

import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


lowercase_									=   {
    'facebook/maskformer-swin-base-ade': (
        'https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

lowercase_									=   logging.get_logger(__name__)





class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE							):
  _a									=  """maskformer"""
  _a									=  {"""hidden_size""": """mask_feature_size"""}
  _a									=  ["""resnet""", """swin"""]
  _a									=  ["""detr"""]




  def __init__(				self							,   lowerCAmelCase = 256							,   lowerCAmelCase = 256							,   lowerCAmelCase = 0.1							,   lowerCAmelCase = False							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = 0.02							,   lowerCAmelCase = 1.0							,   lowerCAmelCase = 1.0							,   lowerCAmelCase = 1.0							,   lowerCAmelCase = 20.0							,   lowerCAmelCase = None							,   **lowerCAmelCase							,   )  -> Any:
        '''simple docstring'''






        if backbone_config is None:
              # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
              _lowercase	       =SwinConfig(
                  image_size=384							,   in_channels=3							,   patch_size=4							,   embed_dim=128							,   depths=[2, 2, 18, 2]							,   num_heads=[4, 8, 16, 32]							,   window_size=12							,   drop_path_rate=0.3							,   out_features=['stage1', 'stage2', 'stage3', 'stage4']							,   )

        if isinstance(lowerCAmelCase							,   lowerCAmelCase					):
              _lowercase	       =backbone_config.pop('model_type'					)
              _lowercase	       =CONFIG_MAPPING[backbone_model_type]
              _lowercase	       =config_class.from_dict(lowerCAmelCase					)

        # verify that the backbone is supported
        if backbone_config.model_type not in self.backbones_supported:
              logger.warning_once(
                  F'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. '''
                  F'''Supported model types: {",".join(self.backbones_supported					)}'''					)

        if decoder_config is None:
              # fall back to https://huggingface.co/facebook/detr-resnet-50
              _lowercase	       =DetrConfig()
        else:
              # verify that the decoder is supported
              _lowercase	       =(
                  decoder_config.pop('model_type'					) if isinstance(lowerCAmelCase							,   lowerCAmelCase					) else decoder_config.model_type
              )
              if decoder_type not in self.decoders_supported:
                    raise ValueError(
                        F'''Transformer Decoder {decoder_type} not supported, please use one of'''
                        F''' {",".join(self.decoders_supported					)}'''					)
              if isinstance(lowerCAmelCase							,   lowerCAmelCase					):
                    _lowercase	       =CONFIG_MAPPING[decoder_type]
                    _lowercase	       =config_class.from_dict(lowerCAmelCase					)

        _lowercase	       =backbone_config
        _lowercase	       =decoder_config
        # main feature dimension for the model
        _lowercase	       =fpn_feature_size
        _lowercase	       =mask_feature_size
        # initializer
        _lowercase	       =init_std
        _lowercase	       =init_xavier_std
        # Hungarian matcher && loss
        _lowercase	       =cross_entropy_weight
        _lowercase	       =dice_weight
        _lowercase	       =mask_weight
        _lowercase	       =use_auxiliary_loss
        _lowercase	       =no_object_weight
        _lowercase	       =output_auxiliary_logits

        _lowercase	       =self.decoder_config.encoder_attention_heads
        _lowercase	       =self.decoder_config.num_hidden_layers
        super().__init__(**lowerCAmelCase					)




  @classmethod
  def   A__     (				cls							,   lowerCAmelCase							,   lowerCAmelCase							,   **lowerCAmelCase					)  -> int:
        '''simple docstring'''






        return cls(
            backbone_config=lowerCAmelCase							,   decoder_config=lowerCAmelCase							,   **lowerCAmelCase							,   )




  def   A__     (				self					)  -> Dict[str, any]:
        '''simple docstring'''






        _lowercase	       =copy.deepcopy(self.__dict__					)
        _lowercase	       =self.backbone_config.to_dict()
        _lowercase	       =self.decoder_config.to_dict()
        _lowercase	       =self.__class__.model_type
        return output





import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


lowercase_									=   logging.getLogger(__name__)





class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE							):




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase=None							,   lowerCAmelCase=None					)  -> List[str]:
        '''simple docstring'''






        _lowercase	       =self.layer[current_layer](lowerCAmelCase							,   lowerCAmelCase							,   head_mask[current_layer]					)

        _lowercase	       =layer_outputs[0]

        return hidden_states





@add_start_docstrings(
    """The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top."""			,			SCREAMING_SNAKE_CASE			,			)
class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE							):




  def __init__(				self							,   lowerCAmelCase					)  -> Union[str, Any]:
        '''simple docstring'''






        super().__init__(lowerCAmelCase					)

        _lowercase	       =BertEncoderWithPabee(lowerCAmelCase					)

        self.init_weights()
        _lowercase	       =0
        _lowercase	       =0
        _lowercase	       =0

        _lowercase	       =0




  def   A__     (				self							,   lowerCAmelCase					)  -> Optional[Any]:
        '''simple docstring'''






        _lowercase	       =threshold




  def   A__     (				self							,   lowerCAmelCase					)  -> List[Any]:
        '''simple docstring'''






        _lowercase	       =patience




  def   A__     (				self					)  -> Dict:
        '''simple docstring'''






        _lowercase	       =0
        _lowercase	       =0




  def   A__     (				self					)  -> int:
        '''simple docstring'''






        _lowercase	       =self.inference_layers_num / self.inference_instances_num
        _lowercase	       =(
            F'''*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='''
            F''' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'''
        )
        print(lowerCAmelCase					)




  @add_start_docstrings_to_model_forward(lowerCAmelCase					)
  def   A__     (				self							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=False							,   )  -> str:
        '''simple docstring'''






        if input_ids is not None and inputs_embeds is not None:
              raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time'					)
        elif input_ids is not None:
              _lowercase	       =input_ids.size()
        elif inputs_embeds is not None:
              _lowercase	       =inputs_embeds.size()[:-1]
        else:
              raise ValueError('You have to specify either input_ids or inputs_embeds'					)

        _lowercase	       =input_ids.device if input_ids is not None else inputs_embeds.device

        if attention_mask is None:
              _lowercase	       =torch.ones(lowerCAmelCase							,   device=lowerCAmelCase					)
        if token_type_ids is None:
              _lowercase	       =torch.zeros(lowerCAmelCase							,   dtype=torch.long							,   device=lowerCAmelCase					)

        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
        # ourselves in which case we just need to make it broadcastable to all heads.
        _lowercase	       =self.get_extended_attention_mask(lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase					)

        # If a 2D ou 3D attention mask is provided for the cross-attention
        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
        if self.config.is_decoder and encoder_hidden_states is not None:
              _lowercase       ,       _lowercase       ,       _lowercase	       =encoder_hidden_states.size()
              _lowercase	       =(encoder_batch_size, encoder_sequence_length)
              if encoder_attention_mask is None:
                    _lowercase	       =torch.ones(lowerCAmelCase							,   device=lowerCAmelCase					)
              _lowercase	       =self.invert_attention_mask(lowerCAmelCase					)
        else:
              _lowercase	       =None

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
        _lowercase	       =self.get_head_mask(lowerCAmelCase							,   self.config.num_hidden_layers					)

        _lowercase	       =self.embeddings(
            input_ids=lowerCAmelCase							,   position_ids=lowerCAmelCase							,   token_type_ids=lowerCAmelCase							,   inputs_embeds=lowerCAmelCase					)
        _lowercase	       =embedding_output

        if self.training:
              _lowercase	       =[]
              for i in range(self.config.num_hidden_layers					):
                    _lowercase	       =self.encoder.adaptive_forward(
                        lowerCAmelCase							,   current_layer=lowerCAmelCase							,   attention_mask=lowerCAmelCase							,   head_mask=lowerCAmelCase					)

                    _lowercase	       =self.pooler(lowerCAmelCase					)
                    _lowercase	       =output_layers[i](output_dropout(lowerCAmelCase					)					)
                    res.append(lowerCAmelCase					)
        elif self.patience == 0:  # Use all layers for inference
              _lowercase	       =self.encoder(
                  lowerCAmelCase							,   attention_mask=lowerCAmelCase							,   head_mask=lowerCAmelCase							,   encoder_hidden_states=lowerCAmelCase							,   encoder_attention_mask=lowerCAmelCase							,   )
              _lowercase	       =self.pooler(encoder_outputs[0]					)
              _lowercase	       =[output_layers[self.config.num_hidden_layers - 1](lowerCAmelCase					)]
        else:
              _lowercase	       =0
              _lowercase	       =None
              _lowercase	       =0
              for i in range(self.config.num_hidden_layers					):
                    calculated_layer_num += 1
                    _lowercase	       =self.encoder.adaptive_forward(
                        lowerCAmelCase							,   current_layer=lowerCAmelCase							,   attention_mask=lowerCAmelCase							,   head_mask=lowerCAmelCase					)

                    _lowercase	       =self.pooler(lowerCAmelCase					)
                    _lowercase	       =output_layers[i](lowerCAmelCase					)
                    if regression:
                          _lowercase	       =logits.detach()
                          if patient_result is not None:
                                _lowercase	       =patient_result.detach()
                          if (patient_result is not None) and torch.abs(patient_result - labels					) < self.regression_threshold:
                                patient_counter += 1
                          else:
                                _lowercase	       =0
                    else:
                          _lowercase	       =logits.detach().argmax(dim=1					)
                          if patient_result is not None:
                                _lowercase	       =patient_result.detach().argmax(dim=1					)
                          if (patient_result is not None) and torch.all(labels.eq(lowerCAmelCase					)					):
                                patient_counter += 1
                          else:
                                _lowercase	       =0

                    _lowercase	       =logits
                    if patient_counter == self.patience:
                          break
              _lowercase	       =[patient_result]
              self.inference_layers_num += calculated_layer_num
              self.inference_instances_num += 1

        return res





@add_start_docstrings(
    """Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. """			,			SCREAMING_SNAKE_CASE			,			)
class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE							):




  def __init__(				self							,   lowerCAmelCase					)  -> List[Any]:
        '''simple docstring'''






        super().__init__(lowerCAmelCase					)
        _lowercase	       =config.num_labels

        _lowercase	       =BertModelWithPabee(lowerCAmelCase					)
        _lowercase	       =nn.Dropout(config.hidden_dropout_prob					)
        _lowercase	       =nn.ModuleList(
            [nn.Linear(config.hidden_size							,   self.config.num_labels					) for _ in range(config.num_hidden_layers					)]					)

        self.init_weights()




  @add_start_docstrings_to_model_forward(lowerCAmelCase					)
  def   A__     (				self							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   )  -> Union[str, Any]:
        '''simple docstring'''






        _lowercase	       =self.bert(
            input_ids=lowerCAmelCase							,   attention_mask=lowerCAmelCase							,   token_type_ids=lowerCAmelCase							,   position_ids=lowerCAmelCase							,   head_mask=lowerCAmelCase							,   inputs_embeds=lowerCAmelCase							,   output_dropout=self.dropout							,   output_layers=self.classifiers							,   regression=self.num_labels == 1							,   )

        _lowercase	       =(logits[-1],)

        if labels is not None:
              _lowercase	       =None
              _lowercase	       =0
              for ix, logits_item in enumerate(lowerCAmelCase					):
                    if self.num_labels == 1:
                          #  We are doing regression
                          _lowercase	       =MSELoss()
                          _lowercase	       =loss_fct(logits_item.view(-1					)							,   labels.view(-1					)					)
                    else:
                          _lowercase	       =CrossEntropyLoss()
                          _lowercase	       =loss_fct(logits_item.view(-1							,   self.num_labels					)							,   labels.view(-1					)					)
                    if total_loss is None:
                          _lowercase	       =loss
                    else:
                          total_loss += loss * (ix + 1)
                    total_weights += ix + 1
              _lowercase	       =(total_loss / total_weights,) + outputs

        return outputs


