


from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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 logging


if is_vision_available():
   import PIL

_snake_case             =     logging.get_logger(__name__)






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  if isinstance(SCREAMING_SNAKE_CASE_      ,						(list, tuple)	) and isinstance(videos[0]      ,						(list, tuple)	) and is_valid_image(videos[0][0]	):
    return videos

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

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

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







class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Any     		=		["pixel_values"]






   def __init__(		self						,				__A = True						,				__A = None						,				__A = PILImageResampling.BILINEAR						,				__A = True						,				__A = None						,				__A = True						,				__A = 1 / 255						,				__A = True						,				__A = True						,				__A = None						,				__A = None						,				**__A						,				):






     """simple docstring"""





     super().__init__(**__A )
     lowerCamelCase :					Tuple    =				size if size is not None else {"shortest_edge": 256}
     lowerCamelCase :					List[Any]    =				get_size_dict(__A						,				default_to_square=__A )
     lowerCamelCase :					List[Any]    =				crop_size if crop_size is not None else {"height": 224, "width": 224}
     lowerCamelCase :					Dict    =				get_size_dict(__A						,				param_name="crop_size" )

     lowerCamelCase :					str    =				do_resize
     lowerCamelCase :					Any    =				size
     lowerCamelCase :					List[str]    =				do_center_crop
     lowerCamelCase :					Tuple    =				crop_size
     lowerCamelCase :					Dict    =				resample
     lowerCamelCase :					Dict    =				do_rescale
     lowerCamelCase :					List[str]    =				rescale_factor
     lowerCamelCase :					Dict    =				offset
     lowerCamelCase :					Dict    =				do_normalize
     lowerCamelCase :					Tuple    =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     lowerCamelCase :					List[Any]    =				image_std if image_std is not None else IMAGENET_STANDARD_STD






   def _snake_case    (		self						,				__A						,				__A						,				__A = PILImageResampling.BILINEAR						,				__A = None						,				**__A						,				):






     """simple docstring"""





     lowerCamelCase :					Union[str, Any]    =				get_size_dict(__A						,				default_to_square=__A )
     if "shortest_edge" in size:
       lowerCamelCase :					Optional[int]    =				get_resize_output_image_size(__A						,				size["shortest_edge"]						,				default_to_square=__A )
     elif "height" in size and "width" in size:
       lowerCamelCase :					Tuple    =				(size["height"], size["width"])
     else:
       raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}""" )
     return resize(__A						,				size=__A						,				resample=__A						,				data_format=__A						,				**__A )






   def _snake_case    (		self						,				__A						,				__A						,				__A = None						,				**__A						,				):






     """simple docstring"""





     lowerCamelCase :					str    =				get_size_dict(__A )
     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(__A						,				size=(size["height"], size["width"])						,				data_format=__A						,				**__A )






   def _snake_case    (		self						,				__A						,				__A						,				__A = True						,				__A = None						,				**__A						,				):






     """simple docstring"""





     lowerCamelCase :					Tuple    =				image.astype(np.floataa )
     if offset:
       lowerCamelCase :					Optional[int]    =				image - (scale / 2)
     return rescale(__A						,				scale=__A						,				data_format=__A						,				**__A )






   def _snake_case    (		self						,				__A						,				__A						,				__A						,				__A = None						,				**__A						,				):






     """simple docstring"""





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






   def _snake_case    (		self						,				__A						,				__A = None						,				__A = None						,				__A = None						,				__A = None						,				__A = None						,				__A = None						,				__A = None						,				__A = None						,				__A = None						,				__A = None						,				__A = None						,				__A = 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." )

     if offset and not do_rescale:
       raise ValueError("For offset, do_rescale must also be set to True." )

     # All transformations expect numpy arrays.
     lowerCamelCase :					Optional[Any]    =				to_numpy_array(__A )

     if do_resize:
       lowerCamelCase :					str    =				self.resize(image=__A						,				size=__A						,				resample=__A )

     if do_center_crop:
       lowerCamelCase :					str    =				self.center_crop(__A						,				size=__A )

     if do_rescale:
       lowerCamelCase :					Optional[int]    =				self.rescale(image=__A						,				scale=__A						,				offset=__A )

     if do_normalize:
       lowerCamelCase :					Dict    =				self.normalize(image=__A						,				mean=__A						,				std=__A )

     lowerCamelCase :					List[str]    =				to_channel_dimension_format(__A						,				__A )
     return image






   def _snake_case    (		self						,				__A						,				__A = None						,				__A = None						,				__A = None						,				__A = None						,				__A = None						,				__A = None						,				__A = None						,				__A = None						,				__A = None						,				__A = None						,				__A = None						,				__A = None						,				__A = ChannelDimension.FIRST						,				**__A						,				):






     """simple docstring"""





     lowerCamelCase :					str    =				do_resize if do_resize is not None else self.do_resize
     lowerCamelCase :					Optional[int]    =				resample if resample is not None else self.resample
     lowerCamelCase :					Optional[int]    =				do_center_crop if do_center_crop is not None else self.do_center_crop
     lowerCamelCase :					Union[str, Any]    =				do_rescale if do_rescale is not None else self.do_rescale
     lowerCamelCase :					str    =				rescale_factor if rescale_factor is not None else self.rescale_factor
     lowerCamelCase :					List[Any]    =				offset if offset is not None else self.offset
     lowerCamelCase :					List[Any]    =				do_normalize if do_normalize is not None else self.do_normalize
     lowerCamelCase :					int    =				image_mean if image_mean is not None else self.image_mean
     lowerCamelCase :					Tuple    =				image_std if image_std is not None else self.image_std

     lowerCamelCase :					List[str]    =				size if size is not None else self.size
     lowerCamelCase :					Tuple    =				get_size_dict(__A						,				default_to_square=__A )
     lowerCamelCase :					Tuple    =				crop_size if crop_size is not None else self.crop_size
     lowerCamelCase :					Tuple    =				get_size_dict(__A						,				param_name="crop_size" )

     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." )

     lowerCamelCase :					Union[str, Any]    =				make_batched(__A )

     lowerCamelCase :					Optional[int]    =				[
         [
             self._preprocess_image(
                 image=__A						,				do_resize=__A						,				size=__A						,				resample=__A						,				do_center_crop=__A						,				crop_size=__A						,				do_rescale=__A						,				rescale_factor=__A						,				offset=__A						,				do_normalize=__A						,				image_mean=__A						,				image_std=__A						,				data_format=__A						,				)
             for img in video
         ]
         for video in videos
     ]

     lowerCamelCase :					int    =				{"pixel_values": videos}
     return BatchFeature(data=__A						,				tensor_type=__A )







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_snake_case             =     {
    '''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''VisionEncoderDecoderOnnxConfig''']
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case             =     ['''VisionEncoderDecoderModel''']

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case             =     ['''TFVisionEncoderDecoderModel''']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case             =     ['''FlaxVisionEncoderDecoderModel''']

if TYPE_CHECKING:
   from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
   import sys

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



