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

lowerCamelCase_    :		Tuple							=			logging.get_logger(__name__)
def 							lowerCAmelCase(     __lowerCamelCase						):






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

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

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

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



class 						a__							(       __snake_case		):
			A__    :       Dict        =      ["pixel_values"]



			def __init__(						self				,						UpperCAmelCase = True				,						UpperCAmelCase = None				,						UpperCAmelCase = PILImageResampling.BILINEAR				,						UpperCAmelCase = True				,						UpperCAmelCase = None				,						UpperCAmelCase = True				,						UpperCAmelCase = 1 / 2_5_5				,						UpperCAmelCase = True				,						UpperCAmelCase = True				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						**UpperCAmelCase				,						)			->   None:
								super().__init__(**UpperCAmelCase   )
								__a		 =       size if size is not None else {'shortest_edge': 2_5_6}
								__a		 =       get_size_dict(UpperCAmelCase				,						default_to_square=UpperCAmelCase   )
								__a		 =       crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
								__a		 =       get_size_dict(UpperCAmelCase				,						param_name='crop_size'   )

								__a		 =       do_resize
								__a		 =       size
								__a		 =       do_center_crop
								__a		 =       crop_size
								__a		 =       resample
								__a		 =       do_rescale
								__a		 =       rescale_factor
								__a		 =       offset
								__a		 =       do_normalize
								__a		 =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								__a		 =       image_std if image_std is not None else IMAGENET_STANDARD_STD



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = PILImageResampling.BILINEAR				,						UpperCAmelCase = None				,						**UpperCAmelCase				,						)			->   np.ndarray:
								__a		 =       get_size_dict(UpperCAmelCase				,						default_to_square=UpperCAmelCase   )
								if "shortest_edge" in size:
													__a		 =       get_resize_output_image_size(UpperCAmelCase				,						size['shortest_edge']				,						default_to_square=UpperCAmelCase   )
								elif "height" in size and "width" in size:
													__a		 =       (size['height'], size['width'])
								else:
													raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''   )
								return resize(UpperCAmelCase				,						size=UpperCAmelCase				,						resample=UpperCAmelCase				,						data_format=UpperCAmelCase				,						**UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = None				,						**UpperCAmelCase				,						)			->   np.ndarray:
								__a		 =       get_size_dict(UpperCAmelCase   )
								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(UpperCAmelCase				,						size=(size['height'], size['width'])				,						data_format=UpperCAmelCase				,						**UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = True				,						UpperCAmelCase = None				,						**UpperCAmelCase				,						)			->   Union[str, Any]:
								__a		 =       image.astype(np.floataa   )
								if offset:
													__a		 =       image - (scale / 2)
								return rescale(UpperCAmelCase				,						scale=UpperCAmelCase				,						data_format=UpperCAmelCase				,						**UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = None				,						**UpperCAmelCase				,						)			->   np.ndarray:
								return normalize(UpperCAmelCase				,						mean=UpperCAmelCase				,						std=UpperCAmelCase				,						data_format=UpperCAmelCase				,						**UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = ChannelDimension.FIRST				,						)			->   np.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_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.
								__a		 =       to_numpy_array(UpperCAmelCase   )

								if do_resize:
													__a		 =       self.resize(image=UpperCAmelCase				,						size=UpperCAmelCase				,						resample=UpperCAmelCase   )

								if do_center_crop:
													__a		 =       self.center_crop(UpperCAmelCase				,						size=UpperCAmelCase   )

								if do_rescale:
													__a		 =       self.rescale(image=UpperCAmelCase				,						scale=UpperCAmelCase				,						offset=UpperCAmelCase   )

								if do_normalize:
													__a		 =       self.normalize(image=UpperCAmelCase				,						mean=UpperCAmelCase				,						std=UpperCAmelCase   )

								__a		 =       to_channel_dimension_format(UpperCAmelCase				,						UpperCAmelCase   )
								return image







			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = ChannelDimension.FIRST				,						**UpperCAmelCase				,						)			->   PIL.Image.Image:
								__a		 =       do_resize if do_resize is not None else self.do_resize
								__a		 =       resample if resample is not None else self.resample
								__a		 =       do_center_crop if do_center_crop is not None else self.do_center_crop
								__a		 =       do_rescale if do_rescale is not None else self.do_rescale
								__a		 =       rescale_factor if rescale_factor is not None else self.rescale_factor
								__a		 =       offset if offset is not None else self.offset
								__a		 =       do_normalize if do_normalize is not None else self.do_normalize
								__a		 =       image_mean if image_mean is not None else self.image_mean
								__a		 =       image_std if image_std is not None else self.image_std

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

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

								__a		 =       make_batched(UpperCAmelCase   )

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

								__a		 =       {'pixel_values': videos}
								return BatchFeature(data=UpperCAmelCase				,						tensor_type=UpperCAmelCase   )





from ..utils import DummyObject, requires_backends


class 						a__							(       metaclass=__snake_case		):
			A__    :       List[Any]        =      ['torch', 'transformers', 'onnx']



			def __init__(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Any:
								requires_backends(self				,						['torch', 'transformers', 'onnx']   )



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Union[str, Any]:
								requires_backends(cls				,						['torch', 'transformers', 'onnx']   )







			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   List[str]:
								requires_backends(cls				,						['torch', 'transformers', 'onnx']   )






class 						a__							(       metaclass=__snake_case		):
			A__    :       Union[str, Any]        =      ['torch', 'transformers', 'onnx']



			def __init__(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   int:
								requires_backends(self				,						['torch', 'transformers', 'onnx']   )



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   int:
								requires_backends(cls				,						['torch', 'transformers', 'onnx']   )







			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Dict:
								requires_backends(cls				,						['torch', 'transformers', 'onnx']   )






class 						a__							(       metaclass=__snake_case		):
			A__    :       Dict        =      ['torch', 'transformers', 'onnx']



			def __init__(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Union[str, Any]:
								requires_backends(self				,						['torch', 'transformers', 'onnx']   )



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Any:
								requires_backends(cls				,						['torch', 'transformers', 'onnx']   )







			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   int:
								requires_backends(cls				,						['torch', 'transformers', 'onnx']   )






class 						a__							(       metaclass=__snake_case		):
			A__    :       Union[str, Any]        =      ['torch', 'transformers', 'onnx']



			def __init__(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Optional[Any]:
								requires_backends(self				,						['torch', 'transformers', 'onnx']   )



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   int:
								requires_backends(cls				,						['torch', 'transformers', 'onnx']   )







			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Union[str, Any]:
								requires_backends(cls				,						['torch', 'transformers', 'onnx']   )






class 						a__							(       metaclass=__snake_case		):
			A__    :       Dict        =      ['torch', 'transformers', 'onnx']



			def __init__(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Optional[int]:
								requires_backends(self				,						['torch', 'transformers', 'onnx']   )



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Any:
								requires_backends(cls				,						['torch', 'transformers', 'onnx']   )







			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   str:
								requires_backends(cls				,						['torch', 'transformers', 'onnx']   )






class 						a__							(       metaclass=__snake_case		):
			A__    :       Tuple        =      ['torch', 'transformers', 'onnx']



			def __init__(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   int:
								requires_backends(self				,						['torch', 'transformers', 'onnx']   )



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   List[str]:
								requires_backends(cls				,						['torch', 'transformers', 'onnx']   )







			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Any:
								requires_backends(cls				,						['torch', 'transformers', 'onnx']   )




