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,
)


_lowerCamelCase   :				List[Any]     			=						logging.getLogger(__name__)


class   lowercase      (       a       ):




       def 		__snake_case(  self				:   Union[str, Any]		,							_UpperCamelCase				:   List[str]		,							_UpperCamelCase				:   Optional[int]		,							_UpperCamelCase				:   Union[str, Any]=None		,							_UpperCamelCase				:   List[str]=None )   ->			List[Any]:



            '''simple docstring'''

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

            SCREAMING_SNAKE_CASE           =    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."""			,    a			,    )
class   lowercase      (       a       ):




       def __init__(  self				:   Any		,							_UpperCamelCase				:   int )   ->			Union[str, Any]:



            '''simple docstring'''

            super().__init__(_UpperCamelCase )

            SCREAMING_SNAKE_CASE           =    BertEncoderWithPabee(_UpperCamelCase )

            self.init_weights()
            SCREAMING_SNAKE_CASE           =    0
            SCREAMING_SNAKE_CASE           =    0
            SCREAMING_SNAKE_CASE           =    0

            SCREAMING_SNAKE_CASE           =    0




       def 		__snake_case(  self				:   Union[str, Any]		,							_UpperCamelCase				:   Any )   ->			List[Any]:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE           =    threshold




       def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   Dict )   ->			Optional[int]:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE           =    patience




       def 		__snake_case(  self				:   Optional[int] )   ->			Optional[int]:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE           =    0
            SCREAMING_SNAKE_CASE           =    0




       def 		__snake_case(  self				:   Tuple )   ->			int:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE           =    self.inference_layers_num / self.inference_instances_num
            SCREAMING_SNAKE_CASE           =    (
                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 		__snake_case(  self				:   List[Any]		,							_UpperCamelCase				:   Optional[int]=None		,							_UpperCamelCase				:   Tuple=None		,							_UpperCamelCase				:   Optional[int]=None		,							_UpperCamelCase				:   int=None		,							_UpperCamelCase				:   Any=None		,							_UpperCamelCase				:   Dict=None		,							_UpperCamelCase				:   List[Any]=None		,							_UpperCamelCase				:   Optional[int]=None		,							_UpperCamelCase				:   str=None		,							_UpperCamelCase				:   Optional[int]=None		,							_UpperCamelCase				:   Tuple=False		,							)   ->			Dict:



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

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

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

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

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

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

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

                      SCREAMING_SNAKE_CASE           =    logits
                      if patient_counter == self.patience:
                           break
                 SCREAMING_SNAKE_CASE           =    [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. """			,    a			,    )
class   lowercase      (       a       ):




       def __init__(  self				:   Optional[Any]		,							_UpperCamelCase				:   Tuple )   ->			List[str]:



            '''simple docstring'''

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

            SCREAMING_SNAKE_CASE           =    BertModelWithPabee(_UpperCamelCase )
            SCREAMING_SNAKE_CASE           =    nn.Dropout(config.hidden_dropout_prob )
            SCREAMING_SNAKE_CASE           =    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 		__snake_case(  self				:   List[str]		,							_UpperCamelCase				:   Dict=None		,							_UpperCamelCase				:   Any=None		,							_UpperCamelCase				:   Optional[Any]=None		,							_UpperCamelCase				:   Union[str, Any]=None		,							_UpperCamelCase				:   Union[str, Any]=None		,							_UpperCamelCase				:   Any=None		,							_UpperCamelCase				:   int=None		,							)   ->			List[Any]:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE           =    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		,							)

            SCREAMING_SNAKE_CASE           =    (logits[-1],)

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

            return outputs





def 		__lowerCamelCase							(UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							int					):
					while b:
										SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    b, a % b
					return a





def 		__lowerCamelCase							(UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							int					):
					return a if b == 0 else euclidean_gcd_recursive(UpperCAmelCase__ ,	a % b					)





def 		__lowerCamelCase							():
					print(F"euclidean_gcd(3, 5) = {euclidean_gcd(3 ,	5					)}"					)
					print(F"euclidean_gcd(5, 3) = {euclidean_gcd(5 ,	3					)}"					)
					print(F"euclidean_gcd(1, 3) = {euclidean_gcd(1 ,	3					)}"					)
					print(F"euclidean_gcd(3, 6) = {euclidean_gcd(3 ,	6					)}"					)
					print(F"euclidean_gcd(6, 3) = {euclidean_gcd(6 ,	3					)}"					)

					print(F"euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 ,	5					)}"					)
					print(F"euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 ,	3					)}"					)
					print(F"euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 ,	3					)}"					)
					print(F"euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 ,	6					)}"					)
					print(F"euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 ,	3					)}"					)


if __name__ == "__main__":
							main()




