
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							:str										=    logging.get_logger(__name__)





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					["pixel_values"]






	def __init__(	self :			Optional[Any]							,A :			bool = True							,A :			Dict[str, int] = None							,A :			int = 0.9							,A :			PILImageResampling = PILImageResampling.BICUBIC							,A :			bool = True							,A :			Dict[str, int] = None							,A :			Union[int, float] = 1 / 2_55							,A :			bool = True							,A :			bool = True							,A :			Optional[Union[float, List[float]]] = None							,A :			Optional[Union[float, List[float]]] = None							,**A :			List[Any]							,):
					super().__init__(**A			)
					__A             =			size if size is not None else {"shortest_edge": 2_24}
					__A             =			get_size_dict(A							,default_to_square=A			)
					__A             =			crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
					__A             =			get_size_dict(A							,param_name="crop_size"			)

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






	def     UpperCamelCase_  (	self :			Dict							,A :			np.ndarray							,A :			Dict[str, int]							,A :			Optional[float] = None							,A :			PILImageResampling = PILImageResampling.BICUBIC							,A :			Optional[Union[str, ChannelDimension]] = None							,**A :			int							,):
					__A             =			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:
													__A             =			int(size["shortest_edge"] / crop_pct			)
									elif "height" in size and "width" in size:
													if size["height"] == size["width"]:
																	__A             =			int(size["height"] / crop_pct			)
													else:
																	__A             =			(int(size["height"] / crop_pct			), int(size["width"] / crop_pct			))
									else:
													raise ValueError("Invalid size for resize: {}".format(A			)			)

									__A             =			get_resize_output_image_size(A							,size=A							,default_to_square=A			)
					else:
									if "shortest_edge" in size:
													__A             =			get_resize_output_image_size(A							,size=size["shortest_edge"]							,default_to_square=A			)
									elif "height" in size and "width" in size:
													__A             =			(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     UpperCamelCase_  (	self :			Any							,A :			np.ndarray							,A :			Dict[str, int]							,A :			Optional[Union[str, ChannelDimension]] = None							,**A :			Union[str, Any]							,):
					__A             =			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     UpperCamelCase_  (	self :			int							,A :			np.ndarray							,A :			Union[int, float]							,A :			Optional[Union[str, ChannelDimension]] = None							,**A :			List[Any]							,):
					return rescale(A							,scale=A							,data_format=A							,**A			)






	def     UpperCamelCase_  (	self :			Tuple							,A :			np.ndarray							,A :			Union[float, List[float]]							,A :			Union[float, List[float]]							,A :			Optional[Union[str, ChannelDimension]] = None							,**A :			int							,):
					return normalize(A							,mean=A							,std=A							,data_format=A							,**A			)







	def     UpperCamelCase_  (	self :			int							,A :			ImageInput							,A :			bool = None							,A :			Dict[str, int] = None							,A :			int = None							,A :			PILImageResampling = None							,A :			bool = None							,A :			Dict[str, 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 :			Optional[Union[str, TensorType]] = None							,A :			ChannelDimension = ChannelDimension.FIRST							,**A :			Dict							,):
					__A             =			do_resize if do_resize is not None else self.do_resize
					__A             =			crop_pct if crop_pct is not None else self.crop_pct
					__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             =			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(A							,default_to_square=A			)
					__A             =			crop_size if crop_size is not None else self.crop_size
					__A             =			get_size_dict(A							,param_name="crop_size"			)

					__A             =			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.
					__A             =			[to_numpy_array(A			) for image in images]

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

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

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

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

					__A             =			[to_channel_dimension_format(A							,A			) for image in images]

					__A             =			{"pixel_values": images}
					return BatchFeature(data=A							,tensor_type=A			)


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE							:Any										=    {
    'configuration_rag': ['RagConfig'],
    'retrieval_rag': ['RagRetriever'],
    'tokenization_rag': ['RagTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:List[Any]										=    [
					    'RagModel',
					    'RagPreTrainedModel',
					    'RagSequenceForGeneration',
					    'RagTokenForGeneration',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Any										=    [
					    'TFRagModel',
					    'TFRagPreTrainedModel',
					    'TFRagSequenceForGeneration',
					    'TFRagTokenForGeneration',
					]


if TYPE_CHECKING:
					from .configuration_rag import RagConfig
					from .retrieval_rag import RagRetriever
					from .tokenization_rag import RagTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_rag import (
										    TFRagModel,
										    TFRagPreTrainedModel,
										    TFRagSequenceForGeneration,
										    TFRagTokenForGeneration,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE							:int										=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
