






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE__   :      Optional[Any]				=		logging.get_logger(__name__)
class     lowerCamelCase_					(   lowerCamelCase			):
				a__       							=	['''pixel_values''']







				def __init__(    self							,       __lowerCAmelCase = True							,       __lowerCAmelCase = None							,       __lowerCAmelCase = PILImageResampling.BILINEAR							,       __lowerCAmelCase = True							,       __lowerCAmelCase = None							,       __lowerCAmelCase = True							,       __lowerCAmelCase = 1 / 2_5_5							,       __lowerCAmelCase = True							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       **__lowerCAmelCase							,       ):
								"""simple docstring"""
								super().__init__(**__lowerCAmelCase  )
								__magic_name__		:Optional[Any]   =   size if size is not None else {'''shortest_edge''': 2_5_6}
								__magic_name__		:List[Any]   =   get_size_dict(__lowerCAmelCase							,       default_to_square=__lowerCAmelCase  )
								__magic_name__		:List[str]   =   crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
								__magic_name__		:int   =   get_size_dict(__lowerCAmelCase  )
								__magic_name__		:Optional[int]   =   do_resize
								__magic_name__		:List[str]   =   size
								__magic_name__		:List[str]   =   resample
								__magic_name__		:str   =   do_center_crop
								__magic_name__		:Union[str, Any]   =   crop_size
								__magic_name__		:Tuple   =   do_rescale
								__magic_name__		:Optional[Any]   =   rescale_factor
								__magic_name__		:Dict   =   do_normalize
								__magic_name__		:List[Any]   =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								__magic_name__		:str   =   image_std if image_std is not None else IMAGENET_STANDARD_STD







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase = PILImageResampling.BICUBIC							,       __lowerCAmelCase = None							,       **__lowerCAmelCase							,       ):
								"""simple docstring"""
								__magic_name__		:int   =   get_size_dict(__lowerCAmelCase							,       default_to_square=__lowerCAmelCase  )
								if "shortest_edge" not in size:
												raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''  )
								__magic_name__		:Dict   =   get_resize_output_image_size(__lowerCAmelCase							,       size=size['''shortest_edge''']							,       default_to_square=__lowerCAmelCase  )
								return resize(__lowerCAmelCase							,       size=__lowerCAmelCase							,       resample=__lowerCAmelCase							,       data_format=__lowerCAmelCase							,       **__lowerCAmelCase  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase = None							,       **__lowerCAmelCase							,       ):
								"""simple docstring"""
								__magic_name__		:Any   =   get_size_dict(__lowerCAmelCase  )
								return center_crop(__lowerCAmelCase							,       size=(size['''height'''], size['''width'''])							,       data_format=__lowerCAmelCase							,       **__lowerCAmelCase  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase = None							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								return rescale(__lowerCAmelCase							,       scale=__lowerCAmelCase							,       data_format=__lowerCAmelCase							,       **__lowerCAmelCase  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase = None							,       **__lowerCAmelCase							,       ):
								"""simple docstring"""
								return normalize(__lowerCAmelCase							,       mean=__lowerCAmelCase							,       std=__lowerCAmelCase							,       data_format=__lowerCAmelCase							,       **__lowerCAmelCase  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       __lowerCAmelCase = ChannelDimension.FIRST							,       **__lowerCAmelCase							,       ):
								"""simple docstring"""
								__magic_name__		:Optional[int]   =   do_resize if do_resize is not None else self.do_resize
								__magic_name__		:Union[str, Any]   =   size if size is not None else self.size
								__magic_name__		:Dict   =   get_size_dict(__lowerCAmelCase							,       default_to_square=__lowerCAmelCase  )
								__magic_name__		:Optional[Any]   =   resample if resample is not None else self.resample
								__magic_name__		:Dict   =   do_center_crop if do_center_crop is not None else self.do_center_crop
								__magic_name__		:List[str]   =   crop_size if crop_size is not None else self.crop_size
								__magic_name__		:str   =   get_size_dict(__lowerCAmelCase  )
								__magic_name__		:Tuple   =   do_rescale if do_rescale is not None else self.do_rescale
								__magic_name__		:Optional[Any]   =   rescale_factor if rescale_factor is not None else self.rescale_factor
								__magic_name__		:Union[str, Any]   =   do_normalize if do_normalize is not None else self.do_normalize
								__magic_name__		:Union[str, Any]   =   image_mean if image_mean is not None else self.image_mean
								__magic_name__		:Union[str, Any]   =   image_std if image_std is not None else self.image_std

								__magic_name__		:List[Any]   =   make_list_of_images(__lowerCAmelCase  )

								if not valid_images(__lowerCAmelCase  ):
												raise ValueError(
												    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
												    '''torch.Tensor, tf.Tensor or jax.ndarray.'''  )

								if do_resize and size is None:
												raise ValueError('''Size must be specified if do_resize is True.'''  )

								if do_center_crop and crop_size is None:
												raise ValueError('''Crop size must be specified if do_center_crop is True.'''  )

								if do_rescale and rescale_factor is None:
												raise ValueError('''Rescale factor must be specified if do_rescale is True.'''  )

								if do_normalize and (image_mean is None or image_std is None):
												raise ValueError('''Image mean and std must be specified if do_normalize is True.'''  )

								# All transformations expect numpy arrays.
								__magic_name__		:Union[str, Any]   =   [to_numpy_array(__lowerCAmelCase  ) for image in images]

								if do_resize:
												__magic_name__		:Tuple   =   [self.resize(image=__lowerCAmelCase							,       size=__lowerCAmelCase							,       resample=__lowerCAmelCase  ) for image in images]

								if do_center_crop:
												__magic_name__		:str   =   [self.center_crop(image=__lowerCAmelCase							,       size=__lowerCAmelCase  ) for image in images]

								if do_rescale:
												__magic_name__		:List[str]   =   [self.rescale(image=__lowerCAmelCase							,       scale=__lowerCAmelCase  ) for image in images]

								if do_normalize:
												__magic_name__		:Optional[int]   =   [self.normalize(image=__lowerCAmelCase							,       mean=__lowerCAmelCase							,       std=__lowerCAmelCase  ) for image in images]

								__magic_name__		:Dict   =   [to_channel_dimension_format(__lowerCAmelCase							,       __lowerCAmelCase  ) for image in images]

								__magic_name__		:Any   =   {'''pixel_values''': images}
								return BatchFeature(data=__lowerCAmelCase							,       tensor_type=__lowerCAmelCase  )














import pytest
import requests

from datasets.utils.file_utils import http_head

from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline

@pytest.mark.integration
def   __lowercase       (			):







				"""simple docstring"""






				with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT			):
								with pytest.raises(snake_case			):
												requests.request('''GET''',	'''https://huggingface.co'''			)
								with pytest.raises(requests.exceptions.ConnectTimeout			):
												requests.request('''GET''',	'''https://huggingface.co''',	timeout=1.0			)

@pytest.mark.integration
def   __lowercase       (			):







				"""simple docstring"""






				with offline(OfflineSimulationMode.CONNECTION_FAILS			):
								with pytest.raises(requests.exceptions.ConnectionError			):
												requests.request('''GET''',	'''https://huggingface.co'''			)

def   __lowercase       (			):







				"""simple docstring"""






				with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1			):
								with pytest.raises(snake_case			):
												http_head('''https://huggingface.co'''			)






