
"""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()
lowercase_       :						Dict     					=	logging.get_logger(__name__)

lowercase_       :						List[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""",
}
lowercase_       :						Any     					=	[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]





def    _lowerCAmelCase    (     lowerCamelCase__							:							Any,						lowerCamelCase__							:							int,						lowerCamelCase__							:							str,						lowerCamelCase__							:							Dict,						lowerCamelCase__							:							Optional[int]				)  ->    Dict:






  for attribute in key.split("."				):
    _SCREAMING_SNAKE_CASE   :				str							    = getattr(UpperCamelCase__,						UpperCamelCase__				)

  if weight_type is not None:
    _SCREAMING_SNAKE_CASE   :				Tuple							    = getattr(UpperCamelCase__,						UpperCamelCase__				).shape
  else:
    _SCREAMING_SNAKE_CASE   :				Dict							    = 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   :				List[str]							    = value
  elif weight_type == "weight_g":
    _SCREAMING_SNAKE_CASE   :				Dict							    = value
  elif weight_type == "weight_v":
    _SCREAMING_SNAKE_CASE   :				Any							    = value
  elif weight_type == "bias":
    _SCREAMING_SNAKE_CASE   :				Tuple							    = value
  else:
    _SCREAMING_SNAKE_CASE   :				Tuple							    = value

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





def    _lowerCAmelCase    (     lowerCamelCase__							:							str,						lowerCamelCase__							:							List[str]				)  ->    Any:
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = []
  _SCREAMING_SNAKE_CASE   :				int							    = fairseq_model.state_dict()

  _SCREAMING_SNAKE_CASE   :				List[Any]							    = hf_model.feature_extractor
  _SCREAMING_SNAKE_CASE   :				str							    = hf_model.adapter

  for name, value in fairseq_dict.items():
    _SCREAMING_SNAKE_CASE   :				Optional[int]							    = False
    if "conv_layers" in name:
      load_conv_layer(
          UpperCamelCase__,						UpperCamelCase__,						UpperCamelCase__,						UpperCamelCase__,						hf_model.config.feat_extract_norm == "group",						)
      _SCREAMING_SNAKE_CASE   :				List[Any]							    = 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   :				str							    = 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   :				Tuple							    = True
          if "*" in mapped_key:
            _SCREAMING_SNAKE_CASE   :				Tuple							    = name.split(UpperCamelCase__				)[0].split("."				)[-2]
            _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = mapped_key.replace("*",						UpperCamelCase__				)
          if "weight_g" in name:
            _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = "weight_g"
          elif "weight_v" in name:
            _SCREAMING_SNAKE_CASE   :				Dict							    = "weight_v"
          elif "bias" in name:
            _SCREAMING_SNAKE_CASE   :				Dict							    = "bias"
          elif "weight" in name:
            _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = "weight"
          else:
            _SCREAMING_SNAKE_CASE   :				Optional[int]							    = 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    (     lowerCamelCase__							:							Union[str, Any],						lowerCamelCase__							:							str,						lowerCamelCase__							:							Optional[Any],						lowerCamelCase__							:							List[Any],						lowerCamelCase__							:							int				)  ->    List[Any]:
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = full_name.split("conv_layers."				)[-1]
  _SCREAMING_SNAKE_CASE   :				Dict							    = name.split("."				)
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = int(items[0]				)
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = 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   :				Tuple							    = 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   :				Tuple							    = 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   :				Tuple							    = 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   :				List[Any]							    = 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    (     lowerCamelCase__							:							Optional[int],						lowerCamelCase__							:							str,						lowerCamelCase__							:							Optional[int],						lowerCamelCase__							:							Tuple				)  ->    Optional[Any]:
  _SCREAMING_SNAKE_CASE   :				int							    = full_name.split("adaptor."				)[-1]
  _SCREAMING_SNAKE_CASE   :				Dict							    = name.split("."				)

  if items[1].isdigit():
    _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = int(items[1]				)
  else:
    _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = 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   :				List[Any]							    = 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   :				List[Any]							    = 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   :				Any							    = 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   :				int							    = 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   :				Any							    = 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   :				List[str]							    = value
      logger.info(f'''Adapter layer {layer_id} bias was initialized from {full_name}.'''				)
  else:
    unused_weights.append(UpperCamelCase__				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							Dict				)  ->    Dict:
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				int							    = emb.weight.shape
  _SCREAMING_SNAKE_CASE   :				Dict							    = nn.Linear(UpperCamelCase__,						UpperCamelCase__,						bias=UpperCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				int							    = emb.weight.data
  return lin_layer





@torch.no_grad()
def    _lowerCAmelCase    (     lowerCamelCase__							:							int,						lowerCamelCase__							:							Any,						lowerCamelCase__							:							Optional[Any],						lowerCamelCase__							:							List[Any],						lowerCamelCase__							:							List[Any],						lowerCamelCase__							:							Optional[Any],						lowerCamelCase__							:							Any,						lowerCamelCase__							:							Optional[int],						lowerCamelCase__							:							List[Any],						lowerCamelCase__							:							Any,						lowerCamelCase__							:							int,						)  ->    str:
  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = 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   :				str							    = MBartConfig.from_pretrained(UpperCamelCase__				)

  # load model
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Dict							    = 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   :				Union[str, Any]							    = model[0].eval()

  # load feature extractor
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = WavaVecaFeatureExtractor.from_pretrained(UpperCamelCase__,						use_auth_token=UpperCamelCase__				)

  # set weights for wav2vec2 encoder
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = WavaVecaModel(UpperCamelCase__				)

  recursively_load_weights_wavaveca(model.encoder,						UpperCamelCase__				)

  # load decoder weights
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = MBartForCausalLM(UpperCamelCase__				)
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Dict							    = 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   :				Any							    = SpeechEncoderDecoderModel(encoder=UpperCamelCase__,						decoder=UpperCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				Tuple							    = False

  _SCREAMING_SNAKE_CASE   :				str							    = MBartaaTokenizer(UpperCamelCase__				)
  tokenizer.save_pretrained(UpperCamelCase__				)

  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = hf_wavavec.config.to_dict()
  _SCREAMING_SNAKE_CASE   :				int							    = tokenizer.pad_token_id
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = tokenizer.bos_token_id
  _SCREAMING_SNAKE_CASE   :				Dict							    = tokenizer.eos_token_id
  _SCREAMING_SNAKE_CASE   :				int							    = "mbart50"
  _SCREAMING_SNAKE_CASE   :				int							    = "wav2vec2"

  _SCREAMING_SNAKE_CASE   :				List[Any]							    = tokenizer.eos_token_id
  _SCREAMING_SNAKE_CASE   :				List[str]							    = 2_5_0_0_0_4
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = tokenizer.eos_token_id

  _SCREAMING_SNAKE_CASE   :				str							    = SpeechEncoderDecoderConfig.from_dict(UpperCamelCase__				)

  hf_wavavec.save_pretrained(UpperCamelCase__				)
  feature_extractor.save_pretrained(UpperCamelCase__				)


if __name__ == "__main__":
      lowercase_       :						List[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('''--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=10_24, type=int, help='''encoder output dim''')
      parser.add_argument('''--start_token_id''', default=25_00_04, type=int, help='''`decoder_start_token_id` of model config''')

      lowercase_       :						Any     					=	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 __future__ import annotations

lowercase_       :						List[str]     					=	'''#'''
class 			UpperCamelCase      :
     def __init__(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				dict							    = {}
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				str							    = self._trie
       for char in text:
         if char not in trie:
           _SCREAMING_SNAKE_CASE   :				List[str]							    = {}
         _SCREAMING_SNAKE_CASE   :				List[str]							    = trie[char]
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = True
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = self._trie
       for char in prefix:
         if char in trie:
           _SCREAMING_SNAKE_CASE   :				str							    = trie[char]
         else:
           return []
       return self._elements(snake_case__				)




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				str							    = []
       for c, v in d.items():
         _SCREAMING_SNAKE_CASE   :				int							    = [" "] if c == END else [(c + s) for s in self._elements(snake_case__				)]
         result.extend(snake_case__				)
       return tuple(snake_case__				)
lowercase_       :						Union[str, Any]     					=	Trie()
lowercase_       :						Optional[Any]     					=	('''depart''', '''detergent''', '''daring''', '''dog''', '''deer''', '''deal''')
for word in words:
      trie.insert_word(word)





def    _lowerCAmelCase    (     lowerCamelCase__							:							str				)  ->    tuple:
  _SCREAMING_SNAKE_CASE   :				Dict							    = trie.find_word(lowerCamelCase__				)
  return tuple(string + word for word in suffixes				)





def    _lowerCAmelCase    (     )  ->    None:
  print(autocomplete_using_trie("de"				)				)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()



