'''simple docstring'''






import numpy as np
from PIL import Image





def      UpperCAmelCase      (		lowerCamelCase_  :int				,      lowerCamelCase_  :Optional[Any]				,      lowerCamelCase_  :str ):







		'''simple docstring'''
		snake_case_		:							List[Any]			       =						np.array(lowerCamelCase_ )
		if arr.shape[0] != arr.shape[1]:
				raise ValueError("""The input array is not a square matrix""" )
		snake_case_		:							Tuple			       =						0
		snake_case_		:							int			       =						0
		snake_case_		:							Optional[int]			       =						0
		snake_case_		:							str			       =						0

		# compute the shape of the output matrix
		snake_case_		:							int			       =						(arr.shape[0] - size) // stride + 1
		# initialize the output matrix with zeros of shape maxpool_shape
		snake_case_		:							Dict			       =						np.zeros((maxpool_shape, maxpool_shape) )

		while i < arr.shape[0]:
				if i + size > arr.shape[0]:
						# if the end of the matrix is reached, break
						break
				while j < arr.shape[1]:
						# if the end of the matrix is reached, break
						if j + size > arr.shape[1]:
								break
						# compute the maximum of the pooling matrix
						snake_case_		:							Dict			       =						np.max(arr[i : i + size, j : j + size] )
						# shift the pooling matrix by stride of column pixels
						j += stride
						mat_j += 1

				# shift the pooling matrix by stride of row pixels
				i += stride
				mat_i += 1

				# reset the column index to 0
				snake_case_		:							List[Any]			       =						0
				snake_case_		:							Optional[int]			       =						0

		return updated_arr





def      UpperCAmelCase      (		lowerCamelCase_  :Tuple				,      lowerCamelCase_  :List[Any]				,      lowerCamelCase_  :Union[str, Any] ):







		'''simple docstring'''
		snake_case_		:							Tuple			       =						np.array(lowerCamelCase_ )
		if arr.shape[0] != arr.shape[1]:
				raise ValueError("""The input array is not a square matrix""" )
		snake_case_		:							Optional[int]			       =						0
		snake_case_		:							int			       =						0
		snake_case_		:							List[str]			       =						0
		snake_case_		:							List[Any]			       =						0

		# compute the shape of the output matrix
		snake_case_		:							str			       =						(arr.shape[0] - size) // stride + 1
		# initialize the output matrix with zeros of shape avgpool_shape
		snake_case_		:							Union[str, Any]			       =						np.zeros((avgpool_shape, avgpool_shape) )

		while i < arr.shape[0]:
				# if the end of the matrix is reached, break
				if i + size > arr.shape[0]:
						break
				while j < arr.shape[1]:
						# if the end of the matrix is reached, break
						if j + size > arr.shape[1]:
								break
						# compute the average of the pooling matrix
						snake_case_		:							List[Any]			       =						int(np.average(arr[i : i + size, j : j + size] ) )
						# shift the pooling matrix by stride of column pixels
						j += stride
						mat_j += 1

				# shift the pooling matrix by stride of row pixels
				i += stride
				mat_i += 1
				# reset the column index to 0
				snake_case_		:							Union[str, Any]			       =						0
				snake_case_		:							Optional[Any]			       =						0

		return updated_arr


# Main Function
if __name__ == "__main__":
							from doctest import testmod

							testmod(name='avgpooling', verbose=True)

							# Loading the image
							__A	:	Union[str, Any]     =     Image.open('path_to_image')

							# Converting the image to numpy array and maxpooling, displaying the result
							# Ensure that the image is a square matrix

							Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()

							# Converting the image to numpy array and averagepooling, displaying the result
							# Ensure that the image is a square matrix

							Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()



'''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