



'''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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
   import PIL


_lowerCamelCase	:       List[str]      	=	logging.get_logger(__name__)







def 				__lowerCamelCase				(   A__    )       ->	List[List[ImageInput]]:






   """simple docstring"""
   if isinstance(A__    ,						(list, tuple)    ) and isinstance(videos[0]    ,						(list, tuple)    ) and is_valid_image(videos[0][0]    ):
      return videos

   elif isinstance(A__    ,						(list, tuple)    ) and is_valid_image(videos[0]    ):
      return [videos]

   elif is_valid_image(A__    ):
      return [[videos]]

   raise ValueError(F"""Could not make batched video from {videos}"""    )

class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  ["""pixel_values"""]






    def __init__(  self      :					Optional[Any]  ,						UpperCamelCase__      :					bool = True  ,						UpperCamelCase__      :					Dict[str, int] = None  ,						UpperCamelCase__      :					PILImageResampling = PILImageResampling.BILINEAR  ,						UpperCamelCase__      :					bool = True  ,						UpperCamelCase__      :					Dict[str, int] = None  ,						UpperCamelCase__      :					bool = True  ,						UpperCamelCase__      :					Union[int, float] = 1 / 2_5_5  ,						UpperCamelCase__      :					bool = True  ,						UpperCamelCase__      :					Optional[Union[float, List[float]]] = None  ,						UpperCamelCase__      :					Optional[Union[float, List[float]]] = None  ,						**UpperCamelCase__      :					List[Any]  ,						):


       """simple docstring"""







       super().__init__(**UpperCamelCase__ )
       UpperCamelCase					=  size if size is not None else {'shortest_edge': 2_2_4}
       UpperCamelCase					=  get_size_dict(UpperCamelCase__  ,						default_to_square=UpperCamelCase__ )
       UpperCamelCase					=  crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
       UpperCamelCase					=  get_size_dict(UpperCamelCase__  ,						param_name='crop_size' )

       UpperCamelCase					=  do_resize
       UpperCamelCase					=  size
       UpperCamelCase					=  do_center_crop
       UpperCamelCase					=  crop_size
       UpperCamelCase					=  resample
       UpperCamelCase					=  do_rescale
       UpperCamelCase					=  rescale_factor
       UpperCamelCase					=  do_normalize
       UpperCamelCase					=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       UpperCamelCase					=  image_std if image_std is not None else IMAGENET_STANDARD_STD






    def  A       (  self      :					Union[str, Any]  ,						UpperCamelCase__      :					np.ndarray  ,						UpperCamelCase__      :					Dict[str, int]  ,						UpperCamelCase__      :					PILImageResampling = PILImageResampling.BILINEAR  ,						UpperCamelCase__      :					Optional[Union[str, ChannelDimension]] = None  ,						**UpperCamelCase__      :					Optional[int]  ,						):


       """simple docstring"""







       UpperCamelCase					=  get_size_dict(UpperCamelCase__  ,						default_to_square=UpperCamelCase__ )
       if "shortest_edge" in size:
          UpperCamelCase					=  get_resize_output_image_size(UpperCamelCase__  ,						size['shortest_edge']  ,						default_to_square=UpperCamelCase__ )
       elif "height" in size and "width" in size:
          UpperCamelCase					=  (size['height'], size['width'])
       else:
          raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}""" )
       return resize(UpperCamelCase__  ,						size=UpperCamelCase__  ,						resample=UpperCamelCase__  ,						data_format=UpperCamelCase__  ,						**UpperCamelCase__ )






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					np.ndarray  ,						UpperCamelCase__      :					Dict[str, int]  ,						UpperCamelCase__      :					Optional[Union[str, ChannelDimension]] = None  ,						**UpperCamelCase__      :					Optional[Any]  ,						):


       """simple docstring"""







       UpperCamelCase					=  get_size_dict(UpperCamelCase__ )
       if "height" not in size or "width" not in size:
          raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}""" )
       return center_crop(UpperCamelCase__  ,						size=(size['height'], size['width'])  ,						data_format=UpperCamelCase__  ,						**UpperCamelCase__ )






    def  A       (  self      :					List[str]  ,						UpperCamelCase__      :					np.ndarray  ,						UpperCamelCase__      :					Union[int, float]  ,						UpperCamelCase__      :					Optional[Union[str, ChannelDimension]] = None  ,						**UpperCamelCase__      :					str  ,						):


       """simple docstring"""







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






    def  A       (  self      :					Any  ,						UpperCamelCase__      :					np.ndarray  ,						UpperCamelCase__      :					Union[float, List[float]]  ,						UpperCamelCase__      :					Union[float, List[float]]  ,						UpperCamelCase__      :					Optional[Union[str, ChannelDimension]] = None  ,						**UpperCamelCase__      :					Dict  ,						):


       """simple docstring"""







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






    def  A       (  self      :					Optional[Any]  ,						UpperCamelCase__      :					ImageInput  ,						UpperCamelCase__      :					bool = None  ,						UpperCamelCase__      :					Dict[str, int] = None  ,						UpperCamelCase__      :					PILImageResampling = None  ,						UpperCamelCase__      :					bool = None  ,						UpperCamelCase__      :					Dict[str, int] = None  ,						UpperCamelCase__      :					bool = None  ,						UpperCamelCase__      :					float = None  ,						UpperCamelCase__      :					bool = None  ,						UpperCamelCase__      :					Optional[Union[float, List[float]]] = None  ,						UpperCamelCase__      :					Optional[Union[float, List[float]]] = None  ,						UpperCamelCase__      :					Optional[ChannelDimension] = ChannelDimension.FIRST  ,						):


       """simple docstring"""







       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_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					=  to_numpy_array(UpperCamelCase__ )

       if do_resize:
          UpperCamelCase					=  self.resize(image=UpperCamelCase__  ,						size=UpperCamelCase__  ,						resample=UpperCamelCase__ )

       if do_center_crop:
          UpperCamelCase					=  self.center_crop(UpperCamelCase__  ,						size=UpperCamelCase__ )

       if do_rescale:
          UpperCamelCase					=  self.rescale(image=UpperCamelCase__  ,						scale=UpperCamelCase__ )

       if do_normalize:
          UpperCamelCase					=  self.normalize(image=UpperCamelCase__  ,						mean=UpperCamelCase__  ,						std=UpperCamelCase__ )

       UpperCamelCase					=  to_channel_dimension_format(UpperCamelCase__  ,						UpperCamelCase__ )
       return image






    def  A       (  self      :					List[str]  ,						UpperCamelCase__      :					ImageInput  ,						UpperCamelCase__      :					bool = None  ,						UpperCamelCase__      :					Dict[str, int] = None  ,						UpperCamelCase__      :					PILImageResampling = None  ,						UpperCamelCase__      :					bool = None  ,						UpperCamelCase__      :					Dict[str, int] = None  ,						UpperCamelCase__      :					bool = None  ,						UpperCamelCase__      :					float = None  ,						UpperCamelCase__      :					bool = None  ,						UpperCamelCase__      :					Optional[Union[float, List[float]]] = None  ,						UpperCamelCase__      :					Optional[Union[float, List[float]]] = None  ,						UpperCamelCase__      :					Optional[Union[str, TensorType]] = None  ,						UpperCamelCase__      :					ChannelDimension = ChannelDimension.FIRST  ,						**UpperCamelCase__      :					Union[str, Any]  ,						):


       """simple docstring"""







       UpperCamelCase					=  do_resize if do_resize is not None else self.do_resize
       UpperCamelCase					=  resample if resample is not None else self.resample
       UpperCamelCase					=  do_center_crop if do_center_crop is not None else self.do_center_crop
       UpperCamelCase					=  do_rescale if do_rescale is not None else self.do_rescale
       UpperCamelCase					=  rescale_factor if rescale_factor is not None else self.rescale_factor
       UpperCamelCase					=  do_normalize if do_normalize is not None else self.do_normalize
       UpperCamelCase					=  image_mean if image_mean is not None else self.image_mean
       UpperCamelCase					=  image_std if image_std is not None else self.image_std

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

       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.' )

       UpperCamelCase					=  make_batched(UpperCamelCase__ )

       UpperCamelCase					=  [
           [
               self._preprocess_image(
                   image=UpperCamelCase__  ,						do_resize=UpperCamelCase__  ,						size=UpperCamelCase__  ,						resample=UpperCamelCase__  ,						do_center_crop=UpperCamelCase__  ,						crop_size=UpperCamelCase__  ,						do_rescale=UpperCamelCase__  ,						rescale_factor=UpperCamelCase__  ,						do_normalize=UpperCamelCase__  ,						image_mean=UpperCamelCase__  ,						image_std=UpperCamelCase__  ,						data_format=UpperCamelCase__  ,						)
               for img in video
           ]
           for video in videos
       ]

       UpperCamelCase					=  {'pixel_values': videos}
       return BatchFeature(data=UpperCamelCase__  ,						tensor_type=UpperCamelCase__ )






'''simple docstring'''


from typing import TYPE_CHECKING

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


_lowerCamelCase	:       List[Any]      	=	{
    "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:
   _lowerCamelCase	:       int      	=	[
       "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

   _lowerCamelCase	:       Optional[int]      	=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

