
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_		(							__SCREAMING_SNAKE_CASE							):
	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        A_   (			nn.Module   ):




	'''simple docstring'''


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





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case=None		,    snake_case=None		,    snake_case=None		,    snake_case=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        A_   (			SCREAMING_SNAKE_CASE_   ):




	'''simple docstring'''


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

		lowercase       =    BertEmbeddings(a__       )
		lowercase       =    DeeBertEncoder(a__       )
		lowercase       =    BertPooler(a__       )

		self.init_weights()





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





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





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		lowercase       =    value





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





	@add_start_docstrings_to_model_forward(a__       )
	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case=None		,    snake_case=None		,    snake_case=None		,    snake_case=None		,    snake_case=None		,    snake_case=None		,    snake_case=None		,    snake_case=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) * -1_0000.0

		# 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        A_   (			SCREAMING_SNAKE_CASE_   ):




	'''simple docstring'''


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







class        A_   (			nn.Module   ):




	'''simple docstring'''


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





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		# 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,\n    also takes care of multi-layer training. """      ,	SCREAMING_SNAKE_CASE_      ,	)
class        A_   (			SCREAMING_SNAKE_CASE_   ):




	'''simple docstring'''


	def __init__(       self		,    snake_case       ):
		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 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case=None		,    snake_case=None		,    snake_case=None		,    snake_case=None		,    snake_case=None		,    snake_case=None		,    snake_case=None		,    snake_case=-1		,    snake_case=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)


"""simple docstring"""




# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel  # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    'stable diffusion controlnet',
    '0.22.0',
    'Importing `StableDiffusionControlNetPipeline` or `MultiControlNetModel` from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import StableDiffusionControlNetPipeline` instead.',
    standard_warn=False,
    stacklevel=3,
)




