'''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 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





				def  snake_case__				(						self					:						int       ,		lowercase__					:						Optional[Any]       ,		lowercase__					:						Dict       ,		lowercase__					:						int=None       ,		lowercase__					:						List[Any]=None					)							->Any:


										'''simple docstring'''




										_UpperCamelCase			:			Any   			=						self.layer[current_layer](lowercase__       ,		lowercase__       ,		head_mask[current_layer]					)

										_UpperCamelCase			:			Tuple   			=						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.'''   , SCREAMING_SNAKE_CASE   , )
class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





				def __init__(						self					:						Optional[Any]       ,		lowercase__					:						List[str]					)							->Union[str, Any]:


										'''simple docstring'''




										super().__init__(lowercase__					)

										_UpperCamelCase			:			Optional[int]   			=						BertEncoderWithPabee(lowercase__					)

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

										_UpperCamelCase			:			List[Any]   			=						0





				def  snake_case__				(						self					:						Optional[Any]       ,		lowercase__					:						Optional[Any]					)							->List[str]:


										'''simple docstring'''




										_UpperCamelCase			:			List[str]   			=						threshold





				def  snake_case__				(						self					:						Optional[Any]       ,		lowercase__					:						Tuple					)							->str:


										'''simple docstring'''




										_UpperCamelCase			:			Tuple   			=						patience





				def  snake_case__				(						self					:						int					)							->str:


										'''simple docstring'''




										_UpperCamelCase			:			Tuple   			=						0
										_UpperCamelCase			:			Optional[Any]   			=						0





				def  snake_case__				(						self					:						Any					)							->Optional[Any]:


										'''simple docstring'''




										_UpperCamelCase			:			List[str]   			=						self.inference_layers_num / self.inference_instances_num
										_UpperCamelCase			:			Optional[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  snake_case__				(						self					:						Union[str, Any]       ,		lowercase__					:						Union[str, Any]=None       ,		lowercase__					:						str=None       ,		lowercase__					:						List[Any]=None       ,		lowercase__					:						Optional[Any]=None       ,		lowercase__					:						List[Any]=None       ,		lowercase__					:						List[Any]=None       ,		lowercase__					:						str=None       ,		lowercase__					:						str=None       ,		lowercase__					:						int=None       ,		lowercase__					:						List[str]=None       ,		lowercase__					:						List[Any]=False       ,		)							->str:


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

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

										if attention_mask is None:
																_UpperCamelCase			:			List[str]   			=						torch.ones(lowercase__       ,		device=lowercase__					)
										if token_type_ids is None:
																_UpperCamelCase			:			str   			=						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.
										_UpperCamelCase			:			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:
																_UpperCamelCase			,       _UpperCamelCase			,       _UpperCamelCase			:			Optional[int]   			=						encoder_hidden_states.size()
																_UpperCamelCase			:			Optional[Any]   			=						(encoder_batch_size, encoder_sequence_length)
																if encoder_attention_mask is None:
																						_UpperCamelCase			:			Any   			=						torch.ones(lowercase__       ,		device=lowercase__					)
																_UpperCamelCase			:			int   			=						self.invert_attention_mask(lowercase__					)
										else:
																_UpperCamelCase			:			str   			=						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			:			Any   			=						self.get_head_mask(lowercase__       ,		self.config.num_hidden_layers					)

										_UpperCamelCase			:			Optional[Any]   			=						self.embeddings(
										    input_ids=lowercase__       ,		position_ids=lowercase__       ,		token_type_ids=lowercase__       ,		inputs_embeds=lowercase__					)
										_UpperCamelCase			:			int   			=						embedding_output

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

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

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

																						_UpperCamelCase			:			Tuple   			=						logits
																						if patient_counter == self.patience:
																												break
																_UpperCamelCase			:			str   			=						[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. '''   , SCREAMING_SNAKE_CASE   , )
class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





				def __init__(						self					:						Optional[int]       ,		lowercase__					:						Dict					)							->int:


										'''simple docstring'''




										super().__init__(lowercase__					)
										_UpperCamelCase			:			str   			=						config.num_labels

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


										'''simple docstring'''




										_UpperCamelCase			:			int   			=						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       ,		)

										_UpperCamelCase			:			Dict   			=						(logits[-1],)

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

										return outputs



'''simple docstring'''





def 						__A							(     UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase=False					)     ->			Optional[int]:




						'''simple docstring'''

						if isinstance(UpperCAmelCase     ,UpperCAmelCase					) and isinstance(UpperCAmelCase     ,UpperCAmelCase					):
												_UpperCamelCase			:			Any   			=						len(set_a.intersection(UpperCAmelCase					)					)

												if alternative_union:
																		_UpperCamelCase			:			List[str]   			=						len(UpperCAmelCase					) + len(UpperCAmelCase					)
												else:
																		_UpperCamelCase			:			List[str]   			=						len(set_a.union(UpperCAmelCase					)					)

												return intersection / union

						if isinstance(UpperCAmelCase     ,(list, tuple)					) and isinstance(UpperCAmelCase     ,(list, tuple)					):
												_UpperCamelCase			:			List[Any]   			=						[element for element in set_a if element in set_b]

												if alternative_union:
																		_UpperCamelCase			:			str   			=						len(UpperCAmelCase					) + len(UpperCAmelCase					)
																		return len(UpperCAmelCase					) / union
												else:
																		_UpperCamelCase			:			int   			=						set_a + [element for element in set_b if element not in set_a]
																		return len(UpperCAmelCase					) / len(UpperCAmelCase					)

												return len(UpperCAmelCase					) / len(UpperCAmelCase					)
						return None


if __name__ == "__main__":
			lowerCAmelCase_	:		List[str]			=     {"""a""", """b""", """c""", """d""", """e"""}
			lowerCAmelCase_	:		Dict			=     {"""c""", """d""", """e""", """f""", """h""", """i"""}
			print(jaccard_similarity(set_a, set_b))


