str(id_ )
__UpperCamelCase = None
__UpperCamelCase... | 263 | 1 |
"""simple docstring"""
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__ ( _lowerCamelCase):
def __init__( self , _SCREAM... | 86 |
"""simple docstring"""
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavin... | 60 | 0 |
"""simple docstring"""
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
_SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__)
class __a :
"""si... | 356 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE : List[str] = {
"""configuration_bigbird_pegasus""": [
"""BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BigBi... | 157 | 0 |
'''simple docstring'''
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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/... | 319 |
'''simple docstring'''
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 = logging.get_logger(__name__)
... | 319 | 1 |
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch
import pyarrow as pa
im... | 257 |
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_collator... | 257 | 1 |
'''simple docstring'''
import os
def UpperCamelCase_ ( ) -> int:
'''simple docstring'''
with open(os.path.dirname(snake_case_ ) + """/p022_names.txt""" ) as file:
__lowerCAmelCase = str(file.readlines()[0] )
__lowerCAmelCase ... | 229 | '''simple docstring'''
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
... | 229 | 1 |
"""simple docstring"""
# Copyright 2023 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... | 365 |
def __UpperCamelCase ( lowercase__ : str ) -> bool:
'''simple docstring'''
lowerCAmelCase_ : Any = 0
for ch in input_str:
lowerCAmelCase_ : Any = ord(lowercase__ )
lowerCAmelCase_ : Dict = pow(2 , lowerc... | 28 | 0 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
log... | 262 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from... | 262 | 1 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, ... | 358 |
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : Union[str, Any] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : int , lowerCAmelCase_ : Dict ) -> List[str]:
__lowerCAmelCase = name
__lowerCAmelCase = value
... | 207 | 0 |
"""simple docstring"""
from __future__ import annotations
snake_case_ = 10
def _lowerCAmelCase ( lowercase_ ):
UpperCAmelCase = 1
UpperCAmelCase = max(lowercase_ )
while placement <= max_digit:
# declare and ini... | 78 |
"""simple docstring"""
from __future__ import annotations
def _lowerCAmelCase ( lowercase_ , lowercase_ , lowercase_ ):
UpperCAmelCase = list(range(len(lowercase_ ) ) )
UpperCAmelCase = [v / w for v, w in zip(lowercase_ , lowercase_ ... | 78 | 1 |
from __future__ import annotations
from typing import Any
class __a :
def __init__( self , lowerCAmelCase__ ) -> None:
'''simple docstring'''
lowercase__: int = num_of_nodes
lowercase__: list[list[int]] = ... | 288 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def snake_case_ ( snake_case = 3 ) -> qiskit.result.counts.Counts:
if isinstance(snake_case , snake_case ):
raise... | 288 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ :List[str] = {
'''configuration_clap''': [
'''CLAP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ClapAudioConfig''',
'''ClapConfig''',
... | 71 |
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
lowerCAmelCase__ : List[Any] =input('''Enter image url: ''').strip()
print(F'''Downloading image from {url} ...''')
lowerCAmelCase__ : int =BeautifulSoup(requests.get(u... | 257 | 0 |
'''simple docstring'''
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class ... | 360 |
'''simple docstring'''
from math import factorial
def _a( UpperCamelCase__ : int = 1_0_0 ):
'''simple docstring'''
return sum(int(UpperCamelCase__ ) for x in str(factorial(UpperCamelCase__ ) ) )
if __name__ == "__main... | 222 | 0 |
import cva
import numpy as np
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self , A_ , A_ ) -> Optional[Any]:
if k in (0.04, 0.06):
__UpperCamelCase =k
__UpperCamelCase =window_size
... | 62 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase : Optional[Any] = {
"configuration_whisper": ["WHISPER_PR... | 204 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowercase : Optional[int] = {
'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'],
}
... | 294 |
'''simple docstring'''
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('To use the rich extension, install rich with `pip install rich`')
| 294 | 1 |
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def _UpperCamelCase ( snake_case__, snake_case__ = True, snake_case__ = math.inf, snake_case__ = -math.inf, snake_case__ = math.inf, snake_case__ = -math.inf, snake_case__ = False, snake_case_... | 157 | def _UpperCamelCase ( snake_case__ ) -> int:
__UpperCAmelCase : list[list[int]] = [[0 for _ in range(snake_case__ )] for _ in range(m + 1 )]
for i in range(m + 1 ):
__UpperCAmelCase : Optional[int] = 1
for n in range(... | 157 | 1 |
def a__ ( __SCREAMING_SNAKE_CASE = 5_0 ) -> int:
__lowerCAmelCase: List[str] = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] +... | 357 |
"""simple docstring"""
def a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> int:
return int((input_a, input_a).count(0 ) == 0 )
def a__ ( ) -> None:
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , 0... | 108 | 0 |
"""simple docstring"""
from math import pi, sqrt
def __UpperCAmelCase ( __lowerCamelCase ) -> float:
if num <= 0:
raise ValueError('''math domain error''' )
if num > 1_7_1.5:
raise OverflowError('''math range error''' )
elif num - int(__lowerCamel... | 16 |
"""simple docstring"""
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> str:
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
raise ValueError('''iterations must be defined as integers''' )
if not isinstance(__lowerCamelC... | 16 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case : Tuple ={
"configuration_git": ["GIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GitConfig", "GitVisionConfig"],
"processing_git": ["GitProcessor"],
}
try:
if not is_torch... | 350 |
def lowerCAmelCase__ ( lowerCamelCase_ : int ,lowerCamelCase_ : int):
'''simple docstring'''
while b:
lowerCAmelCase__ , lowerCAmelCase__ : Optional[Any] = b, a % b
return a
def lowerCAmelCase__ ( lowerCamelCase_ : int ,lowerCamelCase... | 94 | 0 |
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_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include ... | 110 |
import numpy
# List of input, output pairs
UpperCAmelCase : str = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
UpperCAmelCase : Optional[int] = (((515, 22, 13), 555), ((61, 35, 49), 150))
UpperCAmelCase ... | 95 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
snak... | 4 |
'''simple docstring'''
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Optional[Any] , _lowerCamelCase : Union[str, Any] ):
"""simple docstring"""
A_ : Union[st... | 4 | 1 |
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_proce... | 9 | """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_... | 221 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
UpperCAmelCase: Optional[int] = logging.get_logger(__name__)
class UpperCamelCase ( snake_case ):
"""simple docstring"""
... | 336 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCAmelCase: Tuple = logging.get_logger(__name__)
UpperCAmelCase: List[Any] = {
"""CarlCochet/trajectory-transformer-halfcheetah-medium-v2""": (
"""h... | 336 | 1 |
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s""",
datefmt="""%m/%d/%Y %H:%M:%S""",
level=logging.INFO,
)
__lowerCamelCase : Optional[int]... | 52 |
"""simple docstring"""
import qiskit
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> qiskit.result.counts.Counts:
A__ = qiskit.Aer.get_backend("aer_simulator" )
# Create a Quantum Circuit acting on the q register
A__ = qiskit.QuantumCircuit(lower... | 247 | 0 |
'''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_retribert import RetriBertTokenizer
__A : Tuple = ... | 89 |
'''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,
)
__A : List[str] = ... | 89 | 1 |
from __future__ import annotations
from typing import Any
class __A :
"""simple docstring"""
def __init__( self , lowerCamelCase__ ):
"""simple docstring"""
__UpperCamelCase : Optional[Any] =num_... | 71 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_common import... | 17 | 0 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformer... | 33 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
A : List[Any] = logging.get_logger(__name__)
A : ... | 33 | 1 |
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
UpperCAmelCase : Tuple ... | 128 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import ... | 128 | 1 |
'''simple docstring'''
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)... | 275 |
'''simple docstring'''
def lowerCamelCase ( lowerCAmelCase : int ):
"""simple docstring"""
if not isinstance(lowerCAmelCase , lowerCAmelCase ):
__magic_name__ : int = f'Input value of [number={number}] must be an integer'
raise TypeError(lowerCAmelCase )
if nu... | 275 | 1 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class __lowerCAmelCase :
pass
| 205 |
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import Bnb... | 146 | 0 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class A_ :
def __init__( self : Union[str, Any] , snake_case_ : str=2 , snake_case_ : L... | 356 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : int = 100_0000 ) -> int:
'''simple docstring'''
_UpperCAmelCase = limit + 1
_UpperCAmelCase = [0] * limit
for first_term in range(1 , __lowercase ):
for... | 156 | 0 |
"""simple docstring"""
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
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... | 102 |
"""simple docstring"""
import numpy as np
def lowercase ( _snake_case : int , _snake_case : Optional[Any] , _snake_case : Optional[int] , _snake_case : int , _snake_case : Union[str, Any] ) ->Dict:
"""simple docstrin... | 102 | 1 |
"""simple docstring"""
def A (__A : List[str] , __A : List[Any] , __A : Dict , __A : List[str] ) -> Tuple:
"""simple docstring"""
global f # a global dp table for knapsack
if f[i][j] <... | 359 |
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstring... | 7 | 0 |
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
SCREAMING_SNAKE_CASE_:List[Any] = logging.get_logger(__name__)
... | 116 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqCon... | 166 | 0 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')
class __a :
def __init__( ... | 366 |
def lowerCAmelCase__ ( a__: int ) -> None:
'''simple docstring'''
_UpperCAmelCase = generate_pascal_triangle(a__ )
for row_idx in range(a__ ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(end=' ' )
... | 185 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase : List[Any] = logging.get_logger(__name__)
__lowerCamelCase : str = {
"""roberta-b... | 52 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTo... | 22 | 0 |
'''simple docstring'''
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : Optional[Any] ... | 229 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : Tuple = '''bert-generation'''
def __init__( self , _lowercase=50_358 , _low... | 229 | 1 |
"""simple docstring"""
from typing import List
import numpy as np
def UpperCAmelCase__ ( lowerCAmelCase__ :dict ) -> int:
'''simple docstring'''
lowercase = {key: len(lowerCAmelCase__ ) for key, value in gen_kwargs.items() if isinstance(lowerCAmel... | 197 | """simple docstring"""
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
__lowerCAmelCase : int =logging.getLogger(__name__)
class _A :
... | 197 | 1 |
'''simple docstring'''
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 ImageProcessin... | 367 |
'''simple docstring'''
from __future__ import annotations
import math
from collections.abc import Callable
def _snake_case ( A , A , A , A = 100 , ) -> float:
lowerCAmelCase__ = x_start
lowerCAmelCase__ = fnc(A )
... | 228 | 0 |
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
A__ : List[str] = 0b10_11_00_11_11_10_11_00_10_01_00_00_01_11_10_11_10_11_00_01_... | 103 | import math
def A ( _lowercase ):
return math.sqrt(_lowercase ) * math.sqrt(_lowercase ) == num
def A ( _lowercase ):
SCREAMING_SNAKE_CASE : Dict = 0
SCREAMING_SNAKE_CASE : Tuple = n
while left <= right:
... | 182 | 0 |
'''simple docstring'''
import copy
import re
class lowercase :
"""simple docstring"""
UpperCAmelCase = """hp"""
UpperCAmelCase = {}
UpperCAmelCase = None
@classmethod
def _snake_case ( cls ,a_ ,a_ ) -> int:
_UpperCAmelC... | 351 |
'''simple docstring'''
def snake_case_ ( lowerCAmelCase_ )-> int:
'''simple docstring'''
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("""only integers accepted as input""" )
else:
_UpperCAmelCase : Dict ... | 349 | 0 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation ... | 48 |
'''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 (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils i... | 265 | 0 |
"""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
_lowerCAmelCase :Any = logging.get_logger(__name__... | 356 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase :int = logging.get_logger(__name__)
_lowerCAmelCase :Union[str, Any] = {
'xlm-mlm-... | 68 | 0 |
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 ModelTeste... | 307 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def a_ ... | 307 | 1 |
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
if not arr:
return None, No... | 134 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case : int = logging.get_logger(__name__)
_snake_case : int = {
"micro... | 134 | 1 |
"""simple docstring"""
from pathlib import Path
import fire
from tqdm import tqdm
def lowercase ( _snake_case : int="ro" , _snake_case : Dict="en" , _snake_case : int="wmt16" , _snake_case : List[str]=None ) ->None:
"""simple docst... | 102 |
'''simple docstring'''
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"kwargs, expected" , [
({"num_shards": 0, "max_num_jobs": 1}, []),
({"num_shards": 10, "max_num_jobs": 1}, [range... | 22 | 0 |
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
lowercase : str = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of tha... | 160 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class __UpperCAmelCase ( _lowerCamelCase ):
__lowercase = """SpeechT5FeatureExtractor"""
__lowercase = """SpeechT5Tokenizer"""
def __init__( self , lowerCAmelCase_ , lowerCAmelC... | 160 | 1 |
"""simple docstring"""
from heapq import heappop, heappush
import numpy as np
def __lowercase ( _a , _a , _a , _a , ):
snake_case_ : Dict = grid.shape
snake_case_ : str = [-1, 1, 0, 0]
snake_case_ : Union[str, Any] = [0, 0, -1, 1]
if allow_diagonal:
... | 264 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_ava... | 226 | 0 |
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wavlm.WavLM... | 350 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
_UpperCAmelCase = '\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n author={Wang, Alex and Pr... | 328 | 0 |
from collections.abc import Sequence
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
return sum(c * (x**i) for i, c in enumerate(snake_case ) )
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
_l... | 82 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class __lowerCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
@register_to_config
def... | 82 | 1 |
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ran... | 20 | from __future__ import annotations
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : list[int] , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if (direction == 1 and array[ind... | 20 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
__a ... | 66 |
import unittest
from transformers import LiltConfig, 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 Model... | 24 | 0 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokeniz... | 360 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE__ : Any = TypeVar("KEY")
SCREAMING_SNAKE_CASE__ : Dict = TypeVar("VAL")
@dataclass(frozen=__lowercase , slots=__lowercase )... | 339 | 0 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase , _lowercase ) -> int:
return int((input_a, input_a).count(1 ) != 0 )
def __lowerCamelCase ( ) -> None:
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or_gate... | 265 |
'''simple docstring'''
from PIL import Image
def __lowerCamelCase ( _lowercase , _lowercase ) -> Image:
def brightness(_lowercase ) -> float:
return 1_2_8 + level + (c - 1_2_8)
if not -255.0 <= level <= 255.0:
raise ValueError("""level must b... | 265 | 1 |
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
... | 362 | import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
from ...... | 342 | 0 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_obj... | 196 |
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def __A ( lowerCamelCase_ ):
"... | 323 | 0 |
'''simple docstring'''
import operator as op
a_ : List[str] = """scaler.pt"""
a_ : Union[str, Any] = """pytorch_model"""
a_ : int = """random_states"""
a_ : str = """optimizer"""
a_ : Tuple = """scheduler"""
a_ : Dict = """py... | 352 |
'''simple docstring'''
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def a_ ( ) -> Tuple:
"""simple docstring"""
lowerCamelCase_ ={
'''repo_name''': ['''t... | 6 | 0 |
"""simple docstring"""
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
... | 255 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
... | 89 | 0 |
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
SCREAMING_SNAKE_CASE_ = {
'<': operator.lt,
'<=': operator.le,
'==': operator.eq,
'!=': operator.ne,
'>=': operator.ge,
'>': operator.gt,
}
def __SCREA... | 189 |
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 ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoo... | 189 | 1 |
from math import sqrt
def snake_case__ ( SCREAMING_SNAKE_CASE_ : int ):
'''simple docstring'''
lowercase__ : List[str] = 0
for i in range(1 , int(sqrt(SCREAMING_SNAKE_CASE_ ) + 1 ) ):
if n % i == 0 and i != sqrt(SCREAMING_SNAKE_CASE_ ):
... | 214 |
def snake_case__ ( SCREAMING_SNAKE_CASE_ : str ):
'''simple docstring'''
if not all(x.isalpha() for x in string ):
raise ValueError('String must only contain alphabetic characters.' )
lowercase__ : Optional[int] = sorted(string.lower() )
return ... | 214 | 1 |
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig
... | 350 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
__UpperCAmelCase ... | 28 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
"""junnyu/roformer_chinese_smal... | 59 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase = {"""configuration_xglm""": ["""... | 59 | 1 |
"""simple docstring"""
from manim import *
class snake_case__ ( snake_case_ ):
def a__ ( self ):
__a = Rectangle(height=0.5 , width=0.5 )
__a = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
__a = Rectangle(heigh... | 268 | """simple docstring"""
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
SCREAMING_SNAKE_CASE__:List[str] = 3
def _lowerCamelCase( a ):
print("Generating primitive root of p" )
while True:
__a = ... | 268 | 1 |
'''simple docstring'''
def lowercase__( __UpperCamelCase: list ,__UpperCamelCase: int = 0 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = length or len(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE : str = False
for i in range(le... | 251 |
'''simple docstring'''
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
UpperCAmelCase_ = logging.... | 346 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__:Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__:Tuple = {
'facebook/dpr-ctx_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-single-... | 366 | """simple docstring"""
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__:Tu... | 268 | 0 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available... | 285 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {"""vocab_file""": """vocab.txt"""}
__snake_case ... | 176 | 0 |
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTest... | 44 |
from sklearn.metrics import mean_squared_error
import datasets
A__ = '''\
@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 Blondel, M. and Prettenho... | 44 | 1 |
A__ = """Input must be a string of 8 numbers plus letter"""
A__ = """TRWAGMYFPDXBNJZSQVHLCKE"""
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if not isinstance(snake_case , snake_case ):
_lowerCAmelCase = F'Expected string a... | 82 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
... | 217 | 0 |
"""simple docstring"""
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, i... | 312 | """simple docstring"""
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_... | 312 | 1 |
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAM... | 110 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
... | 284 | 0 |
"""simple docstring"""
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
lowerCAmelCase : List[str] = """src/diffusers"""
# Matches is_xxx_available()
lower... | 356 |
"""simple docstring"""
import math
import random
def a__ ( snake_case__ , snake_case__ = False ) -> float:
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
lowerCAmelCase : Dict = 0.0_2
def a__ ( ... | 168 | 0 |
"""simple docstring"""
import random
def _snake_case ( UpperCAmelCase_ : int ):
A__ = num - 1
A__ = 0
while s % 2 == 0:
A__ = s // 2
t += 1
for _ in range(5 ):
A__ = random.ra... | 335 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : int = 10 ):
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or n < 0:
raise ValueError("""Invalid input""" )
A__ = 10**n
A__ = 2_8433 * (pow(2 , 783_0457 , ... | 335 | 1 |
'''simple docstring'''
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... | 360 |
'''simple docstring'''
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def snake_case_ ( __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[str] = None ... | 264 | 0 |
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
UpperCAmelCase : Any = '''\
@inproceedings{wang2019glue,
title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
author={Wang, Alex and... | 280 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( a ) -> int:
if not nums:
return 0
__A : Optional[int] = nums[0]
__A : str = 0
for num in nums[1:]:
__A , __A : Tuple = (
max_... | 280 | 1 |
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> int:
return 1 if input_a == input_a else 0
def lowerCAmelCase_ ( ) -> None:
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate... | 328 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokeniz... | 328 | 1 |
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