






"""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


UpperCAmelCase_   : int   			= logging.get_logger(__name__)



def 					_A			(__a							) ->       str:
    """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__				(			A_ ):






      '''simple docstring'''

      __UpperCamelCase	 =  ["pixel_values"]
      def __init__(						self     :  Any						,	lowercase_     :  bool = True						,	lowercase_     :  Dict[str, int] = None						,	lowercase_     :  PILImageResampling = PILImageResampling.BILINEAR						,	lowercase_     :  bool = True						,	lowercase_     :  Dict[str, int] = None						,	lowercase_     :  bool = True						,	lowercase_     :  Union[int, float] = 1 / 255						,	lowercase_     :  bool = True						,	lowercase_     :  Optional[Union[float, List[float]]] = None						,	lowercase_     :  Optional[Union[float, List[float]]] = None						,	**lowercase_     :  int						,	):




          '''simple docstring'''


          super().__init__(**snake_case__)
          SCREAMING_SNAKE_CASE_      :  Dict    						=		size if size is not None else {"shortest_edge": 224}
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		get_size_dict(snake_case__						,	default_to_square=snake_case__)
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		crop_size if crop_size is not None else {"height": 224, "width": 224}
          SCREAMING_SNAKE_CASE_      :  str    						=		get_size_dict(snake_case__						,	param_name='''crop_size''')

          SCREAMING_SNAKE_CASE_      :  Any    						=		do_resize
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		size
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		do_center_crop
          SCREAMING_SNAKE_CASE_      :  Dict    						=		crop_size
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		resample
          SCREAMING_SNAKE_CASE_      :  Dict    						=		do_rescale
          SCREAMING_SNAKE_CASE_      :  Any    						=		rescale_factor
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		do_normalize
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		image_std if image_std is not None else IMAGENET_STANDARD_STD
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple						,	lowercase_     :  np.ndarray						,	lowercase_     :  Dict[str, int]						,	lowercase_     :  PILImageResampling = PILImageResampling.BILINEAR						,	lowercase_     :  Optional[Union[str, ChannelDimension]] = None						,	**lowercase_     :  Union[str, Any]						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		get_size_dict(snake_case__						,	default_to_square=snake_case__)
          if "shortest_edge" in size:
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		get_resize_output_image_size(snake_case__						,	size['''shortest_edge''']						,	default_to_square=snake_case__)
          elif "height" in size and "width" in size:
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		(size["height"], size["width"])
          else:
              raise ValueError(F'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}')
          return resize(snake_case__						,	size=snake_case__						,	resample=snake_case__						,	data_format=snake_case__						,	**snake_case__)
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple						,	lowercase_     :  np.ndarray						,	lowercase_     :  Dict[str, int]						,	lowercase_     :  Optional[Union[str, ChannelDimension]] = None						,	**lowercase_     :  Any						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		get_size_dict(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(snake_case__						,	size=(size['''height'''], size['''width'''])						,	data_format=snake_case__						,	**snake_case__)
      def _SCREAMING_SNAKE_CASE (						self     :  int						,	lowercase_     :  np.ndarray						,	lowercase_     :  Union[int, float]						,	lowercase_     :  Optional[Union[str, ChannelDimension]] = None						,	**lowercase_     :  List[str]						,	):




          '''simple docstring'''


          return rescale(snake_case__						,	scale=snake_case__						,	data_format=snake_case__						,	**snake_case__)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]						,	lowercase_     :  np.ndarray						,	lowercase_     :  Union[float, List[float]]						,	lowercase_     :  Union[float, List[float]]						,	lowercase_     :  Optional[Union[str, ChannelDimension]] = None						,	**lowercase_     :  List[str]						,	):




          '''simple docstring'''


          return normalize(snake_case__						,	mean=snake_case__						,	std=snake_case__						,	data_format=snake_case__						,	**snake_case__)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]						,	lowercase_     :  ImageInput						,	lowercase_     :  bool = None						,	lowercase_     :  Dict[str, int] = None						,	lowercase_     :  PILImageResampling = None						,	lowercase_     :  bool = None						,	lowercase_     :  Dict[str, int] = None						,	lowercase_     :  bool = None						,	lowercase_     :  float = None						,	lowercase_     :  bool = None						,	lowercase_     :  Optional[Union[float, List[float]]] = None						,	lowercase_     :  Optional[Union[float, List[float]]] = None						,	lowercase_     :  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.
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		to_numpy_array(snake_case__)

          if do_resize:
              SCREAMING_SNAKE_CASE_      :  int    						=		self.resize(image=snake_case__						,	size=snake_case__						,	resample=snake_case__)

          if do_center_crop:
              SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.center_crop(snake_case__						,	size=snake_case__)

          if do_rescale:
              SCREAMING_SNAKE_CASE_      :  int    						=		self.rescale(image=snake_case__						,	scale=snake_case__)

          if do_normalize:
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		self.normalize(image=snake_case__						,	mean=snake_case__						,	std=snake_case__)

          SCREAMING_SNAKE_CASE_      :  List[str]    						=		to_channel_dimension_format(snake_case__						,	snake_case__)
          return image



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




          '''simple docstring'''


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

          SCREAMING_SNAKE_CASE_      :  Dict    						=		size if size is not None else self.size
          SCREAMING_SNAKE_CASE_      :  Any    						=		get_size_dict(snake_case__						,	default_to_square=snake_case__)
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		crop_size if crop_size is not None else self.crop_size
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		get_size_dict(snake_case__						,	param_name='''crop_size''')

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

          SCREAMING_SNAKE_CASE_      :  Tuple    						=		make_batched(snake_case__)

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

          SCREAMING_SNAKE_CASE_      :  List[str]    						=		{"pixel_values": videos}
          return BatchFeature(data=snake_case__						,	tensor_type=snake_case__)




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

__lowerCamelCase							=       TaTokenizer

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

__lowerCamelCase							=       TaTokenizerFast

__lowerCamelCase							=       {"""configuration_mt5""": ["""MT5Config""", """MT5OnnxConfig"""]}

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

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

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       ["""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

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





