





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    : Optional[Any]    	=  logging.getLogger(__name__)

class  __A(						__UpperCAmelCase ):

     def 	_UpperCamelCase     (      self,       A,       A,       A=None,       A=None      ):





            """simple docstring"""

            _UpperCamelCase              =						self.layer[current_layer](A,       A,       head_mask[current_layer]      )

            _UpperCamelCase              =						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."						,			__UpperCAmelCase						,			)
class  __A(						__UpperCAmelCase ):

     def __init__(      self,       A      ):





            """simple docstring"""

            super().__init__(A      )

            _UpperCamelCase              =						BertEncoderWithPabee(A      )

            self.init_weights()
            _UpperCamelCase              =						0
            _UpperCamelCase              =						0
            _UpperCamelCase              =						0

            _UpperCamelCase              =						0

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						threshold

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						patience

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						0
            _UpperCamelCase              =						0

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.inference_layers_num / self.inference_instances_num
            _UpperCamelCase              =						(
                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(A      )



     @add_start_docstrings_to_model_forward(A      )
     def 	_UpperCamelCase     (      self,       A=None,       A=None,       A=None,       A=None,       A=None,       A=None,       A=None,       A=None,       A=None,       A=None,       A=False,       ):





            """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:
                   _UpperCamelCase              =						input_ids.size()
            elif inputs_embeds is not None:
                   _UpperCamelCase              =						inputs_embeds.size()[:-1]
            else:
                   raise ValueError('''You have to specify either input_ids or inputs_embeds'''      )

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

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

            # 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.
            _UpperCamelCase              =						self.get_extended_attention_mask(A,       A,       A      )

            # 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:
                   _UpperCamelCase       ,		_UpperCamelCase       ,		_UpperCamelCase              =						encoder_hidden_states.size()
                   _UpperCamelCase              =						(encoder_batch_size, encoder_sequence_length)
                   if encoder_attention_mask is None:
                          _UpperCamelCase              =						torch.ones(A,       device=A      )
                   _UpperCamelCase              =						self.invert_attention_mask(A      )
            else:
                   _UpperCamelCase              =						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]
            _UpperCamelCase              =						self.get_head_mask(A,       self.config.num_hidden_layers      )

            _UpperCamelCase              =						self.embeddings(
                input_ids=A,       position_ids=A,       token_type_ids=A,       inputs_embeds=A      )
            _UpperCamelCase              =						embedding_output

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

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

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

                          _UpperCamelCase              =						logits
                          if patient_counter == self.patience:
                                 break
                   _UpperCamelCase              =						[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\n    the pooled output) e.g. for GLUE tasks. "						,			__UpperCAmelCase						,			)
class  __A(						__UpperCAmelCase ):

     def __init__(      self,       A      ):





            """simple docstring"""

            super().__init__(A      )
            _UpperCamelCase              =						config.num_labels

            _UpperCamelCase              =						BertModelWithPabee(A      )
            _UpperCamelCase              =						nn.Dropout(config.hidden_dropout_prob      )
            _UpperCamelCase              =						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(A      )
     def 	_UpperCamelCase     (      self,       A=None,       A=None,       A=None,       A=None,       A=None,       A=None,       A=None,       ):





            """simple docstring"""

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

            _UpperCamelCase              =						(logits[-1],)

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

            return outputs













import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowercase    : Optional[int]    	=  logging.get_logger(__name__)

lowercase    : List[Any]    	=  {
    """facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
    # See all DETR models at https://huggingface.co/models?filter=detr
}

class  __A(						__UpperCAmelCase ):
     __A	  =       "detr"
     __A	  =       ["past_key_values"]
     __A	  =       {
         "hidden_size": "d_model",
         "num_attention_heads": "encoder_attention_heads",
     }

     def __init__(      self,       A=True,       A=None,       A=3,       A=100,       A=6,       A=2048,       A=8,       A=6,       A=2048,       A=8,       A=0.0,       A=0.0,       A=True,       A="relu",       A=256,       A=0.1,       A=0.0,       A=0.0,       A=0.02,       A=1.0,       A=False,       A="sine",       A="resnet50",       A=True,       A=False,       A=1,       A=5,       A=2,       A=1,       A=1,       A=5,       A=2,       A=0.1,       **A,       ):





            """simple docstring"""

            if backbone_config is not None and use_timm_backbone:
                   raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''      )

            if not use_timm_backbone:
                   if backbone_config is None:
                          logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''      )
                          _UpperCamelCase              =						CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']      )
                   elif isinstance(A,       A      ):
                          _UpperCamelCase              =						backbone_config.get('''model_type'''      )
                          _UpperCamelCase              =						CONFIG_MAPPING[backbone_model_type]
                          _UpperCamelCase              =						config_class.from_dict(A      )
                   # set timm attributes to None
                   _UpperCamelCase       ,		_UpperCamelCase       ,		_UpperCamelCase              =						None, None, None

            _UpperCamelCase              =						use_timm_backbone
            _UpperCamelCase              =						backbone_config
            _UpperCamelCase              =						num_channels
            _UpperCamelCase              =						num_queries
            _UpperCamelCase              =						d_model
            _UpperCamelCase              =						encoder_ffn_dim
            _UpperCamelCase              =						encoder_layers
            _UpperCamelCase              =						encoder_attention_heads
            _UpperCamelCase              =						decoder_ffn_dim
            _UpperCamelCase              =						decoder_layers
            _UpperCamelCase              =						decoder_attention_heads
            _UpperCamelCase              =						dropout
            _UpperCamelCase              =						attention_dropout
            _UpperCamelCase              =						activation_dropout
            _UpperCamelCase              =						activation_function
            _UpperCamelCase              =						init_std
            _UpperCamelCase              =						init_xavier_std
            _UpperCamelCase              =						encoder_layerdrop
            _UpperCamelCase              =						decoder_layerdrop
            _UpperCamelCase              =						encoder_layers
            _UpperCamelCase              =						auxiliary_loss
            _UpperCamelCase              =						position_embedding_type
            _UpperCamelCase              =						backbone
            _UpperCamelCase              =						use_pretrained_backbone
            _UpperCamelCase              =						dilation
            # Hungarian matcher
            _UpperCamelCase              =						class_cost
            _UpperCamelCase              =						bbox_cost
            _UpperCamelCase              =						giou_cost
            # Loss coefficients
            _UpperCamelCase              =						mask_loss_coefficient
            _UpperCamelCase              =						dice_loss_coefficient
            _UpperCamelCase              =						bbox_loss_coefficient
            _UpperCamelCase              =						giou_loss_coefficient
            _UpperCamelCase              =						eos_coefficient
            super().__init__(is_encoder_decoder=A,       **A      )

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return self.encoder_attention_heads

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return self.d_model

     @classmethod
     def 	_UpperCamelCase     (      cls,       A,       **A      ):





            """simple docstring"""

            return cls(backbone_config=A,       **A      )



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						copy.deepcopy(self.__dict__      )
            if output["backbone_config"] is not None:
                   _UpperCamelCase              =						self.backbone_config.to_dict()
            _UpperCamelCase              =						self.__class__.model_type
            return output






class  __A(						__UpperCAmelCase ):
     __A	  =       version.parse("1.11" )

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return 1E-5



     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return 12






