'''simple docstring'''

import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer


A_     :						int    							=logging.get_logger(__name__)

A_     :						Optional[Any]    							={'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

A_     :						Union[str, Any]    							={
    '''vocab_file''': {
        '''facebook/dpr-ctx_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-ctx_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-ctx_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-ctx_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}
A_     :						Optional[Any]    							={
    '''vocab_file''': {
        '''facebook/dpr-question_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-question_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-question_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-question_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}
A_     :						str    							={
    '''vocab_file''': {
        '''facebook/dpr-reader-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-reader-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-reader-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-reader-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}

A_     :						Dict    							={
    '''facebook/dpr-ctx_encoder-single-nq-base''': 5_12,
    '''facebook/dpr-ctx_encoder-multiset-base''': 5_12,
}
A_     :						List[str]    							={
    '''facebook/dpr-question_encoder-single-nq-base''': 5_12,
    '''facebook/dpr-question_encoder-multiset-base''': 5_12,
}
A_     :						Optional[Any]    							={
    '''facebook/dpr-reader-single-nq-base''': 5_12,
    '''facebook/dpr-reader-multiset-base''': 5_12,
}


A_     :						Optional[Any]    							={
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
A_     :						int    							={
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
A_     :						Optional[int]    							={
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}


class     __UpperCAmelCase       ( __a		):
  __A :				List[Any]											= VOCAB_FILES_NAMES
  __A :				Union[str, Any]											= CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  __A :				List[Any]											= CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __A :				Optional[Any]											= CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class     __UpperCAmelCase       ( __a		):
  __A :				Union[str, Any]											= VOCAB_FILES_NAMES
  __A :				str											= QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  __A :				List[Any]											= QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __A :				Tuple											= QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION






A_     :						Any    							=collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

A_     :						int    							=collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


A_     :						List[str]    							=R'''
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    ```
    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>
    ```

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to
              the maximum acceptable input length for the model if that argument is not provided. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `\'tf\'`: Return TensorFlow `tf.constant` objects.
                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.
                - `\'np\'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer\'s default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Returns:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    '''


@add_start_docstrings(__a		)
class     __UpperCAmelCase       :





  def __call__(	self    ,     _lowerCamelCase    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = False    ,     _lowerCamelCase = False    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     **_lowerCamelCase    ,     ):
     if titles is None and texts is None:
        return super().__call__(
            _lowerCamelCase    ,     padding=_lowerCamelCase    ,     truncation=_lowerCamelCase    ,     max_length=_lowerCamelCase    ,     return_tensors=_lowerCamelCase    ,     return_attention_mask=_lowerCamelCase    ,     **_lowerCamelCase    ,     )
     elif titles is None or texts is None:
        lowerCAmelCase_			=      titles if texts is None else texts
        return super().__call__(
            _lowerCamelCase    ,     _lowerCamelCase    ,     padding=_lowerCamelCase    ,     truncation=_lowerCamelCase    ,     max_length=_lowerCamelCase    ,     return_tensors=_lowerCamelCase    ,     return_attention_mask=_lowerCamelCase    ,     **_lowerCamelCase    ,     )
     lowerCAmelCase_			=      titles if not isinstance(_lowerCamelCase    ,     _lowerCamelCase ) else [titles]
     lowerCAmelCase_			=      texts if not isinstance(_lowerCamelCase    ,     _lowerCamelCase ) else [texts]
     lowerCAmelCase_			=      len(_lowerCamelCase )
     lowerCAmelCase_			=      questions if not isinstance(_lowerCamelCase    ,     _lowerCamelCase ) else [questions] * n_passages
     if len(_lowerCamelCase ) != len(_lowerCamelCase ):
        raise ValueError(
            F'''There should be as many titles than texts but got {len(_lowerCamelCase )} titles and {len(_lowerCamelCase )} texts.''' )
     lowerCAmelCase_			=      super().__call__(_lowerCamelCase    ,     _lowerCamelCase    ,     padding=_lowerCamelCase    ,     truncation=_lowerCamelCase )['''input_ids''']
     lowerCAmelCase_			=      super().__call__(_lowerCamelCase    ,     add_special_tokens=_lowerCamelCase    ,     padding=_lowerCamelCase    ,     truncation=_lowerCamelCase )['''input_ids''']
     lowerCAmelCase_			=      {
         '''input_ids''': [
             (encoded_question_and_title + encoded_text)[:max_length]
             if max_length is not None and truncation
             else encoded_question_and_title + encoded_text
             for encoded_question_and_title, encoded_text in zip(_lowerCamelCase    ,     _lowerCamelCase )
         ]
     }
     if return_attention_mask is not False:
        lowerCAmelCase_			=      []
        for input_ids in encoded_inputs["input_ids"]:
           attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] )
        lowerCAmelCase_			=      attention_mask
     return self.pad(_lowerCamelCase    ,     padding=_lowerCamelCase    ,     max_length=_lowerCamelCase    ,     return_tensors=_lowerCamelCase )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = 16    ,     _lowerCamelCase = 64    ,     _lowerCamelCase = 4    ,     ):
     lowerCAmelCase_			=      reader_input['''input_ids''']
     lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      reader_output[:3]
     lowerCAmelCase_			=      len(_lowerCamelCase )
     lowerCAmelCase_			=      sorted(range(_lowerCamelCase )    ,     reverse=_lowerCamelCase    ,     key=relevance_logits.__getitem__ )
     lowerCAmelCase_			=      []
     for doc_id in sorted_docs:
        lowerCAmelCase_			=      list(input_ids[doc_id] )
        # assuming question & title information is at the beginning of the sequence
        lowerCAmelCase_			=      sequence_ids.index(self.sep_token_id    ,     2 ) + 1  # second sep id
        if sequence_ids[-1] == self.pad_token_id:
           lowerCAmelCase_			=      sequence_ids.index(self.pad_token_id )
        else:
           lowerCAmelCase_			=      len(_lowerCamelCase )

        lowerCAmelCase_			=      self._get_best_spans(
            start_logits=start_logits[doc_id][passage_offset:sequence_len]    ,     end_logits=end_logits[doc_id][passage_offset:sequence_len]    ,     max_answer_length=_lowerCamelCase    ,     top_spans=_lowerCamelCase    ,     )
        for start_index, end_index in best_spans:
           start_index += passage_offset
           end_index += passage_offset
           nbest_spans_predictions.append(
               DPRSpanPrediction(
                   span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]    ,     relevance_score=relevance_logits[doc_id]    ,     doc_id=_lowerCamelCase    ,     start_index=_lowerCamelCase    ,     end_index=_lowerCamelCase    ,     text=self.decode(sequence_ids[start_index : end_index + 1] )    ,     ) )
        if len(_lowerCamelCase ) >= num_spans:
           break
     return nbest_spans_predictions[:num_spans]







  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     ):
     lowerCAmelCase_			=      []
     for start_index, start_score in enumerate(_lowerCamelCase ):
        for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length] ):
           scores.append(((start_index, start_index + answer_length), start_score + end_score) )
     lowerCAmelCase_			=      sorted(_lowerCamelCase    ,     key=lambda _lowerCamelCase : x[1]    ,     reverse=_lowerCamelCase )
     lowerCAmelCase_			=      []
     for (start_index, end_index), score in scores:
        if start_index > end_index:
           raise ValueError(F'''Wrong span indices: [{start_index}:{end_index}]''' )
        lowerCAmelCase_			=      end_index - start_index + 1
        if length > max_answer_length:
           raise ValueError(F'''Span is too long: {length} > {max_answer_length}''' )
        if any(
            start_index <= prev_start_index <= prev_end_index <= end_index
            or prev_start_index <= start_index <= end_index <= prev_end_index
            for (prev_start_index, prev_end_index) in chosen_span_intervals ):
           continue
        chosen_span_intervals.append((start_index, end_index) )

        if len(_lowerCamelCase ) == top_spans:
           break
     return chosen_span_intervals






@add_end_docstrings(__a		)
class     __UpperCAmelCase       ( __a						,       __a		):
  __A :				Dict											= VOCAB_FILES_NAMES
  __A :				int											= READER_PRETRAINED_VOCAB_FILES_MAP
  __A :				Optional[int]											= READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __A :				Dict											= READER_PRETRAINED_INIT_CONFIGURATION
  __A :				Optional[int]											= ['input_ids', 'attention_mask']






'''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()
A_     :						List[Any]    							=logging.get_logger(__name__)

A_     :						Optional[int]    							={
    '''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_     :						Tuple    							=[
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]



def snake_case_      (					__snake_case  :							List[str]	,   __snake_case  :							Tuple	,   __snake_case  :							Union[str, Any]	,   __snake_case  :							Union[str, Any]	,   __snake_case  :							int	,   __snake_case  :							Dict)			->					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
            lowerCAmelCase_			=      '''lm_head'''

      lowerCAmelCase_			=      getattr(__snake_case	,   __snake_case)

   if weight_type is not None:
      lowerCAmelCase_			=      getattr(__snake_case	,   __snake_case).shape
   else:
      lowerCAmelCase_			=      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_			=      value
   elif weight_type == "weight_g":
      lowerCAmelCase_			=      value
   elif weight_type == "weight_v":
      lowerCAmelCase_			=      value
   elif weight_type == "bias":
      lowerCAmelCase_			=      value
   else:
      lowerCAmelCase_			=      value

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



def snake_case_      (					__snake_case  :							Tuple	,   __snake_case  :							Any	,   __snake_case  :							Any)			->					Tuple:
   lowerCAmelCase_			=      []
   lowerCAmelCase_			=      fairseq_model.state_dict()

   lowerCAmelCase_			=      hf_model.unispeech.feature_extractor

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

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



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



@torch.no_grad()
def snake_case_      (					__snake_case  :							Tuple	,   __snake_case  :							Tuple	,   __snake_case  :							str=None	,   __snake_case  :							Union[str, Any]=None	,   __snake_case  :							Union[str, Any]=True)			->					Any:
   if config_path is not None:
      lowerCAmelCase_			=      UniSpeechConfig.from_pretrained(__snake_case)
   else:
      lowerCAmelCase_			=      UniSpeechConfig()

   if is_finetuned:
      if dict_path:
         lowerCAmelCase_			=      Dictionary.load_from_json(__snake_case)

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

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

      lowerCAmelCase_			=      UniSpeechForCTC(__snake_case)
   else:
      lowerCAmelCase_			=      UniSpeechForPreTraining(__snake_case)

   if is_finetuned:
      lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      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_			=      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path])

   lowerCAmelCase_			=      model[0].eval()

   recursively_load_weights(__snake_case	,   __snake_case	,   __snake_case)

   hf_unispeech.save_pretrained(__snake_case)


if __name__ == "__main__":
 A_     :						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_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_     :						Optional[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
 )





