






"""simple docstring"""







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()
lowerCAmelCase_	:    Optional[int]            =      logging.get_logger(__name__)

lowerCAmelCase_	:    Dict            =      {
    '''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''',
}
lowerCAmelCase_	:    Dict            =      [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       for attribute in key.split("""."""		):
              UpperCAmelCase	    =			getattr(lowerCAmelCase	,      lowerCAmelCase		)

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

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






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			[]
       UpperCAmelCase	    =			fairseq_model.state_dict()

       UpperCAmelCase	    =			hf_model.feature_extractor
       UpperCAmelCase	    =			hf_model.adapter

       for name, value in fairseq_dict.items():
              UpperCAmelCase	    =			False
              if "conv_layers" in name:
                     load_conv_layer(
                         lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      hf_model.config.feat_extract_norm == """group"""	,      )
                     UpperCAmelCase	    =			True
              elif any(x in name for x in ["""adaptor""", """w2v_encoder.proj.""", """w2v_proj_ln."""]		):
                     load_adapter(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		)
                     UpperCAmelCase	    =			True
              else:
                     for key, mapped_key in MAPPING.items():
                            if key in name or key.split("""w2v_model."""		)[-1] == name.split("""."""		)[0]:
                                   UpperCAmelCase	    =			True
                                   if "*" in mapped_key:
                                          UpperCAmelCase	    =			name.split(lowerCAmelCase		)[0].split("""."""		)[-2]
                                          UpperCAmelCase	    =			mapped_key.replace("""*"""	,      lowerCAmelCase		)
                                   if "weight_g" in name:
                                          UpperCAmelCase	    =			"""weight_g"""
                                   elif "weight_v" in name:
                                          UpperCAmelCase	    =			"""weight_v"""
                                   elif "bias" in name:
                                          UpperCAmelCase	    =			"""bias"""
                                   elif "weight" in name:
                                          UpperCAmelCase	    =			"""weight"""
                                   else:
                                          UpperCAmelCase	    =			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       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







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






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			full_name.split("""adaptor."""		)[-1]
       UpperCAmelCase	    =			name.split("""."""		)

       if items[1].isdigit():
              UpperCAmelCase	    =			int(items[1]		)
       else:
              UpperCAmelCase	    =			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.'''
                            UpperCAmelCase	    =			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.'''
                            UpperCAmelCase	    =			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.'''
                            UpperCAmelCase	    =			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.'''
                            UpperCAmelCase	    =			value
                            logger.info(F'''Adapter proj layer weight was initialized from {full_name}.'''		)
       elif isinstance(lowerCAmelCase	,      lowerCAmelCase		):
              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.'''
                     UpperCAmelCase	    =			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.'''
                     UpperCAmelCase	    =			value
                     logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.'''		)
       else:
              unused_weights.append(lowerCAmelCase		)






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase    ,       UpperCAmelCase	    =			emb.weight.shape
       UpperCAmelCase	    =			nn.Linear(lowerCAmelCase	,      lowerCAmelCase	,      bias=lowerCAmelCase		)
       UpperCAmelCase	    =			emb.weight.data
       return lin_layer






@torch.no_grad()
def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      ):







       '''simple docstring'''
       UpperCAmelCase	    =			WavaVecaConfig.from_pretrained(
           lowerCAmelCase	,      add_adapter=lowerCAmelCase	,      adapter_stride=lowerCAmelCase	,      adapter_kernel_size=lowerCAmelCase	,      use_auth_token=lowerCAmelCase	,      output_hidden_size=lowerCAmelCase	,      )
       UpperCAmelCase	    =			MBartConfig.from_pretrained(lowerCAmelCase		)

       # load model
       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase	    =			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,
           }	,      )
       UpperCAmelCase	    =			model[0].eval()

       # load feature extractor
       UpperCAmelCase	    =			WavaVecaFeatureExtractor.from_pretrained(lowerCAmelCase	,      use_auth_token=lowerCAmelCase		)

       # set weights for wav2vec2 encoder
       UpperCAmelCase	    =			WavaVecaModel(lowerCAmelCase		)

       recursively_load_weights_wavaveca(model.encoder	,      lowerCAmelCase		)

       # load decoder weights
       UpperCAmelCase	    =			MBartForCausalLM(lowerCAmelCase		)
       UpperCAmelCase    ,       UpperCAmelCase	    =			hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()	,      strict=lowerCAmelCase		)
       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}'''		)

       UpperCAmelCase	    =			SpeechEncoderDecoderModel(encoder=lowerCAmelCase	,      decoder=lowerCAmelCase		)
       UpperCAmelCase	    =			False

       UpperCAmelCase	    =			MBartaaTokenizer(lowerCAmelCase		)
       tokenizer.save_pretrained(lowerCAmelCase		)

       UpperCAmelCase	    =			hf_wavavec.config.to_dict()
       UpperCAmelCase	    =			tokenizer.pad_token_id
       UpperCAmelCase	    =			tokenizer.bos_token_id
       UpperCAmelCase	    =			tokenizer.eos_token_id
       UpperCAmelCase	    =			"""mbart50"""
       UpperCAmelCase	    =			"""wav2vec2"""

       UpperCAmelCase	    =			tokenizer.eos_token_id
       UpperCAmelCase	    =			250004
       UpperCAmelCase	    =			tokenizer.eos_token_id

       UpperCAmelCase	    =			SpeechEncoderDecoderConfig.from_dict(lowerCAmelCase		)

       hf_wavavec.save_pretrained(lowerCAmelCase		)
       feature_extractor.save_pretrained(lowerCAmelCase		)


if __name__ == "__main__":
 lowerCAmelCase_	:    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=1_0_2_4, type=int, help='''encoder output dim''')
 parser.add_argument('''--start_token_id''', default=2_5_0_0_0_4, type=int, help='''`decoder_start_token_id` of model config''')

 lowerCAmelCase_	:    Optional[int]            =      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,
 )








"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase_	:    int            =      logging.get_logger(__name__)

lowerCAmelCase_	:    List[Any]            =      {
    '''facebook/xlm-roberta-xl''': '''https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json''',
    '''facebook/xlm-roberta-xxl''': '''https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json''',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}





class   UpperCamelCase_ (       a_  ):
  _A       :					int				=	'xlm-roberta-xl'



  def __init__(      self ,		snake_case__=25_08_80 ,		snake_case__=25_60 ,		snake_case__=36 ,		snake_case__=32 ,		snake_case__=1_02_40 ,		snake_case__="gelu" ,		snake_case__=0.1 ,		snake_case__=0.1 ,		snake_case__=5_14 ,		snake_case__=1 ,		snake_case__=0.02 ,		snake_case__=1e-05 ,		snake_case__=1 ,		snake_case__=0 ,		snake_case__=2 ,		snake_case__="absolute" ,		snake_case__=True ,		snake_case__=None ,		**snake_case__ ,		)			->			Union[str, Any]:



         """simple docstring"""





         super().__init__(pad_token_id=snake_case__ ,		bos_token_id=snake_case__ ,		eos_token_id=snake_case__ ,		**snake_case__				)
         UpperCAmelCase	    =			vocab_size
         UpperCAmelCase	    =			hidden_size
         UpperCAmelCase	    =			num_hidden_layers
         UpperCAmelCase	    =			num_attention_heads
         UpperCAmelCase	    =			hidden_act
         UpperCAmelCase	    =			intermediate_size
         UpperCAmelCase	    =			hidden_dropout_prob
         UpperCAmelCase	    =			attention_probs_dropout_prob
         UpperCAmelCase	    =			max_position_embeddings
         UpperCAmelCase	    =			type_vocab_size
         UpperCAmelCase	    =			initializer_range
         UpperCAmelCase	    =			layer_norm_eps
         UpperCAmelCase	    =			position_embedding_type
         UpperCAmelCase	    =			use_cache
         UpperCAmelCase	    =			classifier_dropout





class   UpperCamelCase_ (       a_  ):



  @property
  def 		UpperCamelCase_				(      self				)			->			Mapping[str, Mapping[int, str]]:



         """simple docstring"""





         if self.task == "multiple-choice":
                UpperCAmelCase	    =			{0: """batch""", 1: """choice""", 2: """sequence"""}
         else:
                UpperCAmelCase	    =			{0: """batch""", 1: """sequence"""}
         return OrderedDict(
             [
                 ("""input_ids""", dynamic_axis),
                 ("""attention_mask""", dynamic_axis),
             ]				)
