
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

__UpperCAmelCase							=			logging.get_logger(__name__)





class 							UpperCamelCase__						(     __SCREAMING_SNAKE_CASE     ):






       """simple docstring"""


       UpperCAmelCase_							    ="vision-encoder-decoder"
       UpperCAmelCase_							    =True



       def __init__(				self      ,			**_A     )							->							int:
          super().__init__(**_SCREAMING_SNAKE_CASE     )
          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}'''     )

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

          SCREAMING_SNAKE_CASE_     =	AutoConfig.for_model(_SCREAMING_SNAKE_CASE      ,			**_SCREAMING_SNAKE_CASE     )
          SCREAMING_SNAKE_CASE_     =	AutoConfig.for_model(_SCREAMING_SNAKE_CASE      ,			**_SCREAMING_SNAKE_CASE     )
          SCREAMING_SNAKE_CASE_     =	True



       @classmethod
       def   _UpperCamelCase     (				cls      ,			_A      ,			_A      ,			**_A     )							->							PretrainedConfig:
          logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''     )
          SCREAMING_SNAKE_CASE_     =	True
          SCREAMING_SNAKE_CASE_     =	True

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




       def   _UpperCamelCase     (				self     )							->							Union[str, Any]:
          SCREAMING_SNAKE_CASE_     =	copy.deepcopy(self.__dict__     )
          SCREAMING_SNAKE_CASE_     =	self.encoder.to_dict()
          SCREAMING_SNAKE_CASE_     =	self.decoder.to_dict()
          SCREAMING_SNAKE_CASE_     =	self.__class__.model_type
          return output







class 							UpperCamelCase__						(     __SCREAMING_SNAKE_CASE     ):






       """simple docstring"""


       UpperCAmelCase_							    =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 							UpperCamelCase__						(     __SCREAMING_SNAKE_CASE     ):






       """simple docstring"""


       @property
       def   _UpperCamelCase     (				self     )							->							Mapping[str, Mapping[int, str]]:
          SCREAMING_SNAKE_CASE_     =	OrderedDict()
          SCREAMING_SNAKE_CASE_     =	{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
          SCREAMING_SNAKE_CASE_     =	{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
          SCREAMING_SNAKE_CASE_     =	{0: '''batch''', 1: '''encoder_sequence'''}

          return common_inputs




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

          SCREAMING_SNAKE_CASE_     =	OrderedDict()

          SCREAMING_SNAKE_CASE_     =	super().generate_dummy_inputs(
              _SCREAMING_SNAKE_CASE      ,			batch_size=_SCREAMING_SNAKE_CASE      ,			seq_length=_SCREAMING_SNAKE_CASE      ,			is_pair=_SCREAMING_SNAKE_CASE      ,			framework=_SCREAMING_SNAKE_CASE     )

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

          return common_inputs







class 							UpperCamelCase__						(     __SCREAMING_SNAKE_CASE     ):






       """simple docstring"""


       @property
       def   _UpperCamelCase     (				self     )							->							None:
          pass



       def   _UpperCamelCase     (				self      ,			_A     )							->							OnnxConfig:
          return VisionEncoderDecoderEncoderOnnxConfig(_SCREAMING_SNAKE_CASE     )




       def   _UpperCamelCase     (				self      ,			_A      ,			_A      ,			_A = "default"     )							->							OnnxConfig:
          SCREAMING_SNAKE_CASE_     =	encoder_config.hidden_size
          return VisionEncoderDecoderDecoderOnnxConfig(_SCREAMING_SNAKE_CASE      ,			_SCREAMING_SNAKE_CASE     )






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase								=	logging.get_logger(__name__)

UpperCamelCase								=	{
    """vinvino02/glpn-kitti""": """https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json""",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    "glpn"



       def __init__(      self				,       _SCREAMING_SNAKE_CASE=3				,       _SCREAMING_SNAKE_CASE=4				,       _SCREAMING_SNAKE_CASE=[2, 2, 2, 2]				,       _SCREAMING_SNAKE_CASE=[8, 4, 2, 1]				,       _SCREAMING_SNAKE_CASE=[32, 64, 160, 256]				,       _SCREAMING_SNAKE_CASE=[7, 3, 3, 3]				,       _SCREAMING_SNAKE_CASE=[4, 2, 2, 2]				,       _SCREAMING_SNAKE_CASE=[1, 2, 5, 8]				,       _SCREAMING_SNAKE_CASE=[4, 4, 4, 4]				,       _SCREAMING_SNAKE_CASE="gelu"				,       _SCREAMING_SNAKE_CASE=0.0				,       _SCREAMING_SNAKE_CASE=0.0				,       _SCREAMING_SNAKE_CASE=0.0_2				,       _SCREAMING_SNAKE_CASE=0.1				,       _SCREAMING_SNAKE_CASE=1e-6				,       _SCREAMING_SNAKE_CASE=64				,       _SCREAMING_SNAKE_CASE=10				,       _SCREAMING_SNAKE_CASE=-1				,       **_SCREAMING_SNAKE_CASE				,       )->Any:




         '''simple docstring'''





         super().__init__(**_SCREAMING_SNAKE_CASE		)

         A_      :				Optional[int]		     =					num_channels
         A_      :				Union[str, Any]		     =					num_encoder_blocks
         A_      :				int		     =					depths
         A_      :				Dict		     =					sr_ratios
         A_      :				Any		     =					hidden_sizes
         A_      :				int		     =					patch_sizes
         A_      :				Optional[int]		     =					strides
         A_      :				str		     =					mlp_ratios
         A_      :				List[str]		     =					num_attention_heads
         A_      :				str		     =					hidden_act
         A_      :				int		     =					hidden_dropout_prob
         A_      :				List[Any]		     =					attention_probs_dropout_prob
         A_      :				Optional[Any]		     =					initializer_range
         A_      :				Tuple		     =					drop_path_rate
         A_      :				Optional[int]		     =					layer_norm_eps
         A_      :				List[str]		     =					decoder_hidden_size
         A_      :				List[Any]		     =					max_depth
         A_      :				List[Any]		     =					head_in_index




