


'''simple docstring'''






from __future__ import annotations

import math





def      UpperCAmelCase      (		lowerCamelCase_  :list				,      lowerCamelCase_  :list ):







		'''simple docstring'''
		if len(lowerCamelCase_ ) != 2 or len(a[0] ) != 2 or len(lowerCamelCase_ ) != 2 or len(b[0] ) != 2:
				raise Exception("""Matrices are not 2x2""" )
		snake_case_		:							Tuple			       =						[
		    [a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
		    [a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
		]
		return new_matrix





def      UpperCAmelCase      (		lowerCamelCase_  :list				,      lowerCamelCase_  :list ):







		'''simple docstring'''
		return [
		    [matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row] ) )]
		    for row in range(len(lowerCamelCase_ ) )
		]





def      UpperCAmelCase      (		lowerCamelCase_  :list				,      lowerCamelCase_  :list ):







		'''simple docstring'''
		return [
		    [matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row] ) )]
		    for row in range(len(lowerCamelCase_ ) )
		]





def      UpperCAmelCase      (		lowerCamelCase_  :list ):







		'''simple docstring'''
		if len(lowerCamelCase_ ) % 2 != 0 or len(a[0] ) % 2 != 0:
				raise Exception("""Odd matrices are not supported!""" )

		snake_case_		:							Optional[int]			       =						len(lowerCamelCase_ )
		snake_case_		:							List[str]			       =						matrix_length // 2

		snake_case_		:							Union[str, Any]			       =						[[a[i][j] for j in range(lowerCamelCase_				,      lowerCamelCase_ )] for i in range(lowerCamelCase_ )]
		snake_case_		:							Dict			       =						[
		    [a[i][j] for j in range(lowerCamelCase_				,      lowerCamelCase_ )] for i in range(lowerCamelCase_				,      lowerCamelCase_ )
		]

		snake_case_		:							str			       =						[[a[i][j] for j in range(lowerCamelCase_ )] for i in range(lowerCamelCase_ )]
		snake_case_		:							Dict			       =						[[a[i][j] for j in range(lowerCamelCase_ )] for i in range(lowerCamelCase_				,      lowerCamelCase_ )]

		return top_left, top_right, bot_left, bot_right





def      UpperCAmelCase      (		lowerCamelCase_  :list ):







		'''simple docstring'''
		return len(lowerCamelCase_ ), len(matrix[0] )





def      UpperCAmelCase      (		lowerCamelCase_  :list ):







		'''simple docstring'''
		print("""\n""".join(str(lowerCamelCase_ ) for line in matrix ) )





def      UpperCAmelCase      (		lowerCamelCase_  :list				,      lowerCamelCase_  :list ):







		'''simple docstring'''
		if matrix_dimensions(lowerCamelCase_ ) == (2, 2):
				return default_matrix_multiplication(lowerCamelCase_				,      lowerCamelCase_ )

		snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_		:							Any			       =						split_matrix(lowerCamelCase_ )
		snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_		:							List[Any]			       =						split_matrix(lowerCamelCase_ )

		snake_case_		:							Any			       =						actual_strassen(lowerCamelCase_				,      matrix_subtraction(lowerCamelCase_				,      lowerCamelCase_ ) )
		snake_case_		:							str			       =						actual_strassen(matrix_addition(lowerCamelCase_				,      lowerCamelCase_ )				,      lowerCamelCase_ )
		snake_case_		:							str			       =						actual_strassen(matrix_addition(lowerCamelCase_				,      lowerCamelCase_ )				,      lowerCamelCase_ )
		snake_case_		:							List[str]			       =						actual_strassen(lowerCamelCase_				,      matrix_subtraction(lowerCamelCase_				,      lowerCamelCase_ ) )
		snake_case_		:							Optional[int]			       =						actual_strassen(matrix_addition(lowerCamelCase_				,      lowerCamelCase_ )				,      matrix_addition(lowerCamelCase_				,      lowerCamelCase_ ) )
		snake_case_		:							Dict			       =						actual_strassen(matrix_subtraction(lowerCamelCase_				,      lowerCamelCase_ )				,      matrix_addition(lowerCamelCase_				,      lowerCamelCase_ ) )
		snake_case_		:							int			       =						actual_strassen(matrix_subtraction(lowerCamelCase_				,      lowerCamelCase_ )				,      matrix_addition(lowerCamelCase_				,      lowerCamelCase_ ) )

		snake_case_		:							List[Any]			       =						matrix_addition(matrix_subtraction(matrix_addition(lowerCamelCase_				,      lowerCamelCase_ )				,      lowerCamelCase_ )				,      lowerCamelCase_ )
		snake_case_		:							List[Any]			       =						matrix_addition(lowerCamelCase_				,      lowerCamelCase_ )
		snake_case_		:							str			       =						matrix_addition(lowerCamelCase_				,      lowerCamelCase_ )
		snake_case_		:							Any			       =						matrix_subtraction(matrix_subtraction(matrix_addition(lowerCamelCase_				,      lowerCamelCase_ )				,      lowerCamelCase_ )				,      lowerCamelCase_ )

		# construct the new matrix from our 4 quadrants
		snake_case_		:							Dict			       =						[]
		for i in range(len(lowerCamelCase_ ) ):
				new_matrix.append(top_left[i] + top_right[i] )
		for i in range(len(lowerCamelCase_ ) ):
				new_matrix.append(bot_left[i] + bot_right[i] )
		return new_matrix





def      UpperCAmelCase      (		lowerCamelCase_  :list				,      lowerCamelCase_  :list ):







		'''simple docstring'''
		if matrix_dimensions(lowerCamelCase_ )[1] != matrix_dimensions(lowerCamelCase_ )[0]:
				snake_case_		:							Optional[int]			       =						(
				    """Unable to multiply these matrices, please check the dimensions.\n"""
				    F'''Matrix A: {matrixa}\n'''
				    F'''Matrix B: {matrixa}'''
				)
				raise Exception(lowerCamelCase_ )
		snake_case_		:							Tuple			       =						matrix_dimensions(lowerCamelCase_ )
		snake_case_		:							Union[str, Any]			       =						matrix_dimensions(lowerCamelCase_ )

		if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
				return [matrixa, matrixa]

		snake_case_		:							int			       =						max(*lowerCamelCase_				,      *lowerCamelCase_ )
		snake_case_		:							Union[str, Any]			       =						int(math.pow(2				,      math.ceil(math.loga(lowerCamelCase_ ) ) ) )
		snake_case_		:							Dict			       =						matrixa
		snake_case_		:							Optional[int]			       =						matrixa

		# Adding zeros to the matrices so that the arrays dimensions are the same and also
		# power of 2
		for i in range(0				,      lowerCamelCase_ ):
				if i < dimensiona[0]:
						for _ in range(dimensiona[1]				,      lowerCamelCase_ ):
								new_matrixa[i].append(0 )
				else:
						new_matrixa.append([0] * maxim )
				if i < dimensiona[0]:
						for _ in range(dimensiona[1]				,      lowerCamelCase_ ):
								new_matrixa[i].append(0 )
				else:
						new_matrixa.append([0] * maxim )

		snake_case_		:							Dict			       =						actual_strassen(lowerCamelCase_				,      lowerCamelCase_ )

		# Removing the additional zeros
		for i in range(0				,      lowerCamelCase_ ):
				if i < dimensiona[0]:
						for _ in range(dimensiona[1]				,      lowerCamelCase_ ):
								final_matrix[i].pop()
				else:
						final_matrix.pop()
		return final_matrix


if __name__ == "__main__":
							__A	:	Optional[int]     =     [
							    [2, 3, 4, 5],
							    [6, 4, 3, 1],
							    [2, 3, 6, 7],
							    [3, 1, 2, 4],
							    [2, 3, 4, 5],
							    [6, 4, 3, 1],
							    [2, 3, 6, 7],
							    [3, 1, 2, 4],
							    [2, 3, 4, 5],
							    [6, 2, 3, 1],
							]
							__A	:	List[str]     =     [[0, 2, 1, 1], [16, 2, 3, 3], [2, 2, 7, 7], [13, 11, 22, 4]]
							print(strassen(matrixa, matrixa))



'''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,
)


__A	:	Any     =     logging.getLogger(__name__)
class 						__UpperCamelCase	(						lowercase__							):





							def 		a__   (  self		:int  ,_UpperCamelCase		:Union[str, Any]  ,_UpperCamelCase		:Optional[Any]  ,_UpperCamelCase		:List[Any]=None  ,_UpperCamelCase		:Any=None			):
									snake_case_		:							List[str]			       =						self.layer[current_layer](_UpperCamelCase  ,_UpperCamelCase  ,head_mask[current_layer]			)

									snake_case_		:							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.'						,  lowercase__						,  )
class 						__UpperCamelCase	(						lowercase__							):





							def __init__(  self		:Dict  ,_UpperCamelCase		:Optional[Any]			):
									super().__init__(_UpperCamelCase			)

									snake_case_		:							List[Any]			       =						BertEncoderWithPabee(_UpperCamelCase			)

									self.init_weights()
									snake_case_		:							int			       =						0
									snake_case_		:							Optional[Any]			       =						0
									snake_case_		:							Any			       =						0

									snake_case_		:							str			       =						0





							def 		a__   (  self		:Tuple  ,_UpperCamelCase		:int			):
									snake_case_		:							Optional[Any]			       =						threshold





							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:Union[str, Any]			):
									snake_case_		:							str			       =						patience





							def 		a__   (  self		:Optional[int]			):
									snake_case_		:							List[str]			       =						0
									snake_case_		:							List[Any]			       =						0





							def 		a__   (  self		:List[Any]			):
									snake_case_		:							str			       =						self.inference_layers_num / self.inference_instances_num
									snake_case_		:							List[str]			       =						(
									    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 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:Any=None  ,_UpperCamelCase		:List[str]=None  ,_UpperCamelCase		:Any=None  ,_UpperCamelCase		:Optional[int]=None  ,_UpperCamelCase		:Optional[int]=None  ,_UpperCamelCase		:int=None  ,_UpperCamelCase		:Optional[int]=None  ,_UpperCamelCase		:Any=None  ,_UpperCamelCase		:int=None  ,_UpperCamelCase		:Union[str, Any]=None  ,_UpperCamelCase		:int=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:
											snake_case_		:							Any			       =						input_ids.size()
									elif inputs_embeds is not None:
											snake_case_		:							Union[str, Any]			       =						inputs_embeds.size()[:-1]
									else:
											raise ValueError("""You have to specify either input_ids or inputs_embeds"""			)

									snake_case_		:							Tuple			       =						input_ids.device if input_ids is not None else inputs_embeds.device

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

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

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

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

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

													snake_case_		:							List[Any]			       =						logits
													if patient_counter == self.patience:
															break
											snake_case_		:							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. '						,  lowercase__						,  )
class 						__UpperCamelCase	(						lowercase__							):





							def __init__(  self		:Tuple  ,_UpperCamelCase		:List[str]			):
									super().__init__(_UpperCamelCase			)
									snake_case_		:							List[str]			       =						config.num_labels

									snake_case_		:							Dict			       =						BertModelWithPabee(_UpperCamelCase			)
									snake_case_		:							List[str]			       =						nn.Dropout(config.hidden_dropout_prob			)
									snake_case_		:							str			       =						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 		a__   (  self		:Union[str, Any]  ,_UpperCamelCase		:Tuple=None  ,_UpperCamelCase		:Dict=None  ,_UpperCamelCase		:Union[str, Any]=None  ,_UpperCamelCase		:Union[str, Any]=None  ,_UpperCamelCase		:Tuple=None  ,_UpperCamelCase		:int=None  ,_UpperCamelCase		:Union[str, Any]=None  ,):
									snake_case_		:							int			       =						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  ,)

									snake_case_		:							Optional[int]			       =						(logits[-1],)

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

									return outputs