



'''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,
)


lowerCamelCase							:Dict				    =					logging.getLogger(__name__)







class 	_lowerCAmelCase							(			__UpperCAmelCase ):


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase=None       ,      lowercase=None							):
				A_	:						List[str]	=     self.layer[current_layer](lowercase       ,      lowercase       ,      head_mask[current_layer]							)

				A_	:						Dict	=     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 	_lowerCAmelCase							(			__UpperCAmelCase ):


	def __init__(self       ,      lowercase							):
				super().__init__(lowercase							)

				A_	:						Tuple	=     BertEncoderWithPabee(lowercase							)

				self.init_weights()
				A_	:						Any	=     0
				A_	:						Any	=     0
				A_	:						List[Any]	=     0

				A_	:						int	=     0


	def     _a	(self       ,      lowercase							):
				A_	:						Union[str, Any]	=     threshold


	def     _a	(self       ,      lowercase							):
				A_	:						List[str]	=     patience


	def     _a	(self							):
				A_	:						int	=     0
				A_	:						List[str]	=     0


	def     _a	(self							):
				A_	:						List[Any]	=     self.inference_layers_num / self.inference_instances_num
				A_	:						Any	=     (
				    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(lowercase							)


	@add_start_docstrings_to_model_forward(lowercase							)
	def     _a	(self       ,      lowercase=None       ,      lowercase=None       ,      lowercase=None       ,      lowercase=None       ,      lowercase=None       ,      lowercase=None       ,      lowercase=None       ,      lowercase=None       ,      lowercase=None       ,      lowercase=None       ,      lowercase=False       ,      ):

				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_	:						Optional[int]	=     input_ids.size()
				elif inputs_embeds is not None:
							A_	:						str	=     inputs_embeds.size()[:-1]
				else:
							raise ValueError("""You have to specify either input_ids or inputs_embeds"""							)

				A_	:						List[str]	=     input_ids.device if input_ids is not None else inputs_embeds.device

				if attention_mask is None:
							A_	:						Optional[Any]	=     torch.ones(lowercase       ,      device=lowercase							)
				if token_type_ids is None:
							A_	:						Optional[Any]	=     torch.zeros(lowercase       ,      dtype=torch.long       ,      device=lowercase							)

				# 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_	:						torch.Tensor	=     self.get_extended_attention_mask(lowercase       ,      lowercase       ,      lowercase							)

				# 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_	:						Optional[int]	=     encoder_hidden_states.size()
							A_	:						Tuple	=     (encoder_batch_size, encoder_sequence_length)
							if encoder_attention_mask is None:
										A_	:						Optional[Any]	=     torch.ones(lowercase       ,      device=lowercase							)
							A_	:						Any	=     self.invert_attention_mask(lowercase							)
				else:
							A_	:						Any	=     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_	:						List[Any]	=     self.get_head_mask(lowercase       ,      self.config.num_hidden_layers							)

				A_	:						Tuple	=     self.embeddings(
				    input_ids=lowercase       ,      position_ids=lowercase       ,      token_type_ids=lowercase       ,      inputs_embeds=lowercase							)
				A_	:						Optional[Any]	=     embedding_output

				if self.training:
							A_	:						int	=     []
							for i in range(self.config.num_hidden_layers							):
										A_	:						Optional[int]	=     self.encoder.adaptive_forward(
										    lowercase       ,      current_layer=lowercase       ,      attention_mask=lowercase       ,      head_mask=lowercase							)

										A_	:						str	=     self.pooler(lowercase							)
										A_	:						List[str]	=     output_layers[i](output_dropout(lowercase							)							)
										res.append(lowercase							)
				elif self.patience == 0:  # Use all layers for inference
							A_	:						Optional[int]	=     self.encoder(
							    lowercase       ,      attention_mask=lowercase       ,      head_mask=lowercase       ,      encoder_hidden_states=lowercase       ,      encoder_attention_mask=lowercase       ,      )
							A_	:						List[str]	=     self.pooler(encoder_outputs[0]							)
							A_	:						Optional[Any]	=     [output_layers[self.config.num_hidden_layers - 1](lowercase							)]
				else:
							A_	:						Optional[Any]	=     0
							A_	:						List[str]	=     None
							A_	:						Dict	=     0
							for i in range(self.config.num_hidden_layers							):
										calculated_layer_num += 1
										A_	:						Union[str, Any]	=     self.encoder.adaptive_forward(
										    lowercase       ,      current_layer=lowercase       ,      attention_mask=lowercase       ,      head_mask=lowercase							)

										A_	:						Dict	=     self.pooler(lowercase							)
										A_	:						Dict	=     output_layers[i](lowercase							)
										if regression:
													A_	:						str	=     logits.detach()
													if patient_result is not None:
																A_	:						List[str]	=     patient_result.detach()
													if (patient_result is not None) and torch.abs(patient_result - labels							) < self.regression_threshold:
																patient_counter += 1
													else:
																A_	:						str	=     0
										else:
													A_	:						Optional[int]	=     logits.detach().argmax(dim=1							)
													if patient_result is not None:
																A_	:						List[str]	=     patient_result.detach().argmax(dim=1							)
													if (patient_result is not None) and torch.all(labels.eq(lowercase							)							):
																patient_counter += 1
													else:
																A_	:						Any	=     0

										A_	:						Tuple	=     logits
										if patient_counter == self.patience:
													break
							A_	:						int	=     [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 	_lowerCAmelCase							(			__UpperCAmelCase ):


	def __init__(self       ,      lowercase							):
				super().__init__(lowercase							)
				A_	:						str	=     config.num_labels

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

				A_	:						Optional[Any]	=     (logits[-1],)

				if labels is not None:
							A_	:						Tuple	=     None
							A_	:						List[Any]	=     0
							for ix, logits_item in enumerate(lowercase							):
										if self.num_labels == 1:
													#  We are doing regression
													A_	:						List[str]	=     MSELoss()
													A_	:						int	=     loss_fct(logits_item.view(-1							)       ,      labels.view(-1							)							)
										else:
													A_	:						Any	=     CrossEntropyLoss()
													A_	:						Any	=     loss_fct(logits_item.view(-1       ,      self.num_labels							)       ,      labels.view(-1							)							)
										if total_loss is None:
													A_	:						str	=     loss
										else:
													total_loss += loss * (ix + 1)
										total_weights += ix + 1
							A_	:						Tuple	=     (total_loss / total_weights,) + outputs

				return outputs




'''simple docstring'''




class 	_lowerCAmelCase							(			__UpperCAmelCase ):
	pass







class 	_lowerCAmelCase							(			__UpperCAmelCase ):
	pass







class 	_lowerCAmelCase							:


	def __init__(self							):
				A_	:						List[Any]	=     [
				    [],
				    [],
				    [],
				]


	def     _a	(self       ,      lowercase       ,      lowercase							):
				try:
							if len(self.queues[priority]							) >= 100:
										raise OverflowError("""Maximum queue size is 100"""							)
							self.queues[priority].append(lowercase							)
				except IndexError:
							raise ValueError("""Valid priorities are 0, 1, and 2"""							)


	def     _a	(self							):
				for queue in self.queues:
							if queue:
										return queue.pop(0							)
				raise UnderFlowError("""All queues are empty"""							)


	def __str__(self							):
				return "\n".join(F'Priority {i}: {q}' for i, q in enumerate(self.queues							)							)







class 	_lowerCAmelCase							:


	def __init__(self							):
				A_	:						List[str]	=     []


	def     _a	(self       ,      lowercase							):
				if len(self.queue							) == 100:
							raise OverFlowError("""Maximum queue size is 100"""							)
				self.queue.append(lowercase							)


	def     _a	(self							):
				if not self.queue:
							raise UnderFlowError("""The queue is empty"""							)
				else:
							A_	:						List[Any]	=     min(self.queue							)
							self.queue.remove(lowercase							)
							return data


	def __str__(self							):
				return str(self.queue							)






def 	a			( ):
			'''simple docstring'''





			A_	:						Optional[int]	=     FixedPriorityQueue()
			fpq.enqueue(0						,      10    )
			fpq.enqueue(1						,      70    )
			fpq.enqueue(0						,      1_00    )
			fpq.enqueue(2						,      1    )
			fpq.enqueue(2						,      5    )
			fpq.enqueue(1						,      7    )
			fpq.enqueue(2						,      4    )
			fpq.enqueue(1						,      64    )
			fpq.enqueue(0						,      1_28    )
			print(lowerCamelCase__    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(lowerCamelCase__    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )



def 	a			( ):
			'''simple docstring'''





			A_	:						int	=     ElementPriorityQueue()
			epq.enqueue(10    )
			epq.enqueue(70    )
			epq.enqueue(1_00    )
			epq.enqueue(1    )
			epq.enqueue(5    )
			epq.enqueue(7    )
			epq.enqueue(4    )
			epq.enqueue(64    )
			epq.enqueue(1_28    )
			print(lowerCamelCase__    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(lowerCamelCase__    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )


if __name__ == "__main__":
						fixed_priority_queue()
						element_priority_queue()