
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

lowercase__		:Tuple			  =   imread(r"digital_image_processing/image_data/lena_small.jpg")
lowercase__		:int			  =   cvtColor(img, COLOR_BGR2GRAY)


def 	UpperCamelCase   (							):
					'''simple docstring'''
					lowercase						     =						cn.convert_to_negative(__a		)
					# assert negative_img array for at least one True
					assert negative_img.any()


def 	UpperCamelCase   (							):
					'''simple docstring'''
					with Image.open('''digital_image_processing/image_data/lena_small.jpg'''		) as img:
										# Work around assertion for response
										assert str(cc.change_contrast(__a			,      110		)		).startswith(
										    '''<PIL.Image.Image image mode=RGB size=100x100 at'''		)


def 	UpperCamelCase   (							):
					'''simple docstring'''
					lowercase						     =						canny.gen_gaussian_kernel(9			,      sigma=1.4		)
					# Assert ambiguous array
					assert resp.all()


def 	UpperCamelCase   (							):
					'''simple docstring'''
					lowercase						     =						imread('''digital_image_processing/image_data/lena_small.jpg'''			,      0		)
					# assert ambiguous array for all == True
					assert canny_img.all()
					lowercase						     =						canny.canny(__a		)
					# assert canny array for at least one True
					assert canny_array.any()


def 	UpperCamelCase   (							):
					'''simple docstring'''
					assert gg.gaussian_filter(__a			,      5			,      sigma=0.9		).all()


def 	UpperCamelCase   (							):
					'''simple docstring'''
					# laplace diagonals
					lowercase						     =						array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]		)
					lowercase						     =						conv.img_convolve(__a			,      __a		).astype(__a		)
					assert res.any()


def 	UpperCamelCase   (							):
					'''simple docstring'''
					assert med.median_filter(__a			,      3		).any()


def 	UpperCamelCase   (							):
					'''simple docstring'''
					lowercase     ,						lowercase						     =						sob.sobel_filter(__a		)
					assert grad.any() and theta.any()


def 	UpperCamelCase   (							):
					'''simple docstring'''
					lowercase						     =						sp.make_sepia(__a			,      20		)
					assert sepia.all()


def 	UpperCamelCase   (							lowerCAmelCase__ = "digital_image_processing/image_data/lena_small.jpg"		):
					'''simple docstring'''
					lowercase						     =						bs.Burkes(imread(__a			,      1		)			,      120		)
					burkes.process()
					assert burkes.output_img.any()


def 	UpperCamelCase   (							lowerCAmelCase__ = "digital_image_processing/image_data/lena_small.jpg"			,      ):
					'''simple docstring'''
					lowercase						     =						rs.NearestNeighbour(imread(__a			,      1		)			,      400			,      200		)
					nn.process()
					assert nn.output.any()


def 	UpperCamelCase   (							):
					'''simple docstring'''
					lowercase						     =						'''digital_image_processing/image_data/lena.jpg'''

					# Reading the image and converting it to grayscale.
					lowercase						     =						imread(__a			,      0		)

					# Test for get_neighbors_pixel function() return not None
					lowercase						     =						0
					lowercase						     =						0
					lowercase						     =						image[x_coordinate][y_coordinate]

					lowercase						     =						lbp.get_neighbors_pixel(
					    __a			,      __a			,      __a			,      __a		)

					assert neighbors_pixels is not None

					# Test for local_binary_pattern function()
					# Create a numpy array as the same height and width of read image
					lowercase						     =						np.zeros((image.shape[0], image.shape[1])		)

					# Iterating through the image and calculating the local binary pattern value
					# for each pixel.
					for i in range(0			,      image.shape[0]		):
										for j in range(0			,      image.shape[1]		):
															lowercase						     =						lbp.local_binary_value(__a			,      __a			,      __a		)

					assert lbp_image.any()






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,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)



def 	UpperCamelCase   (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						torch.exp(lowerCAmelCase__		)
					lowercase						     =						torch.sum(lowerCAmelCase__			,      dim=1		)  # sum of exp(x_i)
					lowercase						     =						torch.sum(x * exp_x			,      dim=1		)  # sum of x_i * exp(x_i)
					return torch.log(lowerCAmelCase__		) - B / A







class     lowercase		( nn.Module		):




							def __init__(    self       ,A__):
												super().__init__()
												lowercase						     =						config.output_attentions
												lowercase						     =						config.output_hidden_states
												lowercase						     =						nn.ModuleList([BertLayer(A__) for _ in range(config.num_hidden_layers)])
												lowercase						     =						nn.ModuleList([BertHighway(A__) for _ in range(config.num_hidden_layers)])

												lowercase						     =						[-1 for _ in range(config.num_hidden_layers)]




							def 			A__				(    self       ,A__):
												if (type(A__) is float) or (type(A__) is int):
																	for i in range(len(self.early_exit_entropy)):
																						lowercase						     =						x
												else:
																	lowercase						     =						x




							def 			A__				(    self       ,A__):
												lowercase						     =						pooler.state_dict()
												for highway in self.highway:
																	for name, param in highway.pooler.state_dict().items():
																						param.copy_(loaded_model[name])





							def 			A__				(    self       ,A__       ,A__=None       ,A__=None       ,A__=None       ,A__=None       ,):
												lowercase						     =						()
												lowercase						     =						()
												lowercase						     =						()
												for i, layer_module in enumerate(self.layer):
																	if self.output_hidden_states:
																						lowercase						     =						all_hidden_states + (hidden_states,)

																	lowercase						     =						layer_module(
																	    A__       ,A__       ,head_mask[i]       ,A__       ,A__)
																	lowercase						     =						layer_outputs[0]

																	if self.output_attentions:
																						lowercase						     =						all_attentions + (layer_outputs[1],)

																	lowercase						     =						(hidden_states,)
																	if self.output_hidden_states:
																						lowercase						     =						current_outputs + (all_hidden_states,)
																	if self.output_attentions:
																						lowercase						     =						current_outputs + (all_attentions,)

																	lowercase						     =						self.highway[i](A__)
																	# logits, pooled_output

																	if not self.training:
																						lowercase						     =						highway_exit[0]
																						lowercase						     =						entropy(A__)
																						lowercase						     =						highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
																						lowercase						     =						all_highway_exits + (highway_exit,)

																						if highway_entropy < self.early_exit_entropy[i]:
																											lowercase						     =						(highway_logits,) + current_outputs[1:] + (all_highway_exits,)
																											raise HighwayException(A__       ,i + 1)
																	else:
																						lowercase						     =						all_highway_exits + (highway_exit,)

        # Add last layer
												if self.output_hidden_states:
																	lowercase						     =						all_hidden_states + (hidden_states,)

												lowercase						     =						(hidden_states,)
												if self.output_hidden_states:
																	lowercase						     =						outputs + (all_hidden_states,)
												if self.output_attentions:
																	lowercase						     =						outputs + (all_attentions,)

												lowercase						     =						outputs + (all_highway_exits,)
												return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits







@add_start_docstrings(
    '''The Bert Model transformer with early exiting (DeeBERT). '''	,  SCREAMING_SNAKE_CASE__	,  )
class     lowercase		( SCREAMING_SNAKE_CASE__		):




							def __init__(    self       ,A__):
												super().__init__(A__)
												lowercase						     =						config

												lowercase						     =						BertEmbeddings(A__)
												lowercase						     =						DeeBertEncoder(A__)
												lowercase						     =						BertPooler(A__)

												self.init_weights()




							def 			A__				(    self):
												self.encoder.init_highway_pooler(self.pooler)




							def 			A__				(    self):
												return self.embeddings.word_embeddings




							def 			A__				(    self       ,A__):
												lowercase						     =						value




							def 			A__				(    self       ,A__):
												for layer, heads in heads_to_prune.items():
																	self.encoder.layer[layer].attention.prune_heads(A__)





							@add_start_docstrings_to_model_forward(A__)
							def 			A__				(    self       ,A__=None       ,A__=None       ,A__=None       ,A__=None       ,A__=None       ,A__=None       ,A__=None       ,A__=None       ,):
												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:
																	lowercase						     =						input_ids.size()
												elif inputs_embeds is not None:
																	lowercase						     =						inputs_embeds.size()[:-1]
												else:
																	raise ValueError('''You have to specify either input_ids or inputs_embeds''')

												lowercase						     =						input_ids.device if input_ids is not None else inputs_embeds.device

												if attention_mask is None:
																	lowercase						     =						torch.ones(A__       ,device=A__)
												if encoder_attention_mask is None:
																	lowercase						     =						torch.ones(A__       ,device=A__)
												if token_type_ids is None:
																	lowercase						     =						torch.zeros(A__       ,dtype=torch.long       ,device=A__)

												# 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.
												lowercase						     =						self.get_extended_attention_mask(A__       ,A__       ,A__)

												# 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 encoder_attention_mask.dim() == 3:
																	lowercase						     =						encoder_attention_mask[:, None, :, :]
												if encoder_attention_mask.dim() == 2:
																	lowercase						     =						encoder_attention_mask[:, None, None, :]

												lowercase						     =						encoder_extended_attention_mask.to(
												    dtype=next(self.parameters()).dtype)  # fp16 compatibility
												lowercase						     =						(1.0 - encoder_extended_attention_mask) * -10000.0

												# 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]
												lowercase						     =						self.get_head_mask(A__       ,self.config.num_hidden_layers)

												lowercase						     =						self.embeddings(
												    input_ids=A__       ,position_ids=A__       ,token_type_ids=A__       ,inputs_embeds=A__)
												lowercase						     =						self.encoder(
												    A__       ,attention_mask=A__       ,head_mask=A__       ,encoder_hidden_states=A__       ,encoder_attention_mask=A__       ,)
												lowercase						     =						encoder_outputs[0]
												lowercase						     =						self.pooler(A__)

												lowercase						     =						(
												    sequence_output,
												    pooled_output,
												) + encoder_outputs[
												    1:
												]  # add hidden_states and attentions if they are here
												return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits







class     lowercase		( SCREAMING_SNAKE_CASE__		):




							def __init__(    self       ,A__       ,A__):
												lowercase						     =						message
												lowercase						     =						exit_layer  # start from 1!







class     lowercase		( nn.Module		):




							def __init__(    self       ,A__):
												super().__init__()
												lowercase						     =						BertPooler(A__)
												lowercase						     =						nn.Dropout(config.hidden_dropout_prob)
												lowercase						     =						nn.Linear(config.hidden_size       ,config.num_labels)





							def 			A__				(    self       ,A__):
												# Pooler
												lowercase						     =						encoder_outputs[0]
												lowercase						     =						self.pooler(A__)
												# "return" pooler_output

												# BertModel
												lowercase						     =						(pooler_input, pooler_output) + encoder_outputs[1:]
												# "return" bmodel_output

												# Dropout and classification
												lowercase						     =						bmodel_output[1]

												lowercase						     =						self.dropout(A__)
												lowercase						     =						self.classifier(A__)

												return logits, pooled_output







@add_start_docstrings(
    '''Bert Model (with early exiting - DeeBERT) with a classifier on top,
    also takes care of multi-layer training. '''	,  SCREAMING_SNAKE_CASE__	,  )
class     lowercase		( SCREAMING_SNAKE_CASE__		):




							def __init__(    self       ,A__):
												super().__init__(A__)
												lowercase						     =						config.num_labels
												lowercase						     =						config.num_hidden_layers

												lowercase						     =						DeeBertModel(A__)
												lowercase						     =						nn.Dropout(config.hidden_dropout_prob)
												lowercase						     =						nn.Linear(config.hidden_size       ,self.config.num_labels)

												self.init_weights()





							@add_start_docstrings_to_model_forward(A__)
							def 			A__				(    self       ,A__=None       ,A__=None       ,A__=None       ,A__=None       ,A__=None       ,A__=None       ,A__=None       ,A__=-1       ,A__=False       ,):
												lowercase						     =						self.num_layers
												try:
																	lowercase						     =						self.bert(
																	    A__       ,attention_mask=A__       ,token_type_ids=A__       ,position_ids=A__       ,head_mask=A__       ,inputs_embeds=A__       ,)
																	# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

																	lowercase						     =						outputs[1]

																	lowercase						     =						self.dropout(A__)
																	lowercase						     =						self.classifier(A__)
																	lowercase						     =						(logits,) + outputs[2:]  # add hidden states and attention if they are here
												except HighwayException as e:
																	lowercase						     =						e.message
																	lowercase						     =						e.exit_layer
																	lowercase						     =						outputs[0]

												if not self.training:
																	lowercase						     =						entropy(A__)
																	lowercase						     =						[]
																	lowercase						     =						[]
												if labels is not None:
																	if self.num_labels == 1:
																						#  We are doing regression
																						lowercase						     =						MSELoss()
																						lowercase						     =						loss_fct(logits.view(-1)       ,labels.view(-1))
																	else:
																						lowercase						     =						CrossEntropyLoss()
																						lowercase						     =						loss_fct(logits.view(-1       ,self.num_labels)       ,labels.view(-1))

																	# work with highway exits
																	lowercase						     =						[]
																	for highway_exit in outputs[-1]:
																						lowercase						     =						highway_exit[0]
																						if not self.training:
																											highway_logits_all.append(A__)
																											highway_entropy.append(highway_exit[2])
																						if self.num_labels == 1:
																											#  We are doing regression
																											lowercase						     =						MSELoss()
																											lowercase						     =						loss_fct(highway_logits.view(-1)       ,labels.view(-1))
																						else:
																											lowercase						     =						CrossEntropyLoss()
																											lowercase						     =						loss_fct(highway_logits.view(-1       ,self.num_labels)       ,labels.view(-1))
																						highway_losses.append(A__)

																	if train_highway:
																						lowercase						     =						(sum(highway_losses[:-1]),) + outputs
																						# exclude the final highway, of course
																	else:
																						lowercase						     =						(loss,) + outputs
												if not self.training:
																	lowercase						     =						outputs + ((original_entropy, highway_entropy), exit_layer)
																	if output_layer >= 0:
																						lowercase						     =						(
																						    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
																						)  # use the highway of the last layer

												return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)




