


import logging

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,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


lowerCAmelCase			=				logging.getLogger(__name__)


class 			_a				(     UpperCamelCase__     ):

							def  lowerCamelCase_					(				self:      str       ,		UpperCamelCase_:      str       ,		UpperCamelCase_:      Tuple       ,		UpperCamelCase_:      Optional[Any]=None       ,		UpperCamelCase_:      Dict=None				)     ->  Union[str, Any]:





									"""simple docstring"""







									lowercase__				= self.layer[current_layer](UpperCamelCase_       ,		UpperCamelCase_       ,		head_mask[current_layer]				)

									lowercase__				= layer_outputs[0]

									return hidden_states


@add_start_docstrings(
    '''The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.'''	,      UpperCamelCase__	,      )
class 			_a				(     UpperCamelCase__     ):

							def __init__(				self:      List[Any]       ,		UpperCamelCase_:      Union[str, Any]				)     ->  Tuple:





									"""simple docstring"""







									super().__init__(UpperCamelCase_				)

									lowercase__				= BertEncoderWithPabee(UpperCamelCase_				)

									self.init_weights()
									lowercase__				= 0
									lowercase__				= 0
									lowercase__				= 0

									lowercase__				= 0

							def  lowerCamelCase_					(				self:      Optional[int]       ,		UpperCamelCase_:      str				)     ->  Tuple:





									"""simple docstring"""







									lowercase__				= threshold

							def  lowerCamelCase_					(				self:      int       ,		UpperCamelCase_:      Dict				)     ->  Any:





									"""simple docstring"""







									lowercase__				= patience

							def  lowerCamelCase_					(				self:      Optional[int]				)     ->  Tuple:





									"""simple docstring"""







									lowercase__				= 0
									lowercase__				= 0

							def  lowerCamelCase_					(				self:      List[Any]				)     ->  int:





									"""simple docstring"""







									lowercase__				= self.inference_layers_num / self.inference_instances_num
									lowercase__				= (
									    f'*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='
									    f' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'
									)
									print(UpperCamelCase_				)


							@add_start_docstrings_to_model_forward(UpperCamelCase_				)
							def  lowerCamelCase_					(				self:      List[str]       ,		UpperCamelCase_:      List[Any]=None       ,		UpperCamelCase_:      Union[str, Any]=None       ,		UpperCamelCase_:      str=None       ,		UpperCamelCase_:      List[str]=None       ,		UpperCamelCase_:      str=None       ,		UpperCamelCase_:      Optional[Any]=None       ,		UpperCamelCase_:      Tuple=None       ,		UpperCamelCase_:      List[str]=None       ,		UpperCamelCase_:      Union[str, Any]=None       ,		UpperCamelCase_:      str=None       ,		UpperCamelCase_:      str=False       ,		)     ->  List[Any]:





									"""simple docstring"""







									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 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 self.config.is_decoder and encoder_hidden_states is not None:
											lowercase__	,						lowercase__	,						lowercase__				= encoder_hidden_states.size()
											lowercase__				= (encoder_batch_size, encoder_sequence_length)
											if encoder_attention_mask is None:
													lowercase__				= torch.ones(UpperCamelCase_       ,		device=UpperCamelCase_				)
											lowercase__				= self.invert_attention_mask(UpperCamelCase_				)
									else:
											lowercase__				= None

									# 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__				= embedding_output

									if self.training:
											lowercase__				= []
											for i in range(self.config.num_hidden_layers				):
													lowercase__				= self.encoder.adaptive_forward(
													    UpperCamelCase_       ,		current_layer=UpperCamelCase_       ,		attention_mask=UpperCamelCase_       ,		head_mask=UpperCamelCase_				)

													lowercase__				= self.pooler(UpperCamelCase_				)
													lowercase__				= output_layers[i](output_dropout(UpperCamelCase_				)				)
													res.append(UpperCamelCase_				)
									elif self.patience == 0:  # Use all layers for inference
											lowercase__				= self.encoder(
											    UpperCamelCase_       ,		attention_mask=UpperCamelCase_       ,		head_mask=UpperCamelCase_       ,		encoder_hidden_states=UpperCamelCase_       ,		encoder_attention_mask=UpperCamelCase_       ,		)
											lowercase__				= self.pooler(encoder_outputs[0]				)
											lowercase__				= [output_layers[self.config.num_hidden_layers - 1](UpperCamelCase_				)]
									else:
											lowercase__				= 0
											lowercase__				= None
											lowercase__				= 0
											for i in range(self.config.num_hidden_layers				):
													calculated_layer_num += 1
													lowercase__				= self.encoder.adaptive_forward(
													    UpperCamelCase_       ,		current_layer=UpperCamelCase_       ,		attention_mask=UpperCamelCase_       ,		head_mask=UpperCamelCase_				)

													lowercase__				= self.pooler(UpperCamelCase_				)
													lowercase__				= output_layers[i](UpperCamelCase_				)
													if regression:
															lowercase__				= logits.detach()
															if patient_result is not None:
																	lowercase__				= patient_result.detach()
															if (patient_result is not None) and torch.abs(patient_result - labels				) < self.regression_threshold:
																	patient_counter += 1
															else:
																	lowercase__				= 0
													else:
															lowercase__				= logits.detach().argmax(dim=1				)
															if patient_result is not None:
																	lowercase__				= patient_result.detach().argmax(dim=1				)
															if (patient_result is not None) and torch.all(labels.eq(UpperCamelCase_				)				):
																	patient_counter += 1
															else:
																	lowercase__				= 0

													lowercase__				= logits
													if patient_counter == self.patience:
															break
											lowercase__				= [patient_result]
											self.inference_layers_num += calculated_layer_num
											self.inference_instances_num += 1

									return res


@add_start_docstrings(
    '''Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. '''	,      UpperCamelCase__	,      )
class 			_a				(     UpperCamelCase__     ):

							def __init__(				self:      Optional[int]       ,		UpperCamelCase_:      int				)     ->  int:





									"""simple docstring"""







									super().__init__(UpperCamelCase_				)
									lowercase__				= config.num_labels

									lowercase__				= BertModelWithPabee(UpperCamelCase_				)
									lowercase__				= nn.Dropout(config.hidden_dropout_prob				)
									lowercase__				= nn.ModuleList(
									    [nn.Linear(config.hidden_size       ,		self.config.num_labels				) for _ in range(config.num_hidden_layers				)]				)

									self.init_weights()


							@add_start_docstrings_to_model_forward(UpperCamelCase_				)
							def  lowerCamelCase_					(				self:      List[Any]       ,		UpperCamelCase_:      Union[str, Any]=None       ,		UpperCamelCase_:      Optional[Any]=None       ,		UpperCamelCase_:      int=None       ,		UpperCamelCase_:      Optional[Any]=None       ,		UpperCamelCase_:      Any=None       ,		UpperCamelCase_:      Optional[Any]=None       ,		UpperCamelCase_:      List[str]=None       ,		)     ->  List[str]:





									"""simple docstring"""







									lowercase__				= self.bert(
									    input_ids=UpperCamelCase_       ,		attention_mask=UpperCamelCase_       ,		token_type_ids=UpperCamelCase_       ,		position_ids=UpperCamelCase_       ,		head_mask=UpperCamelCase_       ,		inputs_embeds=UpperCamelCase_       ,		output_dropout=self.dropout       ,		output_layers=self.classifiers       ,		regression=self.num_labels == 1       ,		)

									lowercase__				= (logits[-1],)

									if labels is not None:
											lowercase__				= None
											lowercase__				= 0
											for ix, logits_item in enumerate(UpperCamelCase_				):
													if self.num_labels == 1:
															#  We are doing regression
															lowercase__				= MSELoss()
															lowercase__				= loss_fct(logits_item.view(-1				)       ,		labels.view(-1				)				)
													else:
															lowercase__				= CrossEntropyLoss()
															lowercase__				= loss_fct(logits_item.view(-1       ,		self.num_labels				)       ,		labels.view(-1				)				)
													if total_loss is None:
															lowercase__				= loss
													else:
															total_loss += loss * (ix + 1)
													total_weights += ix + 1
											lowercase__				= (total_loss / total_weights,) + outputs

									return outputs





import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
a_     :    Dict     		=   logging.get_logger(__name__)

a_     :    List[str]     		=   {
    "post_extract_proj": "feature_projection.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.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}
a_     :    Union[str, Any]     		=   [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	Dict		,				_UpperCamelCase		:	int		,				_UpperCamelCase		:	Optional[Any]						)		->				Optional[int]:
 '''simple docstring'''


 for attribute in key.split('.'						):
  SCREAMING_SNAKE_CASE		    =      getattr(_UpperCamelCase		,				_UpperCamelCase						)

 if weight_type is not None:
  SCREAMING_SNAKE_CASE		    =      getattr(_UpperCamelCase		,				_UpperCamelCase						).shape
 else:
  SCREAMING_SNAKE_CASE		    =      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":
  SCREAMING_SNAKE_CASE		    =      value
 elif weight_type == "weight_g":
  SCREAMING_SNAKE_CASE		    =      value
 elif weight_type == "weight_v":
  SCREAMING_SNAKE_CASE		    =      value
 elif weight_type == "bias":
  SCREAMING_SNAKE_CASE		    =      value
 else:
  SCREAMING_SNAKE_CASE		    =      value

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


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Dict		,				_UpperCamelCase		:	Optional[Any]						)		->				Tuple:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      []
 SCREAMING_SNAKE_CASE		    =      fairseq_model.state_dict()

 SCREAMING_SNAKE_CASE		    =      hf_model.feature_extractor
 SCREAMING_SNAKE_CASE		    =      hf_model.adapter

 for name, value in fairseq_dict.items():
  SCREAMING_SNAKE_CASE		    =      False
  if "conv_layers" in name:
   load_conv_layer(
       _UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				hf_model.config.feat_extract_norm == 'group'		,				)
   SCREAMING_SNAKE_CASE		    =      True
  elif any(x in name for x in ['adaptor', 'w2v_encoder.proj.', 'w2v_proj_ln.']						):
   load_adapter(_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase						)
   SCREAMING_SNAKE_CASE		    =      True
  else:
   for key, mapped_key in MAPPING.items():
    if key in name or key.split('w2v_model.'						)[-1] == name.split('.'						)[0]:
     SCREAMING_SNAKE_CASE		    =      True
     if "*" in mapped_key:
      SCREAMING_SNAKE_CASE		    =      name.split(_UpperCamelCase						)[0].split('.'						)[-2]
      SCREAMING_SNAKE_CASE		    =      mapped_key.replace('*'		,				_UpperCamelCase						)
     if "weight_g" in name:
      SCREAMING_SNAKE_CASE		    =      'weight_g'
     elif "weight_v" in name:
      SCREAMING_SNAKE_CASE		    =      'weight_v'
     elif "bias" in name:
      SCREAMING_SNAKE_CASE		    =      'bias'
     elif "weight" in name:
      SCREAMING_SNAKE_CASE		    =      'weight'
     else:
      SCREAMING_SNAKE_CASE		    =      None
     set_recursively(_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase						)
    continue
  if not is_used:
   unused_weights.append(_UpperCamelCase						)

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


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	int		,				_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	List[Any]		,				_UpperCamelCase		:	List[Any]						)		->				Optional[Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      full_name.split('conv_layers.'						)[-1]
 SCREAMING_SNAKE_CASE		    =      name.split('.'						)
 SCREAMING_SNAKE_CASE		    =      int(items[0]						)
 SCREAMING_SNAKE_CASE		    =      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."""
   )
   SCREAMING_SNAKE_CASE		    =      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."""
   )
   SCREAMING_SNAKE_CASE		    =      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."
   )
   SCREAMING_SNAKE_CASE		    =      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."""
   )
   SCREAMING_SNAKE_CASE		    =      value
   logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
 else:
  unused_weights.append(_UpperCamelCase						)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	str		,				_UpperCamelCase		:	Any		,				_UpperCamelCase		:	Union[str, Any]						)		->				int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      full_name.split('adaptor.'						)[-1]
 SCREAMING_SNAKE_CASE		    =      name.split('.'						)

 if items[1].isdigit():
  SCREAMING_SNAKE_CASE		    =      int(items[1]						)
 else:
  SCREAMING_SNAKE_CASE		    =      None

 if "adaptor" not in full_name:
  if "proj_ln" in full_name:
   # has to be layer norm
   if "bias" in name:
    assert (
        value.shape == adapter.proj_layer_norm.bias.data.shape
    ), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."""
    SCREAMING_SNAKE_CASE		    =      value
    logger.info(f"""Adapter proj layer norm bias was initialized from {full_name}."""						)
   if "weight" in name:
    assert (
        value.shape == adapter.proj_layer_norm.weight.data.shape
    ), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."""
    SCREAMING_SNAKE_CASE		    =      value
  else:
   # has to be projection layer
   if "bias" in name:
    assert (
        value.shape == adapter.proj.bias.data.shape
    ), f"""{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."""
    SCREAMING_SNAKE_CASE		    =      value
    logger.info(f"""Adapter proj layer bias was initialized from {full_name}."""						)
   if "weight" in name:
    assert (
        value.shape == adapter.proj.weight.data.shape
    ), f"""{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."""
    SCREAMING_SNAKE_CASE		    =      value
    logger.info(f"""Adapter proj layer weight was initialized from {full_name}."""						)
 elif isinstance(_UpperCamelCase		,				_UpperCamelCase						):
  if "bias" in name:
   assert (
       value.shape == adapter.layers[layer_id].conv.bias.data.shape
   ), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."""
   SCREAMING_SNAKE_CASE		    =      value
   logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""						)
  elif "weight" in name:
   assert (
       value.shape == adapter.layers[layer_id].conv.weight.data.shape
   ), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."""
   SCREAMING_SNAKE_CASE		    =      value
   logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""						)
 else:
  unused_weights.append(_UpperCamelCase						)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]						)		->				str:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      emb.weight.shape
 SCREAMING_SNAKE_CASE		    =      nn.Linear(_UpperCamelCase		,				_UpperCamelCase		,				bias=_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      emb.weight.data
 return lin_layer


@torch.no_grad()
def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]		,				_UpperCamelCase		:	Any		,				_UpperCamelCase		:	Optional[int]		,				_UpperCamelCase		:	Tuple		,				_UpperCamelCase		:	str		,				_UpperCamelCase		:	str		,				_UpperCamelCase		:	List[Any]		,				_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	Tuple		,				)		->				int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      WavaVecaConfig.from_pretrained(
     _UpperCamelCase		,				add_adapter=_UpperCamelCase		,				adapter_stride=_UpperCamelCase		,				adapter_kernel_size=_UpperCamelCase		,				use_auth_token=_UpperCamelCase		,				output_hidden_size=_UpperCamelCase		,				)
 SCREAMING_SNAKE_CASE		    =      MBartConfig.from_pretrained(_UpperCamelCase						)

 # load model
 SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
     [checkpoint_path]		,				arg_overrides={
         'config_yaml': config_yaml_path,
         'data': '/'.join(dict_path.split('/'						)[:-1]						),
         'w2v_path': checkpoint_path,
         'load_pretrained_decoder_from': None,
     }		,				)
 SCREAMING_SNAKE_CASE		    =      model[0].eval()

 # load feature extractor
 SCREAMING_SNAKE_CASE		    =      WavaVecaFeatureExtractor.from_pretrained(_UpperCamelCase		,				use_auth_token=_UpperCamelCase						)

 # set weights for wav2vec2 encoder
 SCREAMING_SNAKE_CASE		    =      WavaVecaModel(_UpperCamelCase						)

 recursively_load_weights_wavaveca(model.encoder		,				_UpperCamelCase						)

 # load decoder weights
 SCREAMING_SNAKE_CASE		    =      MBartForCausalLM(_UpperCamelCase						)
 SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()		,				strict=_UpperCamelCase						)
 logger.warning(f"""The following keys are missing when loading the decoder weights: {missing_keys}"""						)
 logger.warning(f"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}"""						)

 SCREAMING_SNAKE_CASE		    =      SpeechEncoderDecoderModel(encoder=_UpperCamelCase		,				decoder=_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      False

 SCREAMING_SNAKE_CASE		    =      MBartaaTokenizer(_UpperCamelCase						)
 tokenizer.save_pretrained(_UpperCamelCase						)

 SCREAMING_SNAKE_CASE		    =      hf_wavavec.config.to_dict()
 SCREAMING_SNAKE_CASE		    =      tokenizer.pad_token_id
 SCREAMING_SNAKE_CASE		    =      tokenizer.bos_token_id
 SCREAMING_SNAKE_CASE		    =      tokenizer.eos_token_id
 SCREAMING_SNAKE_CASE		    =      'mbart50'
 SCREAMING_SNAKE_CASE		    =      'wav2vec2'

 SCREAMING_SNAKE_CASE		    =      tokenizer.eos_token_id
 SCREAMING_SNAKE_CASE		    =      25_00_04
 SCREAMING_SNAKE_CASE		    =      tokenizer.eos_token_id

 SCREAMING_SNAKE_CASE		    =      SpeechEncoderDecoderConfig.from_dict(_UpperCamelCase						)

 hf_wavavec.save_pretrained(_UpperCamelCase						)
 feature_extractor.save_pretrained(_UpperCamelCase						)


if __name__ == "__main__":
      a_     :    List[str]     		=   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_yaml_path", default=None, type=str, help="Path to yaml file of fine-tuned model")
      parser.add_argument(
          "--encoder_config_path",
          default="facebook/wav2vec2-xls-r-1b",
          type=str,
          help="Path to hf encoder wav2vec2 checkpoint config",
      )
      parser.add_argument(
          "--decoder_config_path",
          default="facebook/mbart-large-50-one-to-many-mmt",
          type=str,
          help="Path to hf decoder checkpoint config",
      )
      parser.add_argument("--add_adapter", default=True, type=bool, help="whethere to add model adapter layers")
      parser.add_argument("--adapter_stride", default=2, type=int, help="stride of adapter layers")
      parser.add_argument("--adapter_kernel_size", default=3, type=int, help="kernel size of adapter layers")
      parser.add_argument("--encoder_output_dim", default=1024, type=int, help="encoder output dim")
      parser.add_argument("--start_token_id", default=25_0004, type=int, help="`decoder_start_token_id` of model config")

      a_     :    Dict     		=   parser.parse_args()
      convert_wavaveca_checkpoint(
          args.checkpoint_path,
          args.pytorch_dump_folder_path,
          args.dict_path,
          args.config_yaml_path,
          encoder_config_path=args.encoder_config_path,
          decoder_config_path=args.decoder_config_path,
          add_adapter=args.add_adapter,
          adapter_kernel_size=args.adapter_kernel_size,
          adapter_stride=args.adapter_stride,
          decoder_start_token_id=args.start_token_id,
          encoder_output_dim=args.encoder_output_dim,
      )

