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 numpy as np
def snake_case_ ( A_ : np.ndarray, A_ : float ):
'''simple docstring'''
return np.where(vector > 0, A_, (alpha * (np.exp(A_ ) - 1)) )
if __name__ == "__main__":
import doctest
doctest.t... | 72 |
"""simple docstring"""
import unittest
import numpy as np
def snake_case_ ( A_ : np.ndarray, A_ : np.ndarray, A_ : np.ndarray, A_ : np.ndarray | None = None, ):
'''simple docstring'''
_lowerCamelCase : Union[str, Any] = np.... | 72 | 1 |
"""simple docstring"""
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
__snake_case : Optional[Any] = 500_000
__snake_case , __snake_case : Optional[Any] = os.pa... | 58 |
"""simple docstring"""
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCr... | 58 | 1 |
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,
AutoMode... | 32 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_... | 32 | 1 |
'''simple docstring'''
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__ = {
'... | 354 |
'''simple docstring'''
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
lowerCamelCase__ = logging.get_logger(__n... | 322 | 0 |
"""simple docstring"""
def lowercase ( a__ : float , a__ : float ) -> float:
if density <= 0:
raise ValueError('''Impossible fluid density''' )
if bulk_modulus <= 0:
raise ValueError('''Impossible bulk modulus''' )
return (bulk_modulus / density) **... | 256 | """simple docstring"""
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrog... | 256 | 1 |
from __future__ import annotations
def __lowerCamelCase (UpperCAmelCase__ : int , UpperCAmelCase__ : int ):
if partitions <= 0:
raise ValueError("partitions must be a positive number!" )
if partitions > number_of_bytes:
raise ValueErro... | 206 | from __future__ import annotations
from collections.abc import Iterator
class lowercase :
def __init__( self : str , _UpperCamelCase : int ) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE = value
SCREA... | 206 | 1 |
"""simple docstring"""
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
fr... | 17 |
"""simple docstring"""
def _A ( UpperCamelCase_ : Any) -> List[str]:
'''simple docstring'''
__lowercase ,__lowercase = [], []
while len(UpperCamelCase_) > 1:
__lowercase ,__lowercase = min(UpperCamelCase_), max(UpperCamelCase_)
start.append(Uppe... | 17 | 1 |
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_ut... | 269 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowercase_ = {
"""configuration_efficientformer""": [
"""EFFICIENTFORMER_PRETRAINED_CONFI... | 269 | 1 |
from __future__ import annotations
import os
from typing import Any
import requests
A : Any = "https://api.github.com"
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
A : Optional[int] = BASE_URL + "/user"
# https://githu... | 184 |
import unittest
import numpy as np
from transformers import RobertaConfig, 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():
from transformers.mo... | 178 | 0 |
"""simple docstring"""
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFA... | 155 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common im... | 155 | 1 |
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
__lowerCAmelCase : str = re.compile(r'^(?P<major>\d+)' r'\.(?P<minor>\d+)' r'\.(?P<patch>\d+)$')
@total_ordering
@dataclass
class snake_c... | 107 |
'''simple docstring'''
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
_lowerCAmelCase = datasets.logging.get_logger(__name__)
_lowerCAmelCase = '''\
@inproceedings{bleurt,
title={BLEURT: Learning Robust Metrics for Tex... | 37 | 0 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from trans... | 365 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
"""transfo-xl-wt103""": """https://huggingface.co/transfo-xl-wt103/resolve/main/config.json""",
}
class lowerCamelCase ( lower... | 166 | 0 |
from __future__ import annotations
class __lowerCamelCase :
"""simple docstring"""
def __init__( self , UpperCAmelCase , UpperCAmelCase ):
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = ... | 39 |
"""simple docstring"""
def lowerCAmelCase ( __UpperCamelCase ):
"""simple docstring"""
__A = current_set.copy()
for row_index, row in enumerate(__UpperCamelCase ):
__A = row[0]
for column_index, column in enumerate(__UpperCamelCase ):
if magnitude == 0:... | 266 | 0 |
"""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_utils impor... | 369 |
"""simple docstring"""
def _a ( _SCREAMING_SNAKE_CASE ) -> list:
if len(_SCREAMING_SNAKE_CASE ) <= 1:
return [tuple(_SCREAMING_SNAKE_CASE )]
snake_case_ = []
def generate(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if k == 1:... | 233 | 0 |
'''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_... | 276 |
'''simple docstring'''
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, id... | 276 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase ( metaclass=UpperCamelCase__ ):
__lowercase = ["""torch""", """torchsde"""]
def __init__( self :Optional[Any] , *lowercase_ :int , **lowercase_ :List[Any] )-... | 371 |
'''simple docstring'''
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__lowerCAmelCase : Optional[Any] ="\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation... | 123 | 0 |
'''simple docstring'''
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def _SCREAMING_... | 2 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class __A( __lowerCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = (CMStochasticIterativeScheduler,)
SCREAMING_SNAKE_CASE__ = 10
def UpperCAmel... | 244 | 0 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""huggingface/informer-tourism-monthly""": (
"""https://huggingface.co/huggingface/informer-... | 201 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case = {
"""configuration_autoformer""": [
"""AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AutoformerConfig""",
... | 201 | 1 |
"""simple docstring"""
class lowercase :
def __init__( self , lowercase ) -> None:
lowerCAmelCase = set_counts
lowerCAmelCase = max(lowercase )
lowerCAmelCase = len(lowercase )
lowerCA... | 46 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_tor... | 122 | 0 |
from math import factorial
class lowercase__ :
def __init__( self : int , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = ... | 258 | import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
__UpperCamelCase : Dict ... | 258 | 1 |
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(_lowerCamelCase , n - 1 , _lowerCamelCase ) * a) % mod
else:
... | 36 |
import requests
from bsa import BeautifulSoup
def _a ( UpperCamelCase_ : str = "AAPL" ) -> str:
"""simple docstring"""
lowerCAmelCase__ = F"https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"
lowerCAmelCase__ = BeautifulSoup(requ... | 340 | 0 |
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, requ... | 368 |
import string
def _A ( lowerCAmelCase_ : str ):
"""simple docstring"""
for key in range(len(string.ascii_uppercase ) ):
lowerCAmelCase__ = ""
for symbol in message:
if symbol in string.ascii_uppercase:
... | 221 | 0 |
'''simple docstring'''
def _UpperCamelCase ( UpperCamelCase__ ):
if not grid or not grid[0]:
raise TypeError("""The grid does not contain the appropriate information""" )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n -... | 163 |
'''simple docstring'''
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__A =get_tests_dir('fixtures/s... | 163 | 1 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
# Initialise PyTorch model
SCREAMING_SNA... | 305 | import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
""... | 305 | 1 |
'''simple docstring'''
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
... | 120 |
'''simple docstring'''
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logg... | 83 | 0 |
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils impo... | 262 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
UpperCAmelCase__ : Optional[Any] ={
'''susnato/ernie-m-base_pytorch''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json''',
'''susnato/ernie-m-large_pyt... | 262 | 1 |
import numpy
class _lowercase :
'''simple docstring'''
def __init__( self , snake_case__ , snake_case__ ):
'''simple docstring'''
UpperCamelCase_ = input_array
# Random initial weights are assigned where first argument ... | 128 |
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 roo... | 128 | 1 |
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: str , lowerCAmelCase: str ) -> bool:
_UpperCAmelCase : Optional[Any] = len(lowerCAmelCase ) + 1
_UpperCAmelCase : Optional[int] = len(lowerCAmelCase ) + 1
# dp is a 2d matrix where dp[i][j]... | 189 |
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: str , lowerCAmelCase: str ) -> bool:
_UpperCAmelCase : Optional[Any] = len(lowerCAmelCase ) + 1
_UpperCAmelCase : Optional[int] = len(lowerCAmelCase ) + 1
# dp is a 2d matrix where dp[i][j]... | 189 | 1 |
"""simple docstring"""
def __lowerCamelCase ( __UpperCamelCase ) -> int:
"""simple docstring"""
assert (
isinstance(__UpperCamelCase , __UpperCamelCase ) and number_of_steps > 0
), f'''number_of_steps needs to be positive integer, your input {number_of_steps... | 241 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
lowercase__ = datasets.utils.logging.get_logger(__name__)
@dataclass
class __lowerC... | 241 | 1 |
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return sum(param.float().sum(... | 105 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__lowercase = {
'''configuration_layoutlmv3''': [
'''LAYOUTLMV3_PRETRAINED_CONFIG_ARCH... | 105 | 1 |
from __future__ import annotations
from math import pi, sqrt
def __snake_case ( _lowerCAmelCase : float , _lowerCAmelCase : float ) -> tuple:
if inductance <= 0:
raise ValueError("Inductance cannot be 0 or negative" )
elif capacitance <= 0:
raise Value... | 300 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
_lowerCAmelCase : List[str] = logging.get_logger(__name__)
def __snake_case ( _lowerCAmelCase : int , _lowerCAmelCase : Any ) ... | 300 | 1 |
from __future__ import annotations
from typing import Any
def UpperCamelCase__( UpperCamelCase__ : list )->int:
if not postfix_notation:
return 0
A__ = {'''+''', '''-''', '''*''', '''/'''}
A__ = []
for token in postf... | 39 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
a__: Union[str, Any] = False
class SCREAMING_SNAKE_CASE_... | 39 | 1 |
'''simple docstring'''
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_avail... | 134 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoForm... | 134 | 1 |
"""simple docstring"""
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class a ( unittest... | 353 |
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
'huggingface/autoformer-tourism-monthly': 'https://huggingface.co/huggingface/autoformer-touri... | 189 | 0 |
"""simple docstring"""
from __future__ import annotations
class _UpperCamelCase :
'''simple docstring'''
def __init__( self , __a = 0 ):
__lowerCAmelCase = key
def snake_case ( self , __a , __a ):
assert isinstance(__a , ... | 57 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils i... | 57 | 1 |
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class SCREAMING_SNAKE_CASE__ :
A : Optional[str] = field(
default="codeparrot/codeparrot" , metadata={"help": "Model name or path of model to be trained."} )
A : Optional[str]... | 20 | from __future__ import annotations
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : list , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
__snake_case : str = []
... | 20 | 1 |
import os
from collections.abc import Iterator
def lowerCamelCase ( SCREAMING_SNAKE_CASE = "." ):
'''simple docstring'''
for dir_path, dir_names, filenames in os.walk(UpperCAmelCase__ ):
__UpperCamelCase :str = [d for d in dir_names if d != '''scripts''' and d[0] not in '''._''']
... | 43 |
'''simple docstring'''
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu... | 162 | 0 |
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
_lowerCamelCase : Union[str, Any] = object()
# For specifying empty leaf dict `{}`
_lowerCamelCase : Optiona... | 191 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def _a ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : complex , SCREAMING_SNAKE_CASE__ : str = "x" , SCREAMING_SNAKE_CASE__ : float = 10**-10 , SCREAMING_SN... | 191 | 1 |
from typing import Any
class a__ :
"""simple docstring"""
def __init__( self , lowercase ) -> str:
'''simple docstring'''
A__ = data
A__ = None
def __repr__( self ) -> str:
'''simple docstring'''
return F'Node... | 68 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import To... | 68 | 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
if... | 356 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenizat... | 101 | 0 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
__UpperCamelCase : List[Any] = '\\n@misc{chen2021evaluating,\n title=... | 182 | import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, ... | 182 | 1 |
"""simple docstring"""
def lowercase ( lowerCAmelCase__ : int ) -> bool:
if num < 0:
return False
__a = num
__a = 0
while num > 0:
__a = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if... | 11 |
"""simple docstring"""
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorch... | 11 | 1 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class _A:
"""simple docstring"""
def __init__( self , _A = None ):
if components is None:
__A : int = []
... | 280 |
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...uti... | 280 | 1 |
'''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
__A : Li... | 89 |
'''simple docstring'''
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_avail... | 89 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : List[str] = {
"SenseTime/deformable-detr": "https://huggi... | 21 |
def lowerCAmelCase__ ( lowerCamelCase_ : str):
'''simple docstring'''
lowerCAmelCase__ : Any = [0] * len(lowerCamelCase_)
for i in range(1 ,len(lowerCamelCase_)):
# use last results for better performance - dynamic programming
lowerCAmelCase__ ... | 129 | 0 |
'''simple docstring'''
from ... import PretrainedConfig
A : Optional[Any] = {
'''sijunhe/nezha-cn-base''': '''https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json''',
}
class __lowerCamelCase ( a_ ):
"""simple docstring"""
a = NEZHA_PRETRAI... | 227 |
'''simple docstring'''
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import tor... | 227 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_a = {
"configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
"tokenization_tapas": ["TapasTokenizer"],
}
try:
if not is_torch_av... | 209 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_a = {
"configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
"tokenization_lxme... | 209 | 1 |
"""simple docstring"""
import numpy as np
def a__ ( SCREAMING_SNAKE_CASE : np.ndarray ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def a__ ( SCREAMING_SNAKE_CASE : np.ndarray ):
'''simple docstring'''
return vector * sigmoid(SCREAMING... | 133 |
"""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:
... | 133 | 1 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_m... | 278 |
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
f... | 278 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torc... | 78 | import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Union[str, Any] = ["""image_processor""", """tokenizer"""]
__lowerCamelCase : Union[str, Any]... | 78 | 1 |
'''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 transf... | 41 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A : Dict ={
'''configuration_swinv2''': ['''SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Swinv2Config'''],
}
try:
if ... | 41 | 1 |
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
infer_sha... | 364 |
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> int:
return int((input_a, input_a).count(1 ) != 0 )
def UpperCamelCase_( ) -> None:
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or_gate(1 , 0 ) == ... | 84 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE ... | 180 | from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
... | 180 | 1 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def A__ ( lowerCamelCase ) -> Any:
UpperCamelCase_: Dict = args.pruning_method
UpperCamelCase_: List[str] = args.threshold
UpperCamel... | 223 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer
lowerCamelCa... | 223 | 1 |
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_softmax
... | 207 |
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 _UpperCAmelCase ( A__ ):
"""simple docstring"""
def __init__( self ... | 207 | 1 |
def lowerCAmelCase_ ( _lowercase : list , _lowercase : list , _lowercase : int) -> list:
"""simple docstring"""
a__ : Union[str, Any] = len(_lowercase)
a__ : Optional[Any] = [[0] * n for i in... | 359 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl im... | 266 | 0 |
'''simple docstring'''
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
a_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class... | 152 |
'''simple docstring'''
from __future__ import annotations
def _a( UpperCamelCase__ : list[int] ):
'''simple docstring'''
if not nums:
return 0
SCREAMING_SNAKE_CASE__ : Dict =nums[0]
SCREAMING_SNAKE_CAS... | 152 | 1 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .lo... | 366 |
'''simple docstring'''
from __future__ import annotations
def snake_case_ (UpperCamelCase : list[int] ):
'''simple docstring'''
if not nums:
return 0
_a = nums[0]
_a = 0
for num in nums[1:]:
... | 179 | 0 |
"""simple docstring"""
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors impor... | 66 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
__snake_case =logging... | 4 | 0 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
UpperCAmelCase_ = logging.get_logger(__name__)
def lowerCamelCase__ ( A__ : int , A__ : Union[str, Any] ):
... | 358 |
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import TEXT_GUIDED_IM... | 29 | 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
snake_case : Any = {
# 1536-bit
5: {
'prime': int(
... | 240 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : bytes ) -> str:
return "".join([hex(lowercase )[2:].zfill(2 ).upper() for byte in list(lowercase )] )
def _lowerCamelCase ( lowercase : str ) -> bytes:
# Check data validity, fol... | 63 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = ['image_processor', 'tokenizer']
SCREAMING_SNAKE_CASE = 'CL... | 308 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __magic_name__ ( pl.LightningModule ):
def __init__( self , __snake_case ) -> List[Any]:
... | 308 | 1 |
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def SCREAMING_SNAKE_CASE__ ( *lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__=True , lowerCamelCase__=2 ) -> List[Any]:
from .. import __version__
__lower... | 73 |
from __future__ import annotations
def _a ( lowerCamelCase: list[float] , lowerCamelCase: Tuple ) -> List[str]:
'''simple docstring'''
print(F"""Vertex\tShortest Distance from vertex {src}""" )
for i, d in enumerate(lowerCamelCase ):
... | 117 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case = {
'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}
try:
if not is_torch_available():
... | 199 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, D... | 199 | 1 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
__SCREAMING_SNAKE_CASE : Optional[Any] ... | 31 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers im... | 309 | 0 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def A__ ( ):
SCREAMING_SNAKE_CASE_ = 9
SCREAMING_SNAKE_CASE_ = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
... | 257 |
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
if not arr:
return None, None, 0
if low == high:
return low, ... | 257 | 1 |
'''simple docstring'''
from __future__ import annotations
UpperCAmelCase_ : List[str] = list[list[int]]
# assigning initial values to the grid
UpperCAmelCase_ : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0,... | 200 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import... | 312 | 0 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ =... | 247 |
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
UpperCAmelCase_ = logging.getLogger(__name__)
def lowerCAmelCase_ ( ) -> Any:
UpperCamelCase__ : Dict = argparse.Arg... | 247 | 1 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
... | 59 |
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,
PreTrainedTokenizer,
TFAutoMod... | 59 | 1 |
"""simple docstring"""
import operator as op
snake_case_ = """scaler.pt"""
snake_case_ = """pytorch_model"""
snake_case_ = """random_states"""
snake_case_ = """optimizer"""
snake_case_ = """scheduler"""
snake_case_ = """pytorch_model.bin"""
snake_c... | 181 |
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _lowerCAmelCase ( ):
UpperCAmelCase = ArgumentParser(
description=(
... | 181 | 1 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
__UpperCamelCase = r'''
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control the m... | 69 | """simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaP... | 69 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : List[str] = logging.get_logger(__name__)
lowerCAmelCase_ : List[str] = {
'''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/resol... | 248 |
"""simple docstring"""
import os
def _lowerCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase = len(grid[0] )
UpperCAmelCase = len(lowerCAmelCase )
UpperCAmelCase = 0
UpperCAmelCase ... | 248 | 1 |
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 CLIPModel, CLIPTokeni... | 312 |
import os
from typing import Dict, List, Tuple, TypeVar, Union
__a :Any = TypeVar('T')
__a :Union[str, Any] = Union[List[T], Tuple[T, ...]]
__a :List[str] = Union[T, List[T], Dict[str, T]]
__a :Any = Union[str, bytes, os.PathLike] | 312 | 1 |
"""simple docstring"""
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checke... | 364 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is... | 112 | 0 |
"""simple docstring"""
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import ... | 160 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A = {
'''configuration_ctrl''': ['''CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CTRLConfig'''],
'''tokenizatio... | 160 | 1 |
'''simple docstring'''
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...tes... | 357 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : list ) -> list:
"""simple docstring"""
UpperCAmelCase_ : str = len(_SCREAMING_SNAKE_CASE )
for _ in range(_SCREAMING_SNAKE_CASE ):
for i in range(_ % 2 , arr_size - 1 ... | 67 | 0 |
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
UpperCAmelCase_ : Optional[int] = 4
UpperCAmelCase_ : Dict = 3
class ... | 32 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import... | 32 | 1 |
"""simple docstring"""
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def _SCREAMING_SNAKE_CASE ... | 230 |
"""simple docstring"""
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mo... | 230 | 1 |
'''simple docstring'''
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... | 70 |
'''simple docstring'''
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_co... | 70 | 1 |
'''simple docstring'''
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 FlaxModelTesterMi... | 322 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWi... | 322 | 1 |
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def __lowercase ( _... | 245 |
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
_snake_case = 2_99_79_24_58
# Symbols
_snake_case , _snake_case , _snake_case , _snake_case = symbols('''ct x y z''')
d... | 283 | 0 |
"""simple docstring"""
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase ) -> Tuple:
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase=0 ) -> Optional[Any]:
return sorted(__UpperCAmel... | 212 |
"""simple docstring"""
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precisi... | 212 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_albert impo... | 50 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def a ( __a="ro" , __a="en" , __a="wmt16" , __a=None ) -> None:
'''simple docstring'''
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise Import... | 97 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
... | 367 |
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_uti... | 27 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
_UpperCamelCase : Union[str, Any] = TypeVar('T')
class snake_case__ ( Generic[T]):
def __init__( self : Any , _A : T )... | 304 |
'''simple docstring'''
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 ImagePr... | 304 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class UpperCamelCase :
UpperCamelCase : int
UpperCamelCase : TreeNode | None = None
UpperCamelCase : TreeNode | None = ... | 324 |
"""simple docstring"""
_snake_case = 8.31_44_62 # Unit - J mol-1 K-1
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueE... | 324 | 1 |
'''simple docstring'''
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,... | 79 |
"""simple docstring"""
lowerCamelCase_ : Any = [
"""DownloadConfig""",
"""DownloadManager""",
"""DownloadMode""",
"""StreamingDownloadManager""",
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .st... | 81 | 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
from ..auto import CONFIG_MAPPING
__A =logging.get_logger(__nam... | 283 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
__A =[
'good first issue',
'good second issue',
'good difficult issue',
'enhancement',
'new pipeline/model',
'new scheduler',
'wip',
]
def _Upper... | 283 | 1 |
from __future__ import annotations
import os
from typing import Any
import requests
lowercase = "https://api.github.com"
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
lowercase = BASE_URL + "/user"
# https://github.com/settings/toke... | 178 |
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowercase = logging.get_logger(__name__)
lowercase = {"vocab_... | 178 | 1 |
"""simple docstring"""
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def __UpperCAmelCase ( __lowerCamelCase ) -> Any:
lowercase__ : Optional[Any] = FileLock(str(tmpdir / '''foo.lock''' ) )... | 302 |
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import... | 302 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
fr... | 302 |
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
lowerCamelCase__ = logging.get_logger(__name__)
@add_... | 302 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a_ = logging.get_logger(__name__)
a_ = {
'facebook/convnextv2-tiny-1k-224': 'https://huggingface.co/facebook/convnextv2-tiny-1... | 355 | import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTester... | 50 | 0 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_tor... | 304 |
'''simple docstring'''
def __UpperCAmelCase ( A : int ) -> list:
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError('''The given input must be positive''' )
# get the generated string sequence
UpperCAmelCase_ : int = gr... | 304 | 1 |
"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def lowerCamelCase ( ) -> Union[str, Any]:
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = HfArgumentParser(__lowerCamelCase )
... | 351 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase ( _UpperCamelCase : list[float] , _UpperCamelCase : list[float] ) -> float:
'''simple docstring'''
__UpperCAmelCase : Tuple = sorted(numsa + numsa )
__Up... | 320 | 0 |
def a ( snake_case__: int , snake_case__: int ):
'''simple docstring'''
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
raise ValueError('''iterations must be defined as integers''' )
if not isinstance(UpperCAmelCase_ ,... | 30 |
from maths.prime_factors import prime_factors
def __lowerCamelCase ( UpperCAmelCase_ : int ):
"""simple docstring"""
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
a :Dict = F'''Input value of [number={number}] must be an integer'''... | 94 | 0 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtrac... | 13 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
fr... | 13 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE =logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE ={
'uclanlp/visualbert-vqa': 'https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json',
'uclanlp/vi... | 213 |
"""simple docstring"""
def lowercase_ ( _snake_case ):
SCREAMING_SNAKE_CASE__ : Optional[int] = [1]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = 0, 0, 0
SCREAMING_SNAKE_CASE... | 25 | 0 |
from __future__ import annotations
from math import pi, sqrt
def snake_case (__lowercase , __lowercase ) -> tuple:
'''simple docstring'''
if inductance <= 0:
raise ValueError("Inductance cannot be 0 or negative" )
elif capacitance <= 0:
raise ValueErr... | 284 | # 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
#
# Unless required by applicabl... | 284 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_numpy,
... | 118 | import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from tran... | 118 | 1 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
__magic_name__: List[str] =argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path", default=None, type=str, required=True, help="... | 371 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def UpperCamelCase ( _A ):
"""simple docstring"""
if not isinstance(_A, _A ):
raise TypeError("""Undefined for non-integers""" )
elif precision < 1:
raise ValueError(""... | 138 | 0 |
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