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 |
|---|---|---|---|---|
'''simple docstring'''
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
... | 427 |
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 |
"""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/LICENS... | 346 |
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 |
"""simple docstring"""
def __snake_case ( __A ,__A ) -> Union[str, Any]:
lowercase : Optional[int] = len(__A )
lowercase : Any = []
for i in range(len(__A ) - pat_len + 1 ):
lowercase : Optional[int] = True
... | 607 |
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 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class _A ( ctypes.Structure ):
SCREAMING_SNAKE_CASE_ : str =[("size", ctypes.c_int), ("visible", ctypes.c_byte)]
... | 415 |
# 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 |
def A_ ( lowercase_ , lowercase_ ) -> Tuple:
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
_snake_case : List[str] = str(bin(lowercase_ ) )[2:] # remove the leading "0b"
_snake_case : List[str] ... | 326 |
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 os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
__SCREAMING_SNAKE_CASE = get_tests_dir(... | 688 |
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 |
def lowerCAmelCase_ ( __a ) -> Any:
"""simple docstring"""
lowerCamelCase__: int =len(__a )
while cur > 1:
# Find the maximum number in arr
lowerCamelCase__: str =arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
lowerCamelCase__:... | 59 |
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 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 a_ ... | 246 |
_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 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
a__ : List[Any] = logging.getLogger(__name__)
class UpperCAmelCase_ ( __snake_case ):
def __init__( self ,__snake_... | 188 |
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 |
'''simple docstring'''
def __UpperCamelCase( _A : list , _A : list , _A : int ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = len(_A )
UpperCAmelCase__ : Optional[int] = [[0] * n for i in range(_A )]
for i in range(_A ... | 614 |
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 |
from ..utils import DummyObject, requires_backends
class _SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
lowerCamelCase_ = ['onnx']
def __init__( self : int , *snake_case_ : List[str] , **snake_case_ : int ):
"""simple docstring"""
... | 256 |
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'''
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCamelCase_ ( snake_case_ : List... | 427 |
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 |
"""simple docstring"""
__A = 9.80_665
def a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = g ) -> Optional[int]:
if fluid_density <= 0:
raise ValueError("Impossible fluid density" )
if volume < 0:
... | 346 |
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 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_comm... | 607 |
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 |
__magic_name__ =8.31_44_62 # Unit - J mol-1 K-1
def __UpperCamelCase ( A , A , A ):
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError('''Invalid inputs. Enter positive value.''' )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
... | 415 |
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 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_ = {
'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig', 'DebertaOnnxCon... | 326 |
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 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, pr... | 688 |
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 |
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def lowerCAmelCase_ ( __a , __a=() , __a=None , __a="no" , __a="29500" ) -> ... | 59 |
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 |
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:
if ... | 246 |
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 argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def UpperCAmelCase_ ( _UpperCAmelCase :str , _UpperCAmelCase :str , _UpperCAmelCase :Optional[int] , _UpperCAmelCase :Optional[int] ) -> List[s... | 188 |
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'''
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def __UpperCamelCase( _A : List[str] ):
'''simple docstring'''
if "model" in orig_key:
UpperCAmelCase__ : Optional[int] = orig_key.replace('''model.''' , '''''' ... | 614 |
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 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
UpperCamelCase_ = 2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must ... | 256 |
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'''
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... | 427 |
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 |
"""simple docstring"""
from __future__ import annotations
import math
__A = '2020.9.26'
__A = 'xcodz-dot, cclaus, dhruvmanila'
def a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , ... | 346 |
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 |
"""simple docstring"""
import os
def __snake_case ( __A = "input.txt" ) -> Optional[Any]:
with open(os.path.join(os.path.dirname(__A ) ,__A ) ) as input_file:
lowercase : Any = [
[int(__A ) for element in line.split(""","... | 607 |
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 __future__ import annotations
def __UpperCamelCase ( A ):
if len(A ) == 0:
return array
UpperCamelCase__ , UpperCamelCase__ = min(A ), max(A )
# Compute the variables
UpperCamelCase__ = _max - _min + 1
Up... | 415 |
# 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 |
from __future__ import annotations
from collections.abc import Callable
lowerCAmelCase_ = list[list[float | int]]
def A_ ( lowercase_ , lowercase_ ) -> Union[str, Any]:
_snake_case : Dict = len(lowercase_ )
_snake_case : Union[str, Any] = [[0 ... | 326 |
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'''
def __a ( lowerCAmelCase__ : int ):
if number > 0:
raise ValueError('''input must be a negative integer''' )
a__ : Any = len(bin(lowerCAmelCase__ )[3:] )
a__ : Optional[Any] = bin(abs(lowerCAmelCase_... | 688 |
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 |
def lowerCAmelCase_ ( __a , __a ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase__: Union[str, Any] =len(__a )
lowerCamelCase__: Dict =len(__a )
lowerCamelCase__: Any =(
first_str_length if first_str_length > second_s... | 59 |
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 |
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
Compu... | 246 |
_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 |
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, task_specific_pa... | 188 |
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 |
'''simple docstring'''
def __UpperCamelCase( _A : str ):
'''simple docstring'''
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 614 |
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 |
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, USER, ge... | 256 |
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'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Any = logging.get_logger(__name__)
_A : Any = {
'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}
class ... | 427 |
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 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformer... | 346 |
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 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class lowerCamelCase__ ( __snake_case ):
@staticmethod
@abstractmethod
def _UpperCAmelCase ( snake_case ) -> Any:
"""simple docstring"""
... | 607 |
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 |
# using dfs for finding eulerian path traversal
def __UpperCamelCase ( A , A , A , A=None ):
UpperCamelCase__ = (path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is False:
UpperCamelCase__ , UpperCam... | 415 |
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 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {
'configuration_blip_2': [
'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Blip2Config',
'Blip2QFormerConfig',
'Blip2VisionConfig',
],
... | 326 |
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 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
)
__SCREAMING_SNAKE_CASE = logging.getLogger(__name__)
if __name... | 688 |
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 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required b... | 59 |
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 |
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_f... | 246 |
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 |
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
a__ : Dict = logging.get_logger(__name__)
... | 188 |
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'''
UpperCamelCase__ : dict[tuple[int, int, int], int] = {}
def __UpperCamelCase( _A : int , _A : int , _A : int ):
'''simple docstring'''
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are ... | 614 |
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 |
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 _lowerCamelCase ( ... | 256 |
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'''
# 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]... | 427 |
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 |
"""simple docstring"""
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.test... | 346 |
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 |
"""simple docstring"""
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... | 607 |
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 |
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__magic_name__ =logging.get_logger(__name__)
__magic_name__ ={'vocab_file': 'vocab.json'}
__magic_name__ ={
'vocab_file': {
'mgp-str':... | 415 |
# 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 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():
import to... | 326 |
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'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__SCREAMING_SNAKE_CASE = {
'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_... | 688 |
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 unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
im... | 59 |
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 |
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 246 |
_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 |
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, ids_tensor
fr... | 188 |
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 |
'''simple docstring'''
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
UpperCamelCase__ : str = 10
def __UpperCamelCase( _A : int , _A : int ... | 614 |
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 |
from random import shuffle
import tensorflow as tf
from numpy import array
def _lowerCamelCase ( lowerCamelCase_: List[Any] , lowerCamelCase_: int ):
'''simple docstring'''
A : Tuple = int(lowerCamelCase_ )
assert noofclusters < len(l... | 256 |
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'''
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
_A : List[str] = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', d... | 427 |
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 |
"""simple docstring"""
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xforme... | 346 |
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 |
"""simple docstring"""
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from... | 607 |
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 |
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def __UpperCamelCase ( A ):
UpperCamelCase__ = os.path.join(args.tf_model_dir , '''parameters.json''' )
UpperCamelCase__ ... | 415 |
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 |
def A_ ( lowercase_ , lowercase_ ) -> Dict:
if len(lowercase_ ) != len(lowercase_ ):
raise ValueError('''String lengths must match!''' )
_snake_case : str = 0
for chara, chara in zip(lowercase_ , lowercase_ ):
if chara != char... | 326 |
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 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... | 688 |
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 |
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
import onnxruntime as... | 59 |
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 |
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.modeling_tf_au... | 246 |
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 sys
from collections import defaultdict
class UpperCAmelCase_ :
def __init__( self ):
"""simple docstring"""
A_ = []
def __UpperCAmelCase ( self ,__snake_case ):
"""simple docstring"""
return self.node_position... | 188 |
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 __future__ import annotations
UpperCamelCase__ : Optional[Any] = '#'
class _lowercase :
'''simple docstring'''
def __init__( self ) -> None:
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] ... | 614 |
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 |
from PIL import Image
def _lowerCamelCase ( lowerCamelCase_: Image , lowerCamelCase_: float ):
'''simple docstring'''
def brightness(lowerCamelCase_: int ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
... | 256 |
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'''
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def UpperCamelCase_ ( snake_case_ : Optional[Any] , snake_case_ : List[Any] , snake_case_ : str , snake_case_ : Dict=5 ) -> Optional[int]:
'''s... | 427 |
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 |
"""simple docstring"""
def a__ ( ) -> Dict:
__lowerCAmelCase: int = [3_1, 2_8, 3_1, 3_0, 3_1, 3_0, 3_1, 3_1, 3_0, 3_1, 3_0, 3_1]
__lowerCAmelCase: List[str] = 6
__lowerCAmelCase: Dict = 1
__lowerCAmelCase: int = 1_9_... | 346 |
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 |
"""simple docstring"""
import fire
from utils import calculate_rouge, save_json
def __snake_case ( __A ,__A ,__A=None ,**__A ) -> Dict:
lowercase : int = [x.strip() for x in open(__A ).readlines()]
lowercase : Union[str, Any] = ... | 607 |
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 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available()... | 415 |
# 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 logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
lowerCAmelCase_ = logging.getLogger(__name__)
@dataclass
class A (__snake_case ):
_SCREAMING_SNAKE_CASE = ... | 326 |
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 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_... | 688 |
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 .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
| 59 |
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 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class lowerCAmelCase :
'''simple docstring'''
snake_case = 42
snake_case = None
snake_case = ... | 246 |
_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 |
a__ : str = 'Tobias Carryer'
from time import time
class UpperCAmelCase_ :
def __init__( self ,__snake_case ,__snake_case ,__snake_case ,__snake_case=int(time() ) ): # noqa: B008
"""simple docstring"""
A_ = multiplier
A_ = ... | 188 |
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 |
'''simple docstring'''
def __UpperCamelCase( _A : int ):
'''simple docstring'''
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...')
UpperCamelCase__ :... | 614 |
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 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requir... | 256 |
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'''
from collections import deque
class _lowercase :
'''simple docstring'''
def __init__( self : int , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> None:
__lowe... | 427 |
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 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy a... | 346 |
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 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
lowerCAmelCase: List[str] ='examples/'
lowerCAmelCase: str ={
'examples': (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), 'check_min_version("VERSION")\n'),
'in... | 607 |
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 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def A(__a: int ):
if not isinstance(__a , __a ):
raise TypeError("Undefined for non-integers" )
elif precision < 1:
raise ValueError("Undefined for non-natural numbers" )
lowerCAmelCase_ ... | 122 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transf... | 122 | 1 |
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class __magic_name__ (__lowercase ):
def __init... | 122 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailabl... | 122 | 1 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'''snap-research/efficientformer-l1-300''': (
'''https://huggingface.co/snap-research/efficientforme... | 122 |
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
lowerCamelCase__ = 4
lowerCamelCase__ = 3
class __magic_name__ (__lowercase ):
pass... | 122 | 1 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
lowerCamelCase__ = '''
import os
'''
lowerCamelCase__ = '''
def foo():
import os
return False
'''
lowerCamelCase__ = '''
def foo():
def bar():
if True:
import... | 122 |
import torch
from torch import nn
class __magic_name__ (nn.Module ):
def __init__( self , _a , _a , _a , _a , _a=1 , _a=False ) -> str:
super().__init__()
lowerCAmelCase_ = n_token
lowerCAmelCase_ = d_embed
lowerCAme... | 122 | 1 |
import os
from distutils.util import strtobool
def A(__a: Dict , __a: str ):
for e in env_keys:
lowerCAmelCase_ = int(os.environ.get(__a , -1 ) )
if val >= 0:
return val
return default
def A(__a: int , __a: Union[str, Any]=False ... | 122 |
from collections import defaultdict
class __magic_name__ :
def __init__( self , _a , _a ) -> Tuple:
lowerCAmelCase_ = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
# initially all values are set to -1
lowerCAm... | 122 | 1 |
def A(__a: int ):
lowerCAmelCase_ = int(__a )
if n_element < 1:
lowerCAmelCase_ = ValueError("a should be a positive number" )
raise my_error
lowerCAmelCase_ = [1]
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ... | 122 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, slow... | 122 | 1 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class __magic_name__ (__lowercase ):
def __init__( self , _a , _a = None , _a =... | 122 |
def A(__a: int = 50 ):
lowerCAmelCase_ = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] += ways_number[
row_length... | 122 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
loggin... | 122 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
lowerCamelCase__ = logging.getLogger()
@unittest.skip('''Temporarily disable the doc tests.''' )... | 122 | 1 |
from ..utils import DummyObject, requires_backends
class __magic_name__ (metaclass=__lowercase ):
lowerCamelCase__ = ['''speech''']
def __init__( self , *_a , **_a ) -> str:
requires_backends(self , ["speech"] )
class __magic_name__ (metacl... | 122 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCamelCase__ = {'''configuration_speech_encoder_decoder''': ['''SpeechEncoderDecoderConfig''']}
try:
if not is_torch_available():
raise OptionalDepende... | 122 | 1 |
from scipy.stats import spearmanr
import datasets
lowerCamelCase__ = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlation... | 122 |
from math import factorial
def A(__a: int , __a: int ):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError("Please enter positive integers for n and k where n... | 122 | 1 |
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def A(__a: Dict , __a: str , __a: str , __a: Path , __a: str = None , __a: str = None , __a: str =... | 122 |
from string import ascii_uppercase
lowerCamelCase__ = {char: i for i, char in enumerate(ascii_uppercase)}
lowerCamelCase__ = dict(enumerate(ascii_uppercase))
def A(__a: str , __a: str ):
lowerCAmelCase_ = len(__a )
lowerCAmelCase_ = ... | 122 | 1 |
from math import factorial
def A(__a: int , __a: int ):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError("Please enter positive integers for n and k where n... | 122 |
from itertools import product
def A(__a: int , __a: int ):
lowerCAmelCase_ = sides_number
lowerCAmelCase_ = max_face_number * dice_number
lowerCAmelCase_ = [0] * (max_total + 1)
lowerCAmelCase_ = 1
lowerCAmelCase_ =... | 122 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {
'''configuration_pegasus_x''': ['''PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusXConfig'''],
}
try:
if not is_torch_available():
raise Optio... | 122 |
from ..utils import DummyObject, requires_backends
class __magic_name__ (metaclass=__lowercase ):
lowerCamelCase__ = ['''speech''']
def __init__( self , *_a , **_a ) -> str:
requires_backends(self , ["speech"] )
class __magic_name__ (metacl... | 122 | 1 |
# 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 app... | 122 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'... | 122 | 1 |
from string import ascii_uppercase
lowerCamelCase__ = {char: i for i, char in enumerate(ascii_uppercase)}
lowerCamelCase__ = dict(enumerate(ascii_uppercase))
def A(__a: str , __a: str ):
lowerCAmelCase_ = len(__a )
lowerCAmelCase_ = ... | 122 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNeta... | 122 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase__ = {
'''configuration_blip''': [
'''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Blip... | 122 |
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ... | 122 | 1 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __magic_name__ (__lowercas... | 122 |
from __future__ import annotations
def A(__a: list[int] , __a: list[int] , __a: int ):
lowerCAmelCase_ = list(range(len(__a ) ) )
lowerCAmelCase_ = [v / w for v, w in zip(__a , __a )]
index.sort(key=lambda __a : ratio[i] , ... | 122 | 1 |
def A(__a: int ):
lowerCAmelCase_ = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def A(__a: int = 5000 ):
lowerCAmelCase_ = [(i * (3 * i - 1)) // 2 for i in range(1 , __a )]
for i, pentagonal_i in enumerate(__a ):
for j in range(_... | 122 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
lowerCamelCase__ = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:... | 122 | 1 |
def A(__a: int = 200_0000 ):
lowerCAmelCase_ = [0 for i in range(n + 1 )]
lowerCAmelCase_ = 1
lowerCAmelCase_ = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i , n + 1 , _... | 122 |
from sklearn.metrics import mean_squared_error
import datasets
lowerCamelCase__ = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Pre... | 122 | 1 |
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tenso... | 122 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
raise OptionalDependency... | 122 | 1 |
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