




'''simple docstring'''



from typing import Dict, Iterable, 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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCamelCase_    	=				logging.get_logger(__name__)






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				List[str]        =						['''pixel_values''']







       def __init__(      self,							A = True,							A = None,							A = PILImageResampling.BICUBIC,							A = True,							A = None,							A = True,							A = 1 / 255,							A = True,							A = IMAGENET_DEFAULT_MEAN,							A = IMAGENET_DEFAULT_STD,							**A,							):







             '''simple docstring'''







             super().__init__(**A  )
             SCREAMING_SNAKE_CASE   :      Any    					=						size if size is not None else {'shortest_edge': 224}
             SCREAMING_SNAKE_CASE   :      List[str]    					=						get_size_dict(A,							default_to_square=A  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						crop_size if crop_size is not None else {'height': 224, 'width': 224}
             SCREAMING_SNAKE_CASE   :      List[str]    					=						get_size_dict(A,							param_name='crop_size'  )

             SCREAMING_SNAKE_CASE   :      Any    					=						do_resize
             SCREAMING_SNAKE_CASE   :      List[str]    					=						size
             SCREAMING_SNAKE_CASE   :      Any    					=						resample
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						do_center_crop
             SCREAMING_SNAKE_CASE   :      Any    					=						crop_size
             SCREAMING_SNAKE_CASE   :      Tuple    					=						do_rescale
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						rescale_factor
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						do_normalize
             SCREAMING_SNAKE_CASE   :      int    					=						image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
             SCREAMING_SNAKE_CASE   :      List[str]    					=						image_std if image_std is not None else IMAGENET_DEFAULT_STD







       def        UpperCamelCase_							(      self,							A,							A,							A = PILImageResampling.BICUBIC,							A = None,							**A,							):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						get_size_dict(A,							default_to_square=A  )
             # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
             if "shortest_edge" in size:
                   SCREAMING_SNAKE_CASE   :      str    					=						int((256 / 224) * size['shortest_edge']  )
                   SCREAMING_SNAKE_CASE   :      str    					=						get_resize_output_image_size(A,							size=A,							default_to_square=A  )
                   SCREAMING_SNAKE_CASE   :      Dict    					=						{'height': output_size[0], 'width': output_size[1]}
             if "height" not in size_dict or "width" not in size_dict:
                   raise ValueError(
                       F"Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"  )
             return resize(
                 A,							size=(size_dict['height'], size_dict['width']),							resample=A,							data_format=A,							**A  )







       def        UpperCamelCase_							(      self,							A,							A,							A = None,							**A,							):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						get_size_dict(A  )
             if "height" not in size or "width" not in size:
                   raise ValueError(F"Size dict must have keys 'height' and 'width'. Got {size.keys()}"  )
             return center_crop(A,							size=(size['height'], size['width']),							data_format=A,							**A  )







       def        UpperCamelCase_							(      self,							A,							A,							A = None,							**A,							):







             '''simple docstring'''







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







       def        UpperCamelCase_							(      self,							A,							A,							A,							A = None,							**A,							):







             '''simple docstring'''







             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,							):







             '''simple docstring'''







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

             SCREAMING_SNAKE_CASE   :      Dict    					=						size if size is not None else self.size
             SCREAMING_SNAKE_CASE   :      List[str]    					=						get_size_dict(A,							default_to_square=A  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						crop_size if crop_size is not None else self.crop_size
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						get_size_dict(A,							param_name='crop_size'  )

             SCREAMING_SNAKE_CASE   :      int    					=						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   :      Tuple    					=						[to_numpy_array(A  ) for image in images]

             if do_resize:
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						[self.resize(A,							A,							A  ) for image in images]

             if do_center_crop:
                   SCREAMING_SNAKE_CASE   :      int    					=						[self.center_crop(A,							A  ) for image in images]

             if do_rescale:
                   SCREAMING_SNAKE_CASE   :      int    					=						[self.rescale(A,							A  ) for image in images]

             if do_normalize:
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						[self.normalize(A,							A,							A  ) for image in images]

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						[to_channel_dimension_format(A,							A  ) for image in images]

             SCREAMING_SNAKE_CASE   :      Tuple    					=						{'pixel_values': images}
             return BatchFeature(data=A,							tensor_type=A  )








'''simple docstring'''



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

from packaging import version




def   lowercase__(			*__UpperCamelCase:					Union[str, Any]      ,__UpperCamelCase:					Optional[Union[Dict, Any]] = None      ,__UpperCamelCase:					Dict=True      ,__UpperCamelCase:					List[Any]=2    ):

      """simple docstring"""






      from .. import __version__

      SCREAMING_SNAKE_CASE   :      int    					=						take_from
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						()
      if not isinstance(args[0]      ,__UpperCamelCase    ):
            SCREAMING_SNAKE_CASE   :      List[str]    					=						(args,)

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

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

            if warning is not None:
                  SCREAMING_SNAKE_CASE   :      Dict    					=						warning + ' ' if standard_warn else ''
                  warnings.warn(warning + message      ,__UpperCamelCase      ,stacklevel=__UpperCamelCase    )

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

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


