import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
UpperCamelCase__											=					logging.get_logger(__name__)

UpperCamelCase__											=					{
    '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',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    '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',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
UpperCamelCase__											=					[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]




def    lowerCAmelCase_ (     __A							)     ->				Optional[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  {}
      with open(UpperCamelCase__,  "r"							) as file:
            for line_number, line in enumerate(UpperCamelCase__							):
                  UpperCAmelCase__								=  line.strip()
                  if line:
                        UpperCAmelCase__								=  line.split()
                        UpperCAmelCase__								=  line_number
                        UpperCAmelCase__								=  words[0]
                        UpperCAmelCase__								=  value
      return result




def    lowerCAmelCase_ (     __A,  __A,  __A,  __A,  __A							)     ->				int:







      '''simple docstring'''


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

      UpperCAmelCase__								=  None
      for param_key in PARAM_MAPPING.keys():
            if full_name.endswith(UpperCamelCase__							):
                  UpperCAmelCase__								=  PARAM_MAPPING[full_name.split("."							)[-1]]
                  UpperCAmelCase__								=  """param"""

      if weight_type is not None and weight_type != "param":
            UpperCAmelCase__								=  getattr(UpperCamelCase__,  UpperCamelCase__							).shape
      elif weight_type is not None and weight_type == "param":
            UpperCAmelCase__								=  hf_pointer
            for attribute in hf_param_name.split("."							):
                  UpperCAmelCase__								=  getattr(UpperCamelCase__,  UpperCamelCase__							)
            UpperCAmelCase__								=  shape_pointer.shape

            # let's reduce dimension
            UpperCAmelCase__								=  value[0]
      else:
            UpperCAmelCase__								=  hf_pointer.shape

      if hf_shape != value.shape:
            raise ValueError(
                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
      elif weight_type == "param":
            for attribute in hf_param_name.split("."							):
                  UpperCAmelCase__								=  getattr(UpperCamelCase__,  UpperCamelCase__							)
            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_ (     __A,  __A,  __A,  __A,  __A							)     ->				Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  None
      for param_key in PARAM_MAPPING.keys():
            if full_name.endswith(UpperCamelCase__							):
                  UpperCAmelCase__								=  PARAM_MAPPING[full_name.split("."							)[-1]]
                  UpperCAmelCase__								=  """param"""

      if weight_type is not None and weight_type != "param":
            UpperCAmelCase__								=  """.""".join([key, weight_type]							)
      elif weight_type is not None and weight_type == "param":
            UpperCAmelCase__								=  """.""".join([key, hf_param_name]							)
      else:
            UpperCAmelCase__								=  key

      UpperCAmelCase__								=  value if """lm_head""" in full_key else value[0]


UpperCamelCase__											=					{
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}




def    lowerCAmelCase_ (     __A,  __A,  __A=None,  __A=None							)     ->				Optional[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  False
      for key, mapped_key in MAPPING.items():
            UpperCAmelCase__								=  """wav2vec2.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
            if key in name or key.split("w2v_model."							)[-1] == name.split("."							)[0]:
                  UpperCAmelCase__								=  True
                  if "*" in mapped_key:
                        UpperCAmelCase__								=  name.split(UpperCamelCase__							)[0].split("."							)[-2]
                        UpperCAmelCase__								=  mapped_key.replace("*",  UpperCamelCase__							)
                  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:
                        # TODO: don't match quantizer.weight_proj
                        UpperCAmelCase__								=  """weight"""
                  else:
                        UpperCAmelCase__								=  None
                  if hf_dict is not None:
                        rename_dict(UpperCamelCase__,  UpperCamelCase__,  UpperCamelCase__,  UpperCamelCase__,  UpperCamelCase__							)
                  else:
                        set_recursively(UpperCamelCase__,  UpperCamelCase__,  UpperCamelCase__,  UpperCamelCase__,  UpperCamelCase__							)
                  return is_used
      return is_used




def    lowerCAmelCase_ (     __A,  __A,  __A							)     ->				List[str]:







      '''simple docstring'''


      UpperCAmelCase__								=  []
      UpperCAmelCase__								=  fairseq_model.state_dict()

      UpperCAmelCase__								=  hf_model.wavaveca.feature_extractor

      for name, value in fairseq_dict.items():
            UpperCAmelCase__								=  False
            if "conv_layers" in name:
                  load_conv_layer(
                      UpperCamelCase__,  UpperCamelCase__,  UpperCamelCase__,  UpperCamelCase__,  hf_model.config.feat_extract_norm == "group",  )
                  UpperCAmelCase__								=  True
            else:
                  UpperCAmelCase__								=  load_wavaveca_layer(UpperCamelCase__,  UpperCamelCase__,  UpperCamelCase__							)
            if not is_used:
                  unused_weights.append(UpperCamelCase__							)

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




def    lowerCAmelCase_ (     __A,  __A,  __A,  __A,  __A							)     ->				Dict:







      '''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:
                  if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                        raise ValueError(
                            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:
                  if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                        raise ValueError(
                            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:
                  if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                        raise ValueError(
                            f"""{full_name} has size {value.shape}, but"""
                            f""" {feature_extractor.conv_layers[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:
                  if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                        raise ValueError(
                            f"""{full_name} has size {value.shape}, but"""
                            f""" {feature_extractor.conv_layers[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(UpperCamelCase__							)




@torch.no_grad()
def    lowerCAmelCase_ (     __A,  __A,  __A=None,  __A=None,  __A=True,  __A=False							)     ->				Tuple:







      '''simple docstring'''


      if config_path is not None:
            UpperCAmelCase__								=  WavaVecaConfig.from_pretrained(UpperCamelCase__							)
      else:
            UpperCAmelCase__								=  WavaVecaConfig()

      if is_seq_class:
            UpperCAmelCase__								=  read_txt_into_dict(UpperCamelCase__							)
            UpperCAmelCase__								=  idalabel
            UpperCAmelCase__								=  WavaVecaForSequenceClassification(UpperCamelCase__							)
            UpperCAmelCase__								=  WavaVecaFeatureExtractor(
                feature_size=1,  sampling_rate=16_000,  padding_value=0,  do_normalize=UpperCamelCase__,  return_attention_mask=UpperCamelCase__,  )
            feature_extractor.save_pretrained(UpperCamelCase__							)

      elif is_finetuned:
            if dict_path:
                  UpperCAmelCase__								=  Dictionary.load(UpperCamelCase__							)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  UpperCAmelCase__								=  target_dict.pad_index
                  UpperCAmelCase__								=  target_dict.bos_index
                  UpperCAmelCase__								=  target_dict.eos_index
                  UpperCAmelCase__								=  len(target_dict.symbols							)
                  UpperCAmelCase__								=  os.path.join(UpperCamelCase__,  "vocab.json"							)
                  if not os.path.isdir(UpperCamelCase__							):
                        logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(UpperCamelCase__							)							)
                        return
                  os.makedirs(UpperCamelCase__,  exist_ok=UpperCamelCase__							)
                  UpperCAmelCase__								=  target_dict.indices

                  # fairseq has the <pad> and <s> switched
                  UpperCAmelCase__								=  0
                  UpperCAmelCase__								=  1
                  with open(UpperCamelCase__,  "w",  encoding="utf-8"							) as vocab_handle:
                        json.dump(UpperCamelCase__,  UpperCamelCase__							)
                  UpperCAmelCase__								=  WavaVecaCTCTokenizer(
                      UpperCamelCase__,  unk_token=target_dict.unk_word,  pad_token=target_dict.pad_word,  bos_token=target_dict.bos_word,  eos_token=target_dict.eos_word,  word_delimiter_token="|",  do_lower_case=UpperCamelCase__,  )
                  UpperCAmelCase__								=  True if config.feat_extract_norm == """layer""" else False
                  UpperCAmelCase__								=  WavaVecaFeatureExtractor(
                      feature_size=1,  sampling_rate=16_000,  padding_value=0,  do_normalize=UpperCamelCase__,  return_attention_mask=UpperCamelCase__,  )
                  UpperCAmelCase__								=  WavaVecaProcessor(feature_extractor=UpperCamelCase__,  tokenizer=UpperCamelCase__							)
                  processor.save_pretrained(UpperCamelCase__							)

            UpperCAmelCase__								=  WavaVecaForCTC(UpperCamelCase__							)
      else:
            UpperCAmelCase__								=  WavaVecaForPreTraining(UpperCamelCase__							)

      if is_finetuned or is_seq_class:
            UpperCAmelCase__								=  fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path],  arg_overrides={"data": "/".join(dict_path.split("/"							)[:-1]							)}							)
      else:
            UpperCAmelCase__								=  argparse.Namespace(task="audio_pretraining"							)
            UpperCAmelCase__								=  fairseq.tasks.setup_task(UpperCamelCase__							)

            UpperCAmelCase__								=  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path],  task=UpperCamelCase__							)

      UpperCAmelCase__								=  model[0].eval()

      recursively_load_weights(UpperCamelCase__,  UpperCamelCase__,  not is_finetuned							)

      hf_wavavec.save_pretrained(UpperCamelCase__							)


if __name__ == "__main__":
      UpperCamelCase__											=					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_path', default=None, type=str, help='Path to hf config.json of model to convert')
      parser.add_argument(
          '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
      )
      parser.add_argument(
          '--is_seq_class',
          action='store_true',
          help='Whether the model to convert is a fine-tuned sequence classification model or not',
      )
      UpperCamelCase__											=					parser.parse_args()

      UpperCamelCase__											=					not args.not_finetuned and not args.is_seq_class
      convert_wavaveca_checkpoint(
          args.checkpoint_path,
          args.pytorch_dump_folder_path,
          args.config_path,
          args.dict_path,
          is_finetuned,
          args.is_seq_class,
      )











"""simple docstring"""




import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_snake_case          =   logging.get_logger(__name__)

_snake_case          =   {'vocab_file': 'vocab.json'}

_snake_case          =   {
    'vocab_file': {
        'mgp-str': 'https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json',
    }
}

_snake_case          =   {'mgp-str': 27}






class        UpperCamelCase			(	snake_case_      ):
 UpperCamelCase					:       List[str]			=					VOCAB_FILES_NAMES
 UpperCamelCase					:       Union[str, Any]			=					PRETRAINED_VOCAB_FILES_MAP
 UpperCamelCase					:       Optional[int]			=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


 def __init__(     self		:						Optional[int]							,						UpperCAmelCase__		:						List[str]							,						UpperCAmelCase__		:						List[Any]="[GO]"							,						UpperCAmelCase__		:						Tuple="[GO]"							,						UpperCAmelCase__		:						Optional[int]="[s]"							,						UpperCAmelCase__		:						int="[GO]"							,						**UpperCAmelCase__		:						Dict							)					->       int:
   super().__init__(
       unk_token=UpperCAmelCase__							,						bos_token=UpperCAmelCase__							,						eos_token=UpperCAmelCase__							,						pad_token=UpperCAmelCase__							,						**UpperCAmelCase__							,						)

   with open(UpperCAmelCase__							,						encoding="""utf-8"""							) as vocab_handle:
     _a  :       int             =	json.load(UpperCAmelCase__							)
   _a  :       Optional[int]             =	{v: k for k, v in self.vocab.items()}


 @property
 def _lowercase	(     self		:						Dict							)					->       Union[str, Any]:
   return len(self.vocab							)


 def _lowercase	(     self		:						Union[str, Any]							)					->       str:
   return dict(self.vocab							,						**self.added_tokens_encoder							)


 def _lowercase	(     self		:						Dict							,						UpperCAmelCase__		:						str							)					->       Union[str, Any]:
   _a  :       Tuple             =	[]
   for s in text:
     char_tokens.extend(UpperCAmelCase__							)
   return char_tokens


 def _lowercase	(     self		:						List[Any]							,						UpperCAmelCase__		:						str							)					->       Dict:
   return self.vocab.get(UpperCAmelCase__							,						self.vocab.get(self.unk_token							)							)


 def _lowercase	(     self		:						Optional[int]							,						UpperCAmelCase__		:						Tuple							)					->       List[Any]:
   return self.decoder.get(UpperCAmelCase__							)







 def _lowercase	(     self		:						Optional[int]							,						UpperCAmelCase__		:						str							,						UpperCAmelCase__		:						Optional[str] = None							)					->       Tuple[str]:
   if not os.path.isdir(UpperCAmelCase__							):
     logger.error("""Vocabulary path ({}) should be a directory""".format(UpperCAmelCase__							)							)
     return
   _a  :       Tuple             =	os.path.join(
       UpperCAmelCase__							,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

   with open(UpperCAmelCase__							,						"""w"""							,						encoding="""utf-8"""							) as f:
     f.write(json.dumps(self.vocab							,						indent=2							,						sort_keys=UpperCAmelCase__							,						ensure_ascii=UpperCAmelCase__							) + """\n"""							)

   return (vocab_file,)

