'''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()
__lowerCamelCase    :				Union[str, Any]							     =	logging.get_logger(__name__)

__lowerCamelCase    :				Union[str, Any]							     =	{
    '''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    :				Tuple							     =	[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Any				,  __UpperCamelCase					: Union[str, Any]				,  __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: Dict			)							->      str:







  """simple docstring"""





  for attribute in key.split("""."""			):
    SCREAMING_SNAKE_CASE__    							=   getattr(__snake_case				,  __snake_case			)

  if weight_type is not None:
    SCREAMING_SNAKE_CASE__    							=   getattr(__snake_case				,  __snake_case			).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 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[str]				,  __UpperCamelCase					: List[str]			)							->      Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   []
  SCREAMING_SNAKE_CASE__    							=   fairseq_model.state_dict()

  SCREAMING_SNAKE_CASE__    							=   hf_model.feature_extractor

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

  for name, value in fairseq_dict.items():
    SCREAMING_SNAKE_CASE__    							=   False
    if "conv_layers" in name:
      load_conv_layer(
          __snake_case				,  __snake_case				,  __snake_case				,  __snake_case				,  hf_model.config.feat_extract_norm == """group"""				,  )
      SCREAMING_SNAKE_CASE__    							=   True
    elif name.split("""."""			)[0] == "proj":
      SCREAMING_SNAKE_CASE__    							=   fairseq_model.proj
      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(__snake_case			)[0].split("""."""			)[-2]
            SCREAMING_SNAKE_CASE__    							=   mapped_key.replace("""*"""				,  __snake_case			)
          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(__snake_case				,  __snake_case				,  __snake_case				,  __snake_case				,  __snake_case			)
        continue
    if not is_used:
      unused_weights.append(__snake_case			)

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

  return proj_weight



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Any				,  __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: Dict				,  __UpperCamelCase					: str				,  __UpperCamelCase					: Any			)							->      str:







  """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(__snake_case			)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Any			)							->      str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   emb.weight.shape
  SCREAMING_SNAKE_CASE__    							=   nn.Linear(__snake_case				,  __snake_case				,  bias=__snake_case			)
  SCREAMING_SNAKE_CASE__    							=   emb.weight.data
  return lin_layer



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Optional[int]			)							->      Optional[int]:







  """simple docstring"""





  with open(__snake_case				,  """r"""				,  encoding="""utf-8"""			) as f:
    SCREAMING_SNAKE_CASE__    							=   f.readlines()
    SCREAMING_SNAKE_CASE__    							=   [line.split(""" """			)[0] for line in lines]

  SCREAMING_SNAKE_CASE__    							=   len(__snake_case			)

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

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



@torch.no_grad()
def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: str				,  __UpperCamelCase					: Optional[Any]				,  __UpperCamelCase					: Tuple				,  __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: Union[str, Any]				,  __UpperCamelCase					: int				,  __UpperCamelCase					: Dict				,  )							->      str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   WavaVecaConfig.from_pretrained(__snake_case			)
  SCREAMING_SNAKE_CASE__    							=   SpeechaTextaConfig.from_pretrained(
      __snake_case				,  vocab_size=__snake_case				,  decoder_layers=__snake_case				,  do_stable_layer_norm=__snake_case			)

  SCREAMING_SNAKE_CASE__    							=   WavaVecaFeatureExtractor(
      feature_size=1				,  sampling_rate=1_60_00				,  padding_value=0				,  do_normalize=__snake_case				,  return_attention_mask=__snake_case				,  )

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

  # set weights for wav2vec2 encoder
  SCREAMING_SNAKE_CASE__    							=   WavaVecaModel(__snake_case			)
  SCREAMING_SNAKE_CASE__    							=   recursively_load_weights_wavaveca(model.encoder				,  __snake_case			)

  SCREAMING_SNAKE_CASE__    							=   SpeechaTextaForCausalLM(__snake_case			)
  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()				,  strict=__snake_case			)

  # set output linear layer
  unexpected_keys.remove("""embed_out"""			)
  SCREAMING_SNAKE_CASE__    							=   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}"""			)

  SCREAMING_SNAKE_CASE__    							=   SpeechEncoderDecoderModel(encoder=__snake_case				,  decoder=__snake_case			)
  SCREAMING_SNAKE_CASE__    							=   False

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

  SCREAMING_SNAKE_CASE__    							=   create_vocab_dict(__snake_case			)

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

  SCREAMING_SNAKE_CASE__    							=   SpeechaTextaTokenizer(os.path.join(__snake_case				,  """vocab.json"""			)			)
  tokenizer.save_pretrained(__snake_case			)

  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__    							=   """speech_to_text_2"""
  SCREAMING_SNAKE_CASE__    							=   """wav2vec2"""

  SCREAMING_SNAKE_CASE__    							=   SpeechEncoderDecoderConfig.from_dict(__snake_case			)

  hf_wavavec.save_pretrained(__snake_case			)
  feature_extractor.save_pretrained(__snake_case			)


if __name__ == "__main__":
   __lowerCamelCase    :				Dict							     =	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_0224, type=int, help='''Vocab size of decoder''')
   parser.add_argument('''--num_decoder_layers''', default=7, type=int, help='''Number of decoder layers''')

   __lowerCamelCase    :				Tuple							     =	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,
   )

import enum
import shutil
import sys


__lowerCamelCase     ,		__lowerCamelCase    :				Optional[Any]							     =	shutil.get_terminal_size()

__lowerCamelCase    :				Any							     =	{'''UP''': '''A''', '''DOWN''': '''B''', '''RIGHT''': '''C''', '''LEFT''': '''D'''}







class __snake_case       (		enum.Enum	):
   lowerCAmelCase_														=							0
   lowerCAmelCase_														=							1





def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: List[str]=""			)							->      int:







  """simple docstring"""





  sys.stdout.write(str(__UpperCamelCase			) + end			)
  sys.stdout.flush()



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: List[str]				,  __UpperCamelCase					: Optional[Any]=""			)							->      Dict:







  """simple docstring"""





  forceWrite(f"""\u001b[{color}m{content}\u001b[0m"""				,  __UpperCamelCase			)



def 			__SCREAMING_SNAKE_CASE						(      )							->      Optional[Any]:







  """simple docstring"""





  forceWrite("""\r"""			)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: int				,  __UpperCamelCase					: str			)							->      str:







  """simple docstring"""





  forceWrite(f"""\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}"""			)



def 			__SCREAMING_SNAKE_CASE						(      )							->      Dict:







  """simple docstring"""





  forceWrite(""" """ * TERMINAL_WIDTH			)
  reset_cursor()






def 			__SCREAMING_SNAKE_CASE						(      )							->      int:







  """simple docstring"""





  reset_cursor()
  forceWrite("""-""" * TERMINAL_WIDTH			)
