




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_STANDARD_MEAN,
    IMAGENET_STANDARD_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 a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						UpperCamelCase_	:       Dict       	=		['pixel_values']






						def __init__( self :     int				,							lowerCamelCase__ :     bool = True				,							lowerCamelCase__ :     Dict[str, int] = None				,							lowerCamelCase__ :     float = None				,							lowerCamelCase__ :     PILImageResampling = PILImageResampling.BILINEAR				,							lowerCamelCase__ :     bool = True				,							lowerCamelCase__ :     Union[int, float] = 1 / 255				,							lowerCamelCase__ :     bool = True				,							lowerCamelCase__ :     Optional[Union[float, List[float]]] = None				,							lowerCamelCase__ :     Optional[Union[float, List[float]]] = None				,							**lowerCamelCase__ :     List[Any]				,							)    ->						None:
													"""simple docstring"""


													super().__init__(**lowerCamelCase__				)
													__lowercase      =    size if size is not None else {'''shortest_edge''': 384}
													__lowercase      =    get_size_dict(lowerCamelCase__				,							default_to_square=lowerCamelCase__				)

													__lowercase      =    do_resize
													__lowercase      =    size
													# Default value set here for backwards compatibility where the value in config is None
													__lowercase      =    crop_pct if crop_pct is not None else 224 / 256
													__lowercase      =    resample
													__lowercase      =    do_rescale
													__lowercase      =    rescale_factor
													__lowercase      =    do_normalize
													__lowercase      =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													__lowercase      =    image_std if image_std is not None else IMAGENET_STANDARD_STD






						def     UpperCAmelCase_      ( self :     Dict				,							lowerCamelCase__ :     np.ndarray				,							lowerCamelCase__ :     Dict[str, int]				,							lowerCamelCase__ :     float				,							lowerCamelCase__ :     PILImageResampling = PILImageResampling.BICUBIC				,							lowerCamelCase__ :     Optional[Union[str, ChannelDimension]] = None				,							**lowerCamelCase__ :     str				,							)    ->						np.ndarray:
													"""simple docstring"""


													__lowercase      =    get_size_dict(lowerCamelCase__				,							default_to_square=lowerCamelCase__				)
													if "shortest_edge" not in size:
																				raise ValueError(f'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'				)
													__lowercase      =    size['''shortest_edge''']

													if shortest_edge < 384:
																				# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
																				__lowercase      =    int(shortest_edge / crop_pct				)
																				__lowercase      =    get_resize_output_image_size(lowerCamelCase__				,							size=lowerCamelCase__				,							default_to_square=lowerCamelCase__				)
																				__lowercase      =    resize(image=lowerCamelCase__				,							size=lowerCamelCase__				,							resample=lowerCamelCase__				,							data_format=lowerCamelCase__				,							**lowerCamelCase__				)
																				# then crop to (shortest_edge, shortest_edge)
																				return center_crop(image=lowerCamelCase__				,							size=(shortest_edge, shortest_edge)				,							data_format=lowerCamelCase__				,							**lowerCamelCase__				)
													else:
																				# warping (no cropping) when evaluated at 384 or larger
																				return resize(
																				    lowerCamelCase__				,							size=(shortest_edge, shortest_edge)				,							resample=lowerCamelCase__				,							data_format=lowerCamelCase__				,							**lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     Tuple				,							lowerCamelCase__ :     np.ndarray				,							lowerCamelCase__ :     Union[int, float]				,							lowerCamelCase__ :     Optional[Union[str, ChannelDimension]] = None				,							**lowerCamelCase__ :     Any				,							)    ->						List[str]:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     str				,							lowerCamelCase__ :     np.ndarray				,							lowerCamelCase__ :     Union[float, List[float]]				,							lowerCamelCase__ :     Union[float, List[float]]				,							lowerCamelCase__ :     Optional[Union[str, ChannelDimension]] = None				,							**lowerCamelCase__ :     Optional[int]				,							)    ->						np.ndarray:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     Any				,							lowerCamelCase__ :     ImageInput				,							lowerCamelCase__ :     bool = None				,							lowerCamelCase__ :     Dict[str, int] = None				,							lowerCamelCase__ :     float = None				,							lowerCamelCase__ :     PILImageResampling = None				,							lowerCamelCase__ :     bool = None				,							lowerCamelCase__ :     float = None				,							lowerCamelCase__ :     bool = None				,							lowerCamelCase__ :     Optional[Union[float, List[float]]] = None				,							lowerCamelCase__ :     Optional[Union[float, List[float]]] = None				,							lowerCamelCase__ :     Optional[Union[str, TensorType]] = None				,							lowerCamelCase__ :     ChannelDimension = ChannelDimension.FIRST				,							**lowerCamelCase__ :     List[Any]				,							)    ->						PIL.Image.Image:
													"""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_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      =    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_resize and size["shortest_edge"] < 384 and crop_pct is None:
																				raise ValueError('''crop_pct must be specified if size < 384.'''				)

													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_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 typing import TYPE_CHECKING

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


UpperCAmelCase__				     = {
    "configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "M2M100OnnxConfig"],
    "tokenization_m2m_100": ["M2M100Tokenizer"],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__				     = [
			    "M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "M2M100ForConditionalGeneration",
			    "M2M100Model",
			    "M2M100PreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
			from .tokenization_mam_aaa import MaMaaaTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mam_aaa import (
						    M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
						    MaMaaaForConditionalGeneration,
						    MaMaaaModel,
						    MaMaaaPreTrainedModel,
						)


else:
			import sys

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






