


'''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


lowercase							=logging.get_logger(__name__)





def lowerCamelCase__      (						__lowerCamelCase	:							Union[str, Any]							):







   '''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  __magic_name__							(		a_    ):
       UpperCAmelCase										=['''pixel_values''']






       def __init__(      self  ,							snake_case = True  ,							snake_case = None  ,							snake_case = PILImageResampling.BILINEAR  ,							snake_case = True  ,							snake_case = None  ,							snake_case = True  ,							snake_case = 1 / 2_5_5  ,							snake_case = True  ,							snake_case = None  ,							snake_case = None  ,							**snake_case  ,							)						->  None:




          '''simple docstring'''
          super().__init__(**lowercase_)
          _UpperCAmelCase       :       Dict							   =size if size is not None else {'shortest_edge': 2_2_4}
          _UpperCAmelCase       :       Optional[int]							   =get_size_dict(lowercase_  ,							default_to_square=lowercase_)
          _UpperCAmelCase       :       Tuple							   =crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
          _UpperCAmelCase       :       Optional[Any]							   =get_size_dict(lowercase_  ,							param_name='crop_size')

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






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case  ,							snake_case = PILImageResampling.BILINEAR  ,							snake_case = None  ,							**snake_case  ,							)						->  np.ndarray:




          '''simple docstring'''
          _UpperCAmelCase       :       Tuple							   =get_size_dict(lowercase_  ,							default_to_square=lowercase_)
          if "shortest_edge" in size:
             _UpperCAmelCase       :       List[str]							   =get_resize_output_image_size(lowercase_  ,							size['shortest_edge']  ,							default_to_square=lowercase_)
          elif "height" in size and "width" in size:
             _UpperCAmelCase       :       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(lowercase_  ,							size=lowercase_  ,							resample=lowercase_  ,							data_format=lowercase_  ,							**lowercase_)






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case  ,							snake_case = None  ,							**snake_case  ,							)						->  np.ndarray:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =get_size_dict(lowercase_)
          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(lowercase_  ,							size=(size['height'], size['width'])  ,							data_format=lowercase_  ,							**lowercase_)






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case  ,							snake_case = None  ,							**snake_case  ,							)						->  Optional[int]:




          '''simple docstring'''
          return rescale(lowercase_  ,							scale=lowercase_  ,							data_format=lowercase_  ,							**lowercase_)






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case = None  ,							**snake_case  ,							)						->  np.ndarray:




          '''simple docstring'''
          return normalize(lowercase_  ,							mean=lowercase_  ,							std=lowercase_  ,							data_format=lowercase_  ,							**lowercase_)






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case = None  ,							snake_case = None  ,							snake_case = None  ,							snake_case = None  ,							snake_case = None  ,							snake_case = None  ,							snake_case = None  ,							snake_case = None  ,							snake_case = None  ,							snake_case = None  ,							snake_case = ChannelDimension.FIRST  ,							)						->  np.ndarray:




          '''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       :       int							   =to_numpy_array(lowercase_)

          if do_resize:
             _UpperCAmelCase       :       int							   =self.resize(image=lowercase_  ,							size=lowercase_  ,							resample=lowercase_)

          if do_center_crop:
             _UpperCAmelCase       :       List[Any]							   =self.center_crop(lowercase_  ,							size=lowercase_)

          if do_rescale:
             _UpperCAmelCase       :       Optional[int]							   =self.rescale(image=lowercase_  ,							scale=lowercase_)

          if do_normalize:
             _UpperCAmelCase       :       List[Any]							   =self.normalize(image=lowercase_  ,							mean=lowercase_  ,							std=lowercase_)

          _UpperCAmelCase       :       Optional[Any]							   =to_channel_dimension_format(lowercase_  ,							lowercase_)
          return image






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case = None  ,							snake_case = None  ,							snake_case = None  ,							snake_case = None  ,							snake_case = None  ,							snake_case = None  ,							snake_case = None  ,							snake_case = None  ,							snake_case = None  ,							snake_case = None  ,							snake_case = None  ,							snake_case = ChannelDimension.FIRST  ,							**snake_case  ,							)						->  PIL.Image.Image:




          '''simple docstring'''
          _UpperCAmelCase       :       List[Any]							   =do_resize if do_resize is not None else self.do_resize
          _UpperCAmelCase       :       Optional[int]							   =resample if resample is not None else self.resample
          _UpperCAmelCase       :       str							   =do_center_crop if do_center_crop is not None else self.do_center_crop
          _UpperCAmelCase       :       int							   =do_rescale if do_rescale is not None else self.do_rescale
          _UpperCAmelCase       :       Tuple							   =rescale_factor if rescale_factor is not None else self.rescale_factor
          _UpperCAmelCase       :       Tuple							   =do_normalize if do_normalize is not None else self.do_normalize
          _UpperCAmelCase       :       List[Any]							   =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       :       Union[str, Any]							   =size if size is not None else self.size
          _UpperCAmelCase       :       Dict							   =get_size_dict(lowercase_  ,							default_to_square=lowercase_)
          _UpperCAmelCase       :       Any							   =crop_size if crop_size is not None else self.crop_size
          _UpperCAmelCase       :       List[Any]							   =get_size_dict(lowercase_  ,							param_name='crop_size')

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

          _UpperCAmelCase       :       int							   =make_batched(lowercase_)

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

          _UpperCAmelCase       :       str							   ={'pixel_values': videos}
          return BatchFeature(data=lowercase_  ,							tensor_type=lowercase_)





'''simple docstring'''






import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


lowercase							=logging.get_logger(__name__)

lowercase							='T5Config'





def lowerCamelCase__      (						__lowerCamelCase	:							jnp.array	,   __lowerCamelCase	:							int	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   _UpperCAmelCase       :       List[Any]							   =jnp.zeros_like(__lowerCamelCase							)
   _UpperCAmelCase       :       Union[str, Any]							   =shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]							)
   _UpperCAmelCase       :       List[Any]							   =shifted_input_ids.at[:, 0].set(__lowerCamelCase							)

   _UpperCAmelCase       :       str							   =jnp.where(shifted_input_ids == -1_0_0	,   __lowerCamelCase	,   __lowerCamelCase							)
   return shifted_input_ids






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										="mt5"
       UpperCAmelCase										=MTaConfig






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										="mt5"
       UpperCAmelCase										=MTaConfig






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										="mt5"
       UpperCAmelCase										=MTaConfig

