
'''simple docstring'''







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


_snake_case				:   Any     =				logging.get_logger(__name__)


if is_vision_available():
						import PIL


class 			lowerCAmelCase (				__UpperCAmelCase			):
		a							:					Any       =					["""pixel_values"""]




		def __init__(				self					,						UpperCamelCase = True					,						UpperCamelCase = None					,						UpperCamelCase = PILImageResampling.BICUBIC					,						UpperCamelCase = True					,						UpperCamelCase = None					,						UpperCamelCase = True					,						UpperCamelCase = 1 / 255					,						UpperCamelCase = True					,						UpperCamelCase = None					,						UpperCamelCase = None					,						UpperCamelCase = True					,						**UpperCamelCase					,						):
								super().__init__(**UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= size if size is not None else {"shortest_edge": 224}
								_SCREAMING_SNAKE_CASE				= get_size_dict(UpperCamelCase					,						default_to_square=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= crop_size if crop_size is not None else {"height": 224, "width": 224}
								_SCREAMING_SNAKE_CASE				= get_size_dict(UpperCamelCase					,						default_to_square=UpperCamelCase					,						param_name="crop_size"      )

								_SCREAMING_SNAKE_CASE				= do_resize
								_SCREAMING_SNAKE_CASE				= size
								_SCREAMING_SNAKE_CASE				= resample
								_SCREAMING_SNAKE_CASE				= do_center_crop
								_SCREAMING_SNAKE_CASE				= crop_size
								_SCREAMING_SNAKE_CASE				= do_rescale
								_SCREAMING_SNAKE_CASE				= rescale_factor
								_SCREAMING_SNAKE_CASE				= do_normalize
								_SCREAMING_SNAKE_CASE				= image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								_SCREAMING_SNAKE_CASE				= image_std if image_std is not None else OPENAI_CLIP_STD
								_SCREAMING_SNAKE_CASE				= do_convert_rgb




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase = PILImageResampling.BICUBIC					,						UpperCamelCase = None					,						**UpperCamelCase					,						):
								_SCREAMING_SNAKE_CASE				= get_size_dict(UpperCamelCase					,						default_to_square=UpperCamelCase      )
								if "shortest_edge" not in size:
														raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'      )
								_SCREAMING_SNAKE_CASE				= get_resize_output_image_size(UpperCamelCase					,						size=size["shortest_edge"]					,						default_to_square=UpperCamelCase      )
								return resize(UpperCamelCase					,						size=UpperCamelCase					,						resample=UpperCamelCase					,						data_format=UpperCamelCase					,						**UpperCamelCase      )




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase = None					,						**UpperCamelCase					,						):
								_SCREAMING_SNAKE_CASE				= get_size_dict(UpperCamelCase      )
								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(UpperCamelCase					,						size=(size["height"], size["width"])					,						data_format=UpperCamelCase					,						**UpperCamelCase      )




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase = None					,						**UpperCamelCase					,						):
								return rescale(UpperCamelCase					,						scale=UpperCamelCase					,						data_format=UpperCamelCase					,						**UpperCamelCase      )




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase = None					,						**UpperCamelCase					,						):
								return normalize(UpperCamelCase					,						mean=UpperCamelCase					,						std=UpperCamelCase					,						data_format=UpperCamelCase					,						**UpperCamelCase      )




		def     lowercase       (				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					,						):
								_SCREAMING_SNAKE_CASE				= do_resize if do_resize is not None else self.do_resize
								_SCREAMING_SNAKE_CASE				= size if size is not None else self.size
								_SCREAMING_SNAKE_CASE				= get_size_dict(UpperCamelCase					,						param_name="size"					,						default_to_square=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= resample if resample is not None else self.resample
								_SCREAMING_SNAKE_CASE				= do_center_crop if do_center_crop is not None else self.do_center_crop
								_SCREAMING_SNAKE_CASE				= crop_size if crop_size is not None else self.crop_size
								_SCREAMING_SNAKE_CASE				= get_size_dict(UpperCamelCase					,						param_name="crop_size"					,						default_to_square=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= do_rescale if do_rescale is not None else self.do_rescale
								_SCREAMING_SNAKE_CASE				= rescale_factor if rescale_factor is not None else self.rescale_factor
								_SCREAMING_SNAKE_CASE				= do_normalize if do_normalize is not None else self.do_normalize
								_SCREAMING_SNAKE_CASE				= image_mean if image_mean is not None else self.image_mean
								_SCREAMING_SNAKE_CASE				= image_std if image_std is not None else self.image_std
								_SCREAMING_SNAKE_CASE				= do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								_SCREAMING_SNAKE_CASE				= make_list_of_images(UpperCamelCase      )

								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."      )

								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:
														_SCREAMING_SNAKE_CASE				= [convert_to_rgb(UpperCamelCase      ) for image in images]

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

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

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

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

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

								_SCREAMING_SNAKE_CASE				= [to_channel_dimension_format(UpperCamelCase					,						UpperCamelCase      ) for image in images]

								_SCREAMING_SNAKE_CASE				= {"pixel_values": images}
								return BatchFeature(data=UpperCamelCase					,						tensor_type=UpperCamelCase      )

'''simple docstring'''







from typing import TYPE_CHECKING

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


_snake_case				:   Optional[Any]     =				{
    """configuration_rag""": ["""RagConfig"""],
    """retrieval_rag""": ["""RagRetriever"""],
    """tokenization_rag""": ["""RagTokenizer"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   int     =				[
						    """RagModel""",
						    """RagPreTrainedModel""",
						    """RagSequenceForGeneration""",
						    """RagTokenForGeneration""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   Dict     =				[
						    """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

						_snake_case				:   str     =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)