





'''simple docstring'''

from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


UpperCAmelCase					:		Tuple		=							logging.get_logger(__name__)

UpperCAmelCase					:		Dict		=							{
    'google/umt5-small': 'https://huggingface.co/google/umt5-small/resolve/main/config.json',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}





class lowerCamelCase						(a__					):
  _lowercase  :	Optional[int]       					=				"""umt5"""
  _lowercase  :	Optional[Any]       					=				["""past_key_values"""]







  def __init__(		self     ,     lowercase__=250_112     ,     lowercase__=512     ,     lowercase__=64     ,     lowercase__=1_024     ,     lowercase__=8     ,     lowercase__=None     ,     lowercase__=6     ,     lowercase__=32     ,     lowercase__=128     ,     lowercase__=0.1     ,     lowercase__=1E-6     ,     lowercase__=1.0     ,     lowercase__="gated-gelu"     ,     lowercase__=True     ,     lowercase__=True     ,     lowercase__="T5Tokenizer"     ,     lowercase__=True     ,     lowercase__=0     ,     lowercase__=1     ,     lowercase__=0     ,     **lowercase__     ,     )		->						Dict:






        """simple docstring"""





        super().__init__(
            is_encoder_decoder=lowercase__     ,     tokenizer_class=lowercase__     ,     tie_word_embeddings=lowercase__     ,     pad_token_id=lowercase__     ,     eos_token_id=lowercase__     ,     decoder_start_token_id=lowercase__     ,     **lowercase__     ,     )
        _snake_case		:	str					     =     vocab_size
        _snake_case		:	Any					     =     d_model
        _snake_case		:	Union[str, Any]					     =     d_kv
        _snake_case		:	List[str]					     =     d_ff
        _snake_case		:	Dict					     =     num_layers
        _snake_case		:	Tuple					     =     (
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        _snake_case		:	List[Any]					     =     num_heads
        _snake_case		:	Dict					     =     relative_attention_num_buckets
        _snake_case		:	List[Any]					     =     relative_attention_max_distance
        _snake_case		:	int					     =     dropout_rate
        _snake_case		:	List[Any]					     =     layer_norm_epsilon
        _snake_case		:	Tuple					     =     initializer_factor
        _snake_case		:	List[str]					     =     feed_forward_proj
        _snake_case		:	Optional[Any]					     =     use_cache

        _snake_case		:	Optional[Any]					     =     self.feed_forward_proj.split('''-'''		)
        _snake_case		:	str					     =     act_info[-1]
        _snake_case		:	int					     =     act_info[0] == '''gated'''

        if len(lowercase__		) > 1 and act_info[0] != "gated" or len(lowercase__		) > 2:
              raise ValueError(
                  F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
                  '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
                  '''\'gated-gelu\' or \'relu\''''		)

        if feed_forward_proj == "gated-gelu":
              _snake_case		:	Optional[int]					     =     '''gelu_new'''







  @property
  def 				UpperCAmelCase_				(		self		)		->						Optional[Any]:






        """simple docstring"""





        return self.d_model







  @property
  def 				UpperCAmelCase_				(		self		)		->						Any:






        """simple docstring"""





        return self.num_heads







  @property
  def 				UpperCAmelCase_				(		self		)		->						Dict:






        """simple docstring"""





        return self.num_layers





class lowerCamelCase						(a__					):







  @property
  # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
  def 				UpperCAmelCase_				(		self		)		->						Mapping[str, Mapping[int, str]]:






        """simple docstring"""





        _snake_case		:	Any					     =     {
            '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
            '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
        }
        if self.use_past:
              _snake_case		:	int					     =     '''past_encoder_sequence + sequence'''
              _snake_case		:	Dict					     =     {0: '''batch'''}
              _snake_case		:	List[str]					     =     {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
        else:
              _snake_case		:	str					     =     {0: '''batch''', 1: '''decoder_sequence'''}
              _snake_case		:	str					     =     {0: '''batch''', 1: '''decoder_sequence'''}

        if self.use_past:
              self.fill_with_past_key_values_(lowercase__     ,     direction='''inputs'''		)

        return common_inputs







  @property
  # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
  def 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





        return 13







  @property
  def 				UpperCAmelCase_				(		self		)		->						float:






        """simple docstring"""





        return 5E-4









'''simple docstring'''

from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase					:		Dict		=							logging.get_logger(__name__)





class lowerCamelCase						(a__					):
  _lowercase  :	int       					=				["""pixel_values"""]







  def __init__(		self     ,     lowercase__ = True     ,     lowercase__ = 32     ,     lowercase__=PILImageResampling.BILINEAR     ,     lowercase__ = True     ,     **lowercase__     ,     )		->						None:






        """simple docstring"""





        _snake_case		:	Any					     =     do_resize
        _snake_case		:	List[str]					     =     do_rescale
        _snake_case		:	Any					     =     size_divisor
        _snake_case		:	Optional[Any]					     =     resample
        super().__init__(**lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__ = None     ,     **lowercase__		)		->						np.ndarray:






        """simple docstring"""





        _snake_case     ,							_snake_case		:	Dict					     =     get_image_size(lowercase__		)
        # Rounds the height and width down to the closest multiple of size_divisor
        _snake_case		:	Optional[int]					     =     height // size_divisor * size_divisor
        _snake_case		:	Dict					     =     width // size_divisor * size_divisor
        _snake_case		:	str					     =     resize(lowercase__     ,     (new_h, new_w)     ,     resample=lowercase__     ,     data_format=lowercase__     ,     **lowercase__		)
        return image







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__ = None     ,     **lowercase__		)		->						np.ndarray:






        """simple docstring"""





        return rescale(image=lowercase__     ,     scale=lowercase__     ,     data_format=lowercase__     ,     **lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__=None     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = ChannelDimension.FIRST     ,     **lowercase__     ,     )		->						BatchFeature:






        """simple docstring"""





        _snake_case		:	Any					     =     do_resize if do_resize is not None else self.do_resize
        _snake_case		:	List[Any]					     =     do_rescale if do_rescale is not None else self.do_rescale
        _snake_case		:	List[str]					     =     size_divisor if size_divisor is not None else self.size_divisor
        _snake_case		:	int					     =     resample if resample is not None else self.resample

        if do_resize and size_divisor is None:
              raise ValueError('''size_divisor is required for resizing'''		)

        _snake_case		:	Tuple					     =     make_list_of_images(lowercase__		)

        if not valid_images(lowercase__		):
              raise ValueError('''Invalid image(s)'''		)

        # All transformations expect numpy arrays.
        _snake_case		:	Tuple					     =     [to_numpy_array(lowercase__		) for img in images]

        if do_resize:
              _snake_case		:	Optional[int]					     =     [self.resize(lowercase__     ,     size_divisor=lowercase__     ,     resample=lowercase__		) for image in images]

        if do_rescale:
              _snake_case		:	Union[str, Any]					     =     [self.rescale(lowercase__     ,     scale=1 / 255		) for image in images]

        _snake_case		:	Union[str, Any]					     =     [to_channel_dimension_format(lowercase__     ,     lowercase__		) for image in images]

        _snake_case		:	List[str]					     =     {'''pixel_values''': images}
        return BatchFeature(data=lowercase__     ,     tensor_type=lowercase__		)


