


'''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,
    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, logging


__A		=logging.get_logger(__name__)


class 						_snake_case   (       a__		):
					lowerCAmelCase :Optional[Any]						=   ['''pixel_values''']




					def __init__( self							,							_lowerCamelCase = True							,							_lowerCamelCase = None							,							_lowerCamelCase = PILImageResampling.BILINEAR							,							_lowerCamelCase = True							,							_lowerCamelCase = None							,							_lowerCamelCase = True							,							_lowerCamelCase = 1 / 255							,							_lowerCamelCase = True							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								super().__init__(**_lowerCamelCase)
								UpperCAmelCase__					:   Any       				=       size if size is not None else {"""shortest_edge""": 256}
								UpperCAmelCase__					:   Union[str, Any]       				=       get_size_dict(_lowerCamelCase							,							default_to_square=_lowerCamelCase)
								UpperCAmelCase__					:   Union[str, Any]       				=       crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
								UpperCAmelCase__					:   Union[str, Any]       				=       get_size_dict(_lowerCamelCase)
								UpperCAmelCase__					:   Union[str, Any]       				=       do_resize
								UpperCAmelCase__					:   List[Any]       				=       size
								UpperCAmelCase__					:   Optional[Any]       				=       resample
								UpperCAmelCase__					:   List[str]       				=       do_center_crop
								UpperCAmelCase__					:   Tuple       				=       crop_size
								UpperCAmelCase__					:   Optional[Any]       				=       do_rescale
								UpperCAmelCase__					:   Any       				=       rescale_factor
								UpperCAmelCase__					:   List[Any]       				=       do_normalize
								UpperCAmelCase__					:   List[str]       				=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								UpperCAmelCase__					:   List[str]       				=       image_std if image_std is not None else IMAGENET_STANDARD_STD




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase = PILImageResampling.BICUBIC							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								UpperCAmelCase__					:   Tuple       				=       get_size_dict(_lowerCamelCase							,							default_to_square=_lowerCamelCase)
								if "shortest_edge" not in size:
											raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''')
								UpperCAmelCase__					:   Union[str, Any]       				=       get_resize_output_image_size(_lowerCamelCase							,							size=size["""shortest_edge"""]							,							default_to_square=_lowerCamelCase)
								return resize(_lowerCamelCase							,							size=_lowerCamelCase							,							resample=_lowerCamelCase							,							data_format=_lowerCamelCase							,							**_lowerCamelCase)




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								UpperCAmelCase__					:   List[str]       				=       get_size_dict(_lowerCamelCase)
								return center_crop(_lowerCamelCase							,							size=(size["""height"""], size["""width"""])							,							data_format=_lowerCamelCase							,							**_lowerCamelCase)




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase = None							,							**_lowerCamelCase):
								return rescale(_lowerCamelCase							,							scale=_lowerCamelCase							,							data_format=_lowerCamelCase							,							**_lowerCamelCase)




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								return normalize(_lowerCamelCase							,							mean=_lowerCamelCase							,							std=_lowerCamelCase							,							data_format=_lowerCamelCase							,							**_lowerCamelCase)




					def 			snake_case__  ( 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 = ChannelDimension.FIRST							,							**_lowerCamelCase							,							):
								UpperCAmelCase__					:   Optional[Any]       				=       do_resize if do_resize is not None else self.do_resize
								UpperCAmelCase__					:   Optional[Any]       				=       size if size is not None else self.size
								UpperCAmelCase__					:   Any       				=       get_size_dict(_lowerCamelCase							,							default_to_square=_lowerCamelCase)
								UpperCAmelCase__					:   Tuple       				=       resample if resample is not None else self.resample
								UpperCAmelCase__					:   Any       				=       do_center_crop if do_center_crop is not None else self.do_center_crop
								UpperCAmelCase__					:   List[Any]       				=       crop_size if crop_size is not None else self.crop_size
								UpperCAmelCase__					:   List[Any]       				=       get_size_dict(_lowerCamelCase)
								UpperCAmelCase__					:   str       				=       do_rescale if do_rescale is not None else self.do_rescale
								UpperCAmelCase__					:   List[str]       				=       rescale_factor if rescale_factor is not None else self.rescale_factor
								UpperCAmelCase__					:   int       				=       do_normalize if do_normalize is not None else self.do_normalize
								UpperCAmelCase__					:   str       				=       image_mean if image_mean is not None else self.image_mean
								UpperCAmelCase__					:   Dict       				=       image_std if image_std is not None else self.image_std

								UpperCAmelCase__					:   str       				=       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:
											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.""")

								# All transformations expect numpy arrays.
								UpperCAmelCase__					:   List[str]       				=       [to_numpy_array(_lowerCamelCase) for image in images]

								if do_resize:
											UpperCAmelCase__					:   Union[str, Any]       				=       [self.resize(image=_lowerCamelCase							,							size=_lowerCamelCase							,							resample=_lowerCamelCase) for image in images]

								if do_center_crop:
											UpperCAmelCase__					:   Union[str, Any]       				=       [self.center_crop(image=_lowerCamelCase							,							size=_lowerCamelCase) for image in images]

								if do_rescale:
											UpperCAmelCase__					:   List[Any]       				=       [self.rescale(image=_lowerCamelCase							,							scale=_lowerCamelCase) for image in images]

								if do_normalize:
											UpperCAmelCase__					:   List[str]       				=       [self.normalize(image=_lowerCamelCase							,							mean=_lowerCamelCase							,							std=_lowerCamelCase) for image in images]

								UpperCAmelCase__					:   int       				=       [to_channel_dimension_format(_lowerCamelCase							,							_lowerCamelCase) for image in images]

								UpperCAmelCase__					:   Optional[int]       				=       {"""pixel_values""": images}
								return BatchFeature(data=_lowerCamelCase							,							tensor_type=_lowerCamelCase)



'''simple docstring'''



from typing import TYPE_CHECKING

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


__A		={
    '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:
		__A		=[
		    '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

		__A		=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)