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





from random import randint, random





def 		__lowerCamelCase							(UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							bool = False ,	UpperCAmelCase__       :							bool = False ,	UpperCAmelCase__       :							int = 5 ,	):
					SCREAMING_SNAKE_CASE           =    [[-1] * number_of_cells]  # Create a highway without any car
					SCREAMING_SNAKE_CASE           =    0
					SCREAMING_SNAKE_CASE           =    max(UpperCAmelCase__ ,	0					)
					while i < number_of_cells:
										SCREAMING_SNAKE_CASE           =    (
										    randint(0 ,	UpperCAmelCase__					) if random_speed else initial_speed
										)  # Place the cars
										i += (
										    randint(1 ,	max_speed * 2					) if random_frequency else frequency
										)  # Arbitrary number, may need tuning
					return highway





def 		__lowerCamelCase							(UpperCAmelCase__       :							list ,	UpperCAmelCase__       :							int					):
					SCREAMING_SNAKE_CASE           =    0
					SCREAMING_SNAKE_CASE           =    highway_now[car_index + 1 :]
					for cell in range(len(UpperCAmelCase__					)					):  # May need a better name for this
										if cells[cell] != -1:  # If the cell is not empty then
															return distance  # we have the distance we wanted
										distance += 1
					# Here if the car is near the end of the highway
					return distance + get_distance(UpperCAmelCase__ ,	-1					)





def 		__lowerCamelCase							(UpperCAmelCase__       :							list ,	UpperCAmelCase__       :							float ,	UpperCAmelCase__       :							int					):
					SCREAMING_SNAKE_CASE           =    len(UpperCAmelCase__					)
					# Beforce calculations, the highway is empty
					SCREAMING_SNAKE_CASE           =    [-1] * number_of_cells

					for car_index in range(UpperCAmelCase__					):
										if highway_now[car_index] != -1:
															# Add 1 to the current speed of the car and cap the speed
															SCREAMING_SNAKE_CASE           =    min(highway_now[car_index] + 1 ,	UpperCAmelCase__					)
															# Number of empty cell before the next car
															SCREAMING_SNAKE_CASE           =    get_distance(UpperCAmelCase__ ,	UpperCAmelCase__					) - 1
															# We can't have the car causing an accident
															SCREAMING_SNAKE_CASE           =    min(next_highway[car_index] ,	UpperCAmelCase__					)
															if random() < probability:
																				# Randomly, a driver will slow down
																				SCREAMING_SNAKE_CASE           =    max(next_highway[car_index] - 1 ,	0					)
					return next_highway





def 		__lowerCamelCase							(UpperCAmelCase__       :							list ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							float ,	UpperCAmelCase__       :							int					):
					SCREAMING_SNAKE_CASE           =    len(highway[0]					)

					for i in range(UpperCAmelCase__					):
										SCREAMING_SNAKE_CASE           =    update(highway[i] ,	UpperCAmelCase__ ,	UpperCAmelCase__					)
										SCREAMING_SNAKE_CASE           =    [-1] * number_of_cells

										for car_index in range(UpperCAmelCase__					):
															SCREAMING_SNAKE_CASE           =    next_speeds_calculated[car_index]
															if speed != -1:
																				# Change the position based on the speed (with % to create the loop)
																				SCREAMING_SNAKE_CASE           =    (car_index + speed) % number_of_cells
																				# Commit the change of position
																				SCREAMING_SNAKE_CASE           =    speed
										highway.append(UpperCAmelCase__					)

					return highway


if __name__ == "__main__":
							import doctest

							doctest.testmod()




