code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
def _lowerCAmelCase ( __magic_name__ :Tuple , __magic_name__ :Any ):
UpperCAmelCase_ = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def _lowerCAmelCase ( __magic_nam... | 121 |
from __future__ import annotations
def _lowerCAmelCase ( __magic_name__ :int ):
UpperCAmelCase_ = [True] * limit
UpperCAmelCase_ = False
UpperCAmelCase_ = False
UpperCAmelCase_ = True
for i in range(3 , int(limit**0.5 + 1 ) ... | 121 | 1 |
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 DEFAULTS, ... | 121 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
_lowerCamelCase : str = logging.getLogger(__name__)
if __name__ ==... | 121 | 1 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
_lowerCamelCase : str = logging.getLogger(__name__)
if __name__ ==... | 121 |
import os
from math import logaa
def _lowerCAmelCase ( __magic_name__ :str = "base_exp.txt" ):
UpperCAmelCase_ = 0
UpperCAmelCase_ = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(__magic_name__ ) , __magic_name__ ) )... | 121 | 1 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTim... | 121 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
_lowerCamelCase : Any = 'Usage of script: script_name <size_of_canvas:int>'
_lowerCamelCase : Dict = [0] * 100 + [1] * 10
random.shuffle(choice... | 121 | 1 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_lowerCamelCase : Optional[Any] = {'tokenization_bertweet': ['BertweetTokenizer']}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
_lowerCamelCase :... | 121 |
from __future__ import annotations
def _lowerCAmelCase ( __magic_name__ :list[int | str] ):
create_state_space_tree(__magic_name__ , [] , 0 , [0 for i in range(len(__magic_name__ ) )] )
def _lowerCAmelCase ( ... | 121 | 1 |
from __future__ import annotations
_lowerCamelCase : str = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
_lowerCamelCase : Dict = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def _lowerCAmelCase ( __magic_name__ :list[float... | 121 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switchi... | 121 | 1 |
from math import sqrt
def _lowerCAmelCase ( __magic_name__ :int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3... | 121 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
_lowerCamelCase : Union[str, Any] = {
'tiny.en': 'https://openaipublic.azu... | 121 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
import tran... | 121 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCAmelCase ( __magic_name__ :Union[str, Any] , __magic_na... | 121 | 1 |
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 (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProc... | 121 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowerCamelCase : Dict = {
'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
... | 121 | 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_F... | 121 |
_lowerCamelCase : dict[tuple[int, int, int], int] = {}
def _lowerCAmelCase ( __magic_name__ :int , __magic_name__ :int , __magic_name__ :int ):
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possib... | 121 | 1 |
from math import sqrt
def _lowerCAmelCase ( __magic_name__ :int ):
assert isinstance(__magic_name__ , __magic_name__ ) and (
number >= 0
), "'number' must been an int and positive"
UpperCAmelCase_ = True
# 0 and 1 are none p... | 121 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
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
from ..models.auto.modeli... | 121 | 1 |
_lowerCamelCase : str = 'Tobias Carryer'
from time import time
class snake_case__ :
'''simple docstring'''
def __init__( self : Tuple , lowerCAmelCase_ : int , lowerCAmelCase_ : Any , lowerCAmelCase_... | 121 |
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Acceler... | 121 | 1 |
def _lowerCAmelCase ( __magic_name__ :list ):
if any(not isinstance(__magic_name__ , __magic_name__ ) or x < 0 for x in sequence ):
raise TypeError('''Sequence must be list of non-negative integers''' )
for _ in range(len(__magic_name__ ... | 121 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : Optional[Any] = {
'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_AR... | 121 | 1 |
def _lowerCAmelCase ( __magic_name__ :int , __magic_name__ :float , __magic_name__ :float ):
return round(float(moles / volume ) * nfactor )
def _lowerCAmelCase ( __magic_name__ :float , __magic_name__ :float , __mag... | 121 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import ena... | 121 | 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 _lowerCAmelCa... | 121 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space... | 121 | 1 |
import math
def _lowerCAmelCase ( __magic_name__ :int ):
UpperCAmelCase_ = []
UpperCAmelCase_ = 2
UpperCAmelCase_ = int(math.sqrt(__magic_name__ ) ) # Size of every segment
UpperCAmelCase_ = [True] * (end + 1)
UpperCAmelCase_ ... | 121 |
def _lowerCAmelCase ( __magic_name__ :list ):
if any(not isinstance(__magic_name__ , __magic_name__ ) or x < 0 for x in sequence ):
raise TypeError('''Sequence must be list of non-negative integers''' )
for _ in range(len(__magic_name__ ... | 121 | 1 |
def _lowerCAmelCase ( __magic_name__ :str ):
UpperCAmelCase_ = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def _lowerCAmelCase ( __magic_name_... | 121 |
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def _lowerCAmelCase ( __magic_name__ :Optional[Any] ):
UpperCAmelCase_ = os.path.join(args.tf_model_dir , '''parameters.jso... | 121 | 1 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCAmelCase ( __magic_name__ :str , __magic_name__ :Dict , __magic_name__ :Union[... | 121 |
def _lowerCAmelCase ( __magic_name__ :list[list[int]] , __magic_name__ :int , __magic_name__ :int , __magic_name__ :set ):
UpperCAmelCase_, UpperCAmelCase_ = len(__magic_name__ ), len(grid[0] )
if (
min(__magic_name__ , __ma... | 121 | 1 |
def _lowerCAmelCase ( __magic_name__ :int ):
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print('Program to check whether a number is a Perfect number or not...')
_lowerCamelCase ... | 121 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
_lowerCamelCase : List[Any] = logging.getLogger(__name__)
class snake_case__ ( __snake_case ):
... | 121 | 1 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class snake_case__ :
'''simple docstring'''
def __init__( self : Optional[int] , lowerCAmelCase_ : C... | 121 |
def _lowerCAmelCase ( __magic_name__ :str ):
UpperCAmelCase_ = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def _lowerCAmelCase ( __magic_name_... | 121 | 1 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
_lowerCamelCase : List[str] = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of t... | 121 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : str = {
'configuration_jukebox': [
'JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP',
'JukeboxConfig',
'JukeboxPriorConfig',
... | 121 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCamelCase : str = logging.get_logger(__name__)
_lowerCamelCase : Optional[Any] = {
... | 121 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class snake_case__ :
'''simple docstring'''
__A = 42
__A = None
__A = None
_lowerCamelCas... | 121 | 1 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN... | 121 |
from __future__ import annotations
def _lowerCAmelCase ( __magic_name__ :int ):
UpperCAmelCase_ = [True] * limit
UpperCAmelCase_ = False
UpperCAmelCase_ = False
UpperCAmelCase_ = True
for i in range(3 , int(limit**0.5 + 1 ) ... | 121 | 1 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
_lowerCamelCase : List[Any] = logging.getLogger(__name__)
class snake_case__ ( __snake_case ):
... | 121 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
_lowerCamelCase : str = logging.getLogger(__name__)
if __name__ ==... | 121 | 1 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql impo... | 121 |
import os
from math import logaa
def _lowerCAmelCase ( __magic_name__ :str = "base_exp.txt" ):
UpperCAmelCase_ = 0
UpperCAmelCase_ = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(__magic_name__ ) , __magic_name__ ) )... | 121 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
... | 121 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
_lowerCamelCase : Any = 'Usage of script: script_name <size_of_canvas:int>'
_lowerCamelCase : Dict = [0] * 100 + [1] * 10
random.shuffle(choice... | 121 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Any = logging.get_logger(__name__)
_lowerCamelCase : Any = {
'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}
class ... | 121 |
from __future__ import annotations
def _lowerCAmelCase ( __magic_name__ :list[int | str] ):
create_state_space_tree(__magic_name__ , [] , 0 , [0 for i in range(len(__magic_name__ ) )] )
def _lowerCAmelCase ( ... | 121 | 1 |
from collections import deque
class snake_case__ :
'''simple docstring'''
def __init__( self : int , lowerCAmelCase_ : str , lowerCAmelCase_ : int , lowerCAmelCase_ : int ) -> None:
Uppe... | 121 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switchi... | 121 | 1 |
def _lowerCAmelCase ( __magic_name__ :str ):
UpperCAmelCase_ = len(__magic_name__ )
while cur > 1:
# Find the maximum number in arr
UpperCAmelCase_ = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
UpperCAmel... | 121 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
_lowerCamelCase : Union[str, Any] = {
'tiny.en': 'https://openaipublic.azu... | 121 | 1 |
_lowerCamelCase : dict[tuple[int, int, int], int] = {}
def _lowerCAmelCase ( __magic_name__ :int , __magic_name__ :int , __magic_name__ :int ):
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possib... | 121 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCAmelCase ( __magic_name__ :Union[str, Any] , __magic_na... | 121 | 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 ModelTesterMix... | 121 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowerCamelCase : Dict = {
'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
... | 121 | 1 |
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, i... | 121 |
_lowerCamelCase : dict[tuple[int, int, int], int] = {}
def _lowerCAmelCase ( __magic_name__ :int , __magic_name__ :int , __magic_name__ :int ):
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possib... | 121 | 1 |
_lowerCamelCase : str = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
_lowerCamelCase : Union[str, Any] = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _lowerCAmelCase ( __magic_name__ :dict[int, list[int]] , __magic_name__ :int , ... | 121 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
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
from ..models.auto.modeli... | 121 | 1 |
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load_... | 121 |
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Acceler... | 121 | 1 |
from ..utils import DummyObject, requires_backends
class snake_case__ ( metaclass=__snake_case ):
'''simple docstring'''
__A = ['''onnx''']
def __init__( self : int , *lowerCAmelCase_ : List[str] , *... | 121 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : Optional[Any] = {
'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_AR... | 121 | 1 |
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
_lowerCamelCase : Tuple = logging.get_logger(__name__)
class snake_case__ ( __snake_case ):
'''simple docstring'''
def _... | 121 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import ena... | 121 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | 121 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space... | 121 | 1 |
from __future__ import annotations
def _lowerCAmelCase ( __magic_name__ :list[int | str] ):
create_state_space_tree(__magic_name__ , [] , 0 , [0 for i in range(len(__magic_name__ ) )] )
def _lowerCAmelCase ( ... | 121 |
def _lowerCAmelCase ( __magic_name__ :list ):
if any(not isinstance(__magic_name__ , __magic_name__ ) or x < 0 for x in sequence ):
raise TypeError('''Sequence must be list of non-negative integers''' )
for _ in range(len(__magic_name__ ... | 121 | 1 |
def _lowerCAmelCase ( __magic_name__ :str , __magic_name__ :str ):
UpperCAmelCase_ = len(__magic_name__ )
UpperCAmelCase_ = len(__magic_name__ )
UpperCAmelCase_ = (
first_str_length if first_str_length > second_str_length else second_st... | 121 |
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def _lowerCAmelCase ( __magic_name__ :Optional[Any] ):
UpperCAmelCase_ = os.path.join(args.tf_model_dir , '''parameters.jso... | 121 | 1 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
... | 121 |
def _lowerCAmelCase ( __magic_name__ :list[list[int]] , __magic_name__ :int , __magic_name__ :int , __magic_name__ :set ):
UpperCAmelCase_, UpperCAmelCase_ = len(__magic_name__ ), len(grid[0] )
if (
min(__magic_name__ , __ma... | 121 | 1 |
from math import sqrt
def _lowerCAmelCase ( __magic_name__ :int = 1_0_0_0_0_0_0 ):
UpperCAmelCase_ = 0
UpperCAmelCase_ = 0
UpperCAmelCase_ = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in ran... | 121 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
_lowerCamelCase : List[Any] = logging.getLogger(__name__)
class snake_case__ ( __snake_case ):
... | 121 | 1 |
def _lowerCAmelCase ( __magic_name__ :str , __magic_name__ :str ):
UpperCAmelCase_ = len(__magic_name__ )
UpperCAmelCase_ = []
for i in range(len(__magic_name__ ) - pat_len + 1 ):
UpperCAmelCase_ = True
for j in range(_... | 121 |
def _lowerCAmelCase ( __magic_name__ :str ):
UpperCAmelCase_ = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def _lowerCAmelCase ( __magic_name_... | 121 | 1 |
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 _lowerCAmelCas... | 121 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : str = {
'configuration_jukebox': [
'JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP',
'JukeboxConfig',
'JukeboxPriorConfig',
... | 121 | 1 |
from math import factorial
def _lowerCAmelCase ( __magic_name__ :int = 1_0_0 ):
return sum(map(__magic_name__ , str(factorial(__magic_name__ ) ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip()))... | 121 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class snake_case__ :
'''simple docstring'''
__A = 42
__A = None
__A = None
_lowerCamelCas... | 121 | 1 |
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ..... | 121 |
from __future__ import annotations
def _lowerCAmelCase ( __magic_name__ :int ):
UpperCAmelCase_ = [True] * limit
UpperCAmelCase_ = False
UpperCAmelCase_ = False
UpperCAmelCase_ = True
for i in range(3 , int(limit**0.5 + 1 ) ... | 121 | 1 |
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def ... | 121 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
_lowerCamelCase : str = logging.getLogger(__name__)
if __name__ ==... | 121 | 1 |
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class ... | 121 |
import os
from math import logaa
def _lowerCAmelCase ( __magic_name__ :str = "base_exp.txt" ):
UpperCAmelCase_ = 0
UpperCAmelCase_ = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(__magic_name__ ) , __magic_name__ ) )... | 121 | 1 |
import fire
from utils import calculate_rouge, save_json
def _lowerCAmelCase ( __magic_name__ :List[str] , __magic_name__ :int , __magic_name__ :Any=None , **__magic_name__ :Any ):
UpperCAmelCase_ = [x.strip() for x in open(__magic_name__ ... | 121 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
_lowerCamelCase : Any = 'Usage of script: script_name <size_of_canvas:int>'
_lowerCamelCase : Dict = [0] * 100 + [1] * 10
random.shuffle(choice... | 121 | 1 |
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
... | 121 |
from __future__ import annotations
def _lowerCAmelCase ( __magic_name__ :list[int | str] ):
create_state_space_tree(__magic_name__ , [] , 0 , [0 for i in range(len(__magic_name__ ) )] )
def _lowerCAmelCase ( ... | 121 | 1 |
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
... | 121 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switchi... | 121 | 1 |
import os
def _lowerCAmelCase ( __magic_name__ :str = "input.txt" ):
with open(os.path.join(os.path.dirname(__magic_name__ ) , __magic_name__ ) ) as input_file:
UpperCAmelCase_ = [
[int(__magic_name__ ) for elemen... | 121 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
_lowerCamelCase : Union[str, Any] = {
'tiny.en': 'https://openaipublic.azu... | 121 | 1 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common impo... | 121 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCAmelCase ( __magic_name__ :Union[str, Any] , __magic_na... | 121 | 1 |
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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import... | 121 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowerCamelCase : Dict = {
'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
... | 121 | 1 |
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArgument... | 121 |
_lowerCamelCase : dict[tuple[int, int, int], int] = {}
def _lowerCAmelCase ( __magic_name__ :int , __magic_name__ :int , __magic_name__ :int ):
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possib... | 121 | 1 |
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.p... | 121 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
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
from ..models.auto.modeli... | 121 | 1 |
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 snake_case__ ( __snake_case ):
... | 121 |
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Acceler... | 121 | 1 |
import unittest
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 ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available... | 121 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : Optional[Any] = {
'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_AR... | 121 | 1 |
import re
import string
import numpy as np
import datasets
_lowerCamelCase : Dict = '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'
_lowerCamelCase : Tuple =... | 121 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import ena... | 121 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unl... | 121 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space... | 121 | 1 |
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 : Dict = loggi... | 121 |
def _lowerCAmelCase ( __magic_name__ :list ):
if any(not isinstance(__magic_name__ , __magic_name__ ) or x < 0 for x in sequence ):
raise TypeError('''Sequence must be list of non-negative integers''' )
for _ in range(len(__magic_name__ ... | 121 | 1 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
SCREAMING_SNAKE_CASE__ : List[str] = logging.getLogger(__name__)
if is_torch_tpu_avai... | 0 |
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def _lowerCAmelCase ( __magic_name__ :Optional[Any] ):
UpperCAmelCase_ = os.path.join(args.tf_model_dir , '''parameters.jso... | 121 | 0 |
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
__snake_case = '''src/diffusers'''
# Matches is_xxx_available()
__snake_case = re.compile(r'''is\_(... | 1 |
def _lowerCAmelCase ( __magic_name__ :list[list[int]] , __magic_name__ :int , __magic_name__ :int , __magic_name__ :set ):
UpperCAmelCase_, UpperCAmelCase_ = len(__magic_name__ ), len(grid[0] )
if (
min(__magic_name__ , __ma... | 121 | 0 |
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def SCREAMING_SNAKE_CASE_ ( *_snake_case :Optional[int] ) -> Optional[int]:
if not isinstance(_snake_case , _snake_case ):
_A = list(_snake_case ... | 2 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
_lowerCamelCase : List[Any] = logging.getLogger(__name__)
class snake_case__ ( __snake_case ):
... | 121 | 0 |
'''simple docstring'''
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
lowerCAmelCase : List[str] = '\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Unders... | 3 |
def _lowerCAmelCase ( __magic_name__ :str ):
UpperCAmelCase_ = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def _lowerCAmelCase ( __magic_name_... | 121 | 0 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.... | 4 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : str = {
'configuration_jukebox': [
'JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP',
'JukeboxConfig',
'JukeboxPriorConfig',
... | 121 | 0 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | 5 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class snake_case__ :
'''simple docstring'''
__A = 42
__A = None
__A = None
_lowerCamelCas... | 121 | 0 |
_lowerCamelCase = {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
_lowerCamelCase = ['a', 'b', 'c', 'd', 'e']
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: str , UpperCamelCase__: Union[str, Any] , UpperCamelCase__: Optional[Any] ):
... | 6 |
from __future__ import annotations
def _lowerCAmelCase ( __magic_name__ :int ):
UpperCAmelCase_ = [True] * limit
UpperCAmelCase_ = False
UpperCAmelCase_ = False
UpperCAmelCase_ = True
for i in range(3 , int(limit**0.5 + 1 ) ... | 121 | 0 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, 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 .tokeniza... | 7 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
_lowerCamelCase : str = logging.getLogger(__name__)
if __name__ ==... | 121 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( __snake_case : int , __snake_case : list ) -> Union[str, Any]:
_enforce_args(__snake_case , __snake_case )
if n == 0:
return 0
__A : Any = float('-inf' )
for... | 8 |
import os
from math import logaa
def _lowerCAmelCase ( __magic_name__ :str = "base_exp.txt" ):
UpperCAmelCase_ = 0
UpperCAmelCase_ = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(__magic_name__ ) , __magic_name__ ) )... | 121 | 0 |
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
SCREAMING_SNAKE_CASE__ = '''\
@inproceedings{popovic-2015-chrf,
title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",
author = "Popovi{\'c}, Maja",
booktitle = "Proceedings of... | 9 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
_lowerCamelCase : Any = 'Usage of script: script_name <size_of_canvas:int>'
_lowerCamelCase : Dict = [0] * 100 + [1] * 10
random.shuffle(choice... | 121 | 0 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def _snake_case ( ):
_UpperCamelCase = {
'''repo_name''': ['''test_repo1''', '''test_repo2''', '''test_repo3'''],
'''path''': ['''test_1.py''... | 10 |
from __future__ import annotations
def _lowerCAmelCase ( __magic_name__ :list[int | str] ):
create_state_space_tree(__magic_name__ , [] , 0 , [0 for i in range(len(__magic_name__ ) )] )
def _lowerCAmelCase ( ... | 121 | 0 |
'''simple docstring'''
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
lowercase_ = logging.getLogger(__name__)
class __A ( A ):
'''simple docstring'''
... | 11 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switchi... | 121 | 0 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
lowerCamelCase_... | 12 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
_lowerCamelCase : Union[str, Any] = {
'tiny.en': 'https://openaipublic.azu... | 121 | 0 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simp... | 13 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCAmelCase ( __magic_name__ :Union[str, Any] , __magic_na... | 121 | 0 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUM... | 14 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowerCamelCase : Dict = {
'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
... | 121 | 0 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
' StableDiffusionInpaintPipeline` instead.'
)
| 15 |
_lowerCamelCase : dict[tuple[int, int, int], int] = {}
def _lowerCAmelCase ( __magic_name__ :int , __magic_name__ :int , __magic_name__ :int ):
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possib... | 121 | 0 |
from __future__ import annotations
from math import pow, sqrt
def __a ( A__ : float , A__ : float , A__ : float ):
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance ... | 16 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
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
from ..models.auto.modeli... | 121 | 0 |
def __SCREAMING_SNAKE_CASE ( a__ : int ) -> list[int]:
if num <= 0:
raise ValueError("""Input must be a positive integer""" )
__A : Any = [True] * (num + 1)
__A : Optional[int] = 2
while p * p <= num:
if primes[p]:
for i in range(p * p ,num + 1 ... | 17 |
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Acceler... | 121 | 0 |
'''simple docstring'''
from collections import defaultdict
def __a(SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str ):
'''simple docstring'''
_lowerCAmelCase = first_str.lower().strip()
_lowerCAmelCase = second_str.lower().str... | 18 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : Optional[Any] = {
'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_AR... | 121 | 0 |
"""simple docstring"""
def lowerCamelCase__ ( __snake_case, __snake_case ) -> int:
"""simple docstring"""
return int(input_a == input_a == 0 )
def lowerCamelCase__ ( ) -> None:
"""simple docstring"""
pr... | 19 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import ena... | 121 | 0 |
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(lowercase__ ) , 'Tatoeba dir... | 20 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space... | 121 | 0 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.num... | 21 |
def _lowerCAmelCase ( __magic_name__ :list ):
if any(not isinstance(__magic_name__ , __magic_name__ ) or x < 0 for x in sequence ):
raise TypeError('''Sequence must be list of non-negative integers''' )
for _ in range(len(__magic_name__ ... | 121 | 0 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
excep... | 22 |
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def _lowerCAmelCase ( __magic_name__ :Optional[Any] ):
UpperCAmelCase_ = os.path.join(args.tf_model_dir , '''parameters.jso... | 121 | 0 |
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_im... | 23 |
def _lowerCAmelCase ( __magic_name__ :list[list[int]] , __magic_name__ :int , __magic_name__ :int , __magic_name__ :set ):
UpperCAmelCase_, UpperCAmelCase_ = len(__magic_name__ ), len(grid[0] )
if (
min(__magic_name__ , __ma... | 121 | 0 |
'''simple docstring'''
import sys
UpperCAmelCase_ : int = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''1254069874715852386305071569329096329522744304355... | 24 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
_lowerCamelCase : List[Any] = logging.getLogger(__name__)
class snake_case__ ( __snake_case ):
... | 121 | 0 |
def lowerCamelCase__ ( _a):
if not isinstance(_a , _a):
raise ValueError("multiplicative_persistence() only accepts integral values")
if num < 0:
raise ValueError("multiplicative_persistence() does not accept negative values")
SCREAMING_SNAKE_CASE : int = 0
SCREAMING_SNAKE_C... | 25 |
def _lowerCAmelCase ( __magic_name__ :str ):
UpperCAmelCase_ = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def _lowerCAmelCase ( __magic_name_... | 121 | 0 |
'''simple docstring'''
import numpy
class _A :
def __init__( self : Any , __magic_name__ : numpy.ndarray , __magic_name__ : numpy.ndarray ) -> None:
"""simple docstring"""
__snake_case : Optional[int] ... | 26 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : str = {
'configuration_jukebox': [
'JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP',
'JukeboxConfig',
'JukeboxPriorConfig',
... | 121 | 0 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
__A : str = logging.get_logger(__name__)
class lowerCamelCase( __snake_case ):
'''simple docstring'''
def __init__( self , ... | 27 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class snake_case__ :
'''simple docstring'''
__A = 42
__A = None
__A = None
_lowerCamelCas... | 121 | 0 |
'''simple docstring'''
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def lowercase__( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : str = 9, 14 ... | 28 |
from __future__ import annotations
def _lowerCAmelCase ( __magic_name__ :int ):
UpperCAmelCase_ = [True] * limit
UpperCAmelCase_ = False
UpperCAmelCase_ = False
UpperCAmelCase_ = True
for i in range(3 , int(limit**0.5 + 1 ) ... | 121 | 0 |
"""simple docstring"""
def lowercase ( lowerCAmelCase__ ,lowerCAmelCase__ ):
if number < 0 or shift_amount < 0:
raise ValueError('''both inputs must be positive integers''' )
lowerCamelCase_ = str(bin(lowerCAmelCase__ ) )
binary_number += "0" * shift_amount
return bi... | 29 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
_lowerCamelCase : str = logging.getLogger(__name__)
if __name__ ==... | 121 | 0 |
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
__a = datasets.logging.get_logger(__name__)
__a = '\\n@inproceedings{bleurt,\n title={BLEURT: Learning Robust Metrics for Text Generation},\n author={Thibault Sellam a... | 30 |
import os
from math import logaa
def _lowerCAmelCase ( __magic_name__ :str = "base_exp.txt" ):
UpperCAmelCase_ = 0
UpperCAmelCase_ = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(__magic_name__ ) , __magic_name__ ) )... | 121 | 0 |
from __future__ import annotations
from collections.abc import Generator
def UpperCAmelCase_ ( ) -> Generator[int, None, None]:
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = 2
while True:
SCREAMING_SNAKE_CASE_ = factor_map.pop(__Uppe... | 31 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
_lowerCamelCase : Any = 'Usage of script: script_name <size_of_canvas:int>'
_lowerCamelCase : Dict = [0] * 100 + [1] * 10
random.shuffle(choice... | 121 | 0 |
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(A__ ) , """Tatoeba directory does... | 32 |
from __future__ import annotations
def _lowerCAmelCase ( __magic_name__ :list[int | str] ):
create_state_space_tree(__magic_name__ , [] , 0 , [0 for i in range(len(__magic_name__ ) )] )
def _lowerCAmelCase ( ... | 121 | 0 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowerCamelCase__ : str = logging.get_logger(__name__)
lowerCamelCase__ : Optional[int] = {
"""... | 33 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switchi... | 121 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
'tiiuae/falcon-40b': 'https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json',
't... | 34 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
_lowerCamelCase : Union[str, Any] = {
'tiny.en': 'https://openaipublic.azu... | 121 | 0 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
a_ :str = logging.get_logger(__name__)
a_ :Optional[int] = 'https://openaipubl... | 35 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCAmelCase ( __magic_name__ :Union[str, Any] , __magic_na... | 121 | 0 |
from manim import *
class _A ( snake_case ):
'''simple docstring'''
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Any = Rectangle(height=0.5 ,width=0.5 )
snake_case : Tuple = Rectangle(height=0.46 ,widt... | 36 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowerCamelCase : Dict = {
'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
... | 121 | 0 |
def UpperCamelCase_ ( __a , __a ) -> int:
return x if y == 0 else greatest_common_divisor(__a , x % y )
def UpperCamelCase_ ( __a , __a ) -> int:
return (x * y) // greatest_common_divisor(__a , __a )
def UpperCamelCase_ ( __a = 20 )... | 37 |
_lowerCamelCase : dict[tuple[int, int, int], int] = {}
def _lowerCAmelCase ( __magic_name__ :int , __magic_name__ :int , __magic_name__ :int ):
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possib... | 121 | 0 |
'''simple docstring'''
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __snake_case ( __SCREAMING_SNAKE_CASE ... | 38 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
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
from ..models.auto.modeli... | 121 | 0 |
import unittest
from transformers import DonutProcessor
lowerCAmelCase_ = '''naver-clova-ix/donut-base'''
class snake_case_ ( unittest.TestCase ):
'''simple docstring'''
def snake_case__( self : Union[str, Any] ) ->Any:
... | 39 |
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Acceler... | 121 | 0 |
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