

'''simple docstring'''
from ..utils import DummyObject, requires_backends


class    a__  (							metaclass=lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE    :     List[Any]	       =							['transformers', 'torch', 'note_seq']


   def __init__( self			,				*_UpperCamelCase			,				**_UpperCamelCase  ):





      """simple docstring"""





      requires_backends(self			,				["transformers", "torch", "note_seq"]  )


   @classmethod
   def 						_lowerCamelCase   ( cls			,				*_UpperCamelCase			,				**_UpperCamelCase  ):





      """simple docstring"""





      requires_backends(cls			,				["transformers", "torch", "note_seq"]  )






   @classmethod
   def 						_lowerCamelCase   ( cls			,				*_UpperCamelCase			,				**_UpperCamelCase  ):





      """simple docstring"""





      requires_backends(cls			,				["transformers", "torch", "note_seq"]  )












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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCamelCase				:		Optional[Any]     =   logging.get_logger(__name__)


if is_vision_available():
 import PIL







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 UpperCamelCase						   =						['''pixel_values''']

 def __init__(				self					:  List[Any]						,							A_					:  bool = True						,							A_					:  Dict[str, int] = None						,							A_					:  PILImageResampling = PILImageResampling.BICUBIC						,							A_					:  bool = True						,							A_					:  Dict[str, int] = None						,							A_					:  bool = True						,							A_					:  Union[int, float] = 1 / 255						,							A_					:  bool = True						,							A_					:  Optional[Union[float, List[float]]] = None						,							A_					:  Optional[Union[float, List[float]]] = None						,							A_					:  bool = True						,							**A_					:  Dict						,							)   ->	None:

       """simple docstring"""






       super().__init__(**A_  )
       lowerCamelCase_		     =    size if size is not None else {'shortest_edge': 224}
       lowerCamelCase_		     =    get_size_dict(A_						,							default_to_square=A_  )
       lowerCamelCase_		     =    crop_size if crop_size is not None else {'height': 224, 'width': 224}
       lowerCamelCase_		     =    get_size_dict(A_						,							default_to_square=A_						,							param_name='crop_size'  )

       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 if image_mean is not None else OPENAI_CLIP_MEAN
       lowerCamelCase_		     =    image_std if image_std is not None else OPENAI_CLIP_STD
       lowerCamelCase_		     =    do_convert_rgb

 def 				a__ (				self					:  Dict						,							A_					:  np.ndarray						,							A_					:  Dict[str, int]						,							A_					:  PILImageResampling = PILImageResampling.BICUBIC						,							A_					:  Optional[Union[str, ChannelDimension]] = None						,							**A_					:  Union[str, Any]						,							)   ->	np.ndarray:

       """simple docstring"""






       lowerCamelCase_		     =    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()}"""  )
       lowerCamelCase_		     =    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 				a__ (				self					:  Tuple						,							A_					:  np.ndarray						,							A_					:  Dict[str, int]						,							A_					:  Optional[Union[str, ChannelDimension]] = None						,							**A_					:  Dict						,							)   ->	np.ndarray:

       """simple docstring"""






       lowerCamelCase_		     =    get_size_dict(A_  )
       if "height" not in size or "width" not in size:
             raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""  )
       return center_crop(A_						,							size=(size['height'], size['width'])						,							data_format=A_						,							**A_  )

 def 				a__ (				self					:  str						,							A_					:  np.ndarray						,							A_					:  Union[int, float]						,							A_					:  Optional[Union[str, ChannelDimension]] = None						,							**A_					:  Union[str, Any]						,							)   ->	str:

       """simple docstring"""






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

 def 				a__ (				self					:  Optional[Any]						,							A_					:  np.ndarray						,							A_					:  Union[float, List[float]]						,							A_					:  Union[float, List[float]]						,							A_					:  Optional[Union[str, ChannelDimension]] = None						,							**A_					:  str						,							)   ->	np.ndarray:

       """simple docstring"""






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





 def 				a__ (				self					:  Any						,							A_					:  ImageInput						,							A_					:  bool = None						,							A_					:  Dict[str, int] = None						,							A_					:  PILImageResampling = None						,							A_					:  bool = None						,							A_					:  int = None						,							A_					:  bool = None						,							A_					:  float = None						,							A_					:  bool = None						,							A_					:  Optional[Union[float, List[float]]] = None						,							A_					:  Optional[Union[float, List[float]]] = None						,							A_					:  bool = None						,							A_					:  Optional[Union[str, TensorType]] = None						,							A_					:  Optional[ChannelDimension] = ChannelDimension.FIRST						,							**A_					:  Dict						,							)   ->	PIL.Image.Image:

       """simple docstring"""






       lowerCamelCase_		     =    do_resize if do_resize is not None else self.do_resize
       lowerCamelCase_		     =    size if size is not None else self.size
       lowerCamelCase_		     =    get_size_dict(A_						,							param_name='size'						,							default_to_square=A_  )
       lowerCamelCase_		     =    resample if resample is not None else self.resample
       lowerCamelCase_		     =    do_center_crop if do_center_crop is not None else self.do_center_crop
       lowerCamelCase_		     =    crop_size if crop_size is not None else self.crop_size
       lowerCamelCase_		     =    get_size_dict(A_						,							param_name='crop_size'						,							default_to_square=A_  )
       lowerCamelCase_		     =    do_rescale if do_rescale is not None else self.do_rescale
       lowerCamelCase_		     =    rescale_factor if rescale_factor is not None else self.rescale_factor
       lowerCamelCase_		     =    do_normalize if do_normalize is not None else self.do_normalize
       lowerCamelCase_		     =    image_mean if image_mean is not None else self.image_mean
       lowerCamelCase_		     =    image_std if image_std is not None else self.image_std
       lowerCamelCase_		     =    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       lowerCamelCase_		     =    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.'  )

       # PIL RGBA images are converted to RGB
       if do_convert_rgb:
             lowerCamelCase_		     =    [convert_to_rgb(A_  ) for image in images]

       # All transformations expect numpy arrays.
       lowerCamelCase_		     =    [to_numpy_array(A_  ) for image in images]

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

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

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

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

       lowerCamelCase_		     =    [to_channel_dimension_format(A_						,							A_  ) for image in images]

       lowerCamelCase_		     =    {'pixel_values': images}
       return BatchFeature(data=A_						,							tensor_type=A_  )



