





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


UpperCAmelCase_      							=     logging.get_logger(__name__)
class 			UpperCamelCase_		(  __lowercase					):
					lowerCAmelCase_							=	['''pixel_values''']




					def __init__(		self				,						lowerCAmelCase_ = True				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = 0.9				,						lowerCAmelCase_ = PILImageResampling.BICUBIC				,						lowerCAmelCase_ = True				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = 1 / 255				,						lowerCAmelCase_ = True				,						lowerCAmelCase_ = True				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						**lowerCAmelCase_				,						)		->							Union[str, Any]:
										super().__init__(**_A  )
										_snake_case									=				size if size is not None else {'shortest_edge': 224}
										_snake_case									=				get_size_dict(_A				,						default_to_square=_A  )
										_snake_case									=				crop_size if crop_size is not None else {'height': 224, 'width': 224}
										_snake_case									=				get_size_dict(_A				,						param_name='crop_size'  )

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




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = PILImageResampling.BICUBIC				,						lowerCAmelCase_ = None				,						**lowerCAmelCase_				,						)		->							Any:
										_snake_case									=				get_size_dict(_A				,						default_to_square=_A  )
										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:
																				_snake_case									=				int(size['shortest_edge'] / crop_pct  )
															elif "height" in size and "width" in size:
																				if size["height"] == size["width"]:
																									_snake_case									=				int(size['height'] / crop_pct  )
																				else:
																									_snake_case									=				(int(size['height'] / crop_pct  ), int(size['width'] / crop_pct  ))
															else:
																				raise ValueError('Invalid size for resize: {}'.format(_A  )  )

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

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




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_ = None				,						**lowerCAmelCase_				,						)		->							List[str]:
										_snake_case									=				get_size_dict(_A  )
										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(_A				,						size=(size['height'], size['width'])				,						data_format=_A				,						**_A  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_ = None				,						**lowerCAmelCase_				,						)		->							Tuple:
										return rescale(_A				,						scale=_A				,						data_format=_A				,						**_A  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_ = None				,						**lowerCAmelCase_				,						)		->							Union[str, Any]:
										return normalize(_A				,						mean=_A				,						std=_A				,						data_format=_A				,						**_A  )






					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = ChannelDimension.FIRST				,						**lowerCAmelCase_				,						)		->							int:
										_snake_case									=				do_resize if do_resize is not None else self.do_resize
										_snake_case									=				crop_pct if crop_pct is not None else self.crop_pct
										_snake_case									=				resample if resample is not None else self.resample
										_snake_case									=				do_center_crop if do_center_crop is not None else self.do_center_crop
										_snake_case									=				do_rescale if do_rescale is not None else self.do_rescale
										_snake_case									=				rescale_factor if rescale_factor is not None else self.rescale_factor
										_snake_case									=				do_normalize if do_normalize is not None else self.do_normalize
										_snake_case									=				image_mean if image_mean is not None else self.image_mean
										_snake_case									=				image_std if image_std is not None else self.image_std

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

										_snake_case									=				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 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.
										_snake_case									=				[to_numpy_array(_A  ) for image in images]

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

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

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

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

										_snake_case									=				[to_channel_dimension_format(_A				,						_A  ) for image in images]

										_snake_case									=				{'pixel_values': images}
										return BatchFeature(data=_A				,						tensor_type=_A  )










from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCAmelCase_      							=     {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}


if TYPE_CHECKING:
					from .tokenization_byta import ByTaTokenizer
else:
					import sys

					UpperCAmelCase_      							=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



