from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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 logging


if is_vision_available():
   import PIL

a_    					=       logging.get_logger(__name__)





def    __lowercase     (						lowerCamelCase					:     int	):
 if isinstance(SCREAMING_SNAKE_CASE__  ,  (list, tuple)	) and isinstance(videos[0]  ,  (list, tuple)	) and is_valid_image(videos[0][0]	):
  return videos

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

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

 raise ValueError(F"Could not make batched video from {videos}"	)






class 		_lowercase						(					snake_case_       ):
 lowercase				     =   ["pixel_values"]

 def __init__( self	:							Optional[Any]   ,      snake_case	:							List[Any] = True   ,      snake_case	:							Any = None   ,      snake_case	:							Optional[Any] = PILImageResampling.BILINEAR   ,      snake_case	:							str = True   ,      snake_case	:							Optional[Any] = None   ,      snake_case	:							int = True   ,      snake_case	:							Optional[int] = 1 / 2_5_5   ,      snake_case	:							Any = True   ,      snake_case	:							Optional[Any] = True   ,      snake_case	:							Tuple = None   ,      snake_case	:							List[Any] = None   ,      **snake_case	:							Optional[int]   ,      )   ->						List[str]:

  """simple docstring"""





  super().__init__(**snake_case			)
  UpperCamelCase_					:						int =					size if size is not None else {"""shortest_edge""": 2_5_6}
  UpperCamelCase_					:						Union[str, Any] =					get_size_dict(snake_case   ,      default_to_square=snake_case			)
  UpperCamelCase_					:						str =					crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
  UpperCamelCase_					:						Tuple =					get_size_dict(snake_case   ,      param_name='crop_size'			)

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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]   ,      snake_case	:							Tuple   ,      snake_case	:							List[str]   ,      snake_case	:							Optional[Any] = PILImageResampling.BILINEAR   ,      snake_case	:							Any = None   ,      **snake_case	:							Dict   ,      )   ->						Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					get_size_dict(snake_case   ,      default_to_square=snake_case			)
  if "shortest_edge" in size:
   UpperCamelCase_					:						str =					get_resize_output_image_size(snake_case   ,      size['shortest_edge']   ,      default_to_square=snake_case			)
  elif "height" in size and "width" in size:
   UpperCamelCase_					:						Optional[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(snake_case   ,      size=snake_case   ,      resample=snake_case   ,      data_format=snake_case   ,      **snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str   ,      snake_case	:							Optional[int]   ,      snake_case	:							Any   ,      snake_case	:							int = None   ,      **snake_case	:							List[Any]   ,      )   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					get_size_dict(snake_case			)
  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(snake_case   ,      size=(size['height'], size['width'])   ,      data_format=snake_case   ,      **snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							Optional[int]   ,      snake_case	:							Optional[Any]   ,      snake_case	:							Union[str, Any] = True   ,      snake_case	:							Any = None   ,      **snake_case	:							int   ,      )   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						Union[str, Any] =					image.astype(np.floataa			)
  if offset:
   UpperCamelCase_					:						Optional[Any] =					image - (scale / 2)
  return rescale(snake_case   ,      scale=snake_case   ,      data_format=snake_case   ,      **snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str   ,      snake_case	:							str   ,      snake_case	:							Dict   ,      snake_case	:							Union[str, Any]   ,      snake_case	:							List[Any] = None   ,      **snake_case	:							List[str]   ,      )   ->						Optional[int]:

  """simple docstring"""





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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]   ,      snake_case	:							Tuple   ,      snake_case	:							Dict = None   ,      snake_case	:							Union[str, Any] = None   ,      snake_case	:							List[Any] = None   ,      snake_case	:							int = None   ,      snake_case	:							Union[str, Any] = None   ,      snake_case	:							Union[str, Any] = None   ,      snake_case	:							int = None   ,      snake_case	:							Tuple = None   ,      snake_case	:							List[str] = None   ,      snake_case	:							Optional[int] = None   ,      snake_case	:							List[Any] = None   ,      snake_case	:							Optional[Any] = ChannelDimension.FIRST   ,      )   ->						Dict:

  """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.'			)

  if offset and not do_rescale:
   raise ValueError('For offset, do_rescale must also be set to True.'			)

  # All transformations expect numpy arrays.
  UpperCamelCase_					:						Optional[int] =					to_numpy_array(snake_case			)

  if do_resize:
   UpperCamelCase_					:						Union[str, Any] =					self.resize(image=snake_case   ,      size=snake_case   ,      resample=snake_case			)

  if do_center_crop:
   UpperCamelCase_					:						Optional[Any] =					self.center_crop(snake_case   ,      size=snake_case			)

  if do_rescale:
   UpperCamelCase_					:						Optional[int] =					self.rescale(image=snake_case   ,      scale=snake_case   ,      offset=snake_case			)

  if do_normalize:
   UpperCamelCase_					:						Any =					self.normalize(image=snake_case   ,      mean=snake_case   ,      std=snake_case			)

  UpperCamelCase_					:						Any =					to_channel_dimension_format(snake_case   ,      snake_case			)
  return image


 def 						SCREAMING_SNAKE_CASE__   ( self	:							str   ,      snake_case	:							Any   ,      snake_case	:							str = None   ,      snake_case	:							Dict = None   ,      snake_case	:							str = None   ,      snake_case	:							Optional[int] = None   ,      snake_case	:							Any = None   ,      snake_case	:							Optional[int] = None   ,      snake_case	:							Optional[Any] = None   ,      snake_case	:							Dict = None   ,      snake_case	:							Any = None   ,      snake_case	:							Any = None   ,      snake_case	:							Optional[int] = None   ,      snake_case	:							Tuple = None   ,      snake_case	:							Dict = ChannelDimension.FIRST   ,      **snake_case	:							Union[str, Any]   ,      )   ->						int:

  """simple docstring"""





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

  UpperCamelCase_					:						int =					size if size is not None else self.size
  UpperCamelCase_					:						List[Any] =					get_size_dict(snake_case   ,      default_to_square=snake_case			)
  UpperCamelCase_					:						str =					crop_size if crop_size is not None else self.crop_size
  UpperCamelCase_					:						List[Any] =					get_size_dict(snake_case   ,      param_name='crop_size'			)

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

  UpperCamelCase_					:						List[Any] =					make_batched(snake_case			)

  UpperCamelCase_					:						Any =					[
      [
          self._preprocess_image(
              image=snake_case   ,      do_resize=snake_case   ,      size=snake_case   ,      resample=snake_case   ,      do_center_crop=snake_case   ,      crop_size=snake_case   ,      do_rescale=snake_case   ,      rescale_factor=snake_case   ,      offset=snake_case   ,      do_normalize=snake_case   ,      image_mean=snake_case   ,      image_std=snake_case   ,      data_format=snake_case   ,      )
          for img in video
      ]
      for video in videos
  ]

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





import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version







def 						_snake_case(  *SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__ = None					,	SCREAMING_SNAKE_CASE__=True					,	SCREAMING_SNAKE_CASE__=2				) ->    Optional[Any]:
       from .. import __version__

       lowercase							:     int       	=      take_from
       lowercase							:     Tuple       	=      ()
       if not isinstance(args[0]					,	SCREAMING_SNAKE_CASE__				):
              lowercase							:     Dict       	=      (args,)

       for attribute, version_name, message in args:
              if version.parse(version.parse(SCREAMING_SNAKE_CASE__				).base_version				) >= version.parse(SCREAMING_SNAKE_CASE__				):
                     raise ValueError(
                         f"The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"
                         f" version {__version__} is >= {version_name}"				)

              lowercase							:     int       	=      None
              if isinstance(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) and attribute in deprecated_kwargs:
                     values += (deprecated_kwargs.pop(SCREAMING_SNAKE_CASE__				),)
                     lowercase							:     Union[str, Any]       	=      f"The `{attribute}` argument is deprecated and will be removed in version {version_name}."
              elif hasattr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				):
                     values += (getattr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				),)
                     lowercase							:     int       	=      f"The `{attribute}` attribute is deprecated and will be removed in version {version_name}."
              elif deprecated_kwargs is None:
                     lowercase							:     Dict       	=      f"`{attribute}` is deprecated and will be removed in version {version_name}."

              if warning is not None:
                     lowercase							:     Dict       	=      warning + """ """ if standard_warn else """"""
                     warnings.warn(warning + message					,	SCREAMING_SNAKE_CASE__					,	stacklevel=SCREAMING_SNAKE_CASE__				)

       if isinstance(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) and len(SCREAMING_SNAKE_CASE__				) > 0:
              lowercase							:     str       	=      inspect.getouterframes(inspect.currentframe()				)[1]
              lowercase							:     List[str]       	=      call_frame.filename
              lowercase							:     Tuple       	=      call_frame.lineno
              lowercase							:     List[str]       	=      call_frame.function
              lowercase	,			lowercase							:     Optional[Any]       	=      next(iter(deprecated_kwargs.items()				)				)
              raise TypeError(f"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"				)

       if len(SCREAMING_SNAKE_CASE__				) == 0:
              return
       elif len(SCREAMING_SNAKE_CASE__				) == 1:
              return values[0]
       return values
