


import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
 from ... import PreTrainedTokenizerBase, TensorType

__lowerCAmelCase			=							logging.get_logger(__name__)





class   __a      (   __UpperCamelCase				):
 __lowercase			:							Union[str, Any]       					=			'vision-encoder-decoder'
 __lowercase			:							Union[str, Any]       					=			True
 def __init__(       self    ,			**lowerCAmelCase__			) ->    List[str]:




        '''simple docstring'''
        super().__init__(**lowerCAmelCase__			)
        if "encoder" not in kwargs or "decoder" not in kwargs:
               raise ValueError(
                   F'A configuraton of type {self.model_type} cannot be instantiated because '
                   F'not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}'			)

        lowercase__:       Dict        =       kwargs.pop('encoder'			)
        lowercase__:       List[str]        =       encoder_config.pop('model_type'			)
        lowercase__:       Dict        =       kwargs.pop('decoder'			)
        lowercase__:       List[Any]        =       decoder_config.pop('model_type'			)

        lowercase__:       Union[str, Any]        =       AutoConfig.for_model(lowerCAmelCase__    ,			**lowerCAmelCase__			)
        lowercase__:       Tuple        =       AutoConfig.for_model(lowerCAmelCase__    ,			**lowerCAmelCase__			)
        lowercase__:       Any        =       True
 @classmethod
 def 		SCREAMING_SNAKE_CASE__       (       cls    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			**lowerCAmelCase__			) ->    PretrainedConfig:




        '''simple docstring'''
        logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'			)
        lowercase__:       Any        =       True
        lowercase__:       Optional[int]        =       True

        return cls(encoder=encoder_config.to_dict()    ,			decoder=decoder_config.to_dict()    ,			**lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        lowercase__:       int        =       copy.deepcopy(self.__dict__			)
        lowercase__:       str        =       self.encoder.to_dict()
        lowercase__:       Tuple        =       self.decoder.to_dict()
        lowercase__:       List[str]        =       self.__class__.model_type
        return output





class   __a      (   __UpperCamelCase				):
 __lowercase			:							Optional[int]       					=			version.parse('1.11'				)
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Mapping[str, Mapping[int, str]]:




        '''simple docstring'''
        return OrderedDict(
            [
                ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
            ]			)
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    float:




        '''simple docstring'''
        return 1E-4
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Mapping[str, Mapping[int, str]]:




        '''simple docstring'''
        return OrderedDict({'last_hidden_state': {0: 'batch', 1: 'encoder_sequence'}}			)





class   __a      (   __UpperCamelCase				):
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Mapping[str, Mapping[int, str]]:




        '''simple docstring'''
        lowercase__:       Optional[Any]        =       OrderedDict()
        lowercase__:       Tuple        =       {0: 'batch', 1: 'past_decoder_sequence + sequence'}
        lowercase__:       Dict        =       {0: 'batch', 1: 'past_decoder_sequence + sequence'}
        lowercase__:       str        =       {0: 'batch', 1: 'encoder_sequence'}

        return common_inputs
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = -1    ,			lowerCAmelCase__ = -1    ,			lowerCAmelCase__ = False    ,			lowerCAmelCase__ = None    ,			) ->    Mapping[str, Any]:




        '''simple docstring'''
        import torch

        lowercase__:       List[str]        =       OrderedDict()

        lowercase__:       Tuple        =       super().generate_dummy_inputs(
            lowerCAmelCase__    ,			batch_size=lowerCAmelCase__    ,			seq_length=lowerCAmelCase__    ,			is_pair=lowerCAmelCase__    ,			framework=lowerCAmelCase__			)

        lowercase__      ,						lowercase__:       Tuple        =       dummy_input['input_ids'].shape
        lowercase__:       Dict        =       (batch, encoder_sequence, self._config.encoder_hidden_size)
        lowercase__:       List[Any]        =       dummy_input.pop('input_ids'			)
        lowercase__:       int        =       dummy_input.pop('attention_mask'			)
        lowercase__:       Any        =       torch.zeros(lowerCAmelCase__			)

        return common_inputs





class   __a      (   __UpperCamelCase				):
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    None:




        '''simple docstring'''
        pass
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    OnnxConfig:




        '''simple docstring'''
        return VisionEncoderDecoderEncoderOnnxConfig(lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = "default"			) ->    OnnxConfig:




        '''simple docstring'''
        lowercase__:       List[Any]        =       encoder_config.hidden_size
        return VisionEncoderDecoderDecoderOnnxConfig(lowerCAmelCase__    ,			lowerCAmelCase__			)






def        snake_case_					(  snake_case					)  ->    list[int]:
       lowercase__:       Dict        =       [0 for i in range(len(snake_case					)					)]

       # initialize interval's left pointer and right pointer
       lowercase__      ,						lowercase__:       Union[str, Any]        =       0, 0

       for i in range(1      ,							len(snake_case					)					):
              # case when current index is inside the interval
              if i <= right_pointer:
                     lowercase__:       List[Any]        =       min(right_pointer - i + 1      ,							z_result[i - left_pointer]					)
                     lowercase__:       List[str]        =       min_edge

              while go_next(snake_case      ,							snake_case      ,							snake_case					):
                     z_result[i] += 1

              # if new index's result gives us more right interval,
              # we've to update left_pointer and right_pointer
              if i + z_result[i] - 1 > right_pointer:
                     lowercase__      ,						lowercase__:       List[Any]        =       i, i + z_result[i] - 1

       return z_result




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case					)  ->    bool:
       return i + z_result[i] < len(snake_case					) and s[z_result[i]] == s[i + z_result[i]]




def        snake_case_					(  snake_case      ,							snake_case					)  ->    int:
       lowercase__:       Tuple        =       0
       # concatenate 'pattern' and 'input_str' and call z_function
       # with concatenated string
       lowercase__:       Any        =       z_function(pattern + input_str					)

       for val in z_result:
              # if value is greater then length of the pattern string
              # that means this index is starting position of substring
              # which is equal to pattern string
              if val >= len(snake_case					):
                     answer += 1

       return answer


if __name__ == "__main__":
 import doctest

 doctest.testmod()


