
'''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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
    import PIL


__snake_case:  int								=			logging.get_logger(__name__)






def 	_snake_case						(      A_       :    Optional[Any]   ):
  """simple docstring"""







  if isinstance(A_     ,		(list, tuple)   ) and isinstance(videos[0]     ,		(list, tuple)   ) and is_valid_image(videos[0][0]   ):
    return videos

  elif isinstance(A_     ,		(list, tuple)   ) and is_valid_image(videos[0]   ):
    return [videos]

  elif is_valid_image(A_   ):
    return [[videos]]

  raise ValueError(f'''Could not make batched video from {videos}'''   )




class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					["pixel_values"]


     def __init__(      self							,		lowerCAmelCase_ = True							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = PILImageResampling.BILINEAR							,		lowerCAmelCase_ = True							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = True							,		lowerCAmelCase_ = 1 / 2_55							,		lowerCAmelCase_ = True							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		**lowerCAmelCase_							,		):

       '''simple docstring'''

       super().__init__(**lowerCAmelCase_ )
       a_      :	Dict      =							size if size is not None else {"""shortest_edge""": 2_24}
       a_      :	Union[str, Any]      =							get_size_dict(lowerCAmelCase_							,		default_to_square=lowerCAmelCase_ )
       a_      :	Tuple      =							crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
       a_      :	List[Any]      =							get_size_dict(lowerCAmelCase_							,		param_name="""crop_size""" )

       a_      :	List[Any]      =							do_resize
       a_      :	int      =							size
       a_      :	Dict      =							do_center_crop
       a_      :	int      =							crop_size
       a_      :	Union[str, Any]      =							resample
       a_      :	Optional[int]      =							do_rescale
       a_      :	Optional[int]      =							rescale_factor
       a_      :	Dict      =							do_normalize
       a_      :	Union[str, Any]      =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       a_      :	Dict      =							image_std if image_std is not None else IMAGENET_STANDARD_STD


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ = PILImageResampling.BILINEAR							,		lowerCAmelCase_ = None							,		**lowerCAmelCase_							,		):

       '''simple docstring'''

       a_      :	List[Any]      =							get_size_dict(lowerCAmelCase_							,		default_to_square=lowerCAmelCase_ )
       if "shortest_edge" in size:
         a_      :	Optional[int]      =							get_resize_output_image_size(lowerCAmelCase_							,		size["""shortest_edge"""]							,		default_to_square=lowerCAmelCase_ )
       elif "height" in size and "width" in size:
         a_      :	int      =							(size["""height"""], size["""width"""])
       else:
         raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''' )
       return resize(lowerCAmelCase_							,		size=lowerCAmelCase_							,		resample=lowerCAmelCase_							,		data_format=lowerCAmelCase_							,		**lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ = None							,		**lowerCAmelCase_							,		):

       '''simple docstring'''

       a_      :	Optional[Any]      =							get_size_dict(lowerCAmelCase_ )
       if "height" not in size or "width" not in size:
         raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}''' )
       return center_crop(lowerCAmelCase_							,		size=(size["""height"""], size["""width"""])							,		data_format=lowerCAmelCase_							,		**lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ = None							,		**lowerCAmelCase_							,		):

       '''simple docstring'''

       return rescale(lowerCAmelCase_							,		scale=lowerCAmelCase_							,		data_format=lowerCAmelCase_							,		**lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ = None							,		**lowerCAmelCase_							,		):

       '''simple docstring'''

       return normalize(lowerCAmelCase_							,		mean=lowerCAmelCase_							,		std=lowerCAmelCase_							,		data_format=lowerCAmelCase_							,		**lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = ChannelDimension.FIRST							,		):

       '''simple docstring'''

       if do_resize and size is None or resample is None:
         raise ValueError("""Size and resample 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.
       a_      :	List[str]      =							to_numpy_array(lowerCAmelCase_ )

       if do_resize:
         a_      :	Optional[Any]      =							self.resize(image=lowerCAmelCase_							,		size=lowerCAmelCase_							,		resample=lowerCAmelCase_ )

       if do_center_crop:
         a_      :	str      =							self.center_crop(lowerCAmelCase_							,		size=lowerCAmelCase_ )

       if do_rescale:
         a_      :	int      =							self.rescale(image=lowerCAmelCase_							,		scale=lowerCAmelCase_ )

       if do_normalize:
         a_      :	Dict      =							self.normalize(image=lowerCAmelCase_							,		mean=lowerCAmelCase_							,		std=lowerCAmelCase_ )

       a_      :	str      =							to_channel_dimension_format(lowerCAmelCase_							,		lowerCAmelCase_ )
       return image






     def 							_lowerCAmelCase  (      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'''

       a_      :	Any      =							do_resize if do_resize is not None else self.do_resize
       a_      :	Dict      =							resample if resample is not None else self.resample
       a_      :	str      =							do_center_crop if do_center_crop is not None else self.do_center_crop
       a_      :	Any      =							do_rescale if do_rescale is not None else self.do_rescale
       a_      :	List[Any]      =							rescale_factor if rescale_factor is not None else self.rescale_factor
       a_      :	Dict      =							do_normalize if do_normalize is not None else self.do_normalize
       a_      :	Union[str, Any]      =							image_mean if image_mean is not None else self.image_mean
       a_      :	List[str]      =							image_std if image_std is not None else self.image_std

       a_      :	Union[str, Any]      =							size if size is not None else self.size
       a_      :	Optional[Any]      =							get_size_dict(lowerCAmelCase_							,		default_to_square=lowerCAmelCase_ )
       a_      :	List[str]      =							crop_size if crop_size is not None else self.crop_size
       a_      :	int      =							get_size_dict(lowerCAmelCase_							,		param_name="""crop_size""" )

       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.""" )

       a_      :	List[Any]      =							make_batched(lowerCAmelCase_ )

       a_      :	int      =							[
           [
               self._preprocess_image(
                   image=lowerCAmelCase_							,		do_resize=lowerCAmelCase_							,		size=lowerCAmelCase_							,		resample=lowerCAmelCase_							,		do_center_crop=lowerCAmelCase_							,		crop_size=lowerCAmelCase_							,		do_rescale=lowerCAmelCase_							,		rescale_factor=lowerCAmelCase_							,		do_normalize=lowerCAmelCase_							,		image_mean=lowerCAmelCase_							,		image_std=lowerCAmelCase_							,		data_format=lowerCAmelCase_							,		)
               for img in video
           ]
           for video in videos
       ]

       a_      :	Optional[int]      =							{"""pixel_values""": videos}
       return BatchFeature(data=lowerCAmelCase_							,		tensor_type=lowerCAmelCase_ )




'''simple docstring'''

from datasets.utils.patching import _PatchedModuleObj, patch_submodule

from . import _test_patching






def 	_snake_case						(      ):
  """simple docstring"""







  import os as original_os
  from os import path as original_path
  from os import rename as original_rename
  from os.path import dirname as original_dirname
  from os.path import join as original_join

  assert _test_patching.os is original_os
  assert _test_patching.path is original_path
  assert _test_patching.join is original_join

  assert _test_patching.renamed_os is original_os
  assert _test_patching.renamed_path is original_path
  assert _test_patching.renamed_join is original_join

  a_      :	List[str]      =							"""__test_patch_submodule_mock__"""
  with patch_submodule(_test_patching     ,		"""os.path.join"""     ,		A_   ):
    # Every way to access os.path.join must be patched, and the rest must stay untouched

    # check os.path.join
    assert isinstance(_test_patching.os     ,		_PatchedModuleObj   )
    assert isinstance(_test_patching.os.path     ,		_PatchedModuleObj   )
    assert _test_patching.os.path.join is mock

    # check path.join
    assert isinstance(_test_patching.path     ,		_PatchedModuleObj   )
    assert _test_patching.path.join is mock

    # check join
    assert _test_patching.join is mock

    # check that the other attributes are untouched
    assert _test_patching.os.rename is original_rename
    assert _test_patching.path.dirname is original_dirname
    assert _test_patching.os.path.dirname is original_dirname

    # Even renamed modules or objects must be patched

    # check renamed_os.path.join
    assert isinstance(_test_patching.renamed_os     ,		_PatchedModuleObj   )
    assert isinstance(_test_patching.renamed_os.path     ,		_PatchedModuleObj   )
    assert _test_patching.renamed_os.path.join is mock

    # check renamed_path.join
    assert isinstance(_test_patching.renamed_path     ,		_PatchedModuleObj   )
    assert _test_patching.renamed_path.join is mock

    # check renamed_join
    assert _test_patching.renamed_join is mock

    # check that the other attributes are untouched
    assert _test_patching.renamed_os.rename is original_rename
    assert _test_patching.renamed_path.dirname is original_dirname
    assert _test_patching.renamed_os.path.dirname is original_dirname

  # check that everthing is back to normal when the patch is over

  assert _test_patching.os is original_os
  assert _test_patching.path is original_path
  assert _test_patching.join is original_join

  assert _test_patching.renamed_os is original_os
  assert _test_patching.renamed_path is original_path
  assert _test_patching.renamed_join is original_join






def 	_snake_case						(      ):
  """simple docstring"""







  assert _test_patching.open is open

  a_      :	Tuple      =							"""__test_patch_submodule_builtin_mock__"""
  # _test_patching has "open" in its globals
  assert _test_patching.open is open
  with patch_submodule(_test_patching     ,		"""open"""     ,		A_   ):
    assert _test_patching.open is mock

  # check that everthing is back to normal when the patch is over

  assert _test_patching.open is open






def 	_snake_case						(      ):
  """simple docstring"""







  a_      :	Any      =							"""__test_patch_submodule_missing_mock__"""
  with patch_submodule(_test_patching     ,		"""pandas.read_csv"""     ,		A_   ):
    pass






def 	_snake_case						(      ):
  """simple docstring"""







  a_      :	int      =							"""__test_patch_submodule_missing_builtin_mock__"""
  # _test_patching doesn't have "len" in its globals
  assert getattr(_test_patching     ,		"""len"""     ,		A_   ) is None
  with patch_submodule(_test_patching     ,		"""len"""     ,		A_   ):
    assert _test_patching.len is mock
  assert _test_patching.len is len






def 	_snake_case						(      ):
  """simple docstring"""







  a_      :	List[str]      =							"""__test_patch_submodule_start_and_stop_mock__"""
  a_      :	int      =							patch_submodule(_test_patching     ,		"""open"""     ,		A_   )
  assert _test_patching.open is open
  patch.start()
  assert _test_patching.open is mock
  patch.stop()
  assert _test_patching.open is open






def 	_snake_case						(      ):
  """simple docstring"""







  from os import rename as original_rename
  from os.path import dirname as original_dirname
  from os.path import join as original_join

  a_      :	Any      =							"""__test_patch_submodule_successive_join__"""
  a_      :	Optional[Any]      =							"""__test_patch_submodule_successive_dirname__"""
  a_      :	Optional[int]      =							"""__test_patch_submodule_successive_rename__"""
  assert _test_patching.os.path.join is original_join
  assert _test_patching.os.path.dirname is original_dirname
  assert _test_patching.os.rename is original_rename

  with patch_submodule(_test_patching     ,		"""os.path.join"""     ,		A_   ):
    with patch_submodule(_test_patching     ,		"""os.rename"""     ,		A_   ):
      with patch_submodule(_test_patching     ,		"""os.path.dirname"""     ,		A_   ):
        assert _test_patching.os.path.join is mock_join
        assert _test_patching.os.path.dirname is mock_dirname
        assert _test_patching.os.rename is mock_rename

    # try another order
  with patch_submodule(_test_patching     ,		"""os.rename"""     ,		A_   ):
    with patch_submodule(_test_patching     ,		"""os.path.join"""     ,		A_   ):
      with patch_submodule(_test_patching     ,		"""os.path.dirname"""     ,		A_   ):
        assert _test_patching.os.path.join is mock_join
        assert _test_patching.os.path.dirname is mock_dirname
        assert _test_patching.os.rename is mock_rename

  assert _test_patching.os.path.join is original_join
  assert _test_patching.os.path.dirname is original_dirname
  assert _test_patching.os.rename is original_rename






def 	_snake_case						(      ):
  """simple docstring"""







  a_      :	Optional[int]      =							"""__test_patch_submodule_doesnt_exist_mock__"""
  with patch_submodule(_test_patching     ,		"""__module_that_doesn_exist__.__attribute_that_doesn_exist__"""     ,		A_   ):
    pass
  with patch_submodule(_test_patching     ,		"""os.__attribute_that_doesn_exist__"""     ,		A_   ):
    pass


