code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE = TypeVar('KT')
SCREAMING_SNAKE_CASE = TypeVar('VT')
class __UpperCAmelCase ( Generic[KT, VT] ):
"""simple docstring"""... | 99 |
'''simple docstring'''
def A_ ( snake_case = 600851475143 ):
try:
SCREAMING_SNAKE_CASE:str = int(snake_case )
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or castable to int." )
if n <= 0:
raise ValueError("Parameter n must be greater tha... | 143 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase = {
'configuration_blenderbot': [
... | 344 |
'''simple docstring'''
def _snake_case ( _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : List[Any] ) -> str:
"""simple docstring"""
global f # a global dp table for k... | 344 | 1 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
lowercase_ = namedtuple('covid_data', 'cases deaths recovered')
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase = "https://www.worldometers.info/coronavirus/" ) -> covid_data:
_a = '''... | 562 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json',
}
class UpperCAme... | 322 | 0 |
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_optuna,
default_hp... | 714 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a__ : Tuple = {
'''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP... | 333 | 0 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rou... | 16 | import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class a__ ( __SCREAMING_SNAKE_CASE ):
_A = (KDPMaDiscreteScheduler,)
_A = 10
def ... | 423 | 0 |
def _lowercase ( lowercase__ = 1_0_0_0 ):
__lowerCAmelCase : Dict = -1
__lowerCAmelCase : Optional[Any] = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
__lowerCAmelCase : ... | 712 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
_UpperCamelCase = {
"configuration_gpt_neox_japanese": ["GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXJapaneseConfig"],
"tokenization_gpt_neox_j... | 583 | 0 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine impor... | 122 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_... | 83 | 0 |
"""simple docstring"""
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqdm_lib
SCREAMING_SNAKE_CASE... | 720 | """simple docstring"""
import math
def __lowerCAmelCase( __UpperCAmelCase ):
"""simple docstring"""
if not isinstance(__UpperCAmelCase ,__UpperCAmelCase ):
_lowercase : List[Any] = F'''Input value of [number={number}] must be an integer'''
raise TypeError(__UpperCA... | 283 | 0 |
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 __snake_case ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAme... | 100 |
'''simple docstring'''
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
A : int = TypeVar("""T""")
class lowerCAmelCase_ ( Gen... | 349 | 0 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class snake_case_ ( __A , __A ):
'''simple docstring'''
lo... | 253 |
from __future__ import annotations
def __a ( __UpperCAmelCase : list[int | str] ) -> None:
"""simple docstring"""
create_state_space_tree(__UpperCAmelCase , [] , 0 , [0 for i in range(len(__UpperCAmelCase ) )] )
def __a ( __Upper... | 253 | 1 |
"""simple docstring"""
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ : int = logging.get_logger(__name__)
A__... | 153 |
def lowercase ( a , a , a , a ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ :int = [False] * len(a )
SCREAMING_SNAKE_CASE_ :List[Any] = []
queue.append(a )
SCREAMING_SNAKE_CASE_ :int = True
while queue:
SCREAMING_SNAKE_CASE_... | 631 | 0 |
'''simple docstring'''
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
A_ : List[str] = collections.namedtuple("_D... | 706 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def UpperCamelCase__ ... | 419 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'caidas/swin2sr-classicalsr-x2-64': (
'https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json'
),
}
... | 306 |
from __future__ import annotations
_lowercase = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
_lowercase = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def __lowerCAmelCase ( _UpperCamelCase ) -> list[float]:
'''simple docstrin... | 306 | 1 |
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tens... | 703 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokeniz... | 26 | 0 |
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
_snake_case : Union[str, Any] = (
'This metric will be removed from the... | 53 |
def _UpperCamelCase ( lowercase__ = 10**9 ):
__SCREAMING_SNAKE_CASE : List[str] = 1
__SCREAMING_SNAKE_CASE : int = 2
__SCREAMING_SNAKE_CASE : Union[str, Any] = 0
__SCREAMING_SNAKE_CASE : Dict = 0
__SCREAMING_SNAKE_CA... | 696 | 0 |
import argparse
import copy
def __magic_name__( SCREAMING_SNAKE_CASE__ : List[str] ) -> int:
'''simple docstring'''
A__ = {}
with open(SCREAMING_SNAKE_CASE__ ) as f:
for line in f:
if line.split()[0] not in dict_of_neig... | 709 |
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Toke... | 586 | 0 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __magic_name__ ( ):
'''simple docstring'''
UpperCamelCase__ = ArgumentParser(
description=(
... | 513 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMSc... | 513 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_... | 490 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def SCREAMING_SNAKE_CASE ( a_ : Tuple ):
# This defines a "chinese character" as anything in the CJK Unicode block:
# https:/... | 490 | 1 |
"""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_m... | 549 | import os
import pytest
from attr import dataclass
SCREAMING_SNAKE_CASE__ : int = "us-east-1" # defaults region
@dataclass
class snake_case :
lowercase_ = 42
lowercase_ = 'arn:aws:iam::558105141721:role/sagemaker_execution_role'
lowercase_ = {
'... | 85 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : List[str] = logging.get_logger(__name__)
lowercase : Dict = {
'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json',
... | 703 |
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavave... | 105 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
_A = TypeVar('T')
class lowerCamelCase (Generic[T] ):
'''simple docstring'''
def __init__( self : Tuple , ... | 159 | """simple docstring"""
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
_A = 'http://w... | 159 | 1 |
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCamelCa... | 613 |
def __UpperCAmelCase( lowercase_ ):
# if the collection is empty, returns empty
if collection == []:
return []
# get some information about the collection
_lowerCamelCase : Tuple = len(lowercase_ )
_lowerCamelCase : List[str]... | 613 | 1 |
from __future__ import annotations
from collections.abc import Generator
def snake_case ( ) -> Generator[int, None, None]:
_A = {}
_A = 2
while True:
_A = factor_map.pop(snake_case__ , snake_case__)
if factor:
... | 401 | from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class a ( __lowerCAmelCase ):
"""simple docstring"""
... | 401 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ :Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase__ :List[str] = {
"""SCUT-DLVCLab/lilt-roberta-en-base""": (
"""https://huggingface.... | 721 |
'''simple docstring'''
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def __lowercase () -> str:
"""simple docstring"""
__lowerCamelCase : Any = HfArgumentParser(_lowercase )
__lowerCamelCase : List[str]... | 483 | 0 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"Visual-Attention-Network/van-base": (
"https://huggingface.co/Visual-Attention-Network/v... | 574 |
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_xformers_available
from diffusers.utils.tes... | 81 | 0 |
"""simple docstring"""
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
__UpperCAmelCase =logging.get_l... | 717 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import T... | 261 | 0 |
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.sch... | 287 |
'''simple docstring'''
import heapq
def a__ ( _SCREAMING_SNAKE_CASE : dict ) -> set[int]:
"""simple docstring"""
UpperCAmelCase_ : list[list] = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq... | 71 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE :List[Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE :int = {}
class A_ ( _UpperCAmelCase ):
_lowerCamelCase ... | 704 |
'''simple docstring'''
import sys
from collections import defaultdict
class A_ :
def __init__( self : Dict ):
_UpperCAmelCase = []
def lowercase ( self : Union[str, Any] , snake_case_ : List[str] ):
return self.node_positi... | 119 | 0 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
__SCREAMING... | 357 |
"""simple docstring"""
def A_ ( __lowercase , __lowercase , __lowercase ):
if len(__lowercase ) != len(__lowercase ):
raise ValueError('The length of profit and weight must be same.' )
if max_weight <= 0:
raise ValueError('max_weight must greater than zero.' )
if any(p < 0 for p in pro... | 357 | 1 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __UpperCAmelCase ( lowerCAmelCase__ ):
A__ : Dict = ["image_processor", "tokenizer"]
A__ : List[str] = "AutoImageProcessor"
A... | 709 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a ={
'configuration_albert': ['ALBERT_PRET... | 132 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
UpperCAmelCase__ : int
UpperCAmelCase__ : TreeNode | None = None
... | 664 |
'''simple docstring'''
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingf... | 664 | 1 |
'''simple docstring'''
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # n... | 603 | '''simple docstring'''
import argparse
__snake_case = """docs/source/_static/js/custom.js"""
def A_ ( SCREAMING_SNAKE_CASE_ ) ->Any:
with open(SCREAMING_SNAKE_CASE_ , encoding="""utf-8""" , newline="""\n""" ) as f:
lowercase_ = f.readlines()
lowercase_ = 0
# First le... | 603 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A_ : Tuple = {
'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
'configuration_maskformer_swin': ['MaskForm... | 456 |
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_toke... | 456 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a : List[Any] = logging.get_logger(__name__)
__a : Any = {
"""facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/config.json""",
# See all XGLM models at ht... | 522 | import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
__a ... | 522 | 1 |
from collections.abc import Sequence
def lowerCAmelCase_ ( lowerCamelCase = None ):
if nums is None or not nums:
raise ValueError("""Input sequence should not be empty""" )
__magic_name__ : str =nums[0]
for i in range(1 , len(lowerCamelCase ) ... | 21 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_lowercase = {
"configuration_layoutlmv2": ["LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMv2Config"],
"p... | 632 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipe... | 169 |
def A (__A : str ) -> list:
"""simple docstring"""
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(__A ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__("doctest").testmod()
... | 169 | 1 |
import numpy
class _snake_case :
def __init__( self : Dict, __lowercase : numpy.ndarray, __lowercase : numpy.ndarray ):
lowercase__ = input_array
# Random initial weights are assigned where first argument is the
# number... | 413 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggi... | 413 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline,
... | 706 |
import argparse
import os
from . import (
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DPR_CO... | 380 | 0 |
"""simple docstring"""
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
lowerCAmelCase__ = logging.get_logge... | 645 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class SCREAMING_SNAKE_CASE__ ( lowercase ):
"""simple docstring"""
a : int ="SpeechT5FeatureExtractor"
a : Any ="SpeechT5Tokenizer"
def __init__( self , snake_case... | 645 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
"""tanreinama/GPTSAN-2.8B-spout_is_uniform""": (
"""https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/ma... | 355 |
'''simple docstring'''
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def __lowercase (_SCREAMING_SNAKE_CASE :List[Any] ):
return x + 2
class a__ ( unittest.TestCase ):
def lowercase__ ... | 355 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
... | 433 |
'''simple docstring'''
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument(
'--ch... | 433 | 1 |
"""simple docstring"""
def UpperCamelCase_ ( lowerCamelCase : int , lowerCamelCase : Tuple ) -> Tuple:
"""simple docstring"""
__magic_name__ : Dict = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k... | 713 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
A = TypeVar("""T""")
A = TypeVar("""U""")
class _UpperCamelCase ( Generic[T, U] ):
"""simple docstring"""
def __init__( self : Any , snake_case : ... | 147 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
class _a :
'''simple docstring'''
def __init__( self, A ):
'''simple docstring'''
S... | 28 |
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 t... | 225 | 0 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.se... | 109 |
"""simple docstring"""
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTest... | 109 | 1 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
lowerCamelCase__ : int = '2.13.1'
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('3.7')... | 31 |
import operator as op
def UpperCAmelCase_ ( __UpperCAmelCase : str ) -> Any:
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = lambda __UpperCAmelCase , __UpperCAmelCase : int(x / y ) # noqa: E731 integer division operation
SCREAMING_SNA... | 31 | 1 |
import os
def __SCREAMING_SNAKE_CASE ( ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = os.path.dirname(os.path.realpath(__UpperCamelCase ) )
SCREAMING_SNAKE_CASE__ = os.path.join(__UpperCamelCase , """triangle.txt"... | 379 | import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
__lowerCamelCase : Union[str, Any] = datasets.utils.logging.get_logger(__name__)
@da... | 379 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging... | 482 |
"""simple docstring"""
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class A__( unittest.TestCase ):
def _a ( self : List[str] ) -> str:
"""simple docstring"""
__SCREAM... | 482 | 1 |
"""simple docstring"""
from __future__ import annotations
import time
__snake_case = list[tuple[int, int]]
__snake_case = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, ... | 285 |
"""simple docstring"""
def A_ ( _lowerCAmelCase : int = 10_00 ):
"""simple docstring"""
_a , _a = 1, 1
_a = 2
while True:
_a = 0
_a = fa + fa
_a , _a = fa, f
index += 1
... | 285 | 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
#
# Unless re... | 99 |
from __future__ import annotations
def lowerCAmelCase__(__snake_case ,__snake_case ) -> list[int]:
'''simple docstring'''
lowerCamelCase__ = 0
lowerCamelCase__ = len(__snake_case ) - 1
while i < j:
if nums[i] + nums[j] == target:
retur... | 481 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config... | 709 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class ... | 324 | 0 |
'''simple docstring'''
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transf... | 161 |
def lowerCamelCase_ ( lowerCAmelCase: str )-> str:
_snake_case : str = 0
# if input_string is "aba" than new_input_string become "a|b|a"
_snake_case : List[Any] = ''
_snake_case : Dict = ''
# append each character + "|" in new_strin... | 411 | 0 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin im... | 707 |
'''simple docstring'''
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy a... | 178 | 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.apach... | 26 |
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tok... | 225 | 0 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase ( __a):
'''simple docstring'''
__magic_name__ : int = ["image_processor", "tokenizer"]
__magic_nam... | 41 |
'''simple docstring'''
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import ... | 41 | 1 |
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 |
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 dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class a__ ( _snake_case ):
"""sim... | 314 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testin... | 314 | 1 |
'''simple docstring'''
from collections import deque
from .hash_table import HashTable
class lowerCamelCase ( lowerCamelCase ):
'''simple docstring'''
def __init__( self : Union[str, Any] , *UpperCAmelCase__ : Optional[Any] , **UpperCAmelCase__ : ... | 390 | '''simple docstring'''
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
lowercase__ : Union[str, Any] = [
... | 390 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase : Union[str, Any] = {
"configuration_rag": ["RagConfig"],
"retrieval_rag": ["RagRetriever"],
"tokenization_rag": ["RagTokenizer"],
}
... | 423 |
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
lowercase : int = logging.get_logger(__name__)
... | 423 | 1 |
'''simple docstring'''
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
_UpperCamelCase : Any = False... | 284 |
'''simple docstring'''
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODE... | 284 | 1 |
def __magic_name__ ( __a : List[Any] , __a : Optional[Any] ):
'''simple docstring'''
UpperCamelCase__ = len(__a )
UpperCamelCase__ = []
for i in range(len(__a ) - pat_len + 1 ):
UpperCamelCase__ = True
for j in range(__a ):
... | 701 |
from __future__ import annotations
lowerCamelCase_ = '''#'''
class __A:
"""simple docstring"""
def __init__(self ):
UpperCamelCase__ = {}
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase__ = self._trie
for char in text:
... | 86 | 0 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_environm... | 511 |
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logging
... | 511 | 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
if is_sentencepiec... | 450 |
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime as ort
__A : ... | 450 | 1 |
"""simple docstring"""
from __future__ import annotations
__A = list[tuple[int, int]]
__A = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, ... | 346 |
'''simple docstring'''
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... | 119 | 0 |
'''simple docstring'''
from sklearn.metrics import mean_squared_error
import datasets
a : Union[str, Any] = """\
@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. ... | 672 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ) -> np.array:
UpperCAmelCase : Optional[Any] = int(np.ceil((x_end - xa) / st... | 672 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a = {
"configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "D... | 109 |
'''simple docstring'''
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import ... | 109 | 1 |
def _A ( _UpperCamelCase , _UpperCamelCase ):
_UpperCAmelCase : Tuple = len(_UpperCamelCase )
_UpperCAmelCase : Tuple = len(_UpperCamelCase )
_UpperCAmelCase : Dict = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
_UpperCAmelCase : List[A... | 416 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class lowerCAmelCase_ :
def __init__( self : Any , UpperCAmelCase_ : Collection[float] | None = None ) -> None:
'''simple... | 416 | 1 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import At... | 10 |
def lowerCamelCase__ ( snake_case_ : int = 1000 ) -> int:
__snake_case = 2**power
__snake_case = str(snake_case_ )
__snake_case = list(snake_case_ )
__snake_case = 0
for i in list_num:
sum_of_num += int(snake_case_ )
... | 592 | 0 |
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
A__ = False
try:
A__ = _is_package_available("goo... | 184 |
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class __UpperCamelCase ( SCREAMING_SNAKE_CASE , unittest.TestCase ):
_lowercase : str = DownBlockaD # noqa... | 184 | 1 |
import re
def SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ ):
UpperCamelCase__ : Optional[int] = re.compile(
R'''^(?:0|94|\+94|0{2}94)''' R'''7(0|1|2|4|5|6|7|8)''' R'''(-| |)''' R'''\d{7}$''' )
return bool(re.search(UpperCamelCase__ , UpperCamelCase__ ) )
if _... | 285 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_model... | 285 | 1 |
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('Googling.....')
_SCREAMING_SNAKE_CASE = 'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
_SCREAMING_SNAKE_CASE = requ... | 718 | import colorsys
from PIL import Image # type: ignore
def snake_case ( snake_case__ :float , snake_case__ :float , snake_case__ :int) -> float:
_A = x
_A = y
for step in range(snake_case__): # noqa: B007
_A ... | 83 | 0 |
'''simple docstring'''
from scipy.stats import spearmanr
import datasets
SCREAMING_SNAKE_CASE = '\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no co... | 94 | '''simple docstring'''
def UpperCamelCase_ ( _UpperCAmelCase : int ) -> list:
"""simple docstring"""
_UpperCAmelCase : Tuple = int(_UpperCAmelCase )
if n_element < 1:
_UpperCAmelCase : Tuple = ValueError("a s... | 244 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE = {
'configuration_ctrl': ['CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CTRLConfig'],
'tokenization_ctrl': ['CTRLTok... | 283 | """simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_tru... | 283 | 1 |
import numpy as np
def UpperCAmelCase__( __UpperCAmelCase : List[str] ):
return 1 / (1 + np.exp(-vector ))
def UpperCAmelCase__( __UpperCAmelCase : int ):
return vector * sigmoid(SCREAMING_SNAKE_CASE__ )
if __name__ == "__main__":
import doc... | 576 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : Union[str, Any] = {
"""configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
"""feature_extraction_mctct""": ["""MCTCTFeatureEx... | 336 | 0 |
import os
def A_ ( ) ->Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE = os.path.join(os.path.dirname(lowercase_ ) , 'num.txt' )
with open(lowercase_ ) as file_hand:
return str(sum(int(lowercase_ ) for line in file_hand ) )[:1_0]
if __name__ == "__main__":
... | 259 |
import numpy as np
class a_:
"""simple docstring"""
def __init__( self : Any) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE = (0, 0)
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE... | 259 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils impor... | 597 |
'''simple docstring'''
from statistics import mean, stdev
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = 3 ):
__a : List[str] = min(SCREAMING_SNAKE_CASE__ )
__a : Tuple = max(SCREAMING_SNAKE_CASE... | 597 | 1 |
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALU... | 705 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path", default=None, type=str, required=True, help="Path to the chec... | 526 | 0 |
"""simple docstring"""
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
a : Tuple = '''src/transformers'''
# ... | 555 |
"""simple docstring"""
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, loggin... | 555 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class snake_case_ ( unittest.TestCase ):
"""simple docstr... | 102 |
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datas... | 102 | 1 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
__snake_case :Tuple ='https://www.indeed.co.in/jobs?q=mobile+app+development&l='
def lowerCamelCase_ ( lowerCAmelCase__ : str = "mumbai" ) -> Generator[tu... | 106 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class lowercase__ ( __SCRE... | 475 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
UpperCAmelCase : str = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
el... | 711 |
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class _A( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase_ ( self ):
debug_launcher(test_script.main )
de... | 77 | 0 |
'''simple docstring'''
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class lowerCAmelCase__ ( a , a ):
"""simple docs... | 627 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)... | 627 | 1 |
'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_proper... | 145 |
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
def UpperCamelCase ( ) -> None:
'''simple docstring'''
lowercase =input('''Enter message: ''' )
lowercase =input('''Enter key [alphanumeric]: ''' )
lowercase =i... | 145 | 1 |
"""simple docstring"""
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, to... | 391 |
"""simple docstring"""
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
Charac... | 391 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET... | 633 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ :Tuple = {
"configuration_instructblip": [
"INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"InstructBlipConfig",
"InstructBlipQFormerConfig",
... | 633 | 1 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelC... | 271 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class snake_case__(_UpperCamelCase ):
"""simple docstring"""
lowercase_ = (IPNDMScheduler,)
lowercase_ = (("""num_inference_steps""", 5_0),)
... | 496 | 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()
ex... | 707 | """simple docstring"""
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
lowerCamelCase : Tuple =logging.get_logger(__name__)
def _lowercase ( ... | 237 | 0 |
import math
def __lowerCAmelCase ( a__ , a__ ) -> int:
__a = len(_UpperCamelCase )
__a = int(math.floor(math.sqrt(_UpperCamelCase ) ) )
__a = 0
while arr[min(_UpperCamelCase , _UpperCamelCase ) - 1] < x:
... | 219 |
'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase : Union[str, Any] = logging... | 405 | 0 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, l... | 718 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"""google/realm-cc-news-pretrained-embedder""": (
"""https://huggingface.co/google/realm-cc-news-pretra... | 492 | 0 |
"""simple docstring"""
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_to... | 142 |
"""simple docstring"""
import os
def lowerCamelCase_ ( ):
lowerCamelCase_ = os.path.dirname(os.path.realpath(_lowerCamelCase ) )
lowerCamelCase_ = os.path.join(_lowerCamelCase , '''triangle.txt''' )
with open(_lowerCamelCase ) as f:
... | 142 | 1 |
"""simple docstring"""
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.... | 614 |
"""simple docstring"""
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
d... | 614 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCAmelCase : List[str] = {
"configuration_whisper": ["WHI... | 261 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ..... | 261 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from typing import Any
class a_ :
def __init__( self ):
a_ = []
a_ = 0
a_ = 0
def lowerCAmelCase__ ( self ):
return self.head == self.tail
... | 715 |
'''simple docstring'''
import math
def UpperCamelCase_ ( A__ ):
a_ = [True] * n
a_ = False
a_ = False
a_ = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
a_ = i * 2
while index < n:
a_ = False
a_ = index + i
... | 511 | 0 |
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
a_ = logging.get_logger(__name__)
class lowercase__ :
a_ =None
@experimental
def _a ( UpperCamelCase_ : str , UpperCamelCase_ : ... | 339 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class lowercase ( UpperCamelCase__ ):
_a = ["i... | 307 | 0 |
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : Tuple ) -> list[int]:
if num <= 0:
raise ValueError('Input must be a positive integer' )
__lowercase = [True] * (num + 1)
__lowercase = 2
while p * p <= num:
if primes[p]:
for i in r... | 721 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
"""google/umt5-small""": """https://huggingface.co/g... | 688 | 0 |
from __future__ import annotations
import time
a_ = list[tuple[int, int]]
a_ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0... | 25 |
"""simple docstring"""
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
A : Dict = get_logger(__name__)
A : Dict = R"\n Args:\n input_ids (`jnp.ndarray` of shape `(... | 636 | 0 |
from collections.abc import Callable
import numpy as np
def A__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> np.ndarray:
lowerCamelCase : List[Any] ... | 262 |
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
snake_case_ = '''
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
auth... | 262 | 1 |
"""simple docstring"""
from typing import Any
def __UpperCAmelCase ( lowercase ,lowercase ,lowercase ,lowercase ,lowercase ,):
"""simple docstring"""
_validation(
lowercase ,lowercase ,lowercase ,lowercase ,lowercase ,)
# Creates data structures and fill initial step
_UpperC... | 277 | """simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDi... | 277 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"google/bit-50": "htt... | 707 |
# 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
#
# U... | 291 | 0 |
import string
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int) -> List[Any]:
'''simple docstring'''
for key in range(len(string.ascii_uppercase)):
__UpperCamelCase : int = ""
for symbol in message... | 557 |
"""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]... | 34 | 0 |
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
fr... | 720 |
from __future__ import annotations
a__ = 10
def __UpperCAmelCase ( __a : list[int] ) -> list[int]:
"""simple docstring"""
_a : Union[str, Any] = 1
_a : str = max(__a )
while placement <= max_digit:
# declare and initi... | 578 | 0 |
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