
"""simple docstring"""
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					     =	{
    '''post_extract_proj''': '''feature_projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.upsample.0''': '''encoder.upsample.projection''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						for attribute in key.split("""."""						):
												__lowerCAmelCase     =  getattr(_lowerCAmelCase ,    _lowerCAmelCase						)

						if weight_type is not None:
												__lowerCAmelCase     =  getattr(_lowerCAmelCase ,    _lowerCAmelCase						).shape
						else:
												__lowerCAmelCase     =  hf_pointer.shape

						assert hf_shape == value.shape, (
						    f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
						    f""" {value.shape} for {full_name}"""
						)

						if weight_type == "weight":
												__lowerCAmelCase     =  value
						elif weight_type == "weight_g":
												__lowerCAmelCase     =  value
						elif weight_type == "weight_v":
												__lowerCAmelCase     =  value
						elif weight_type == "bias":
												__lowerCAmelCase     =  value
						else:
												__lowerCAmelCase     =  value

						logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""						)

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						__lowerCAmelCase     =  []
						__lowerCAmelCase     =  fairseq_model.state_dict()

						__lowerCAmelCase     =  hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												__lowerCAmelCase     =  False
												if "conv_layers" in name:
																		load_conv_layer(
																		    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    hf_model.config.feat_extract_norm == """group""" ,    )
																		__lowerCAmelCase     =  True
												else:
																		for key, mapped_key in MAPPING.items():
																								__lowerCAmelCase     =  """sew.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key

																								if key in name or key.split("""w2v_model."""						)[-1] == name.split("""."""						)[0]:
																														__lowerCAmelCase     =  True
																														if "*" in mapped_key:
																																				__lowerCAmelCase     =  name.split(_lowerCAmelCase						)[0].split("""."""						)[-2]
																																				__lowerCAmelCase     =  mapped_key.replace("""*""" ,    _lowerCAmelCase						)
																														if "weight_g" in name:
																																				__lowerCAmelCase     =  """weight_g"""
																														elif "weight_v" in name:
																																				__lowerCAmelCase     =  """weight_v"""
																														elif "weight" in name:
																																				__lowerCAmelCase     =  """weight"""
																														elif "bias" in name:
																																				__lowerCAmelCase     =  """bias"""
																														else:
																																				__lowerCAmelCase     =  None
																														set_recursively(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						)
																								continue
												if not is_used:
																		unused_weights.append(_lowerCAmelCase						)

						logger.warning(f"""Unused weights: {unused_weights}"""						)

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						__lowerCAmelCase     =  full_name.split("""conv_layers."""						)[-1]
						__lowerCAmelCase     =  name.split("""."""						)
						__lowerCAmelCase     =  int(items[0]						)
						__lowerCAmelCase     =  int(items[1]						)

						if type_id == 0:
												if "bias" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																		    f"""{full_name} has size {value.shape}, but"""
																		    f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
																		)
																		__lowerCAmelCase     =  value
																		logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""						)
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																		    f"""{full_name} has size {value.shape}, but"""
																		    f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
																		)
																		__lowerCAmelCase     =  value
																		logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""						)
						elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
												if "bias" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																		    f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
																		    " found."
																		)
																		__lowerCAmelCase     =  value
																		logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																		    f"""{full_name} has size {value.shape}, but"""
																		    f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
																		)
																		__lowerCAmelCase     =  value
																		logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
						else:
												unused_weights.append(_lowerCAmelCase						)

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						__lowerCAmelCase     =  SEWConfig()
						if is_finetuned:
												__lowerCAmelCase     =  model.wav_encoder.wav_model.cfg
						else:
												__lowerCAmelCase     =  model.cfg

						__lowerCAmelCase     =  fs_config.conv_bias
						__lowerCAmelCase     =  eval(fs_config.conv_feature_layers						)
						__lowerCAmelCase     =  [x[0] for x in conv_layers]
						__lowerCAmelCase     =  [x[1] for x in conv_layers]
						__lowerCAmelCase     =  [x[2] for x in conv_layers]
						__lowerCAmelCase     =  """gelu"""
						__lowerCAmelCase     =  """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
						__lowerCAmelCase     =  0.0
						__lowerCAmelCase     =  fs_config.activation_fn.name
						__lowerCAmelCase     =  fs_config.encoder_embed_dim
						__lowerCAmelCase     =  0.02
						__lowerCAmelCase     =  fs_config.encoder_ffn_embed_dim
						__lowerCAmelCase     =  1E-5
						__lowerCAmelCase     =  fs_config.encoder_layerdrop
						__lowerCAmelCase     =  fs_config.encoder_attention_heads
						__lowerCAmelCase     =  fs_config.conv_pos_groups
						__lowerCAmelCase     =  fs_config.conv_pos
						__lowerCAmelCase     =  len(_lowerCAmelCase						)
						__lowerCAmelCase     =  fs_config.encoder_layers
						__lowerCAmelCase     =  fs_config.squeeze_factor

						# take care of any params that are overridden by the Wav2VecCtc model
						if is_finetuned:
												__lowerCAmelCase     =  model.cfg
												__lowerCAmelCase     =  fs_config.final_dropout
												__lowerCAmelCase     =  fs_config.layerdrop
						__lowerCAmelCase     =  fs_config.activation_dropout
						__lowerCAmelCase     =  fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
						__lowerCAmelCase     =  fs_config.attention_dropout
						__lowerCAmelCase     =  fs_config.dropout_input
						__lowerCAmelCase     =  fs_config.dropout
						__lowerCAmelCase     =  fs_config.mask_channel_length
						__lowerCAmelCase     =  fs_config.mask_channel_prob
						__lowerCAmelCase     =  fs_config.mask_length
						__lowerCAmelCase     =  fs_config.mask_prob

						__lowerCAmelCase     =  """Wav2Vec2FeatureExtractor"""
						__lowerCAmelCase     =  """Wav2Vec2CTCTokenizer"""

						return config

@torch.no_grad()
def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase=None ,    _lowerCAmelCase=None ,    _lowerCAmelCase=True						):

						if is_finetuned:
												__lowerCAmelCase   ,     __lowerCAmelCase   ,     __lowerCAmelCase     =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
												    [checkpoint_path] ,    arg_overrides={"""data""": """/""".join(dict_path.split("""/"""						)[:-1]						)}						)
						else:
												__lowerCAmelCase   ,     __lowerCAmelCase   ,     __lowerCAmelCase     =  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						)

						if config_path is not None:
												__lowerCAmelCase     =  SEWConfig.from_pretrained(_lowerCAmelCase						)
						else:
												__lowerCAmelCase     =  convert_config(model[0] ,    _lowerCAmelCase						)
						__lowerCAmelCase     =  model[0].eval()

						__lowerCAmelCase     =  True if config.feat_extract_norm == """layer""" else False
						__lowerCAmelCase     =  WavaVecaFeatureExtractor(
						    feature_size=1 ,    sampling_rate=1_6000 ,    padding_value=0 ,    do_normalize=_lowerCAmelCase ,    return_attention_mask=_lowerCAmelCase ,    )

						if is_finetuned:
												if dict_path:
																		__lowerCAmelCase     =  Dictionary.load(_lowerCAmelCase						)

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		__lowerCAmelCase     =  target_dict.pad_index
																		__lowerCAmelCase     =  target_dict.bos_index
																		__lowerCAmelCase     =  target_dict.pad_index
																		__lowerCAmelCase     =  target_dict.bos_index
																		__lowerCAmelCase     =  target_dict.eos_index
																		__lowerCAmelCase     =  len(target_dict.symbols						)
																		__lowerCAmelCase     =  os.path.join(_lowerCAmelCase ,    """vocab.json"""						)
																		if not os.path.isdir(_lowerCAmelCase						):
																								logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(_lowerCAmelCase						)						)
																								return
																		os.makedirs(_lowerCAmelCase ,    exist_ok=_lowerCAmelCase						)
																		with open(_lowerCAmelCase ,    """w""" ,    encoding="""utf-8"""						) as vocab_handle:
																								json.dump(target_dict.indices ,    _lowerCAmelCase						)
																		__lowerCAmelCase     =  WavaVecaCTCTokenizer(
																		    _lowerCAmelCase ,    unk_token=target_dict.unk_word ,    pad_token=target_dict.pad_word ,    bos_token=target_dict.bos_word ,    eos_token=target_dict.eos_word ,    word_delimiter_token="""|""" ,    do_lower_case=_lowerCAmelCase ,    )
																		__lowerCAmelCase     =  WavaVecaProcessor(feature_extractor=_lowerCAmelCase ,    tokenizer=_lowerCAmelCase						)
																		processor.save_pretrained(_lowerCAmelCase						)

												__lowerCAmelCase     =  SEWForCTC(_lowerCAmelCase						)
						else:
												__lowerCAmelCase     =  SEWModel(_lowerCAmelCase						)
												feature_extractor.save_pretrained(_lowerCAmelCase						)

						recursively_load_weights(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						)

						hf_model.save_pretrained(_lowerCAmelCase						)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE_					     =	argparse.ArgumentParser()
			parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
			parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
			parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
			parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
			parser.add_argument(
			    '''--is_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
			)
			SCREAMING_SNAKE_CASE_					     =	parser.parse_args()
			convert_sew_checkpoint(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
			)


"""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  lowercase     (_lowerCAmelCase						):
						__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 ):



							'''simple docstring'''





							def __init__( self    ,				snake_case_				)       ->  Optional[Any]:
													super().__init__()
													__lowerCAmelCase     =  config.output_attentions
													__lowerCAmelCase     =  config.output_hidden_states
													__lowerCAmelCase     =  nn.ModuleList([BertLayer(snake_case_				) for _ in range(config.num_hidden_layers				)]				)
													__lowerCAmelCase     =  nn.ModuleList([BertHighway(snake_case_				) for _ in range(config.num_hidden_layers				)]				)

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




							def    A__						( self    ,				snake_case_				)       ->  List[Any]:
													if (type(snake_case_				) is float) or (type(snake_case_				) is int):
																			for i in range(len(self.early_exit_entropy				)				):
																									__lowerCAmelCase     =  x
													else:
																			__lowerCAmelCase     =  x




							def    A__						( self    ,				snake_case_				)       ->  Dict:
													__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    ,				snake_case_    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				)       ->  List[str]:
													__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(
																			    snake_case_    ,				snake_case_    ,				head_mask[i]    ,				snake_case_    ,				snake_case_				)
																			__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](snake_case_				)
																			# logits, pooled_output

																			if not self.training:
																									__lowerCAmelCase     =  highway_exit[0]
																									__lowerCAmelCase     =  entropy(snake_case_				)
																									__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(snake_case_    ,				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__ ):



							'''simple docstring'''





							def __init__( self    ,				snake_case_				)       ->  Tuple:
													super().__init__(snake_case_				)
													__lowerCAmelCase     =  config

													__lowerCAmelCase     =  BertEmbeddings(snake_case_				)
													__lowerCAmelCase     =  DeeBertEncoder(snake_case_				)
													__lowerCAmelCase     =  BertPooler(snake_case_				)

													self.init_weights()




							def    A__						( self				)       ->  Dict:
													self.encoder.init_highway_pooler(self.pooler				)




							def    A__						( self				)       ->  List[Any]:
													return self.embeddings.word_embeddings




							def    A__						( self    ,				snake_case_				)       ->  Tuple:
													__lowerCAmelCase     =  value




							def    A__						( self    ,				snake_case_				)       ->  List[str]:
													for layer, heads in heads_to_prune.items():
																			self.encoder.layer[layer].attention.prune_heads(snake_case_				)




							@add_start_docstrings_to_model_forward(snake_case_				)
							def    A__						( 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    ,				)       ->  Optional[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:
																			__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(snake_case_    ,				device=snake_case_				)
													if encoder_attention_mask is None:
																			__lowerCAmelCase     =  torch.ones(snake_case_    ,				device=snake_case_				)
													if token_type_ids is None:
																			__lowerCAmelCase     =  torch.zeros(snake_case_    ,				dtype=torch.long    ,				device=snake_case_				)

													# 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(snake_case_    ,				snake_case_    ,				snake_case_				)

													# 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) * -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]
													__lowerCAmelCase     =  self.get_head_mask(snake_case_    ,				self.config.num_hidden_layers				)

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

													__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__ ):



							'''simple docstring'''





							def __init__( self    ,				snake_case_    ,				snake_case_				)       ->  Optional[int]:
													__lowerCAmelCase     =  message
													__lowerCAmelCase     =  exit_layer  # start from 1!
class       lowerCAmelCase_		(				nn.Module ):



							'''simple docstring'''





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




							def    A__						( self    ,				snake_case_				)       ->  Optional[int]:
													# Pooler
													__lowerCAmelCase     =  encoder_outputs[0]
													__lowerCAmelCase     =  self.pooler(snake_case_				)
													# "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(snake_case_				)
													__lowerCAmelCase     =  self.classifier(snake_case_				)

													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__ ):



							'''simple docstring'''





							def __init__( self    ,				snake_case_				)       ->  Optional[Any]:
													super().__init__(snake_case_				)
													__lowerCAmelCase     =  config.num_labels
													__lowerCAmelCase     =  config.num_hidden_layers

													__lowerCAmelCase     =  DeeBertModel(snake_case_				)
													__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(snake_case_				)
							def    A__						( 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    ,				)       ->  Optional[int]:
													__lowerCAmelCase     =  self.num_layers
													try:
																			__lowerCAmelCase     =  self.bert(
																			    snake_case_    ,				attention_mask=snake_case_    ,				token_type_ids=snake_case_    ,				position_ids=snake_case_    ,				head_mask=snake_case_    ,				inputs_embeds=snake_case_    ,				)
																			# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

																			__lowerCAmelCase     =  outputs[1]

																			__lowerCAmelCase     =  self.dropout(snake_case_				)
																			__lowerCAmelCase     =  self.classifier(snake_case_				)
																			__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(snake_case_				)
																			__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(snake_case_				)
																															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(snake_case_				)

																			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)
