"""simple docstring"""
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()
lowercase__ 	=     logging.get_logger(__name__)

lowercase__ 	=     {
    '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',
}
lowercase__ 	=     [
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Tuple:





  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
        a__:					Any      							=     """lm_head"""

    a__:					int      							=     getattr(lowerCAmelCase__		,					lowerCAmelCase__       )

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

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







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Optional[Any]:
  a__:					List[Any]      							=     []
  a__:					List[str]      							=     fairseq_model.state_dict()

  a__:					Tuple      							=     hf_model.unispeech.feature_extractor

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

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







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







@torch.no_grad()
def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE=None		,					_SCREAMING_SNAKE_CASE=None		,					_SCREAMING_SNAKE_CASE=True       )     ->List[Any]:





  if config_path is not None:
    a__:					str      							=     UniSpeechConfig.from_pretrained(lowerCAmelCase__       )
  else:
    a__:					Any      							=     UniSpeechConfig()

  if is_finetuned:
    if dict_path:
      a__:					str      							=     Dictionary.load_from_json(lowerCAmelCase__       )

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

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

    a__:					Optional[Any]      							=     UniSpeechForCTC(lowerCAmelCase__       )
  else:
    a__:					Dict      							=     UniSpeechForPreTraining(lowerCAmelCase__       )

  if is_finetuned:
    a__:					List[Any]      							=     fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]		,					arg_overrides={'data': '/'.join(dict_path.split('/'       )[:-1]       ), 'w2v_path': checkpoint_path}       )
  else:
    a__:					List[str]      							=     fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       )

  a__:					int      							=     model[0].eval()

  recursively_load_weights(lowerCAmelCase__		,					lowerCAmelCase__		,					lowerCAmelCase__       )

  hf_unispeech.save_pretrained(lowerCAmelCase__       )


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



"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_mbart import MBartTokenizer
else:
 lowercase__ 	=     None


lowercase__ 	=     logging.get_logger(__name__)


lowercase__ 	=     {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

lowercase__ 	=     {
    'vocab_file': {
        'facebook/mbart-large-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
        'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
    },
}

lowercase__ 	=     {
    'facebook/mbart-large-en-ro': 1024,
    'facebook/mbart-large-cc25': 1024,
}

# fmt: off
lowercase__ 	=     ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']




class        __snake_case	(					__lowerCAmelCase    ):
      a__				=		VOCAB_FILES_NAMES
      a__				=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a__				=		PRETRAINED_VOCAB_FILES_MAP
      a__				=		["""input_ids""", """attention_mask"""]
      a__				=		MBartTokenizer

      a__				=		[]
      a__				=		[]



      def __init__(						self   ,      lowercase=None   ,      lowercase=None   ,      lowercase="<s>"   ,      lowercase="</s>"   ,      lowercase="</s>"   ,      lowercase="<s>"   ,      lowercase="<unk>"   ,      lowercase="<pad>"   ,      lowercase="<mask>"   ,      lowercase=None   ,      lowercase=None   ,      lowercase=None   ,      **lowercase   ,      )     ->      Tuple:





        '''simple docstring'''





        a__:					List[str]      							=     AddedToken(lowercase   ,      lstrip=lowercase   ,      rstrip=lowercase) if isinstance(lowercase   ,      lowercase) else mask_token

        super().__init__(
            vocab_file=lowercase   ,      tokenizer_file=lowercase   ,      bos_token=lowercase   ,      eos_token=lowercase   ,      sep_token=lowercase   ,      cls_token=lowercase   ,      unk_token=lowercase   ,      pad_token=lowercase   ,      mask_token=lowercase   ,      src_lang=lowercase   ,      tgt_lang=lowercase   ,      additional_special_tokens=lowercase   ,      **lowercase   ,      )

        a__:					Tuple      							=     vocab_file
        a__:					Union[str, Any]      							=     False if not self.vocab_file else True

        a__:					Union[str, Any]      							=     FAIRSEQ_LANGUAGE_CODES.copy()

        if additional_special_tokens is not None:
          # Only add those special tokens if they are not already there.
          _additional_special_tokens.extend(
              [t for t in additional_special_tokens if t not in _additional_special_tokens])

        self.add_special_tokens({'additional_special_tokens': _additional_special_tokens})
        a__:					int      							=     {
            lang_code: self.convert_tokens_to_ids(lowercase) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }

        a__:					List[Any]      							=     src_lang if src_lang is not None else 'en_XX'
        a__:					Tuple      							=     self.convert_tokens_to_ids(self._src_lang)
        a__:					str      							=     tgt_lang
        self.set_src_lang_special_tokens(self._src_lang)



      @property
      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        return self._src_lang



      @src_lang.setter
      def   lowerCamelCase_       (						self   ,      lowercase)     ->      None:





        '''simple docstring'''





        a__:					Tuple      							=     new_src_lang
        self.set_src_lang_special_tokens(self._src_lang)



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = None)     ->      List[int]:





        '''simple docstring'''





        if token_ids_a is None:
          return self.prefix_tokens + token_ids_a + self.suffix_tokens
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = None)     ->      List[int]:





        '''simple docstring'''





        a__:					Any      							=     [self.sep_token_id]
        a__:					List[str]      							=     [self.cls_token_id]

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



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase   ,      **lowercase)     ->      Union[str, Any]:





        '''simple docstring'''





        if src_lang is None or tgt_lang is None:
          raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model')
        a__:					Union[str, Any]      							=     src_lang
        a__:					Any      							=     self(lowercase   ,      add_special_tokens=lowercase   ,      return_tensors=lowercase   ,      **lowercase)
        a__:					str      							=     self.convert_tokens_to_ids(lowercase)
        a__:					Any      							=     tgt_lang_id
        return inputs



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = "en_XX"   ,      lowercase = None   ,      lowercase = "ro_RO"   ,      **lowercase   ,      )     ->      BatchEncoding:





        '''simple docstring'''





        a__:					Any      							=     src_lang
        a__:					List[Any]      							=     tgt_lang
        return super().prepare_seqaseq_batch(lowercase   ,      lowercase   ,      **lowercase)



      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        return self.set_src_lang_special_tokens(self.src_lang)



      def   lowerCamelCase_       (						self)     ->      List[Any]:





        '''simple docstring'''





        return self.set_tgt_lang_special_tokens(self.tgt_lang)



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      None:





        '''simple docstring'''





        a__:					int      							=     self.convert_tokens_to_ids(lowercase)
        a__:					List[Any]      							=     []
        a__:					List[str]      							=     [self.eos_token_id, self.cur_lang_code]

        a__:					Dict      							=     self.convert_ids_to_tokens(self.prefix_tokens)
        a__:					Any      							=     self.convert_ids_to_tokens(self.suffix_tokens)

        a__:					int      							=     processors.TemplateProcessing(
            single=prefix_tokens_str + ['$A'] + suffix_tokens_str   ,      pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str   ,      special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str   ,      self.prefix_tokens + self.suffix_tokens))   ,      )



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      None:





        '''simple docstring'''





        a__:					str      							=     self.convert_tokens_to_ids(lowercase)
        a__:					List[Any]      							=     []
        a__:					Dict      							=     [self.eos_token_id, self.cur_lang_code]

        a__:					Any      							=     self.convert_ids_to_tokens(self.prefix_tokens)
        a__:					Optional[Any]      							=     self.convert_ids_to_tokens(self.suffix_tokens)

        a__:					str      							=     processors.TemplateProcessing(
            single=prefix_tokens_str + ['$A'] + suffix_tokens_str   ,      pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str   ,      special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str   ,      self.prefix_tokens + self.suffix_tokens))   ,      )



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = None)     ->      Tuple[str]:





        '''simple docstring'''





        if not self.can_save_slow_tokenizer:
          raise ValueError(
              'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
              'tokenizer.')

        if not os.path.isdir(lowercase):
          logger.error(f'Vocabulary path ({save_directory}) should be a directory.')
          return
        a__:					Any      							=     os.path.join(
            lowercase   ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

        if os.path.abspath(self.vocab_file) != os.path.abspath(lowercase):
          copyfile(self.vocab_file   ,      lowercase)

        return (out_vocab_file,)


