




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


UpperCamelCase								=	logging.get_logger(__name__)



def 				_A  (		lowerCAmelCase_	:   Optional[Any]     ):
     """simple docstring"""

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

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

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

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






class 					__lowerCamelCase			(       UpperCamelCase__       ):




    """simple docstring"""

    snake_case__   = ["pixel_values"]


    def __init__(			self	:	Any  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	Dict[str, int] = None  ,      SCREAMING_SNAKE_CASE__	:	PILImageResampling = PILImageResampling.BILINEAR  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	Dict[str, int] = None  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	Union[int, float] = 1 / 255  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[float, List[float]]] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[float, List[float]]] = None  ,      **SCREAMING_SNAKE_CASE__	:	Optional[Any]  ,      ) ->			None:
         super().__init__(**SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					size if size is not None else {"shortest_edge": 224}
         lowerCAmelCase__            =					get_size_dict(SCREAMING_SNAKE_CASE__  ,      default_to_square=SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					crop_size if crop_size is not None else {"height": 224, "width": 224}
         lowerCAmelCase__            =					get_size_dict(SCREAMING_SNAKE_CASE__  ,      param_name="crop_size"  )

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


    def   a (			self	:	Optional[Any]  ,      SCREAMING_SNAKE_CASE__	:	np.ndarray  ,      SCREAMING_SNAKE_CASE__	:	Dict[str, int]  ,      SCREAMING_SNAKE_CASE__	:	PILImageResampling = PILImageResampling.BILINEAR  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[str, ChannelDimension]] = None  ,      **SCREAMING_SNAKE_CASE__	:	int  ,      ) ->			np.ndarray:
         lowerCAmelCase__            =					get_size_dict(SCREAMING_SNAKE_CASE__  ,      default_to_square=SCREAMING_SNAKE_CASE__  )
         if "shortest_edge" in size:
              lowerCAmelCase__            =					get_resize_output_image_size(SCREAMING_SNAKE_CASE__  ,      size["shortest_edge"]  ,      default_to_square=SCREAMING_SNAKE_CASE__  )
         elif "height" in size and "width" in size:
              lowerCAmelCase__            =					(size["height"], size["width"])
         else:
              raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'  )
         return resize(SCREAMING_SNAKE_CASE__  ,      size=SCREAMING_SNAKE_CASE__  ,      resample=SCREAMING_SNAKE_CASE__  ,      data_format=SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	Optional[int]  ,      SCREAMING_SNAKE_CASE__	:	np.ndarray  ,      SCREAMING_SNAKE_CASE__	:	Dict[str, int]  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[str, ChannelDimension]] = None  ,      **SCREAMING_SNAKE_CASE__	:	Dict  ,      ) ->			np.ndarray:
         lowerCAmelCase__            =					get_size_dict(SCREAMING_SNAKE_CASE__  )
         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(SCREAMING_SNAKE_CASE__  ,      size=(size["height"], size["width"])  ,      data_format=SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	Optional[int]  ,      SCREAMING_SNAKE_CASE__	:	np.ndarray  ,      SCREAMING_SNAKE_CASE__	:	Union[int, float]  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[str, ChannelDimension]] = None  ,      **SCREAMING_SNAKE_CASE__	:	Optional[Any]  ,      ) ->			Optional[Any]:
         return rescale(SCREAMING_SNAKE_CASE__  ,      scale=SCREAMING_SNAKE_CASE__  ,      data_format=SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	int  ,      SCREAMING_SNAKE_CASE__	:	np.ndarray  ,      SCREAMING_SNAKE_CASE__	:	Union[float, List[float]]  ,      SCREAMING_SNAKE_CASE__	:	Union[float, List[float]]  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[str, ChannelDimension]] = None  ,      **SCREAMING_SNAKE_CASE__	:	str  ,      ) ->			np.ndarray:
         return normalize(SCREAMING_SNAKE_CASE__  ,      mean=SCREAMING_SNAKE_CASE__  ,      std=SCREAMING_SNAKE_CASE__  ,      data_format=SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	Optional[Any]  ,      SCREAMING_SNAKE_CASE__	:	ImageInput  ,      SCREAMING_SNAKE_CASE__	:	bool = None  ,      SCREAMING_SNAKE_CASE__	:	Dict[str, int] = None  ,      SCREAMING_SNAKE_CASE__	:	PILImageResampling = None  ,      SCREAMING_SNAKE_CASE__	:	bool = None  ,      SCREAMING_SNAKE_CASE__	:	Dict[str, int] = None  ,      SCREAMING_SNAKE_CASE__	:	bool = None  ,      SCREAMING_SNAKE_CASE__	:	float = None  ,      SCREAMING_SNAKE_CASE__	:	bool = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[float, List[float]]] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[float, List[float]]] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[ChannelDimension] = ChannelDimension.FIRST  ,      ) ->			np.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_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.
         lowerCAmelCase__            =					to_numpy_array(SCREAMING_SNAKE_CASE__  )

         if do_resize:
              lowerCAmelCase__            =					self.resize(image=SCREAMING_SNAKE_CASE__  ,      size=SCREAMING_SNAKE_CASE__  ,      resample=SCREAMING_SNAKE_CASE__  )

         if do_center_crop:
              lowerCAmelCase__            =					self.center_crop(SCREAMING_SNAKE_CASE__  ,      size=SCREAMING_SNAKE_CASE__  )

         if do_rescale:
              lowerCAmelCase__            =					self.rescale(image=SCREAMING_SNAKE_CASE__  ,      scale=SCREAMING_SNAKE_CASE__  )

         if do_normalize:
              lowerCAmelCase__            =					self.normalize(image=SCREAMING_SNAKE_CASE__  ,      mean=SCREAMING_SNAKE_CASE__  ,      std=SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					to_channel_dimension_format(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )
         return image


    def   a (			self	:	Union[str, Any]  ,      SCREAMING_SNAKE_CASE__	:	ImageInput  ,      SCREAMING_SNAKE_CASE__	:	bool = None  ,      SCREAMING_SNAKE_CASE__	:	Dict[str, int] = None  ,      SCREAMING_SNAKE_CASE__	:	PILImageResampling = None  ,      SCREAMING_SNAKE_CASE__	:	bool = None  ,      SCREAMING_SNAKE_CASE__	:	Dict[str, int] = None  ,      SCREAMING_SNAKE_CASE__	:	bool = None  ,      SCREAMING_SNAKE_CASE__	:	float = None  ,      SCREAMING_SNAKE_CASE__	:	bool = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[float, List[float]]] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[float, List[float]]] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[str, TensorType]] = None  ,      SCREAMING_SNAKE_CASE__	:	ChannelDimension = ChannelDimension.FIRST  ,      **SCREAMING_SNAKE_CASE__	:	int  ,      ) ->			PIL.Image.Image:
         lowerCAmelCase__            =					do_resize if do_resize is not None else self.do_resize
         lowerCAmelCase__            =					resample if resample is not None else self.resample
         lowerCAmelCase__            =					do_center_crop if do_center_crop is not None else self.do_center_crop
         lowerCAmelCase__            =					do_rescale if do_rescale is not None else self.do_rescale
         lowerCAmelCase__            =					rescale_factor if rescale_factor is not None else self.rescale_factor
         lowerCAmelCase__            =					do_normalize if do_normalize is not None else self.do_normalize
         lowerCAmelCase__            =					image_mean if image_mean is not None else self.image_mean
         lowerCAmelCase__            =					image_std if image_std is not None else self.image_std

         lowerCAmelCase__            =					size if size is not None else self.size
         lowerCAmelCase__            =					get_size_dict(SCREAMING_SNAKE_CASE__  ,      default_to_square=SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					crop_size if crop_size is not None else self.crop_size
         lowerCAmelCase__            =					get_size_dict(SCREAMING_SNAKE_CASE__  ,      param_name="crop_size"  )

         if not valid_images(SCREAMING_SNAKE_CASE__  ):
              raise ValueError(
                  "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                  "torch.Tensor, tf.Tensor or jax.ndarray."  )

         lowerCAmelCase__            =					make_batched(SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					[
             [
                 self._preprocess_image(
                     image=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=SCREAMING_SNAKE_CASE__  ,      image_std=SCREAMING_SNAKE_CASE__  ,      data_format=SCREAMING_SNAKE_CASE__  ,      )
                 for img in video
             ]
             for video in videos
         ]

         lowerCAmelCase__            =					{"pixel_values": videos}
         return BatchFeature(data=SCREAMING_SNAKE_CASE__  ,      tensor_type=SCREAMING_SNAKE_CASE__  )




'''simple docstring'''



from typing import TYPE_CHECKING

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


if is_sentencepiece_available():
      from ..ta.tokenization_ta import TaTokenizer
else:
      from ...utils.dummy_sentencepiece_objects import TaTokenizer

A__						:   int				=    TaTokenizer

if is_tokenizers_available():
      from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
      from ...utils.dummy_tokenizers_objects import TaTokenizerFast

A__						:   Dict				=    TaTokenizerFast

A__						:   Dict				=    {"""configuration_mt5""": ["""MT5Config""", """MT5OnnxConfig"""]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__						:   Any				=    [
          """MT5EncoderModel""",
          """MT5ForConditionalGeneration""",
          """MT5ForQuestionAnswering""",
          """MT5Model""",
          """MT5PreTrainedModel""",
          """MT5Stack""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__						:   str				=    ["""TFMT5EncoderModel""", """TFMT5ForConditionalGeneration""", """TFMT5Model"""]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__						:   str				=    ["""FlaxMT5EncoderModel""", """FlaxMT5ForConditionalGeneration""", """FlaxMT5Model"""]


if TYPE_CHECKING:
      from .configuration_mta import MTaConfig, MTaOnnxConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mta import (
                MTaEncoderModel,
                MTaForConditionalGeneration,
                MTaForQuestionAnswering,
                MTaModel,
                MTaPreTrainedModel,
                MTaStack,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
      import sys

      A__						:   Union[str, Any]				=    _LazyModule(
          __name__,
          globals()["""__file__"""],
          _import_structure,
          extra_objects={"""MT5Tokenizer""": MTaTokenizer, """MT5TokenizerFast""": MTaTokenizerFast},
          module_spec=__spec__,
      )






