'''simple docstring'''






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,
)


_UpperCAmelCase    :     Dict         =       logging.getLogger(__name__)







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   def 	__UpperCAmelCase   (      self      :			List[str], UpperCamelCase__      :			str, UpperCamelCase__      :			List[Any], UpperCamelCase__      :			str=None, UpperCamelCase__      :			Tuple=None     )  ->			List[str]:
        _A    		=  self.layer[current_layer](UpperCamelCase__, UpperCamelCase__, head_mask[current_layer]     )

        _A    		=  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 lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   def __init__(      self      :			Tuple, UpperCamelCase__      :			Tuple     )  ->			List[Any]:
        super().__init__(UpperCamelCase__     )

        _A    		=  BertEncoderWithPabee(UpperCamelCase__     )

        self.init_weights()
        _A    		=  0
        _A    		=  0
        _A    		=  0

        _A    		=  0
   def 	__UpperCAmelCase   (      self      :			Union[str, Any], UpperCamelCase__      :			Optional[int]     )  ->			Dict:
        _A    		=  threshold
   def 	__UpperCAmelCase   (      self      :			int, UpperCamelCase__      :			List[Any]     )  ->			List[str]:
        _A    		=  patience
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			List[str]:
        _A    		=  0
        _A    		=  0
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			Any:
        _A    		=  self.inference_layers_num / self.inference_instances_num
        _A    		=  (
            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(UpperCamelCase__     )

   @add_start_docstrings_to_model_forward(UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			List[Any], UpperCamelCase__      :			Optional[Any]=None, UpperCamelCase__      :			Any=None, UpperCamelCase__      :			List[str]=None, UpperCamelCase__      :			str=None, UpperCamelCase__      :			Dict=None, UpperCamelCase__      :			Dict=None, UpperCamelCase__      :			Dict=None, UpperCamelCase__      :			Any=None, UpperCamelCase__      :			Union[str, Any]=None, UpperCamelCase__      :			List[Any]=None, UpperCamelCase__      :			Any=False, )  ->			str:

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

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

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

        # 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.
        _A    		=  self.get_extended_attention_mask(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__     )

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

        _A    		=  self.embeddings(
            input_ids=UpperCamelCase__, position_ids=UpperCamelCase__, token_type_ids=UpperCamelCase__, inputs_embeds=UpperCamelCase__     )
        _A    		=  embedding_output

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

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

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

                  _A    		=  logits
                  if patient_counter == self.patience:
                       break
             _A    		=  [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 lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   def __init__(      self      :			Dict, UpperCamelCase__      :			List[str]     )  ->			Any:
        super().__init__(UpperCamelCase__     )
        _A    		=  config.num_labels

        _A    		=  BertModelWithPabee(UpperCamelCase__     )
        _A    		=  nn.Dropout(config.hidden_dropout_prob     )
        _A    		=  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(UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Dict, UpperCamelCase__      :			int=None, UpperCamelCase__      :			str=None, UpperCamelCase__      :			Any=None, UpperCamelCase__      :			List[Any]=None, UpperCamelCase__      :			Union[str, Any]=None, UpperCamelCase__      :			Tuple=None, UpperCamelCase__      :			List[str]=None, )  ->			str:
        _A    		=  self.bert(
            input_ids=UpperCamelCase__, attention_mask=UpperCamelCase__, token_type_ids=UpperCamelCase__, position_ids=UpperCamelCase__, head_mask=UpperCamelCase__, inputs_embeds=UpperCamelCase__, output_dropout=self.dropout, output_layers=self.classifiers, regression=self.num_labels == 1, )

        _A    		=  (logits[-1],)

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

        return outputs





"""simple docstring"""





from collections.abc import Callable

import numpy as np



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case      )				->						np.array:


					"""simple docstring"""





					_UpperCamelCase									=    int(np.ceil((x_end - xa) / step_size      )      )
					_UpperCamelCase									=    np.zeros((n + 1,)      )
					_UpperCamelCase									=    ya
					_UpperCamelCase									=    xa

					for k in range(__snake_case      ):
										_UpperCamelCase									=    y[k] + step_size * ode_func(__snake_case,	y[k]      )
										_UpperCamelCase									=    y[k] + (
										    (step_size / 2) * (ode_func(__snake_case,	y[k]      ) + ode_func(x + step_size,	__snake_case      ))
										)
										x += step_size

					return y


if __name__ == "__main__":
				import doctest

				doctest.testmod()






