

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()
_lowerCamelCase				:   List[str]					   =							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": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
_lowerCamelCase				:   Optional[Any]					   =							[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]

def 			a__     (		UpperCAmelCase							:			str				, UpperCAmelCase							:			Any				, UpperCAmelCase							:			List[Any]				, UpperCAmelCase							:			Dict				, UpperCAmelCase							:			Optional[Any]				, UpperCAmelCase							:			Any			)  ->						Optional[Any]:





  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
        UpperCAmelCase       :							Optional[int]  = "lm_head"

    UpperCAmelCase       :							Dict  = getattr(__a				, __a			)

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

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

def 			a__     (		UpperCAmelCase							:			Union[str, Any]				, UpperCAmelCase							:			Tuple				, UpperCAmelCase							:			List[str]			)  ->						Any:
  UpperCAmelCase       :							List[Any]  = []
  UpperCAmelCase       :							List[str]  = fairseq_model.state_dict()

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

  for name, value in fairseq_dict.items():
    UpperCAmelCase       :							int  = False
    if "conv_layers" in name:
      load_conv_layer(
          __a				, __a				, __a				, __a				, hf_model.config.feat_extract_norm == '''group'''				, )
      UpperCAmelCase       :							str  = True
    else:
      for key, mapped_key in MAPPING.items():
        UpperCAmelCase       :							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]:
          UpperCAmelCase       :							Optional[Any]  = True
          if "*" in mapped_key:
            UpperCAmelCase       :							Optional[Any]  = name.split(__a			)[0].split('''.'''			)[-2]
            UpperCAmelCase       :							List[str]  = mapped_key.replace('''*'''				, __a			)
          if "weight_g" in name:
            UpperCAmelCase       :							List[str]  = "weight_g"
          elif "weight_v" in name:
            UpperCAmelCase       :							Union[str, Any]  = "weight_v"
          elif "bias" in name:
            UpperCAmelCase       :							Dict  = "bias"
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            UpperCAmelCase       :							Tuple  = "weight"
          else:
            UpperCAmelCase       :							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 			a__     (		UpperCAmelCase							:			List[str]				, UpperCAmelCase							:			Union[str, Any]				, UpperCAmelCase							:			Union[str, Any]				, UpperCAmelCase							:			Union[str, Any]				, UpperCAmelCase							:			int			)  ->						Union[str, Any]:
  UpperCAmelCase       :							Tuple  = full_name.split('''conv_layers.'''			)[-1]
  UpperCAmelCase       :							List[str]  = name.split('''.'''			)
  UpperCAmelCase       :							str  = int(items[0]			)
  UpperCAmelCase       :							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.'''
      )
      UpperCAmelCase       :							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.'''
      )
      UpperCAmelCase       :							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."
      )
      UpperCAmelCase       :							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.'''
      )
      UpperCAmelCase       :							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 			a__     (		UpperCAmelCase							:			Optional[Any]				, UpperCAmelCase							:			Union[str, Any]				, UpperCAmelCase							:			Optional[int]=None				, UpperCAmelCase							:			List[Any]=None				, UpperCAmelCase							:			Union[str, Any]=True			)  ->						int:





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

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

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      UpperCAmelCase       :							Any  = target_dict.pad_index
      UpperCAmelCase       :							int  = target_dict.bos_index
      UpperCAmelCase       :							Any  = target_dict.eos_index
      UpperCAmelCase       :							Dict  = len(target_dict.symbols			)
      UpperCAmelCase       :							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			)
      UpperCAmelCase       :							Optional[Any]  = target_dict.indices

      # fairseq has the <pad> and <s> switched
      UpperCAmelCase       :							Optional[Any]  = 42
      UpperCAmelCase       :							List[Any]  = 43
      with open(__a				, '''w'''				, encoding='''utf-8'''			) as vocab_handle:
        json.dump(__a				, __a			)
      UpperCAmelCase       :							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				, )
      UpperCAmelCase       :							Dict  = True if config.feat_extract_norm == "layer" else False
      UpperCAmelCase       :							Tuple  = WavaVecaFeatureExtractor(
          feature_size=1				, sampling_rate=16_000				, padding_value=0				, do_normalize=__a				, return_attention_mask=__a				, )
      UpperCAmelCase       :							List[Any]  = WavaVecaProcessor(feature_extractor=__a				, tokenizer=__a			)
      processor.save_pretrained(__a			)

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

  if is_finetuned:
    UpperCAmelCase       :							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:
    UpperCAmelCase       :							Tuple  = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			)

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

  recursively_load_weights(__a				, __a				, __a			)

  hf_unispeech.save_pretrained(__a			)


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("--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"
     )
     _lowerCamelCase				:   Any					   =							parser.parse_args()
     convert_unispeech_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )












import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

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


__A									=				logging.get_logger(__name__)

__A									=				"▁"

__A									=				{"vocab_file": "prophetnet.tokenizer"}

__A									=				{
    "vocab_file": {
        "microsoft/xprophetnet-large-wiki100-cased": (
            "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"
        ),
    }
}

__A									=				{
    "microsoft/xprophetnet-large-wiki100-cased": {"do_lower_case": False},
}

__A									=				{
    "microsoft/xprophetnet-large-wiki100-cased": 512,
}





def      lowerCAmelCase_ (       __a		)     ->		int:







  """simple docstring"""





  lowerCamelCase__:      Optional[Any]			  =collections.OrderedDict()
  with open(__a	,    "r"	,    encoding="utf-8"		) as reader:
    lowerCamelCase__:      int			  =reader.readlines()
  for index, token in enumerate(__a		):
    lowerCamelCase__:      List[str]			  =token.rstrip("\n"		)
    lowerCamelCase__:      List[Any]			  =index
  return vocab






class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			VOCAB_FILES_NAMES
 lowercase_		=			PRETRAINED_VOCAB_FILES_MAP
 lowercase_		=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_		=			["input_ids", "attention_mask"]

 def __init__(self				:   Union[str, Any]       , UpperCAmelCase_				:   Union[str, Any]       , UpperCAmelCase_				:   Optional[Any]="[SEP]"       , UpperCAmelCase_				:   List[Any]="[SEP]"       , UpperCAmelCase_				:   Optional[Any]="[SEP]"       , UpperCAmelCase_				:   int="[UNK]"       , UpperCAmelCase_				:   Optional[Any]="[PAD]"       , UpperCAmelCase_				:   Dict="[CLS]"       , UpperCAmelCase_				:   Dict="[MASK]"       , UpperCAmelCase_				:   Optional[Dict[str, Any]] = None       , **UpperCAmelCase_				:   Tuple       , )       ->None:



   '''simple docstring'''



   lowerCamelCase__:      int			  ={} if sp_model_kwargs is None else sp_model_kwargs

   super().__init__(
       bos_token=UpperCAmelCase_       , eos_token=UpperCAmelCase_       , sep_token=UpperCAmelCase_       , unk_token=UpperCAmelCase_       , pad_token=UpperCAmelCase_       , cls_token=UpperCAmelCase_       , mask_token=UpperCAmelCase_       , sp_model_kwargs=self.sp_model_kwargs       , **UpperCAmelCase_       , )

   try:
     import sentencepiece as spm
   except ImportError:
     logger.warning(
         "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
         " pip install sentencepiece")
     raise

   lowerCamelCase__:      Optional[int]			  =spm.SentencePieceProcessor(**self.sp_model_kwargs)
   self.sp_model.Load(str(UpperCAmelCase_))
   lowerCamelCase__:      Optional[int]			  =vocab_file

   # Original fairseq vocab and spm vocab must be "aligned":
   # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
   # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
   # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
   # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

   # put special tokens and [unused] tokens into the vocab
   lowerCamelCase__:      Optional[int]			  ={"[PAD]": 0, "[CLS]": 1, "[SEP]": 2, "[UNK]": 3, "[MASK]": 4}

   for i in range(10):
     lowerCamelCase__:      Optional[int]			  =F"""[unused{i}]"""
     lowerCamelCase__:      int			  =5 + i

   # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
   lowerCamelCase__:      int			  =12
   lowerCamelCase__:      Optional[Any]			  ={v: k for k, v in self.fairseq_tokens_to_ids.items()}
   for k in self.fairseq_tokens_to_ids.keys():
     self.unique_no_split_tokens.append(UpperCAmelCase_)

 def __getstate__(self				:   List[str])       ->Dict:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =self.__dict__.copy()
   lowerCamelCase__:      Dict			  =None
   return state

 def __setstate__(self				:   List[str]       , UpperCAmelCase_				:   Union[str, Any])       ->Dict:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =d
   try:
     import sentencepiece as spm
   except ImportError:
     logger.warning(
         "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
         " pip install sentencepiece")
     raise

   # for backward compatibility
   if not hasattr(self       , "sp_model_kwargs"):
     lowerCamelCase__:      Dict			  ={}

   lowerCamelCase__:      Tuple			  =spm.SentencePieceProcessor(**self.sp_model_kwargs)
   self.sp_model.Load(self.vocab_file)

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str]       , UpperCAmelCase_				:   List[int]       , UpperCAmelCase_				:   Optional[List[int]] = None       , UpperCAmelCase_				:   bool = False)       ->List[int]:



   '''simple docstring'''



   if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=UpperCAmelCase_       , token_ids_a=UpperCAmelCase_       , already_has_special_tokens=UpperCAmelCase_)

   if token_ids_a is None:
     return ([0] * len(UpperCAmelCase_)) + [1]
   return ([0] * len(UpperCAmelCase_)) + [1] + ([0] * len(UpperCAmelCase_)) + [1]

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict       , UpperCAmelCase_				:   List[int]       , UpperCAmelCase_				:   Optional[List[int]] = None)       ->List[int]:



   '''simple docstring'''



   lowerCamelCase__:      Any			  =[self.sep_token_id]

   if token_ids_a is None:
     return len(token_ids_a + sep) * [0]
   return len(token_ids_a + sep + sep + token_ids_a + sep) * [0]

 @property
 def 							SCREAMING_SNAKE_CASE_					(self				:   str)       ->Dict:



   '''simple docstring'''



   return len(self.sp_model) + self.fairseq_offset

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



   '''simple docstring'''



   lowerCamelCase__:      str			  ={self.convert_ids_to_tokens(UpperCAmelCase_): i for i in range(self.vocab_size)}
   vocab.update(self.added_tokens_encoder)
   return vocab

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



   '''simple docstring'''



   return self.sp_model.encode(UpperCAmelCase_       , out_type=UpperCAmelCase_)

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



   '''simple docstring'''



   if token in self.fairseq_tokens_to_ids:
     return self.fairseq_tokens_to_ids[token]
   lowerCamelCase__:      str			  =self.sp_model.PieceToId(UpperCAmelCase_)

   # Need to return unknown token if the SP model returned 0
   return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

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



   '''simple docstring'''



   if index in self.fairseq_ids_to_tokens:
     return self.fairseq_ids_to_tokens[index]
   return self.sp_model.IdToPiece(index - self.fairseq_offset)

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



   '''simple docstring'''



   lowerCamelCase__:      Union[str, Any]			  ="".join(UpperCAmelCase_).replace(UpperCAmelCase_       , " ").strip()
   return out_string

 def 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any]       , UpperCAmelCase_				:   str       , UpperCAmelCase_				:   Optional[str] = None)       ->Tuple[str]:



   '''simple docstring'''



   if not os.path.isdir(UpperCAmelCase_):
     logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
     return
   lowerCamelCase__:      List[str]			  =os.path.join(
       UpperCAmelCase_       , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

   if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCAmelCase_) and os.path.isfile(self.vocab_file):
     copyfile(self.vocab_file       , UpperCAmelCase_)
   elif not os.path.isfile(self.vocab_file):
     with open(UpperCAmelCase_       , "wb") as fi:
       lowerCamelCase__:      Dict			  =self.sp_model.serialized_model_proto()
       fi.write(UpperCAmelCase_)

   return (out_vocab_file,)







 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str]       , UpperCAmelCase_				:   List[int]       , UpperCAmelCase_				:   Optional[List[int]] = None)       ->List[int]:



   '''simple docstring'''



   if token_ids_a is None:
     return token_ids_a + [self.sep_token_id]
   lowerCamelCase__:      Union[str, Any]			  =[self.sep_token_id]
   return token_ids_a + sep + token_ids_a + sep

