






"""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

lowercase_				     =     logging.get_logger(__name__)


class 					snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   A_						:	Any						 = "vision-encoder-decoder"
   A_						:	Optional[int]						 = True


   def __init__(	self :     Union[str, Any],       **_lowerCamelCase :     Optional[int]  ):

     '''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}'  )

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

     __A									= AutoConfig.for_model(_lowerCamelCase,       **_lowerCamelCase  )
     __A									= AutoConfig.for_model(_lowerCamelCase,       **_lowerCamelCase  )
     __A									= True


   @classmethod
   def 				_SCREAMING_SNAKE_CASE				(	cls :     Optional[int],       _lowerCamelCase :     PretrainedConfig,       _lowerCamelCase :     PretrainedConfig,       **_lowerCamelCase :     Optional[Any]  ):

     '''simple docstring'''
     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(),       **_lowerCamelCase  )






   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     __A									= copy.deepcopy(self.__dict__  )
     __A									= self.encoder.to_dict()
     __A									= self.decoder.to_dict()
     __A									= self.__class__.model_type
     return output





class 					snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   A_						:	Any						 = version.parse("1.11"						)


   @property
   def 				_SCREAMING_SNAKE_CASE				(	self :     str  ):

     '''simple docstring'''
     return OrderedDict(
         [
             ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
         ]  )


   @property
   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     return 1e-4






   @property
   def 				_SCREAMING_SNAKE_CASE				(	self :     Any  ):

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





class 					snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   @property
   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     __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 				_SCREAMING_SNAKE_CASE				(	self :     str,       _lowerCamelCase :     "PreTrainedTokenizerBase",       _lowerCamelCase :     int = -1,       _lowerCamelCase :     int = -1,       _lowerCamelCase :     bool = False,       _lowerCamelCase :     Optional["TensorType"] = None,       ):

     '''simple docstring'''
     import torch

     __A									= OrderedDict()

     __A									= super().generate_dummy_inputs(
         _lowerCamelCase,       batch_size=_lowerCamelCase,       seq_length=_lowerCamelCase,       is_pair=_lowerCamelCase,       framework=_lowerCamelCase  )

     __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(_lowerCamelCase  )

     return common_inputs





class 					snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   @property
   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     pass


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any],       _lowerCamelCase :     PretrainedConfig  ):

     '''simple docstring'''
     return VisionEncoderDecoderEncoderOnnxConfig(_lowerCamelCase  )






   def 				_SCREAMING_SNAKE_CASE				(	self :     Any,       _lowerCamelCase :     PretrainedConfig,       _lowerCamelCase :     PretrainedConfig,       _lowerCamelCase :     str = "default"  ):

     '''simple docstring'''
     __A									= encoder_config.hidden_size
     return VisionEncoderDecoderDecoderOnnxConfig(_lowerCamelCase,       _lowerCamelCase  )












"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


lowercase_				     =     logging.get_logger(__name__)


class 					snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   def __init__(	self :     Optional[int],       *_lowerCamelCase :     Union[str, Any],       **_lowerCamelCase :     Dict  ):

     '''simple docstring'''
     warnings.warn(
         '''The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
         ''' Please use SegformerImageProcessor instead.''',       _lowerCamelCase,       )
     super().__init__(*_lowerCamelCase,       **_lowerCamelCase  )




