


'''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, 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, is_vision_available, logging


if is_vision_available():
		import PIL


__UpperCAmelCase					       =							logging.get_logger(__name__)




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     Any					=   ["pixel_values"]







							def __init__(	self      ,					lowerCamelCase_ = True      ,					lowerCamelCase_ = None      ,					lowerCamelCase_ = PIL.Image.BICUBIC      ,					lowerCamelCase_ = True      ,					lowerCamelCase_ = None      ,					lowerCamelCase_ = 1 / 2_55      ,					lowerCamelCase_ = True      ,					lowerCamelCase_ = True      ,					lowerCamelCase_ = None      ,					lowerCamelCase_ = None      ,					**lowerCamelCase_      ,					) ->							None:
														super().__init__(**lowerCamelCase_     )
														lowerCAmelCase__      =			size if size is not None else {'''height''': 2_56, '''width''': 2_56}
														lowerCAmelCase__      =			get_size_dict(lowerCamelCase_     )
														lowerCAmelCase__      =			crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
														lowerCAmelCase__      =			get_size_dict(lowerCamelCase_      ,					param_name='''crop_size'''     )

														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 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_ = PIL.Image.BICUBIC      ,					lowerCamelCase_ = None      ,					**lowerCamelCase_      ,					) ->							np.ndarray:
														lowerCAmelCase__      =			get_size_dict(lowerCamelCase_     )
														if "height" not in size or "width" not in size:
																					raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""     )
														return resize(
														    lowerCamelCase_      ,					size=(size['''height'''], size['''width'''])      ,					resample=lowerCamelCase_      ,					data_format=lowerCamelCase_      ,					**lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_ = None      ,					**lowerCamelCase_      ,					) ->							np.ndarray:
														lowerCAmelCase__      =			get_size_dict(lowerCamelCase_     )
														if "height" not in size or "width" not in size:
																					raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""     )
														return center_crop(lowerCamelCase_      ,					size=(size['''height'''], size['''width'''])      ,					data_format=lowerCamelCase_      ,					**lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_ = None      ,					**lowerCamelCase_      ,					) ->							List[Any]:
														return rescale(lowerCamelCase_      ,					scale=lowerCamelCase_      ,					data_format=lowerCamelCase_      ,					**lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_ = None      ,					**lowerCamelCase_      ,					) ->							np.ndarray:
														return normalize(lowerCamelCase_      ,					mean=lowerCamelCase_      ,					std=lowerCamelCase_      ,					data_format=lowerCamelCase_      ,					**lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_ = None      ,					lowerCamelCase_ = None      ,					lowerCamelCase_=None      ,					lowerCamelCase_ = None      ,					lowerCamelCase_ = None      ,					lowerCamelCase_ = None      ,					lowerCamelCase_ = None      ,					lowerCamelCase_ = None      ,					lowerCamelCase_ = None      ,					lowerCamelCase_ = None      ,					lowerCamelCase_ = None      ,					lowerCamelCase_ = ChannelDimension.FIRST      ,					**lowerCamelCase_      ,					) ->							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(lowerCamelCase_     )
														lowerCAmelCase__      =			crop_size if crop_size is not None else self.crop_size
														lowerCAmelCase__      =			get_size_dict(lowerCamelCase_      ,					param_name='''crop_size'''     )

														lowerCAmelCase__      =			make_list_of_images(lowerCamelCase_     )

														if not valid_images(lowerCamelCase_     ):
																					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 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(lowerCamelCase_     ) for image in images]

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

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

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

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

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

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



'''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


__UpperCAmelCase					       =							logging.get_logger(__name__)

__UpperCAmelCase					       =							'''T5Config'''


def       _snake_case     (  A				,					A				,					A )		->					jnp.ndarray:
							lowerCAmelCase__      =			jnp.zeros_like(A )
							lowerCAmelCase__      =			shifted_input_ids.at[:, 1:].set(input_ids[:, :-1] )
							lowerCAmelCase__      =			shifted_input_ids.at[:, 0].set(A )

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




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     int					=   "mt5"
							lowercase__    :     Dict					=   MTaConfig




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     int					=   "mt5"
							lowercase__    :     Any					=   MTaConfig




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     Union[str, Any]					=   "mt5"
							lowercase__    :     Tuple					=   MTaConfig