


'''simple docstring'''






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

a_   :      str				    =      logging.get_logger(__name__)




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		"""vision-encoder-decoder"""
 _lowerCAmelCase	   =		True
 def __init__(	self ,			**__magic_name__  )   ->							Optional[int]:
      super().__init__(**__magic_name__  )
      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}'  )

      _a      =							kwargs.pop('encoder'  )
      _a      =							encoder_config.pop('model_type'  )
      _a      =							kwargs.pop('decoder'  )
      _a      =							decoder_config.pop('model_type'  )

      _a      =							AutoConfig.for_model(__magic_name__ ,			**__magic_name__  )
      _a      =							AutoConfig.for_model(__magic_name__ ,			**__magic_name__  )
      _a      =							True
 @classmethod
 def 							__UpperCAmelCase						(	cls ,			__magic_name__ ,			__magic_name__ ,			**__magic_name__  )   ->							PretrainedConfig:
      logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'  )
      _a      =							True
      _a      =							True

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




 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      _a      =							copy.deepcopy(self.__dict__  )
      _a      =							self.encoder.to_dict()
      _a      =							self.decoder.to_dict()
      _a      =							self.__class__.model_type
      return output





class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		version.parse("""1.11"""    )
 @property
 def 							__UpperCAmelCase						(	self  )   ->							Mapping[str, Mapping[int, str]]:
      return OrderedDict(
          [
              ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
          ]  )
 @property
 def 							__UpperCAmelCase						(	self  )   ->							float:
      return 1e-4




 @property
 def 							__UpperCAmelCase						(	self  )   ->							Mapping[str, Mapping[int, str]]:
      return OrderedDict({'last_hidden_state': {0: 'batch', 1: 'encoder_sequence'}}  )





class 	a       (       _SCREAMING_SNAKE_CASE    ):
 @property
 def 							__UpperCAmelCase						(	self  )   ->							Mapping[str, Mapping[int, str]]:
      _a      =							OrderedDict()
      _a      =							{0: 'batch', 1: 'past_decoder_sequence + sequence'}
      _a      =							{0: 'batch', 1: 'past_decoder_sequence + sequence'}
      _a      =							{0: 'batch', 1: 'encoder_sequence'}

      return common_inputs




 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = -1 ,			__magic_name__ = -1 ,			__magic_name__ = False ,			__magic_name__ = None ,			)   ->							Mapping[str, Any]:
      import torch

      _a      =							OrderedDict()

      _a      =							super().generate_dummy_inputs(
          __magic_name__ ,			batch_size=__magic_name__ ,			seq_length=__magic_name__ ,			is_pair=__magic_name__ ,			framework=__magic_name__  )

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

      return common_inputs





class 	a       (       _SCREAMING_SNAKE_CASE    ):
 @property
 def 							__UpperCAmelCase						(	self  )   ->							None:
      pass
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							OnnxConfig:
      return VisionEncoderDecoderEncoderOnnxConfig(__magic_name__  )




 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ = "default"  )   ->							OnnxConfig:
      _a      =							encoder_config.hidden_size
      return VisionEncoderDecoderDecoderOnnxConfig(__magic_name__ ,			__magic_name__  )





'''simple docstring'''






from __future__ import annotations






def 		_A			(lowerCAmelCase__					:int						)				->					list[int]:




     '''simple docstring'''



     _a      =							2
     _a      =							[]
     while i * i <= n:
          if n % i:
               i += 1
          else:
               n //= i
               factors.append(lowerCAmelCase__						)
     if n > 1:
          factors.append(lowerCAmelCase__						)
     return factors


if __name__ == "__main__":
 import doctest

 doctest.testmod()

