from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


UpperCamelCase__											=					{'configuration_dpt': ['DPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPTConfig']}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase__											=					['DPTFeatureExtractor']
      UpperCamelCase__											=					['DPTImageProcessor']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase__											=					[
          'DPT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'DPTForDepthEstimation',
          'DPTForSemanticSegmentation',
          'DPTModel',
          'DPTPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_dpt import DPTFeatureExtractor
            from .image_processing_dpt import DPTImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dpt import (
                DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DPTForDepthEstimation,
                DPTForSemanticSegmentation,
                DPTModel,
                DPTPreTrainedModel,
            )


else:
      import sys

      UpperCamelCase__											=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput





@dataclass
class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : Optional[torch.FloatTensor]      =			None
   __UpperCAmelCase : torch.FloatTensor      =			None
   __UpperCAmelCase : Optional[Tuple[torch.FloatTensor]]      =			None
   __UpperCAmelCase : Optional[Tuple[torch.FloatTensor]]      =			None





class      A							(				UpperCAmelCase_     ):


   def __init__(self     :						Union[str, Any]       ,  __UpperCAmelCase     :						Tuple=1       ,  __UpperCAmelCase     :						str=0       ,  __UpperCAmelCase     :						str=2       ,  __UpperCAmelCase     :						Union[str, Any]=5_1_2       ,  __UpperCAmelCase     :						List[str]="cls"       ,  __UpperCAmelCase     :						Optional[int]=False       ,  __UpperCAmelCase     :						str=True       ,  **__UpperCAmelCase     :						str       ,  )						->				int:
         """simple docstring"""



         super().__init__(pad_token_id=__UpperCAmelCase       ,  bos_token_id=__UpperCAmelCase       ,  eos_token_id=__UpperCAmelCase       ,  **__UpperCAmelCase		)
         UpperCAmelCase__								=  project_dim
         UpperCAmelCase__								=  pooler_fn
         UpperCAmelCase__								=  learn_encoder
         UpperCAmelCase__								=  use_attention_mask





class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : Tuple      =			[r'pooler', r'logit_scale']
   __UpperCAmelCase : int      =			[r'position_ids', r'predictions.decoder.bias']
   __UpperCAmelCase : Any      =			'roberta'
   __UpperCAmelCase : List[str]      =			RobertaSeriesConfig


   def __init__(self     :						Tuple       ,  __UpperCAmelCase     :						Optional[int]		)						->				int:
         """simple docstring"""



         super().__init__(__UpperCAmelCase		)
         UpperCAmelCase__								=  XLMRobertaModel(__UpperCAmelCase		)
         UpperCAmelCase__								=  nn.Linear(config.hidden_size       ,  config.project_dim		)
         UpperCAmelCase__								=  getattr(__UpperCAmelCase       ,  "has_pre_transformation"       ,  __UpperCAmelCase		)
         if self.has_pre_transformation:
               UpperCAmelCase__								=  nn.Linear(config.hidden_size       ,  config.project_dim		)
               UpperCAmelCase__								=  nn.LayerNorm(config.hidden_size       ,  eps=config.layer_norm_eps		)
         self.post_init()





   def 			lowercase_      (self     :						Optional[Any]       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[bool] = None       ,  __UpperCAmelCase     :						Optional[bool] = None       ,  __UpperCAmelCase     :						Optional[bool] = None       ,  )						->				Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  return_dict if return_dict is not None else self.config.use_return_dict

         UpperCAmelCase__								=  self.base_model(
             input_ids=__UpperCAmelCase       ,  attention_mask=__UpperCAmelCase       ,  token_type_ids=__UpperCAmelCase       ,  position_ids=__UpperCAmelCase       ,  head_mask=__UpperCAmelCase       ,  inputs_embeds=__UpperCAmelCase       ,  encoder_hidden_states=__UpperCAmelCase       ,  encoder_attention_mask=__UpperCAmelCase       ,  output_attentions=__UpperCAmelCase       ,  output_hidden_states=True if self.has_pre_transformation else output_hidden_states       ,  return_dict=__UpperCAmelCase       ,  )

         if self.has_pre_transformation:
               UpperCAmelCase__								=  outputs["hidden_states"][-2]
               UpperCAmelCase__								=  self.pre_LN(__UpperCAmelCase		)
               UpperCAmelCase__								=  self.transformation_pre(__UpperCAmelCase		)

               return TransformationModelOutput(
                   projection_state=__UpperCAmelCase       ,  last_hidden_state=outputs.last_hidden_state       ,  hidden_states=outputs.hidden_states       ,  attentions=outputs.attentions       ,  )
         else:
               UpperCAmelCase__								=  self.transformation(outputs.last_hidden_state		)
               return TransformationModelOutput(
                   projection_state=__UpperCAmelCase       ,  last_hidden_state=outputs.last_hidden_state       ,  hidden_states=outputs.hidden_states       ,  attentions=outputs.attentions       ,  )



