code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from math import factorial
def _snake_case ( _snake_case : int = 100 ):
return sum(int(_snake_case ) for x in str(factorial(_snake_case ) ) )
if __name__ == "__main__":
print(solution(int(input('''Enter the Number: ''').strip())))
| 314 |
"""simple docstring"""
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_te... | 314 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : int ):
lowerCAmelCase : Any = 0
lowerCAmelCase : Optional[Any] = len(_snake_case )
for i in range(n - 1 ):
for j in range(i + 1 , _snake_case ):
if arr[i] >... | 314 |
"""simple docstring"""
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
snake_case__ : Optional[Any] = False
class snake_case_( unittest.T... | 314 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from datasets import load_dataset
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, p... | 314 |
"""simple docstring"""
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
snake_case__ : List[str] = logging.get_logger(__name__)
class snake_case_( a__ ):
... | 314 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
snake_case__ : int = {
'''configuration_conditional_detr''': [
'''CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''... | 314 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
snake_case__ : str = logging.get_logger... | 314 | 1 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
Wav... | 314 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class snake_case_( a__ ):
__UpperCamelCase = (DDPMScheduler,)
def lowerCamelCase__ ( self : List[Any] , **UpperCamelCase_ :... | 314 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : int ):
return str(_snake_case ) == str(_snake_case )[::-1]
def _snake_case ( _snake_case : int ):
return int(_snake_case ) + int(str(_snake_case )[::-1] )
def _snake_case ( _... | 314 |
"""simple docstring"""
def _snake_case ( _snake_case : int = 50000000 ):
lowerCAmelCase : List[str] = set()
lowerCAmelCase : List[Any] = int((limit - 24) ** (1 / 2) )
lowerCAmelCase : Optional[int] = set(range(3 , prime_square_limit + 1 , ... | 314 | 1 |
"""simple docstring"""
import unittest
from knapsack import knapsack as k
class snake_case_( unittest.TestCase ):
def lowerCamelCase__ ( self : List[Any] ):
lowerCAmelCase : Optional[int] = 0
lowerCAmelCase : List[str] = [... | 314 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
snake_case__ : Tuple = {
'''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerCon... | 314 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : int = 50000000 ):
lowerCAmelCase : List[str] = set()
lowerCAmelCase : List[Any] = int((limit - 24) ** (1 / 2) )
lowerCAmelCase : Optional[int] = set(range(3 , prime_square_limit + 1 , ... | 314 |
"""simple docstring"""
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class snake_case_:
def __init__( self : Dict , UpperCamel... | 314 | 1 |
"""simple docstring"""
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class snake_case_( a__ , unittest.TestCase ):
__UpperCamelCase = Pho... | 314 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def _snake_case ( _snake_case : Dict ):
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org... | 314 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
snake_case__ : str = logging.get_logger... | 314 |
"""simple docstring"""
import numpy as np
from PIL import Image
def _snake_case ( _snake_case : np.ndarray , _snake_case : int , _snake_case : int ):
lowerCAmelCase : Dict = np.array(_snake_case )
if arr.shape[0] != arr.shape[1]:
raise ... | 314 | 1 |
"""simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
snake_case__ : Optional[int] = '''\
@inproceedings{popovic-2015-chrf,
title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",
author = "Popo... | 314 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_case_( a__ ):
def __init__( self : Dic... | 314 | 1 |
"""simple docstring"""
from typing import Dict
from .base import GenericTensor, Pipeline
class snake_case_( a__ ):
def lowerCamelCase__ ( self : Any , UpperCamelCase_ : Any=None , UpperCamelCase_ : List[Any]=None , UpperCamelCase_ : L... | 314 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
snake_case__ : int = {'''configuration_plbart''': ['''PLBART_PRETRAIN... | 314 | 1 |
"""simple docstring"""
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subproc... | 314 |
"""simple docstring"""
import os
import pytest
from transformers.dynamic_module_utils import get_imports
snake_case__ : Optional[Any] = '''
import os
'''
snake_case__ : Tuple = '''
def foo():
import os
return False
'''
snake_case__ : Any ... | 314 | 1 |
"""simple docstring"""
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 snake_case_( a__ ):
def __init__( self :... | 314 |
"""simple docstring"""
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class snake_case_( tf.keras.optimizers.schedules.LearningRateSche... | 314 | 1 |
"""simple docstring"""
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
if is_torch_available():
import t... | 314 |
"""simple docstring"""
import collections
import importlib.util
import os
import re
from pathlib import Path
snake_case__ : Union[str, Any] = '''src/transformers'''
# Matches is_xxx_available()
snake_case__ : int = re.compile(R'''is\_([a-z_]*)_available()''')
# Catc... | 314 | 1 |
"""simple docstring"""
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
snake_case__ : List[str] = logging.get_logger(__name__)
snake_case__ ... | 314 |
"""simple docstring"""
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def _snake_case ( _snake_case ... | 314 | 1 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_case_( a__ ):
... | 314 |
"""simple docstring"""
from math import sqrt
def _snake_case ( _snake_case : int ):
assert isinstance(_snake_case , _snake_case ) and (
number >= 0
), "'number' must been an int and positive"
lowerCAmelCase : Dict = True
# 0 and 1 are none... | 314 | 1 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allo... | 314 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : Any = logging.get_logger(__name__)
snake_case__ : Any = {
'''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve... | 314 | 1 |
"""simple docstring"""
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
snake_case__ : int = {'''UserAgent''': UserAgent().random}
def _snake_case ( _snake_case : Optional[int] ):
... | 314 |
"""simple docstring"""
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
snake_case__ ... | 314 | 1 |
"""simple docstring"""
import collections
import importlib.util
import os
import re
from pathlib import Path
snake_case__ : Union[str, Any] = '''src/transformers'''
# Matches is_xxx_available()
snake_case__ : int = re.compile(R'''is\_([a-z_]*)_available()''')
# Catc... | 314 |
"""simple docstring"""
def _snake_case ( _snake_case : int ):
assert isinstance(_snake_case , _snake_case ), f'''The input value of [n={number}] is not an integer'''
if number == 1:
return 2
elif number < 1:
lowerCAmelCase : Tuple = f... | 314 | 1 |
"""simple docstring"""
from ... import PretrainedConfig
snake_case__ : List[str] = {
'''sijunhe/nezha-cn-base''': '''https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json''',
}
class snake_case_( a__ ):
__UpperCamelCase = NEZHA_PRETRAINED_CON... | 314 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def _snake_case ( _snake_case : List[str] ):
lowerCAmelCase : Union[str, Any] = SwinConfig(image_size... | 314 | 1 |
"""simple docstring"""
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
snake_case__ : Tuple = '''Usage of script: script_name <size_of_canvas:int>'''
snake_case__ : str = [0] * 100 + [1] * ... | 314 |
"""simple docstring"""
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPip... | 314 | 1 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
SegformerForSe... | 314 |
"""simple docstring"""
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_te... | 314 | 1 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def _snake_case ( _snake_case : str , _snake_case : float | Decimal , _snake_case : float = 10**-10 ):
lowerCAmelCase : ... | 314 |
"""simple docstring"""
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
snake_case__ : Optional[Any] = False
class snake_case_( unittest.T... | 314 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
snake_case__ : Dict = {'''configuration_fnet''': ['''FNET_PRETRAINED_... | 314 |
"""simple docstring"""
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
snake_case__ : List[str] = logging.get_logger(__name__)
class snake_case_( a__ ):
... | 314 | 1 |
"""simple docstring"""
import argparse
snake_case__ : Union[str, Any] = '''docs/source/_static/js/custom.js'''
def _snake_case ( _snake_case : Dict ):
with open(_snake_case , encoding='''utf-8''' , newline='''\n''' ) as f:
lowerCAmelCase ... | 314 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
snake_case__ : str = logging.get_logger... | 314 | 1 |
"""simple docstring"""
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : Tuple = logging.get_logger(__name__)
snake_case__ : int = {
'''facebook/encodec_24k... | 314 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class snake_case_( a__ ):
__UpperCamelCase = (DDPMScheduler,)
def lowerCamelCase__ ( self : List[Any] , **UpperCamelCase_ :... | 314 | 1 |
"""simple docstring"""
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
snake_case__ : int = '''src/transformers'''
# This is to make su... | 314 |
"""simple docstring"""
def _snake_case ( _snake_case : int = 50000000 ):
lowerCAmelCase : List[str] = set()
lowerCAmelCase : List[Any] = int((limit - 24) ** (1 / 2) )
lowerCAmelCase : Optional[int] = set(range(3 , prime_square_limit + 1 , ... | 314 | 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProce... | 314 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
snake_case__ : Tuple = {
'''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerCon... | 314 | 1 |
"""simple docstring"""
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
snake_case__ : List[str] = {
'''E''': 1_2.7_0,
'''T''': 9.0_6,
'''A''': 8.1_7,
'''O''': 7.5_1,
'''I''': 6.9_7,
'''N''': 6.7_5,
'''S''': 6.3_3,
'''H''':... | 314 |
"""simple docstring"""
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class snake_case_:
def __init__( self : Dict , UpperCamel... | 314 | 1 |
"""simple docstring"""
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def _snake_case ( _snake_case ... | 314 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def _snake_case ( _snake_case : Dict ):
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org... | 314 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
snake_case__ : List[Any] = logging.get_logger(__name__)
snake_case__ : Optional[int] = {
'''nielsr/cani... | 314 |
"""simple docstring"""
import numpy as np
from PIL import Image
def _snake_case ( _snake_case : np.ndarray , _snake_case : int , _snake_case : int ):
lowerCAmelCase : Dict = np.array(_snake_case )
if arr.shape[0] != arr.shape[1]:
raise ... | 314 | 1 |
"""simple docstring"""
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def _snake_case ( _snake_case : List[str] , _snake_case : int=1 ):
if n_shave_prefix_segments >= 0:
return ".".join(path.split(... | 314 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_case_( a__ ):
def __init__( self : Dic... | 314 | 1 |
"""simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as... | 314 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
snake_case__ : int = {'''configuration_plbart''': ['''PLBART_PRETRAIN... | 314 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : list[list[float]] ):
lowerCAmelCase : list[list[float]] = []
for data in source_data:
for i, el in enumerate(_snake_case ):
if len(_snake_case ) < i + 1:
d... | 314 |
"""simple docstring"""
import os
import pytest
from transformers.dynamic_module_utils import get_imports
snake_case__ : Optional[Any] = '''
import os
'''
snake_case__ : Tuple = '''
def foo():
import os
return False
'''
snake_case__ : Any ... | 314 | 1 |
"""simple docstring"""
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('''>=''', FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distr... | 314 |
"""simple docstring"""
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class snake_case_( tf.keras.optimizers.schedules.LearningRateSche... | 314 | 1 |
"""simple docstring"""
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
... | 314 |
"""simple docstring"""
import collections
import importlib.util
import os
import re
from pathlib import Path
snake_case__ : Union[str, Any] = '''src/transformers'''
# Matches is_xxx_available()
snake_case__ : int = re.compile(R'''is\_([a-z_]*)_available()''')
# Catc... | 314 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import... | 314 |
"""simple docstring"""
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def _snake_case ( _snake_case ... | 314 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
from collections.abc import Callable
def _snake_case ( _snake_case : Callable[[int | float], int | float] , _snake_case : int | float , _snake_case : int | float , _snake_case : int = 100 , ):
... | 314 |
"""simple docstring"""
from math import sqrt
def _snake_case ( _snake_case : int ):
assert isinstance(_snake_case , _snake_case ) and (
number >= 0
), "'number' must been an int and positive"
lowerCAmelCase : Dict = True
# 0 and 1 are none... | 314 | 1 |
"""simple docstring"""
from math import factorial
def _snake_case ( _snake_case : int , _snake_case : 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 <... | 314 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : Any = logging.get_logger(__name__)
snake_case__ : Any = {
'''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve... | 314 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : list ):
lowerCAmelCase : List[str] = len(_snake_case )
for i in range(1 , _snake_case ):
lowerCAmelCase : Tuple = collection[i]
lowerCAmelCase : Any = 0
... | 314 |
"""simple docstring"""
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
snake_case__ ... | 314 | 1 |
"""simple docstring"""
from cva import destroyAllWindows, imread, imshow, waitKey
def _snake_case ( _snake_case : Dict ):
# getting number of pixels in the image
lowerCAmelCase, lowerCAmelCase : str = img.shape[0], img.shape[1]
# converting each pixel's color to ... | 314 |
"""simple docstring"""
def _snake_case ( _snake_case : int ):
assert isinstance(_snake_case , _snake_case ), f'''The input value of [n={number}] is not an integer'''
if number == 1:
return 2
elif number < 1:
lowerCAmelCase : Tuple = f... | 314 | 1 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ..... | 314 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def _snake_case ( _snake_case : List[str] ):
lowerCAmelCase : Union[str, Any] = SwinConfig(image_size... | 314 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : int = 1000000 ):
lowerCAmelCase : List[str] = set(range(3 , _snake_case , 2 ) )
primes.add(2 )
for p in range(3 , _snake_case , 2 ):
if p not in primes:
... | 314 |
"""simple docstring"""
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPip... | 314 | 1 |
"""simple docstring"""
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 TokenizerTe... | 314 |
"""simple docstring"""
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_te... | 314 | 1 |
"""simple docstring"""
import functools
from typing import Any
def _snake_case ( _snake_case : str , _snake_case : list[str] ):
# Validation
if not isinstance(_snake_case , _snake_case ) or len(_snake_case ) == 0:
raise ValueError('''the string s... | 314 |
"""simple docstring"""
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
snake_case__ : Optional[Any] = False
class snake_case_( unittest.T... | 314 | 1 |
"""simple docstring"""
import torch
from diffusers import DiffusionPipeline
class snake_case_( a__ ):
def __init__( self : Any , UpperCamelCase_ : Tuple , UpperCamelCase_ : List[Any] ):
super().__init__()
self.register_modules(unet=... | 314 |
"""simple docstring"""
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
snake_case__ : List[str] = logging.get_logger(__name__)
class snake_case_( a__ ):
... | 314 | 1 |
"""simple docstring"""
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
snake_case__ : List[Any] = '''2.13.1'''
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_... | 314 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
snake_case__ : str = logging.get_logger... | 314 | 1 |
"""simple docstring"""
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPip... | 314 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class snake_case_( a__ ):
__UpperCamelCase = (DDPMScheduler,)
def lowerCamelCase__ ( self : List[Any] , **UpperCamelCase_ :... | 314 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkAr... | 314 |
"""simple docstring"""
def _snake_case ( _snake_case : int = 50000000 ):
lowerCAmelCase : List[str] = set()
lowerCAmelCase : List[Any] = int((limit - 24) ** (1 / 2) )
lowerCAmelCase : Optional[int] = set(range(3 , prime_square_limit + 1 , ... | 314 | 1 |
"""simple docstring"""
snake_case__ : Dict = '''Input must be a string of 8 numbers plus letter'''
snake_case__ : Union[str, Any] = '''TRWAGMYFPDXBNJZSQVHLCKE'''
def _snake_case ( _snake_case : str ):
if not isinstance(_snake_case , _snake... | 314 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
snake_case__ : Tuple = {
'''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerCon... | 314 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vi... | 314 |
"""simple docstring"""
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class snake_case_:
def __init__( self : Dict , UpperCamel... | 314 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, 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... | 314 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def _snake_case ( _snake_case : Dict ):
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org... | 314 | 1 |
"""simple docstring"""
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_log... | 314 |
"""simple docstring"""
import numpy as np
from PIL import Image
def _snake_case ( _snake_case : np.ndarray , _snake_case : int , _snake_case : int ):
lowerCAmelCase : Dict = np.array(_snake_case )
if arr.shape[0] != arr.shape[1]:
raise ... | 314 | 1 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu... | 314 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_case_( a__ ):
def __init__( self : Dic... | 314 | 1 |
"""simple docstring"""
from math import sqrt
def _snake_case ( _snake_case : int ):
assert isinstance(_snake_case , _snake_case ) and (
number >= 0
), "'number' must been an int and positive"
lowerCAmelCase : Dict = True
# 0 and 1 are none... | 314 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
snake_case__ : int = {'''configuration_plbart''': ['''PLBART_PRETRAIN... | 314 | 1 |
"""simple docstring"""
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import Nest... | 314 |
"""simple docstring"""
import os
import pytest
from transformers.dynamic_module_utils import get_imports
snake_case__ : Optional[Any] = '''
import os
'''
snake_case__ : Tuple = '''
def foo():
import os
return False
'''
snake_case__ : Any ... | 314 | 1 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def _snake_case ( _snake_case : List[str] ):
lowerCAmelCase : Union[str, Any] = SwinConfig(image_size... | 314 |
"""simple docstring"""
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class snake_case_( tf.keras.optimizers.schedules.LearningRateSche... | 314 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : str ):
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 314 |
"""simple docstring"""
import collections
import importlib.util
import os
import re
from pathlib import Path
snake_case__ : Union[str, Any] = '''src/transformers'''
# Matches is_xxx_available()
snake_case__ : int = re.compile(R'''is\_([a-z_]*)_available()''')
# Catc... | 314 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#... | 314 |
"""simple docstring"""
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def _snake_case ( _snake_case ... | 314 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : int = 1000000 ):
lowerCAmelCase : Optional[int] = limit + 1
lowerCAmelCase : int = [0] * limit
for first_term in range(1 , _snake_case ):
for n in range(_snake_case , _snake_case ,... | 314 |
"""simple docstring"""
from math import sqrt
def _snake_case ( _snake_case : int ):
assert isinstance(_snake_case , _snake_case ) and (
number >= 0
), "'number' must been an int and positive"
lowerCAmelCase : Dict = True
# 0 and 1 are none... | 314 | 1 |
"""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, lo... | 314 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : Any = logging.get_logger(__name__)
snake_case__ : Any = {
'''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve... | 314 | 1 |
"""simple docstring"""
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 impo... | 314 |
"""simple docstring"""
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
snake_case__ ... | 314 | 1 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : int , _snake_case : int ):
if partitions <= 0:
raise ValueError('''partitions must be a positive number!''' )
if partitions > number_of_bytes:
raise Val... | 314 |
"""simple docstring"""
def _snake_case ( _snake_case : int ):
assert isinstance(_snake_case , _snake_case ), f'''The input value of [n={number}] is not an integer'''
if number == 1:
return 2
elif number < 1:
lowerCAmelCase : Tuple = f... | 314 | 1 |
"""simple docstring"""
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case__ : Tuple = logging.get_logger(__name__)
snake_case__ : Optional[int] ... | 314 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def _snake_case ( _snake_case : List[str] ):
lowerCAmelCase : Union[str, Any] = SwinConfig(image_size... | 314 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : Optional[int] , _snake_case : Optional[Any] ):
lowerCAmelCase : Optional[int] = [1]
for i in range(2 , _snake_case ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factor... | 314 |
"""simple docstring"""
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPip... | 314 | 1 |
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor... | 314 |
"""simple docstring"""
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_te... | 314 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class snake_case_( unit... | 314 |
"""simple docstring"""
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
snake_case__ : Optional[Any] = False
class snake_case_( unittest.T... | 314 | 1 |
"""simple docstring"""
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_c... | 314 |
"""simple docstring"""
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
snake_case__ : List[str] = logging.get_logger(__name__)
class snake_case_( a__ ):
... | 314 | 1 |
"""simple docstring"""
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
snake_case__ : List[Any] = logging... | 314 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
snake_case__ : str = logging.get_logger... | 314 | 1 |
"""simple docstring"""
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
snake_case__ : Optional[Any] = False
class snake_case_( unittest.T... | 314 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class snake_case_( a__ ):
__UpperCamelCase = (DDPMScheduler,)
def lowerCamelCase__ ( self : List[Any] , **UpperCamelCase_ :... | 314 | 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,
is_vision_available,
)
snake_case__ : Dict = {
'''c... | 314 |
"""simple docstring"""
def _snake_case ( _snake_case : int = 50000000 ):
lowerCAmelCase : List[str] = set()
lowerCAmelCase : List[Any] = int((limit - 24) ** (1 / 2) )
lowerCAmelCase : Optional[int] = set(range(3 , prime_square_limit + 1 , ... | 314 | 1 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_pa... | 314 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
snake_case__ : Tuple = {
'''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerCon... | 314 | 1 |
"""simple docstring"""
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
snake_case__ : str = TypeVar('''T''')
class snake_case_( Generic[T] ):
__UpperCamelCase = 42 # Cache store of keys
__Uppe... | 314 |
"""simple docstring"""
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class snake_case_:
def __init__( self : Dict , UpperCamel... | 314 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : int = 4000000 ):
lowerCAmelCase : Union[str, Any] = []
lowerCAmelCase, lowerCAmelCase : Union[str, Any] = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(_snake_case ... | 314 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def _snake_case ( _snake_case : Dict ):
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org... | 314 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
... | 314 |
"""simple docstring"""
import numpy as np
from PIL import Image
def _snake_case ( _snake_case : np.ndarray , _snake_case : int , _snake_case : int ):
lowerCAmelCase : Dict = np.array(_snake_case )
if arr.shape[0] != arr.shape[1]:
raise ... | 314 | 1 |
"""simple docstring"""
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
... | 314 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_case_( a__ ):
def __init__( self : Dic... | 314 | 1 |
"""simple docstring"""
from __future__ import annotations
from statistics import mean
def _snake_case ( _snake_case : list[int] , _snake_case : list[int] , _snake_case : int ):
lowerCAmelCase : List[Any] = [0] * no_of_processes
lowerCAmelCase : Dic... | 314 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
snake_case__ : int = {'''configuration_plbart''': ['''PLBART_PRETRAIN... | 314 | 1 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : int ):
lowerCAmelCase : Optional[int] = [True] * limit
lowerCAmelCase : Union[str, Any] = False
lowerCAmelCase : Tuple = False
lowerCAmelCase : int... | 314 |
"""simple docstring"""
import os
import pytest
from transformers.dynamic_module_utils import get_imports
snake_case__ : Optional[Any] = '''
import os
'''
snake_case__ : Tuple = '''
def foo():
import os
return False
'''
snake_case__ : Any ... | 314 | 1 |
"""simple docstring"""
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class snake_case_( a__ ):
__UpperCamelCase = '''Speech2TextFeatureExtractor'''
__UpperCamelCase = '''Speech2TextTokenizer'''
def __init__(... | 314 |
"""simple docstring"""
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class snake_case_( tf.keras.optimizers.schedules.LearningRateSche... | 314 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : int ):
if n == 1 or not isinstance(_snake_case , _snake_case ):
return 0
elif n == 2:
return 1
else:
lowerCAmelCase : Optional[Any] = [0, 1]
for i in range(... | 314 |
"""simple docstring"""
import collections
import importlib.util
import os
import re
from pathlib import Path
snake_case__ : Union[str, Any] = '''src/transformers'''
# Matches is_xxx_available()
snake_case__ : int = re.compile(R'''is\_([a-z_]*)_available()''')
# Catc... | 314 | 1 |
"""simple docstring"""
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet im... | 314 |
"""simple docstring"""
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def _snake_case ( _snake_case ... | 314 | 1 |
"""simple docstring"""
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord i... | 314 |
"""simple docstring"""
from math import sqrt
def _snake_case ( _snake_case : int ):
assert isinstance(_snake_case , _snake_case ) and (
number >= 0
), "'number' must been an int and positive"
lowerCAmelCase : Dict = True
# 0 and 1 are none... | 314 | 1 |
"""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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_ver... | 314 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : Any = logging.get_logger(__name__)
snake_case__ : Any = {
'''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve... | 314 | 1 |
"""simple docstring"""
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def _snake_case ( _snake_case : int , _snake_case : str , _snake_case : Optional[A... | 314 |
"""simple docstring"""
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
snake_case__ ... | 314 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
snake_case__ : Dict = {
'''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP''',... | 314 |
"""simple docstring"""
def _snake_case ( _snake_case : int ):
assert isinstance(_snake_case , _snake_case ), f'''The input value of [n={number}] is not an integer'''
if number == 1:
return 2
elif number < 1:
lowerCAmelCase : Tuple = f... | 314 | 1 |
"""simple docstring"""
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('''Googling.....''')
snake_case__ : Optional[Any] = '''https://www.google.com/search?q=''' + ''' '''.join(sys.argv[1:])... | 314 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def _snake_case ( _snake_case : List[str] ):
lowerCAmelCase : Union[str, Any] = SwinConfig(image_size... | 314 | 1 |
"""simple docstring"""
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class snake_case_:
def __init__( self : Dict , UpperCamel... | 314 |
"""simple docstring"""
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPip... | 314 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_cha... | 314 |
"""simple docstring"""
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_te... | 314 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
f... | 314 |
"""simple docstring"""
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
snake_case__ : Optional[Any] = False
class snake_case_( unittest.T... | 314 | 1 |
"""simple docstring"""
import os
import numpy
import onnx
def _snake_case ( _snake_case : Tuple , _snake_case : Optional[int] ):
lowerCAmelCase : int = a.name
lowerCAmelCase : Union[str, Any] = b.name
lowerCAmelCase : List[Any] = '''''... | 314 |
"""simple docstring"""
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
snake_case__ : List[str] = logging.get_logger(__name__)
class snake_case_( a__ ):
... | 314 | 1 |
"""simple docstring"""
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
... | 314 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
snake_case__ : str = logging.get_logger... | 314 | 1 |
"""simple docstring"""
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
snake_case__ : List[str] = logging.get_lo... | 314 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class snake_case_( a__ ):
__UpperCamelCase = (DDPMScheduler,)
def lowerCamelCase__ ( self : List[Any] , **UpperCamelCase_ :... | 314 | 1 |
"""simple docstring"""
# Function to print upper half of diamond (pyramid)
def _snake_case ( _snake_case : List[Any] ):
for i in range(0 , _snake_case ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(''' ''' , end='''''' )
... | 314 |
"""simple docstring"""
def _snake_case ( _snake_case : int = 50000000 ):
lowerCAmelCase : List[str] = set()
lowerCAmelCase : List[Any] = int((limit - 24) ** (1 / 2) )
lowerCAmelCase : Optional[int] = set(range(3 , prime_square_limit + 1 , ... | 314 | 1 |
"""simple docstring"""
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.test... | 314 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
snake_case__ : Tuple = {
'''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerCon... | 314 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
snake_case__ : Tuple = logging.get_logger(__name__)
class snake_case_( a__ ):
def __init__( self : Optional[Any] , *... | 314 |
"""simple docstring"""
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class snake_case_:
def __init__( self : Dict , UpperCamel... | 314 | 1 |
"""simple docstring"""
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import f... | 314 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def _snake_case ( _snake_case : Dict ):
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org... | 314 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeli... | 314 |
"""simple docstring"""
import numpy as np
from PIL import Image
def _snake_case ( _snake_case : np.ndarray , _snake_case : int , _snake_case : int ):
lowerCAmelCase : Dict = np.array(_snake_case )
if arr.shape[0] != arr.shape[1]:
raise ... | 314 | 1 |
"""simple docstring"""
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu,... | 314 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_case_( a__ ):
def __init__( self : Dic... | 314 | 1 |
"""simple docstring"""
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavin... | 314 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
snake_case__ : int = {'''configuration_plbart''': ['''PLBART_PRETRAIN... | 314 | 1 |
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