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


if is_vision_available():
							import PIL


SCREAMING_SNAKE_CASE__							=				logging.get_logger(__name__)
class 	A__   (						SCREAMING_SNAKE_CASE__				):
							lowerCAmelCase__		:						Optional[int]    			=							["pixel_values"]





							def __init__( self  :   int     , _UpperCAmelCase  :   str = True     , _UpperCAmelCase  :   List[str] = None     , _UpperCAmelCase  :   Tuple = 0.9     , _UpperCAmelCase  :   Optional[Any] = PILImageResampling.BICUBIC     , _UpperCAmelCase  :   List[str] = True     , _UpperCAmelCase  :   List[str] = None     , _UpperCAmelCase  :   List[str] = 1 / 2_55     , _UpperCAmelCase  :   str = True     , _UpperCAmelCase  :   Any = True     , _UpperCAmelCase  :   Dict = None     , _UpperCAmelCase  :   Optional[int] = None     , **_UpperCAmelCase  :   Any     , )						->		Union[str, Any]:



											"""simple docstring"""


											super().__init__(**lowerCamelCase__				)
											__lowercase								=					size if size is not None else {"""shortest_edge""": 2_24}
											__lowercase								=					get_size_dict(lowerCamelCase__     , default_to_square=lowerCamelCase__				)
											__lowercase								=					crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
											__lowercase								=					get_size_dict(lowerCamelCase__     , param_name='crop_size'				)

											__lowercase								=					do_resize
											__lowercase								=					size
											__lowercase								=					crop_pct
											__lowercase								=					resample
											__lowercase								=					do_center_crop
											__lowercase								=					crop_size
											__lowercase								=					do_rescale
											__lowercase								=					rescale_factor
											__lowercase								=					do_normalize
											__lowercase								=					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
											__lowercase								=					image_std if image_std is not None else IMAGENET_DEFAULT_STD





							def 			a__ ( self  :   Tuple     , _UpperCAmelCase  :   Union[str, Any]     , _UpperCAmelCase  :   Any     , _UpperCAmelCase  :   str = None     , _UpperCAmelCase  :   int = PILImageResampling.BICUBIC     , _UpperCAmelCase  :   str = None     , **_UpperCAmelCase  :   Optional[int]     , )						->		int:



											"""simple docstring"""


											__lowercase								=					get_size_dict(lowerCamelCase__     , default_to_square=lowerCamelCase__				)
											if "shortest_edge" not in size and ("height" not in size or "width" not in size):
															raise ValueError(f"""size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}"""				)
											if crop_pct is not None:
															if "shortest_edge" in size:
																			__lowercase								=					int(size['shortest_edge'] / crop_pct				)
															elif "height" in size and "width" in size:
																			if size["height"] == size["width"]:
																							__lowercase								=					int(size['height'] / crop_pct				)
																			else:
																							__lowercase								=					(int(size['height'] / crop_pct				), int(size['width'] / crop_pct				))
															else:
																			raise ValueError('Invalid size for resize: {}'.format(lowerCamelCase__				)				)

															__lowercase								=					get_resize_output_image_size(lowerCamelCase__     , size=lowerCamelCase__     , default_to_square=lowerCamelCase__				)
											else:
															if "shortest_edge" in size:
																			__lowercase								=					get_resize_output_image_size(lowerCamelCase__     , size=size['shortest_edge']     , default_to_square=lowerCamelCase__				)
															elif "height" in size and "width" in size:
																			__lowercase								=					(size["""height"""], size["""width"""])
															else:
																			raise ValueError('Invalid size for resize: {}'.format(lowerCamelCase__				)				)

											return resize(lowerCamelCase__     , size=lowerCamelCase__     , resample=lowerCamelCase__     , data_format=lowerCamelCase__     , **lowerCamelCase__				)





							def 			a__ ( self  :   str     , _UpperCAmelCase  :   List[str]     , _UpperCAmelCase  :   str     , _UpperCAmelCase  :   List[Any] = None     , **_UpperCAmelCase  :   List[Any]     , )						->		int:



											"""simple docstring"""


											__lowercase								=					get_size_dict(lowerCamelCase__				)
											if "height" not in size or "width" not in size:
															raise ValueError(f"""size must contain \'height\' and \'width\' as keys. Got {size.keys()}"""				)
											return center_crop(lowerCamelCase__     , size=(size['height'], size['width'])     , data_format=lowerCamelCase__     , **lowerCamelCase__				)





							def 			a__ ( self  :   Any     , _UpperCAmelCase  :   List[Any]     , _UpperCAmelCase  :   Dict     , _UpperCAmelCase  :   Optional[Any] = None     , **_UpperCAmelCase  :   Tuple     , )						->		Union[str, Any]:



											"""simple docstring"""


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





							def 			a__ ( self  :   str     , _UpperCAmelCase  :   Any     , _UpperCAmelCase  :   Optional[Any]     , _UpperCAmelCase  :   Tuple     , _UpperCAmelCase  :   List[str] = None     , **_UpperCAmelCase  :   Any     , )						->		str:



											"""simple docstring"""


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





							def 			a__ ( self  :   Any     , _UpperCAmelCase  :   List[Any]     , _UpperCAmelCase  :   Optional[int] = None     , _UpperCAmelCase  :   Optional[int] = None     , _UpperCAmelCase  :   int = None     , _UpperCAmelCase  :   Any = None     , _UpperCAmelCase  :   Any = None     , _UpperCAmelCase  :   int = None     , _UpperCAmelCase  :   Dict = None     , _UpperCAmelCase  :   Dict = None     , _UpperCAmelCase  :   str = None     , _UpperCAmelCase  :   Tuple = None     , _UpperCAmelCase  :   List[Any] = None     , _UpperCAmelCase  :   List[Any] = None     , _UpperCAmelCase  :   int = ChannelDimension.FIRST     , **_UpperCAmelCase  :   Dict     , )						->		Optional[Any]:



											"""simple docstring"""


											__lowercase								=					do_resize if do_resize is not None else self.do_resize
											__lowercase								=					crop_pct if crop_pct is not None else self.crop_pct
											__lowercase								=					resample if resample is not None else self.resample
											__lowercase								=					do_center_crop if do_center_crop is not None else self.do_center_crop
											__lowercase								=					do_rescale if do_rescale is not None else self.do_rescale
											__lowercase								=					rescale_factor if rescale_factor is not None else self.rescale_factor
											__lowercase								=					do_normalize if do_normalize is not None else self.do_normalize
											__lowercase								=					image_mean if image_mean is not None else self.image_mean
											__lowercase								=					image_std if image_std is not None else self.image_std

											__lowercase								=					size if size is not None else self.size
											__lowercase								=					get_size_dict(lowerCamelCase__     , default_to_square=lowerCamelCase__				)
											__lowercase								=					crop_size if crop_size is not None else self.crop_size
											__lowercase								=					get_size_dict(lowerCamelCase__     , param_name='crop_size'				)

											__lowercase								=					make_list_of_images(lowerCamelCase__				)

											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.'				)

											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_pct is None:
															raise ValueError('Crop_pct 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.
											__lowercase								=					[to_numpy_array(lowerCamelCase__				) for image in images]

											if do_resize:
															__lowercase								=					[self.resize(image=lowerCamelCase__     , size=lowerCamelCase__     , crop_pct=lowerCamelCase__     , resample=lowerCamelCase__				) for image in images]

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

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

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

											__lowercase								=					[to_channel_dimension_format(lowerCamelCase__     , lowerCamelCase__				) for image in images]

											__lowercase								=					{"""pixel_values""": images}
											return BatchFeature(data=lowerCamelCase__     , tensor_type=lowerCamelCase__				)










from ..utils import DummyObject, requires_backends






class SCREAMING_SNAKE_CASE__     (		metaclass=SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: List[str]					=      ["torch", "transformers", "onnx"]






    def __init__(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(self,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






class SCREAMING_SNAKE_CASE__     (		metaclass=SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Dict					=      ["torch", "transformers", "onnx"]






    def __init__(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(self,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






class SCREAMING_SNAKE_CASE__     (		metaclass=SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Any					=      ["torch", "transformers", "onnx"]






    def __init__(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(self,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






class SCREAMING_SNAKE_CASE__     (		metaclass=SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Optional[int]					=      ["torch", "transformers", "onnx"]






    def __init__(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(self,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






class SCREAMING_SNAKE_CASE__     (		metaclass=SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Optional[int]					=      ["torch", "transformers", "onnx"]






    def __init__(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(self,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






class SCREAMING_SNAKE_CASE__     (		metaclass=SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Optional[int]					=      ["torch", "transformers", "onnx"]






    def __init__(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(self,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)


