





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

lowercase_					     =	logging.get_logger(__name__)
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):
   _UpperCamelCase					:	Union[str, Any]				      = 'vision-encoder-decoder'
   _UpperCamelCase					:	Union[str, Any]				      = True


   def __init__(					self    :       Dict    ,  **a    :       Union[str, Any] )->     Any:





     """simple docstring"""







     super().__init__(**a )
     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__			    =						kwargs.pop('encoder' )
     lowercase__			    =						encoder_config.pop('model_type' )
     lowercase__			    =						kwargs.pop('decoder' )
     lowercase__			    =						decoder_config.pop('model_type' )

     lowercase__			    =						AutoConfig.for_model(a    ,  **a )
     lowercase__			    =						AutoConfig.for_model(a    ,  **a )
     lowercase__			    =						True


   @classmethod
   def  SCREAMING_SNAKE_CASE_							(					cls    :       Optional[int]    ,  a    :       PretrainedConfig    ,  a    :       PretrainedConfig    ,  **a    :       Dict )->     PretrainedConfig:





     """simple docstring"""







     logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config' )
     lowercase__			    =						True
     lowercase__			    =						True

     return cls(encoder=encoder_config.to_dict()    ,  decoder=decoder_config.to_dict()    ,  **a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     Tuple:





     """simple docstring"""







     lowercase__			    =						copy.deepcopy(self.__dict__ )
     lowercase__			    =						self.encoder.to_dict()
     lowercase__			    =						self.decoder.to_dict()
     lowercase__			    =						self.__class__.model_type
     return output






class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):
   _UpperCamelCase					:	str				      = version.parse('1.11'   )


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[int] )->     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    :       Optional[int] )->     float:





     """simple docstring"""







     return 1E-4


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str] )->     Mapping[str, Mapping[int, str]]:





     """simple docstring"""







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






class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any] )->     Mapping[str, Mapping[int, str]]:





     """simple docstring"""







     lowercase__			    =						OrderedDict()
     lowercase__			    =						{0: 'batch', 1: 'past_decoder_sequence + sequence'}
     lowercase__			    =						{0: 'batch', 1: 'past_decoder_sequence + sequence'}
     lowercase__			    =						{0: 'batch', 1: 'encoder_sequence'}

     return common_inputs


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str]    ,  a    :       "PreTrainedTokenizerBase"    ,  a    :       int = -1    ,  a    :       int = -1    ,  a    :       bool = False    ,  a    :       Optional["TensorType"] = None    ,  )->     Mapping[str, Any]:





     """simple docstring"""







     import torch

     lowercase__			    =						OrderedDict()

     lowercase__			    =						super().generate_dummy_inputs(
         a    ,  batch_size=a    ,  seq_length=a    ,  is_pair=a    ,  framework=a )

     lowercase__    ,					lowercase__			    =						dummy_input['input_ids'].shape
     lowercase__			    =						(batch, encoder_sequence, self._config.encoder_hidden_size)
     lowercase__			    =						dummy_input.pop('input_ids' )
     lowercase__			    =						dummy_input.pop('attention_mask' )
     lowercase__			    =						torch.zeros(a )

     return common_inputs






class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any] )->     None:





     """simple docstring"""







     pass


   def  SCREAMING_SNAKE_CASE_							(					self    :       Dict    ,  a    :       PretrainedConfig )->     OnnxConfig:





     """simple docstring"""







     return VisionEncoderDecoderEncoderOnnxConfig(a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[int]    ,  a    :       PretrainedConfig    ,  a    :       PretrainedConfig    ,  a    :       str = "default" )->     OnnxConfig:





     """simple docstring"""







     lowercase__			    =						encoder_config.hidden_size
     return VisionEncoderDecoderDecoderOnnxConfig(a    ,  a )









class        SCREAMING_SNAKE_CASE     :  # Public class to implement a graph


   def __init__(					self    :       int    ,  a    :       int    ,  a    :       int    ,  a    :       list[list[bool]] )->     None:





     """simple docstring"""







     lowercase__			    =						row
     lowercase__			    =						col
     lowercase__			    =						graph


   def  SCREAMING_SNAKE_CASE_							(					self    :       Dict    ,  a    :       int    ,  a    :       int    ,  a    :       list[list[bool]] )->     bool:





     """simple docstring"""







     return (
         0 <= i < self.ROW
         and 0 <= j < self.COL
         and not visited[i][j]
         and self.graph[i][j]
     )


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str]    ,  a    :       int    ,  a    :       int    ,  a    :       list[list[bool]] )->     None:





     """simple docstring"""







     lowercase__			    =						[-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
     lowercase__			    =						[-1, 0, 1, -1, 1, -1, 0, 1]
     lowercase__			    =						True  # Make those cells visited
     for k in range(8 ):
       if self.is_safe(i + row_nbr[k]    ,  j + col_nbr[k]    ,  a ):
         self.diffs(i + row_nbr[k]    ,  j + col_nbr[k]    ,  a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str] )->     int:  # And finally, count all islands.





     """simple docstring"""







     lowercase__			    =						[[False for j in range(self.COL )] for i in range(self.ROW )]
     lowercase__			    =						0
     for i in range(self.ROW ):
       for j in range(self.COL ):
         if visited[i][j] is False and self.graph[i][j] == 1:
           self.diffs(a    ,  a    ,  a )
           count += 1
     return count


