





"""simple docstring"""




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    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Dict      )				->int:






	'''simple docstring'''






	a   :	str			=       torch.exp(_lowercase      )
	a   :	int			=       torch.sum(_lowercase					, dim=1      )  # sum of exp(x_i)
	a   :	Union[str, Any]			=       torch.sum(x * exp_x					, dim=1      )  # sum of x_i * exp(x_i)
	return torch.log(_lowercase      ) - B / A


class  __UpperCamelCase       (		nn.Module		):






							def __init__(     self   ,      lowerCAmelCase__   )			->				Tuple:
								super().__init__()
								a   :	Any			=       config.output_attentions
								a   :	Dict			=       config.output_hidden_states
								a   :	Any			=       nn.ModuleList([BertLayer(lowerCAmelCase__   ) for _ in range(config.num_hidden_layers   )]   )
								a   :	List[Any]			=       nn.ModuleList([BertHighway(lowerCAmelCase__   ) for _ in range(config.num_hidden_layers   )]   )

								a   :	Optional[int]			=       [-1 for _ in range(config.num_hidden_layers   )]






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Any:
								if (type(lowerCAmelCase__   ) is float) or (type(lowerCAmelCase__   ) is int):
									for i in range(len(self.early_exit_entropy   )   ):
										a   :	str			=       x
								else:
									a   :	Tuple			=       x






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				int:
								a   :	str			=       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   ,      lowerCAmelCase__   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   ,      )			->				Dict:
								a   :	Union[str, Any]			=       ()
								a   :	Optional[Any]			=       ()
								a   :	Any			=       ()
								for i, layer_module in enumerate(self.layer   ):
									if self.output_hidden_states:
										a   :	Dict			=       all_hidden_states + (hidden_states,)

									a   :	str			=       layer_module(
									    lowerCAmelCase__   ,      lowerCAmelCase__   ,      head_mask[i]   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )
									a   :	Dict			=       layer_outputs[0]

									if self.output_attentions:
										a   :	Optional[Any]			=       all_attentions + (layer_outputs[1],)

									a   :	List[Any]			=       (hidden_states,)
									if self.output_hidden_states:
										a   :	Dict			=       current_outputs + (all_hidden_states,)
									if self.output_attentions:
										a   :	Any			=       current_outputs + (all_attentions,)

									a   :	Any			=       self.highway[i](lowerCAmelCase__   )
									# logits, pooled_output

									if not self.training:
										a   :	List[str]			=       highway_exit[0]
										a   :	int			=       entropy(lowerCAmelCase__   )
										a   :	List[Any]			=       highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
										a   :	Tuple			=       all_highway_exits + (highway_exit,)

										if highway_entropy < self.early_exit_entropy[i]:
											a   :	Optional[Any]			=       (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
											raise HighwayException(lowerCAmelCase__   ,      i + 1   )
									else:
										a   :	Optional[Any]			=       all_highway_exits + (highway_exit,)

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

								a   :	Any			=       (hidden_states,)
								if self.output_hidden_states:
									a   :	Union[str, Any]			=       outputs + (all_hidden_states,)
								if self.output_attentions:
									a   :	Optional[Any]			=       outputs + (all_attentions,)

								a   :	Union[str, Any]			=       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). """	, a__	, )
class  __UpperCamelCase       (		a__		):






							def __init__(     self   ,      lowerCAmelCase__   )			->				Any:
								super().__init__(lowerCAmelCase__   )
								a   :	int			=       config

								a   :	Tuple			=       BertEmbeddings(lowerCAmelCase__   )
								a   :	str			=       DeeBertEncoder(lowerCAmelCase__   )
								a   :	List[str]			=       BertPooler(lowerCAmelCase__   )

								self.init_weights()






							def 					__a     (     self   )			->				List[Any]:
								self.encoder.init_highway_pooler(self.pooler   )






							def 					__a     (     self   )			->				Union[str, Any]:
								return self.embeddings.word_embeddings






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Any:
								a   :	Dict			=       value






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Union[str, Any]:
								for layer, heads in heads_to_prune.items():
									self.encoder.layer[layer].attention.prune_heads(lowerCAmelCase__   )






							@add_start_docstrings_to_model_forward(lowerCAmelCase__   )
							def 					__a     (     self   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   ,      )			->				List[Any]:
								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:
									a   :	List[Any]			=       input_ids.size()
								elif inputs_embeds is not None:
									a   :	Any			=       inputs_embeds.size()[:-1]
								else:
									raise ValueError("You have to specify either input_ids or inputs_embeds"   )

								a   :	Union[str, Any]			=       input_ids.device if input_ids is not None else inputs_embeds.device

								if attention_mask is None:
									a   :	Any			=       torch.ones(lowerCAmelCase__   ,      device=lowerCAmelCase__   )
								if encoder_attention_mask is None:
									a   :	Optional[Any]			=       torch.ones(lowerCAmelCase__   ,      device=lowerCAmelCase__   )
								if token_type_ids is None:
									a   :	str			=       torch.zeros(lowerCAmelCase__   ,      dtype=torch.long   ,      device=lowerCAmelCase__   )

								# 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.
								a   :	torch.Tensor			=       self.get_extended_attention_mask(lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )

								# 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:
									a   :	Optional[int]			=       encoder_attention_mask[:, None, :, :]
								if encoder_attention_mask.dim() == 2:
									a   :	Union[str, Any]			=       encoder_attention_mask[:, None, None, :]

								a   :	Union[str, Any]			=       encoder_extended_attention_mask.to(
								    dtype=next(self.parameters()   ).dtype   )  # fp16 compatibility
								a   :	Tuple			=       (1.0 - encoder_extended_attention_mask) * -10_000.0

								# 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]
								a   :	Any			=       self.get_head_mask(lowerCAmelCase__   ,      self.config.num_hidden_layers   )

								a   :	Dict			=       self.embeddings(
								    input_ids=lowerCAmelCase__   ,      position_ids=lowerCAmelCase__   ,      token_type_ids=lowerCAmelCase__   ,      inputs_embeds=lowerCAmelCase__   )
								a   :	Optional[int]			=       self.encoder(
								    lowerCAmelCase__   ,      attention_mask=lowerCAmelCase__   ,      head_mask=lowerCAmelCase__   ,      encoder_hidden_states=lowerCAmelCase__   ,      encoder_attention_mask=lowerCAmelCase__   ,      )
								a   :	str			=       encoder_outputs[0]
								a   :	Any			=       self.pooler(lowerCAmelCase__   )

								a   :	Tuple			=       (
								    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  __UpperCamelCase       (		a__		):






							def __init__(     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Tuple:
								a   :	List[Any]			=       message
								a   :	List[str]			=       exit_layer  # start from 1!


class  __UpperCamelCase       (		nn.Module		):






							def __init__(     self   ,      lowerCAmelCase__   )			->				Dict:
								super().__init__()
								a   :	List[str]			=       BertPooler(lowerCAmelCase__   )
								a   :	Any			=       nn.Dropout(config.hidden_dropout_prob   )
								a   :	str			=       nn.Linear(config.hidden_size   ,      config.num_labels   )






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Union[str, Any]:
								# Pooler
								a   :	str			=       encoder_outputs[0]
								a   :	Optional[int]			=       self.pooler(lowerCAmelCase__   )
								# "return" pooler_output

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

								# Dropout and classification
								a   :	int			=       bmodel_output[1]

								a   :	List[Any]			=       self.dropout(lowerCAmelCase__   )
								a   :	str			=       self.classifier(lowerCAmelCase__   )

								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. """	, a__	, )
class  __UpperCamelCase       (		a__		):






							def __init__(     self   ,      lowerCAmelCase__   )			->				Optional[int]:
								super().__init__(lowerCAmelCase__   )
								a   :	Dict			=       config.num_labels
								a   :	Optional[int]			=       config.num_hidden_layers

								a   :	str			=       DeeBertModel(lowerCAmelCase__   )
								a   :	str			=       nn.Dropout(config.hidden_dropout_prob   )
								a   :	str			=       nn.Linear(config.hidden_size   ,      self.config.num_labels   )

								self.init_weights()






							@add_start_docstrings_to_model_forward(lowerCAmelCase__   )
							def 					__a     (     self   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=-1   ,      lowerCAmelCase__=False   ,      )			->				Optional[int]:
								a   :	int			=       self.num_layers
								try:
									a   :	List[Any]			=       self.bert(
									    lowerCAmelCase__   ,      attention_mask=lowerCAmelCase__   ,      token_type_ids=lowerCAmelCase__   ,      position_ids=lowerCAmelCase__   ,      head_mask=lowerCAmelCase__   ,      inputs_embeds=lowerCAmelCase__   ,      )
									# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

									a   :	str			=       outputs[1]

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

								if not self.training:
									a   :	Tuple			=       entropy(lowerCAmelCase__   )
									a   :	Optional[Any]			=       []
									a   :	Optional[Any]			=       []
								if labels is not None:
									if self.num_labels == 1:
										#  We are doing regression
										a   :	int			=       MSELoss()
										a   :	str			=       loss_fct(logits.view(-1   )   ,      labels.view(-1   )   )
									else:
										a   :	List[Any]			=       CrossEntropyLoss()
										a   :	List[Any]			=       loss_fct(logits.view(-1   ,      self.num_labels   )   ,      labels.view(-1   )   )

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

									if train_highway:
										a   :	Tuple			=       (sum(highway_losses[:-1]   ),) + outputs
										# exclude the final highway, of course
									else:
										a   :	List[Any]			=       (loss,) + outputs
								if not self.training:
									a   :	Optional[Any]			=       outputs + ((original_entropy, highway_entropy), exit_layer)
									if output_layer >= 0:
										a   :	List[str]			=       (
										    (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"""




from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin


@dataclass
class  __UpperCamelCase       (		a__		):
							lowerCamelCase     :      torch.FloatTensor
							lowerCamelCase     :      torch.FloatTensor
							lowerCamelCase     :      Optional[torch.FloatTensor]         =None


class  __UpperCamelCase       (		a__	, a__		):
							lowerCamelCase     :      Tuple         =2






							@register_to_config
							def __init__(     self   ,      lowerCAmelCase__ = 0.02   ,      lowerCAmelCase__ = 100   ,      lowerCAmelCase__ = 1.007   ,      lowerCAmelCase__ = 80   ,      lowerCAmelCase__ = 0.05   ,      lowerCAmelCase__ = 50   ,      )			->				Union[str, Any]:
								# standard deviation of the initial noise distribution
								a   :	Tuple			=       sigma_max

								# setable values
								a   :	int			=       None
								a   :	np.IntTensor			=       None
								a   :	torch.FloatTensor			=       None  # sigma(t_i)






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				torch.FloatTensor:
								return sample






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				List[str]:
								a   :	List[Any]			=       num_inference_steps
								a   :	List[str]			=       np.arange(0   ,      self.num_inference_steps   )[::-1].copy()
								a   :	int			=       torch.from_numpy(lowerCAmelCase__   ).to(lowerCAmelCase__   )
								a   :	List[str]			=       [
								    (
								        self.config.sigma_max**2
								        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
								    )
								    for i in self.timesteps
								]
								a   :	Any			=       torch.tensor(lowerCAmelCase__   ,      dtype=torch.floataa   ,      device=lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				Tuple[torch.FloatTensor, float]:
								if self.config.s_min <= sigma <= self.config.s_max:
									a   :	str			=       min(self.config.s_churn / self.num_inference_steps   ,      2**0.5 - 1   )
								else:
									a   :	Dict			=       0

								# sample eps ~ N(0, S_noise^2 * I)
								a   :	Union[str, Any]			=       self.config.s_noise * randn_tensor(sample.shape   ,      generator=lowerCAmelCase__   ).to(sample.device   )
								a   :	Any			=       sigma + gamma * sigma
								a   :	Tuple			=       sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

								return sample_hat, sigma_hat






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = True   ,      )			->				Union[KarrasVeOutput, Tuple]:
								a   :	Union[str, Any]			=       sample_hat + sigma_hat * model_output
								a   :	Tuple			=       (sample_hat - pred_original_sample) / sigma_hat
								a   :	List[Any]			=       sample_hat + (sigma_prev - sigma_hat) * derivative

								if not return_dict:
									return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=lowerCAmelCase__   ,      derivative=lowerCAmelCase__   ,      pred_original_sample=lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = True   ,      )			->				Union[KarrasVeOutput, Tuple]:
								a   :	Optional[int]			=       sample_prev + sigma_prev * model_output
								a   :	str			=       (sample_prev - pred_original_sample) / sigma_prev
								a   :	Dict			=       sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

								if not return_dict:
									return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=lowerCAmelCase__   ,      derivative=lowerCAmelCase__   ,      pred_original_sample=lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				str:
								raise NotImplementedError()


