

"""simple docstring"""



import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

SCREAMING_SNAKE_CASE_					:	Optional[int]							  =						{
    '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',
}
SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]





def  _snake_case     (    UpperCAmelCase_  :					int					,   UpperCAmelCase_  :					int					,   UpperCAmelCase_  :					Optional[Any]					,   UpperCAmelCase_  :					Dict					,   UpperCAmelCase_  :					Any			):
     for attribute in key.split("""."""			):
          A__										=  getattr(UpperCAmelCase_					,   UpperCAmelCase_			)

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

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





def  _snake_case     (    UpperCAmelCase_  :					int					,   UpperCAmelCase_  :					List[Any]			):
     A__										=  []
     A__										=  fairseq_model.state_dict()

     A__										=  hf_model.feature_extractor

     # if encoder has different dim to decoder -> use proj_weight
     A__										=  None

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

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

     return proj_weight





def  _snake_case     (    UpperCAmelCase_  :					Optional[int]					,   UpperCAmelCase_  :					Optional[Any]					,   UpperCAmelCase_  :					int					,   UpperCAmelCase_  :					Any					,   UpperCAmelCase_  :					Tuple			):
     A__										=  full_name.split("""conv_layers."""			)[-1]
     A__										=  name.split("""."""			)
     A__										=  int(items[0]			)
     A__										=  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."""
               )
               A__										=  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."""
               )
               A__										=  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."
               )
               A__										=  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."""
               )
               A__										=  value
               logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""			)
     else:
          unused_weights.append(UpperCAmelCase_			)





def  _snake_case     (    UpperCAmelCase_  :					List[Any]			):
     A__			,				A__										=  emb.weight.shape
     A__										=  nn.Linear(UpperCAmelCase_					,   UpperCAmelCase_					,   bias=UpperCAmelCase_			)
     A__										=  emb.weight.data
     return lin_layer





def  _snake_case     (    UpperCAmelCase_  :					str			):
     with open(UpperCAmelCase_					,   """r"""					,   encoding="""utf-8"""			) as f:
          A__										=  f.readlines()
          A__										=  [line.split(""" """			)[0] for line in lines]

     A__										=  len(UpperCAmelCase_			)

     A__										=  {
         """<s>""": 0,
         """<pad>""": 1,
         """</s>""": 2,
         """<unk>""": 3,
     }

     vocab_dict.update(dict(zip(UpperCAmelCase_					,   range(4					,   num_words + 4			)			)			)			)
     return vocab_dict





@torch.no_grad()
def  _snake_case     (    UpperCAmelCase_  :					str					,   UpperCAmelCase_  :					Dict					,   UpperCAmelCase_  :					Optional[int]					,   UpperCAmelCase_  :					int					,   UpperCAmelCase_  :					Dict					,   UpperCAmelCase_  :					str					,   UpperCAmelCase_  :					Dict					,   ):
     A__										=  WavaVecaConfig.from_pretrained(UpperCAmelCase_			)
     A__										=  SpeechaTextaConfig.from_pretrained(
         UpperCAmelCase_					,   vocab_size=UpperCAmelCase_					,   decoder_layers=UpperCAmelCase_					,   do_stable_layer_norm=UpperCAmelCase_			)

     A__										=  WavaVecaFeatureExtractor(
         feature_size=1					,   sampling_rate=1_6000					,   padding_value=0					,   do_normalize=UpperCAmelCase_					,   return_attention_mask=UpperCAmelCase_					,   )

     A__			,				A__			,				A__										=  fairseq.checkpoint_utils.load_model_ensemble_and_task(
         [checkpoint_path]					,   arg_overrides={"""data""": """/""".join(dict_path.split("""/"""			)[:-1]			)}			)
     A__										=  model[0].eval()

     # set weights for wav2vec2 encoder
     A__										=  WavaVecaModel(UpperCAmelCase_			)
     A__										=  recursively_load_weights_wavaveca(model.encoder					,   UpperCAmelCase_			)

     A__										=  SpeechaTextaForCausalLM(UpperCAmelCase_			)
     A__			,				A__										=  hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()					,   strict=UpperCAmelCase_			)

     # set output linear layer
     unexpected_keys.remove("""embed_out"""			)
     A__										=  nn.Parameter(model.decoder.embed_out.detach()			)

     # layer norm is init to identity matrix so leaving it is fine
     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}"""			)

     A__										=  SpeechEncoderDecoderModel(encoder=UpperCAmelCase_					,   decoder=UpperCAmelCase_			)
     A__										=  False

     # add projection layer
     A__										=  nn.Parameter(projection_layer.weight			)
     A__										=  nn.Parameter(projection_layer.bias			)

     A__										=  create_vocab_dict(UpperCAmelCase_			)

     with open(os.path.join(UpperCAmelCase_					,   """vocab.json"""			)					,   """w"""			) as fp:
          json.dump(UpperCAmelCase_					,   UpperCAmelCase_			)

     A__										=  SpeechaTextaTokenizer(os.path.join(UpperCAmelCase_					,   """vocab.json"""			)			)
     tokenizer.save_pretrained(UpperCAmelCase_			)

     A__										=  hf_wavavec.config.to_dict()
     A__										=  tokenizer.pad_token_id
     A__										=  tokenizer.bos_token_id
     A__										=  tokenizer.eos_token_id
     A__										=  """speech_to_text_2"""
     A__										=  """wav2vec2"""

     A__										=  SpeechEncoderDecoderConfig.from_dict(UpperCAmelCase_			)

     hf_wavavec.save_pretrained(UpperCAmelCase_			)
     feature_extractor.save_pretrained(UpperCAmelCase_			)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_					:	Optional[Any]							  =						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(
     '--encoder_config_path',
     default='facebook/wav2vec2-large-lv60',
     type=str,
     help='Path to hf encoder wav2vec2 checkpoint config',
 )
 parser.add_argument(
     '--decoder_config_path',
     default='facebook/s2t-small-mustc-en-fr-st',
     type=str,
     help='Path to hf decoder s2t checkpoint config',
 )
 parser.add_argument('--vocab_size', default=1_0_2_2_4, type=int, help='Vocab size of decoder')
 parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')

 SCREAMING_SNAKE_CASE_					:	int							  =						parser.parse_args()
 convert_wavaveca_checkpoint(
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.dict_path,
     encoder_config_path=args.encoder_config_path,
     decoder_config_path=args.decoder_config_path,
     vocab_size=args.vocab_size,
     num_decoder_layers=args.num_decoder_layers,
 )









"""simple docstring"""



import baseaa





def  _snake_case     (    UpperCAmelCase_  :					str			):
     return baseaa.aaaencode(string.encode("""utf-8"""			)			)





def  _snake_case     (    UpperCAmelCase_  :					bytes			):
     return baseaa.aaadecode(UpperCAmelCase_			).decode("""utf-8"""			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()






