
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    lowerCamelCase__				(				__lowerCAmelCase  :  List[str]				):




			"""simple docstring"""


			lowerCAmelCase_						=	torch.exp(__lowerCAmelCase				)
			lowerCAmelCase_						=	torch.sum(__lowerCAmelCase    ,							dim=1				)  # sum of exp(x_i)
			lowerCAmelCase_						=	torch.sum(x * exp_x    ,							dim=1				)  # sum of x_i * exp(x_i)
			return torch.log(__lowerCAmelCase				) - B / A





class 	_lowerCAmelCase					(	nn.Module       ):




				def __init__(					self				,				_UpperCamelCase     )				->      int:
							super().__init__()
							lowerCAmelCase_						=	config.output_attentions
							lowerCAmelCase_						=	config.output_hidden_states
							lowerCAmelCase_						=	nn.ModuleList([BertLayer(_UpperCamelCase     ) for _ in range(config.num_hidden_layers     )]     )
							lowerCAmelCase_						=	nn.ModuleList([BertHighway(_UpperCamelCase     ) for _ in range(config.num_hidden_layers     )]     )

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




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




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

										lowerCAmelCase_						=	layer_module(
										    _UpperCamelCase				,				_UpperCamelCase				,				head_mask[i]				,				_UpperCamelCase				,				_UpperCamelCase     )
										lowerCAmelCase_						=	layer_outputs[0]

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

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

										lowerCAmelCase_						=	self.highway[i](_UpperCamelCase     )
										# logits, pooled_output

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

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

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

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

							lowerCAmelCase_						=	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 	_lowerCAmelCase					(	__a       ):




				def __init__(					self				,				_UpperCamelCase     )				->      Union[str, Any]:
							super().__init__(_UpperCamelCase     )
							lowerCAmelCase_						=	config

							lowerCAmelCase_						=	BertEmbeddings(_UpperCamelCase     )
							lowerCAmelCase_						=	DeeBertEncoder(_UpperCamelCase     )
							lowerCAmelCase_						=	BertPooler(_UpperCamelCase     )

							self.init_weights()




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




				def      __a						(					self     )				->      str:
							return self.embeddings.word_embeddings




				def      __a						(					self				,				_UpperCamelCase     )				->      Tuple:
							lowerCAmelCase_						=	value




				def      __a						(					self				,				_UpperCamelCase     )				->      Union[str, Any]:
							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				,				_UpperCamelCase=None				,				_UpperCamelCase=None				,				_UpperCamelCase=None				,				_UpperCamelCase=None				,				_UpperCamelCase=None				,				_UpperCamelCase=None				,				_UpperCamelCase=None				,				_UpperCamelCase=None				,				)				->      Dict:
							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:
										lowerCAmelCase_						=	input_ids.size()
							elif inputs_embeds is not None:
										lowerCAmelCase_						=	inputs_embeds.size()[:-1]
							else:
										raise ValueError("You have to specify either input_ids or inputs_embeds"     )

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

							if attention_mask is None:
										lowerCAmelCase_						=	torch.ones(_UpperCamelCase				,				device=_UpperCamelCase     )
							if encoder_attention_mask is None:
										lowerCAmelCase_						=	torch.ones(_UpperCamelCase				,				device=_UpperCamelCase     )
							if token_type_ids is None:
										lowerCAmelCase_						=	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.
							lowerCAmelCase_						=	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:
										lowerCAmelCase_						=	encoder_attention_mask[:, None, :, :]
							if encoder_attention_mask.dim() == 2:
										lowerCAmelCase_						=	encoder_attention_mask[:, None, None, :]

							lowerCAmelCase_						=	encoder_extended_attention_mask.to(
							    dtype=next(self.parameters()     ).dtype     )  # fp16 compatibility
							lowerCAmelCase_						=	(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]
							lowerCAmelCase_						=	self.get_head_mask(_UpperCamelCase				,				self.config.num_hidden_layers     )

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

							lowerCAmelCase_						=	(
							    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 	_lowerCAmelCase					(	__a       ):




				def __init__(					self				,				_UpperCamelCase				,				_UpperCamelCase     )				->      List[str]:
							lowerCAmelCase_						=	message
							lowerCAmelCase_						=	exit_layer  # start from 1!





class 	_lowerCAmelCase					(	nn.Module       ):




				def __init__(					self				,				_UpperCamelCase     )				->      Optional[int]:
							super().__init__()
							lowerCAmelCase_						=	BertPooler(_UpperCamelCase     )
							lowerCAmelCase_						=	nn.Dropout(config.hidden_dropout_prob     )
							lowerCAmelCase_						=	nn.Linear(config.hidden_size				,				config.num_labels     )




				def      __a						(					self				,				_UpperCamelCase     )				->      str:
							# Pooler
							lowerCAmelCase_						=	encoder_outputs[0]
							lowerCAmelCase_						=	self.pooler(_UpperCamelCase     )
							# "return" pooler_output

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

							# Dropout and classification
							lowerCAmelCase_						=	bmodel_output[1]

							lowerCAmelCase_						=	self.dropout(_UpperCamelCase     )
							lowerCAmelCase_						=	self.classifier(_UpperCamelCase     )

							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 	_lowerCAmelCase					(	__a       ):




				def __init__(					self				,				_UpperCamelCase     )				->      Union[str, Any]:
							super().__init__(_UpperCamelCase     )
							lowerCAmelCase_						=	config.num_labels
							lowerCAmelCase_						=	config.num_hidden_layers

							lowerCAmelCase_						=	DeeBertModel(_UpperCamelCase     )
							lowerCAmelCase_						=	nn.Dropout(config.hidden_dropout_prob     )
							lowerCAmelCase_						=	nn.Linear(config.hidden_size				,				self.config.num_labels     )

							self.init_weights()




				@add_start_docstrings_to_model_forward(_UpperCamelCase     )
				def      __a						(					self				,				_UpperCamelCase=None				,				_UpperCamelCase=None				,				_UpperCamelCase=None				,				_UpperCamelCase=None				,				_UpperCamelCase=None				,				_UpperCamelCase=None				,				_UpperCamelCase=None				,				_UpperCamelCase=-1				,				_UpperCamelCase=False				,				)				->      str:
							lowerCAmelCase_						=	self.num_layers
							try:
										lowerCAmelCase_						=	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

										lowerCAmelCase_						=	outputs[1]

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

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

										# work with highway exits
										lowerCAmelCase_						=	[]
										for highway_exit in outputs[-1]:
													lowerCAmelCase_						=	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
																lowerCAmelCase_						=	MSELoss()
																lowerCAmelCase_						=	loss_fct(highway_logits.view(-1     )				,				labels.view(-1     )     )
													else:
																lowerCAmelCase_						=	CrossEntropyLoss()
																lowerCAmelCase_						=	loss_fct(highway_logits.view(-1				,				self.num_labels     )				,				labels.view(-1     )     )
													highway_losses.append(_UpperCamelCase     )

										if train_highway:
													lowerCAmelCase_						=	(sum(highway_losses[:-1]     ),) + outputs
													# exclude the final highway, of course
										else:
													lowerCAmelCase_						=	(loss,) + outputs
							if not self.training:
										lowerCAmelCase_						=	outputs + ((original_entropy, highway_entropy), exit_layer)
										if output_layer >= 0:
													lowerCAmelCase_						=	(
													    (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 functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A   =     logging.get_logger(__name__)

_A   =     {
    "asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
    # See all SEW models at https://huggingface.co/models?filter=sew
}





class 	_lowerCAmelCase					(	__a       ):
				_lowercase					='''sew'''




				def __init__(					self				,				_UpperCamelCase=32				,				_UpperCamelCase=768				,				_UpperCamelCase=12				,				_UpperCamelCase=12				,				_UpperCamelCase=3_072				,				_UpperCamelCase=2				,				_UpperCamelCase="gelu"				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.0				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.02				,				_UpperCamelCase=1e-5				,				_UpperCamelCase="group"				,				_UpperCamelCase="gelu"				,				_UpperCamelCase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)				,				_UpperCamelCase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)				,				_UpperCamelCase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)				,				_UpperCamelCase=False				,				_UpperCamelCase=128				,				_UpperCamelCase=16				,				_UpperCamelCase=True				,				_UpperCamelCase=0.05				,				_UpperCamelCase=10				,				_UpperCamelCase=2				,				_UpperCamelCase=0.0				,				_UpperCamelCase=10				,				_UpperCamelCase=0				,				_UpperCamelCase="mean"				,				_UpperCamelCase=False				,				_UpperCamelCase=False				,				_UpperCamelCase=256				,				_UpperCamelCase=0				,				_UpperCamelCase=1				,				_UpperCamelCase=2				,				**_UpperCamelCase				,				)				->      Union[str, Any]:
							super().__init__(**_UpperCamelCase				,				pad_token_id=_UpperCamelCase				,				bos_token_id=_UpperCamelCase				,				eos_token_id=_UpperCamelCase     )
							lowerCAmelCase_						=	hidden_size
							lowerCAmelCase_						=	feat_extract_norm
							lowerCAmelCase_						=	feat_extract_activation
							lowerCAmelCase_						=	list(_UpperCamelCase     )
							lowerCAmelCase_						=	list(_UpperCamelCase     )
							lowerCAmelCase_						=	list(_UpperCamelCase     )
							lowerCAmelCase_						=	conv_bias
							lowerCAmelCase_						=	num_conv_pos_embeddings
							lowerCAmelCase_						=	num_conv_pos_embedding_groups
							lowerCAmelCase_						=	len(self.conv_dim     )
							lowerCAmelCase_						=	num_hidden_layers
							lowerCAmelCase_						=	intermediate_size
							lowerCAmelCase_						=	squeeze_factor
							lowerCAmelCase_						=	hidden_act
							lowerCAmelCase_						=	num_attention_heads
							lowerCAmelCase_						=	hidden_dropout
							lowerCAmelCase_						=	attention_dropout
							lowerCAmelCase_						=	activation_dropout
							lowerCAmelCase_						=	feat_proj_dropout
							lowerCAmelCase_						=	final_dropout
							lowerCAmelCase_						=	layerdrop
							lowerCAmelCase_						=	layer_norm_eps
							lowerCAmelCase_						=	initializer_range
							lowerCAmelCase_						=	vocab_size

							if (
							    (len(self.conv_stride     ) != self.num_feat_extract_layers)
							    or (len(self.conv_kernel     ) != self.num_feat_extract_layers)
							    or (len(self.conv_dim     ) != self.num_feat_extract_layers)
							):
										raise ValueError(
										    "Configuration for convolutional layers is incorrect."
										    "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
										    f"""but is `len(config.conv_dim) = {len(self.conv_dim     )}`, `len(config.conv_stride)"""
										    f"""= {len(self.conv_stride     )}`, `len(config.conv_kernel) = {len(self.conv_kernel     )}`."""     )

							# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
							lowerCAmelCase_						=	apply_spec_augment
							lowerCAmelCase_						=	mask_time_prob
							lowerCAmelCase_						=	mask_time_length
							lowerCAmelCase_						=	mask_time_min_masks
							lowerCAmelCase_						=	mask_feature_prob
							lowerCAmelCase_						=	mask_feature_length
							lowerCAmelCase_						=	mask_feature_min_masks

							# ctc loss
							lowerCAmelCase_						=	ctc_loss_reduction
							lowerCAmelCase_						=	ctc_zero_infinity

							# sequence classification
							lowerCAmelCase_						=	use_weighted_layer_sum
							lowerCAmelCase_						=	classifier_proj_size




				@property
				def      __a						(					self     )				->      Optional[Any]:
							return functools.reduce(operator.mul				,				self.conv_stride				,				1     )



