






import jax.numpy as jnp

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


__snake_case      :      Union[str, Any]             =       logging.get_logger(__name__)

__snake_case      :      int             =       """T5Config"""







def 		_UpperCamelCase				(      UpperCamelCase_    :   jnp.array      ,    UpperCamelCase_    :   int      ,    UpperCamelCase_    :   int						)					->				jnp.ndarray:

						"""simple docstring"""



						lowerCAmelCase__        =  jnp.zeros_like(UpperCamelCase_						)
						lowerCAmelCase__        =  shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]						)
						lowerCAmelCase__        =  shifted_input_ids.at[:, 0].set(UpperCamelCase_						)

						lowerCAmelCase__        =  jnp.where(shifted_input_ids == -100      ,    UpperCamelCase_      ,    UpperCamelCase_						)
						return shifted_input_ids


class       __SCREAMING_SNAKE_CASE   (				__lowercase):
						_SCREAMING_SNAKE_CASE		:    Optional[Any]						=   '''mt5'''
						_SCREAMING_SNAKE_CASE		:    Optional[int]						=   MTaConfig




class       __SCREAMING_SNAKE_CASE   (				__lowercase):
						_SCREAMING_SNAKE_CASE		:    Optional[Any]						=   '''mt5'''
						_SCREAMING_SNAKE_CASE		:    Dict						=   MTaConfig




class       __SCREAMING_SNAKE_CASE   (				__lowercase):
						_SCREAMING_SNAKE_CASE		:    List[str]						=   '''mt5'''
						_SCREAMING_SNAKE_CASE		:    Dict						=   MTaConfig













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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__snake_case      :      Any             =       logging.get_logger(__name__)


class       __SCREAMING_SNAKE_CASE   (				__lowercase):
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   ['''pixel_values''']






						def __init__(		self       ,  _UpperCamelCase = True       ,  _UpperCamelCase = None       ,  _UpperCamelCase = PILImageResampling.BILINEAR       ,  _UpperCamelCase = True       ,  _UpperCamelCase = None       ,  _UpperCamelCase = True       ,  _UpperCamelCase = 1 / 2_55       ,  _UpperCamelCase = True       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												super().__init__(**_UpperCamelCase				)
												lowerCAmelCase__        =  size if size is not None else {'shortest_edge': 2_56}
												lowerCAmelCase__        =  get_size_dict(_UpperCamelCase       ,  default_to_square=_UpperCamelCase				)
												lowerCAmelCase__        =  crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
												lowerCAmelCase__        =  get_size_dict(_UpperCamelCase				)
												lowerCAmelCase__        =  do_resize
												lowerCAmelCase__        =  size
												lowerCAmelCase__        =  resample
												lowerCAmelCase__        =  do_center_crop
												lowerCAmelCase__        =  crop_size
												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  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase = PILImageResampling.BICUBIC       ,  _UpperCamelCase = None       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  get_size_dict(_UpperCamelCase       ,  default_to_square=_UpperCamelCase				)
												if "shortest_edge" not in size:
																		raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"				)
												lowerCAmelCase__        =  get_resize_output_image_size(_UpperCamelCase       ,  size=size['shortest_edge']       ,  default_to_square=_UpperCamelCase				)
												return resize(_UpperCamelCase       ,  size=_UpperCamelCase       ,  resample=_UpperCamelCase       ,  data_format=_UpperCamelCase       ,  **_UpperCamelCase				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  get_size_dict(_UpperCamelCase				)
												return center_crop(_UpperCamelCase       ,  size=(size['height'], size['width'])       ,  data_format=_UpperCamelCase       ,  **_UpperCamelCase				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  **_UpperCamelCase				):






												"""simple docstring"""
												return rescale(_UpperCamelCase       ,  scale=_UpperCamelCase       ,  data_format=_UpperCamelCase       ,  **_UpperCamelCase				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												return normalize(_UpperCamelCase       ,  mean=_UpperCamelCase       ,  std=_UpperCamelCase       ,  data_format=_UpperCamelCase       ,  **_UpperCamelCase				)







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = ChannelDimension.FIRST       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  do_resize if do_resize is not None else self.do_resize
												lowerCAmelCase__        =  size if size is not None else self.size
												lowerCAmelCase__        =  get_size_dict(_UpperCamelCase       ,  default_to_square=_UpperCamelCase				)
												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__        =  crop_size if crop_size is not None else self.crop_size
												lowerCAmelCase__        =  get_size_dict(_UpperCamelCase				)
												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__        =  make_list_of_images(_UpperCamelCase				)

												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.'				)

												if do_resize and size is None:
																		raise ValueError('Size 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(_UpperCamelCase				) for image in images]

												if do_resize:
																		lowerCAmelCase__        =  [self.resize(image=_UpperCamelCase       ,  size=_UpperCamelCase       ,  resample=_UpperCamelCase				) for image in images]

												if do_center_crop:
																		lowerCAmelCase__        =  [self.center_crop(image=_UpperCamelCase       ,  size=_UpperCamelCase				) for image in images]

												if do_rescale:
																		lowerCAmelCase__        =  [self.rescale(image=_UpperCamelCase       ,  scale=_UpperCamelCase				) for image in images]

												if do_normalize:
																		lowerCAmelCase__        =  [self.normalize(image=_UpperCamelCase       ,  mean=_UpperCamelCase       ,  std=_UpperCamelCase				) for image in images]

												lowerCAmelCase__        =  [to_channel_dimension_format(_UpperCamelCase       ,  _UpperCamelCase				) for image in images]

												lowerCAmelCase__        =  {'pixel_values': images}
												return BatchFeature(data=_UpperCamelCase       ,  tensor_type=_UpperCamelCase				)





