
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(_A		)
					lowercase							=						torch.sum(_A ,			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(_A		) - 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(UpperCamelCase__) for _ in range(config.num_hidden_layers)])
												lowercase							=						nn.ModuleList([BertHighway(UpperCamelCase__) for _ in range(config.num_hidden_layers)])

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




							def  A__      (				self   ,A__):



												if (type(UpperCamelCase__) is float) or (type(UpperCamelCase__) 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(
																	    UpperCamelCase__   ,UpperCamelCase__   ,head_mask[i]   ,UpperCamelCase__   ,UpperCamelCase__)
																	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](UpperCamelCase__)
																	# logits, pooled_output

																	if not self.training:
																						lowercase							=						highway_exit[0]
																						lowercase							=						entropy(UpperCamelCase__)
																						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(UpperCamelCase__   ,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). '''       ,  __UpperCAmelCase       ,  )
class 				lowercase		(					__UpperCAmelCase		):




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

												lowercase							=						BertEmbeddings(UpperCamelCase__)
												lowercase							=						DeeBertEncoder(UpperCamelCase__)
												lowercase							=						BertPooler(UpperCamelCase__)

												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(UpperCamelCase__)





							@add_start_docstrings_to_model_forward(UpperCamelCase__)
							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(UpperCamelCase__   ,device=UpperCamelCase__)
												if encoder_attention_mask is None:
																	lowercase							=						torch.ones(UpperCamelCase__   ,device=UpperCamelCase__)
												if token_type_ids is None:
																	lowercase							=						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.
												lowercase							=						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 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) * -1_0_0_0_0.0

												# 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(UpperCamelCase__   ,self.config.num_hidden_layers)

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

												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		(					__UpperCAmelCase		):




							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(UpperCamelCase__)
												lowercase							=						nn.Dropout(config.hidden_dropout_prob)
												lowercase							=						nn.Linear(config.hidden_size   ,config.num_labels)





							def  A__      (				self   ,A__):
												lowercase							=						encoder_outputs[0]
												lowercase							=						self.pooler(UpperCamelCase__)
												# "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(UpperCamelCase__)
												lowercase							=						self.classifier(UpperCamelCase__)

												return logits, pooled_output







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




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

												lowercase							=						DeeBertModel(UpperCamelCase__)
												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(UpperCamelCase__)
							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(
																	    UpperCamelCase__   ,attention_mask=UpperCamelCase__   ,token_type_ids=UpperCamelCase__   ,position_ids=UpperCamelCase__   ,head_mask=UpperCamelCase__   ,inputs_embeds=UpperCamelCase__   ,)
																	# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

																	lowercase							=						outputs[1]

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

																	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)










import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class  A		(  unittest.TestCase     ):



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								torch.manual_seed(0   )
								lowerCAmelCase_      			=  UNetaDModel(
								    block_out_channels=(32, 64),	layers_per_block=2,	sample_size=32,	in_channels=3,	out_channels=3,	down_block_types=('''DownBlock2D''', '''AttnDownBlock2D'''),	up_block_types=('''AttnUpBlock2D''', '''UpBlock2D'''),	)
								return model







		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.dummy_uncond_unet
								lowerCAmelCase_      			=  KarrasVeScheduler()

								lowerCAmelCase_      			=  KarrasVePipeline(unet=UpperCamelCase__,	scheduler=UpperCamelCase__   )
								pipe.to(UpperCamelCase__   )
								pipe.set_progress_bar_config(disable=UpperCamelCase__   )

								lowerCAmelCase_      			=  torch.manual_seed(0   )
								lowerCAmelCase_      			=  pipe(num_inference_steps=2,	generator=UpperCamelCase__,	output_type='''numpy'''   ).images

								lowerCAmelCase_      			=  torch.manual_seed(0   )
								lowerCAmelCase_      			=  pipe(num_inference_steps=2,	generator=UpperCamelCase__,	output_type='''numpy''',	return_dict=UpperCamelCase__   )[0]

								lowerCAmelCase_      			=  image[0, -3:, -3:, -1]
								lowerCAmelCase_      			=  image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								lowerCAmelCase_      			=  np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]   )

								assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
								assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2







@slow
@require_torch
class  A		(  unittest.TestCase     ):



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  '''google/ncsnpp-celebahq-256'''
								lowerCAmelCase_      			=  UNetaDModel.from_pretrained(UpperCamelCase__   )
								lowerCAmelCase_      			=  KarrasVeScheduler()

								lowerCAmelCase_      			=  KarrasVePipeline(unet=UpperCamelCase__,	scheduler=UpperCamelCase__   )
								pipe.to(UpperCamelCase__   )
								pipe.set_progress_bar_config(disable=UpperCamelCase__   )

								lowerCAmelCase_      			=  torch.manual_seed(0   )
								lowerCAmelCase_      			=  pipe(num_inference_steps=20,	generator=UpperCamelCase__,	output_type='''numpy'''   ).images

								lowerCAmelCase_      			=  image[0, -3:, -3:, -1]
								assert image.shape == (1, 256, 256, 3)
								lowerCAmelCase_      			=  np.array([0.578, 0.5_811, 0.5_924, 0.5_809, 0.587, 0.5_886, 0.5_861, 0.5_802, 0.586]   )

								assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2




