





import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


__A									=				get_tests_dir("fixtures/test_sentencepiece_bpe.model")



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ,							unittest.TestCase    ):







 '''simple docstring'''
 lowercase_		=			BartphoTokenizer
 lowercase_		=			False
 lowercase_		=			True

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any])       ->Tuple:



   '''simple docstring'''



   super().setUp()

   lowerCamelCase__:      int			  =["▁This", "▁is", "▁a", "▁t", "est"]
   lowerCamelCase__:      Tuple			  =dict(zip(UpperCAmelCase_       , range(len(UpperCAmelCase_))))
   lowerCamelCase__:      List[Any]			  ={"unk_token": "<unk>"}

   lowerCamelCase__:      Dict			  =os.path.join(self.tmpdirname       , VOCAB_FILES_NAMES["monolingual_vocab_file"])
   with open(self.monolingual_vocab_file       , "w"       , encoding="utf-8") as fp:
     for token in vocab_tokens:
       fp.write(F"""{token} {vocab_tokens[token]}\n""")

   lowerCamelCase__:      Dict			  =BartphoTokenizer(UpperCAmelCase_       , self.monolingual_vocab_file       , **self.special_tokens_map)
   tokenizer.save_pretrained(self.tmpdirname)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[int]       , **UpperCAmelCase_				:   Optional[Any])       ->str:



   '''simple docstring'''



   kwargs.update(self.special_tokens_map)
   return BartphoTokenizer.from_pretrained(self.tmpdirname       , **UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any]       , UpperCAmelCase_				:   Optional[Any])       ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  ="This is a là test"
   lowerCamelCase__:      Optional[Any]			  ="This is a<unk><unk> test"
   return input_text, output_text







 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any])       ->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:      str			  =BartphoTokenizer(UpperCAmelCase_       , self.monolingual_vocab_file       , **self.special_tokens_map)
   lowerCamelCase__:      List[Any]			  ="This is a là test"
   lowerCamelCase__:      Optional[int]			  ="▁This ▁is ▁a ▁l à ▁t est".split()
   lowerCamelCase__:      Optional[int]			  =tokenizer.tokenize(UpperCAmelCase_)
   self.assertListEqual(UpperCAmelCase_       , UpperCAmelCase_)

   lowerCamelCase__:      Tuple			  =tokens + [tokenizer.unk_token]
   lowerCamelCase__:      List[Any]			  =[4, 5, 6, 3, 3, 7, 8, 3]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_)       , UpperCAmelCase_)








import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


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

__A									=				{
    "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": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
__A									=				[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a	,    __a	,    __a		)     ->		Optional[Any]:







  """simple docstring"""





  for attribute in key.split("."		):
    if is_finetuned:
      if attribute in ["quantizer", "project_q", "project_hid"]:
        # those layers are only relevant for pretraining and should be dropped
        return

      if attribute == "ctc_proj":
        # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
        lowerCamelCase__:      Optional[int]			  ="lm_head"

    lowerCamelCase__:      Dict			  =getattr(__a	,    __a		)

  if weight_type is not None:
    lowerCamelCase__:      str			  =getattr(__a	,    __a		).shape
  else:
    lowerCamelCase__:      int			  =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":
    lowerCamelCase__:      Dict			  =value
  elif weight_type == "weight_g":
    lowerCamelCase__:      Optional[Any]			  =value
  elif weight_type == "weight_v":
    lowerCamelCase__:      int			  =value
  elif weight_type == "bias":
    lowerCamelCase__:      List[str]			  =value
  else:
    lowerCamelCase__:      Union[str, Any]			  =value

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





def      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		Any:







  """simple docstring"""





  lowerCamelCase__:      List[Any]			  =[]
  lowerCamelCase__:      List[str]			  =fairseq_model.state_dict()

  lowerCamelCase__:      Optional[int]			  =hf_model.unispeech.feature_extractor

  for name, value in fairseq_dict.items():
    lowerCamelCase__:      int			  =False
    if "conv_layers" in name:
      load_conv_layer(
          __a	,    __a	,    __a	,    __a	,    hf_model.config.feat_extract_norm == "group"	,    )
      lowerCamelCase__:      str			  =True
    else:
      for key, mapped_key in MAPPING.items():
        lowerCamelCase__:      List[str]			  ="unispeech." + 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]:
          lowerCamelCase__:      Optional[Any]			  =True
          if "*" in mapped_key:
            lowerCamelCase__:      Optional[Any]			  =name.split(__a		)[0].split("."		)[-2]
            lowerCamelCase__:      List[str]			  =mapped_key.replace("*"	,    __a		)
          if "weight_g" in name:
            lowerCamelCase__:      List[str]			  ="weight_g"
          elif "weight_v" in name:
            lowerCamelCase__:      Union[str, Any]			  ="weight_v"
          elif "bias" in name:
            lowerCamelCase__:      Dict			  ="bias"
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            lowerCamelCase__:      Tuple			  ="weight"
          else:
            lowerCamelCase__:      List[Any]			  =None
          set_recursively(__a	,    __a	,    __a	,    __a	,    __a	,    __a		)
        continue
    if not is_used:
      unused_weights.append(__a		)

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





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a	,    __a		)     ->		Union[str, Any]:







  """simple docstring"""





  lowerCamelCase__:      Tuple			  =full_name.split("conv_layers."		)[-1]
  lowerCamelCase__:      List[str]			  =name.split("."		)
  lowerCamelCase__:      str			  =int(items[0]		)
  lowerCamelCase__:      Union[str, 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."""
      )
      lowerCamelCase__:      List[str]			  =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."""
      )
      lowerCamelCase__:      Dict			  =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."
      )
      lowerCamelCase__:      List[Any]			  =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."""
      )
      lowerCamelCase__:      List[str]			  =value
      logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""		)
  else:
    unused_weights.append(__a		)





@torch.no_grad()
def      lowerCAmelCase_ (       __a	,    __a	,    __a=None	,    __a=None	,    __a=True		)     ->		int:







  """simple docstring"""





  if config_path is not None:
    lowerCamelCase__:      str			  =UniSpeechConfig.from_pretrained(__a		)
  else:
    lowerCamelCase__:      List[Any]			  =UniSpeechConfig()

  if is_finetuned:
    if dict_path:
      lowerCamelCase__:      str			  =Dictionary.load_from_json(__a		)

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

      # fairseq has the <pad> and <s> switched
      lowerCamelCase__:      Optional[Any]			  =42
      lowerCamelCase__:      List[Any]			  =43
      with open(__a	,    "w"	,    encoding="utf-8"		) as vocab_handle:
        json.dump(__a	,    __a		)
      lowerCamelCase__:      List[str]			  =WavaVecaPhonemeCTCTokenizer(
          __a	,    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=__a	,    )
      lowerCamelCase__:      Dict			  =True if config.feat_extract_norm == "layer" else False
      lowerCamelCase__:      Tuple			  =WavaVecaFeatureExtractor(
          feature_size=1	,    sampling_rate=16000	,    padding_value=0	,    do_normalize=__a	,    return_attention_mask=__a	,    )
      lowerCamelCase__:      List[Any]			  =WavaVecaProcessor(feature_extractor=__a	,    tokenizer=__a		)
      processor.save_pretrained(__a		)

    lowerCamelCase__:      int			  =UniSpeechForCTC(__a		)
  else:
    lowerCamelCase__:      int			  =UniSpeechForPreTraining(__a		)

  if is_finetuned:
    lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__:      Optional[int]			  =fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]	,    arg_overrides={"data": "/".join(dict_path.split("/"		)[:-1]		), "w2v_path": checkpoint_path}		)
  else:
    lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__:      Tuple			  =fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		)

  lowerCamelCase__:      List[str]			  =model[0].eval()

  recursively_load_weights(__a	,    __a	,    __a		)

  hf_unispeech.save_pretrained(__a		)


if __name__ == "__main__":
     __A									=				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"
     )
     __A									=				parser.parse_args()
     convert_unispeech_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )

