
'''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_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


UpperCamelCase__      :		Any       							=  logging.get_logger(__name__)

class    _a				(_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  ['pixel_values']






       def __init__(							self       ,							A__ = True       ,							A__ = None       ,							A__ = PILImageResampling.BILINEAR       ,							A__ = True       ,							A__ = None       ,							A__ = True       ,							A__ = 1 / 2_55       ,							A__ = True       ,							A__ = None       ,							A__ = None       ,							**A__       ,							)    ->			None:
          super().__init__(**A__      )
          _SCREAMING_SNAKE_CASE      =				size if size is not None else {"""shortest_edge""": 2_56}
          _SCREAMING_SNAKE_CASE      =				get_size_dict(A__       ,							default_to_square=A__      )
          _SCREAMING_SNAKE_CASE      =				crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
          _SCREAMING_SNAKE_CASE      =				get_size_dict(A__      )
          _SCREAMING_SNAKE_CASE      =				do_resize
          _SCREAMING_SNAKE_CASE      =				size
          _SCREAMING_SNAKE_CASE      =				resample
          _SCREAMING_SNAKE_CASE      =				do_center_crop
          _SCREAMING_SNAKE_CASE      =				crop_size
          _SCREAMING_SNAKE_CASE      =				do_rescale
          _SCREAMING_SNAKE_CASE      =				rescale_factor
          _SCREAMING_SNAKE_CASE      =				do_normalize
          _SCREAMING_SNAKE_CASE      =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          _SCREAMING_SNAKE_CASE      =				image_std if image_std is not None else IMAGENET_STANDARD_STD






       def        UpperCamelCase				(							self       ,							A__       ,							A__       ,							A__ = PILImageResampling.BICUBIC       ,							A__ = None       ,							**A__       ,							)    ->			np.ndarray:
          _SCREAMING_SNAKE_CASE      =				get_size_dict(A__       ,							default_to_square=A__      )
          if "shortest_edge" not in size:
             raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"      )
          _SCREAMING_SNAKE_CASE      =				get_resize_output_image_size(A__       ,							size=size["""shortest_edge"""]       ,							default_to_square=A__      )
          return resize(A__       ,							size=A__       ,							resample=A__       ,							data_format=A__       ,							**A__      )






       def        UpperCamelCase				(							self       ,							A__       ,							A__       ,							A__ = None       ,							**A__       ,							)    ->			np.ndarray:
          _SCREAMING_SNAKE_CASE      =				get_size_dict(A__      )
          return center_crop(A__       ,							size=(size["""height"""], size["""width"""])       ,							data_format=A__       ,							**A__      )






       def        UpperCamelCase				(							self       ,							A__       ,							A__       ,							A__ = None       ,							**A__      )    ->			np.ndarray:
          return rescale(A__       ,							scale=A__       ,							data_format=A__       ,							**A__      )






       def        UpperCamelCase				(							self       ,							A__       ,							A__       ,							A__       ,							A__ = None       ,							**A__       ,							)    ->			np.ndarray:
          return normalize(A__       ,							mean=A__       ,							std=A__       ,							data_format=A__       ,							**A__      )






       def        UpperCamelCase				(							self       ,							A__       ,							A__ = None       ,							A__ = None       ,							A__ = None       ,							A__ = None       ,							A__ = None       ,							A__ = None       ,							A__ = None       ,							A__ = None       ,							A__ = None       ,							A__ = None       ,							A__ = None       ,							A__ = ChannelDimension.FIRST       ,							**A__       ,							)    ->			Tuple:
          _SCREAMING_SNAKE_CASE      =				do_resize if do_resize is not None else self.do_resize
          _SCREAMING_SNAKE_CASE      =				size if size is not None else self.size
          _SCREAMING_SNAKE_CASE      =				get_size_dict(A__       ,							default_to_square=A__      )
          _SCREAMING_SNAKE_CASE      =				resample if resample is not None else self.resample
          _SCREAMING_SNAKE_CASE      =				do_center_crop if do_center_crop is not None else self.do_center_crop
          _SCREAMING_SNAKE_CASE      =				crop_size if crop_size is not None else self.crop_size
          _SCREAMING_SNAKE_CASE      =				get_size_dict(A__      )
          _SCREAMING_SNAKE_CASE      =				do_rescale if do_rescale is not None else self.do_rescale
          _SCREAMING_SNAKE_CASE      =				rescale_factor if rescale_factor is not None else self.rescale_factor
          _SCREAMING_SNAKE_CASE      =				do_normalize if do_normalize is not None else self.do_normalize
          _SCREAMING_SNAKE_CASE      =				image_mean if image_mean is not None else self.image_mean
          _SCREAMING_SNAKE_CASE      =				image_std if image_std is not None else self.image_std

          _SCREAMING_SNAKE_CASE      =				make_list_of_images(A__      )

          if not valid_images(A__      ):
             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.
          _SCREAMING_SNAKE_CASE      =				[to_numpy_array(A__      ) for image in images]

          if do_resize:
             _SCREAMING_SNAKE_CASE      =				[self.resize(image=A__       ,							size=A__       ,							resample=A__      ) for image in images]

          if do_center_crop:
             _SCREAMING_SNAKE_CASE      =				[self.center_crop(image=A__       ,							size=A__      ) for image in images]

          if do_rescale:
             _SCREAMING_SNAKE_CASE      =				[self.rescale(image=A__       ,							scale=A__      ) for image in images]

          if do_normalize:
             _SCREAMING_SNAKE_CASE      =				[self.normalize(image=A__       ,							mean=A__       ,							std=A__      ) for image in images]

          _SCREAMING_SNAKE_CASE      =				[to_channel_dimension_format(A__       ,							A__      ) for image in images]

          _SCREAMING_SNAKE_CASE      =				{"""pixel_values""": images}
          return BatchFeature(data=A__       ,							tensor_type=A__      )








'''simple docstring'''



import pytest
import requests

from datasets.utils.file_utils import http_head

from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def    lowerCAmelCase_	(				)     ->     List[Any]:




   """simple docstring"""
   with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT						):
      with pytest.raises(SCREAMING_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    lowerCAmelCase_	(				)     ->     int:




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







def    lowerCAmelCase_	(				)     ->     Optional[Any]:




   """simple docstring"""
   with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1						):
      with pytest.raises(SCREAMING_SNAKE_CASE_						):
         http_head("""https://huggingface.co"""						)






