import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation


snake_case__				= logging.get_logger(__name__)

snake_case__				= {'vocab_file': 'vocab.txt', 'emoji_file': 'emoji.json'}

snake_case__				= {
    'vocab_file': {
        'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt',
    },
    'emoji_file': {
        'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json',
    },
}

snake_case__				= {
    'abeja/gpt-neox-japanese-2.7b': 2048,
}
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    Union[str, Any]:



      '''simple docstring'''

      with open(__UpperCAmelCase ,						'''r''' ,						encoding='''utf-8'''		) as f:
            _lowerCamelCase			    =				json.loads(f.read()		)

      _lowerCamelCase			    =				collections.OrderedDict()
      _lowerCamelCase			    =				collections.OrderedDict()
      _lowerCamelCase			    =				collections.OrderedDict()
      with open(__UpperCAmelCase ,						'''r''' ,						encoding='''utf-8'''		) as f:
            _lowerCamelCase			    =				f.readlines()
      _lowerCamelCase			    =				[[t.rstrip('''\n'''		)] if (t == ''',''' or ''',''' not in t) else t.rstrip('''\n'''		).split(''','''		) for t in token]
      for idx, b in enumerate(__UpperCAmelCase		):
            _lowerCamelCase			    =				b
            _lowerCamelCase			    =				idx
            for wd in b:
                  _lowerCamelCase			    =				idx

      return vocab, raw_vocab, ids_to_tokens, emoji



class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  VOCAB_FILES_NAMES
     A_										=  PRETRAINED_VOCAB_FILES_MAP
     A_										=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A_										=  ['input_ids', 'attention_mask']



     def __init__(					self				,				A_				,				A_				,				A_="<|endoftext|>"				,				A_="<|endoftext|>"				,				A_="<|startoftext|>"				,				A_="<|endoftext|>"				,				A_=False				,				**A_				,				) ->				Dict:






           """simple docstring"""
           super().__init__(
               unk_token=A_				,				pad_token=A_				,				bos_token=A_				,				eos_token=A_				,				do_clean_text=A_				,				**A_				,				)
           if not os.path.isfile(A_						):
                 raise ValueError(
                     F'Can\'t find a vocabulary file at path \'{vocab_file}\'. To load the vocabulary from a Google pretrained'
                     ''' model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`'''						)
           if not os.path.isfile(A_						):
                 raise ValueError(
                     F'Can\'t find a emoji file at path \'{emoji_file}\'. To load the emoji information from a Google'
                     ''' pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`'''						)
           _lowerCamelCase			    =				do_clean_text
           _lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				load_vocab_and_emoji(A_				,				A_						)
           _lowerCamelCase			    =				SubWordJapaneseTokenizer(
               vocab=self.vocab				,				ids_to_tokens=self.ids_to_tokens				,				emoji=self.emoji						)



     @property
     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           return len(self.raw_vocab						)



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






           """simple docstring"""
           return dict(self.raw_vocab				,				**self.added_tokens_encoder						)



     def 		UpperCamelCase_						(					self				,				A_						) ->				Tuple:






           """simple docstring"""
           return self.subword_tokenizer.tokenize(A_				,				clean=self.do_clean_text						)



     def 		UpperCamelCase_						(					self				,				A_						) ->				List[str]:






           """simple docstring"""
           return self.vocab.get(A_				,				self.vocab.get(self.unk_token						)						)



     def 		UpperCamelCase_						(					self				,				A_						) ->				Dict:






           """simple docstring"""
           return self.subword_tokenizer.convert_id_to_token(A_						)



     def 		UpperCamelCase_						(					self				,				A_						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase			    =				''''''.join(A_						).strip()
           return out_string



     def 		UpperCamelCase_						(					self				,				A_						) ->				List[int]:






           """simple docstring"""
           _lowerCamelCase			    =				[]
           for is_user, text in conversation.iter_texts():
                 input_ids.extend(self.encode(A_				,				add_special_tokens=A_						) + [self.eos_token_id]						)

           if len(A_						) > self.model_max_length:
                 _lowerCamelCase			    =				input_ids[-self.model_max_length :]
           return input_ids





     def 		UpperCamelCase_						(					self				,				A_				,				A_ = None						) ->				Tuple[str]:






           """simple docstring"""
           _lowerCamelCase			    =				0
           if os.path.isdir(A_						):
                 _lowerCamelCase			    =				os.path.join(
                     A_				,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)
                 _lowerCamelCase			    =				os.path.join(
                     A_				,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''emoji_file''']						)
           else:
                 _lowerCamelCase			    =				(
                     (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory + VOCAB_FILES_NAMES['''vocab_file''']
                 )
                 _lowerCamelCase			    =				(
                     (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory + VOCAB_FILES_NAMES['''emoji_file''']
                 )
           with open(A_				,				'''w'''				,				encoding='''utf-8'''						) as writer:
                 for token_index, token in self.ids_to_tokens.items():
                       if index != token_index:
                             logger.warning(
                                 F'Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'
                                 ''' Please check that the vocabulary is not corrupted!'''						)
                             _lowerCamelCase			    =				token_index
                       writer.write(''','''.join(A_						) + '''\n'''						)
                       index += 1
           with open(A_				,				'''w'''				,				encoding='''utf-8'''						) as writer:
                 json.dump(self.emoji				,				A_						)
           return vocab_file, emoji_file





class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     def __init__(					self				,				A_				,				A_				,				A_						) ->				List[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				vocab  # same as swe
           _lowerCamelCase			    =				ids_to_tokens  # same as bpe
           _lowerCamelCase			    =				emoji
           _lowerCamelCase			    =				np.max([len(A_						) for w in self.vocab.keys()]						)
           _lowerCamelCase			    =				re.compile(r'''(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)'''						)
           _lowerCamelCase			    =				re.compile(r'''[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*'''						)
           _lowerCamelCase			    =				re.compile(r'''[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}'''						)
           _lowerCamelCase			    =				re.compile(
               r'''([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*'''						)
           _lowerCamelCase			    =				re.compile(
               r'''(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*'''						)
           _lowerCamelCase			    =				re.compile(
               r'''((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*'''						)
           _lowerCamelCase			    =				'''─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿'''
           _lowerCamelCase			    =				'''▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟'''
           _lowerCamelCase			    =				str.maketrans({k: '''<BLOCK>''' for k in keisen + blocks}						)



     def __len__(					self						) ->				Optional[int]:






           """simple docstring"""
           return len(self.ids_to_tokens						)



     def 		UpperCamelCase_						(					self				,				A_						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				self.content_repattera.sub('''<URL>'''				,				A_						)
           _lowerCamelCase			    =				self.content_repattera.sub('''<EMAIL>'''				,				A_						)
           _lowerCamelCase			    =				self.content_repattera.sub('''<TEL>'''				,				A_						)
           _lowerCamelCase			    =				self.content_repattera.sub('''<DATE>'''				,				A_						)
           _lowerCamelCase			    =				self.content_repattera.sub('''<DATE>'''				,				A_						)
           _lowerCamelCase			    =				self.content_repattera.sub('''<PRICE>'''				,				A_						)
           _lowerCamelCase			    =				content.translate(self.content_transa						)
           while "<BLOCK><BLOCK>" in content:
                 _lowerCamelCase			    =				content.replace('''<BLOCK><BLOCK>'''				,				'''<BLOCK>'''						)
           return content



     def 		UpperCamelCase_						(					self				,				A_				,				A_=False						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase			    =				text.replace(''' '''				,				'''<SP>'''						)
           _lowerCamelCase			    =				text.replace('''　'''				,				'''<SP>'''						)
           _lowerCamelCase			    =				text.replace('''\r\n'''				,				'''<BR>'''						)
           _lowerCamelCase			    =				text.replace('''\n'''				,				'''<BR>'''						)
           _lowerCamelCase			    =				text.replace('''\r'''				,				'''<BR>'''						)
           _lowerCamelCase			    =				text.replace('''\t'''				,				'''<TAB>'''						)
           _lowerCamelCase			    =				text.replace('''—'''				,				'''ー'''						)
           _lowerCamelCase			    =				text.replace('''−'''				,				'''ー'''						)
           for k, v in self.emoji["emoji"].items():
                 if k in text:
                       _lowerCamelCase			    =				text.replace(A_				,				A_						)
           if clean:
                 _lowerCamelCase			    =				self.clean_text(A_						)

           def check_simbol(A_						):
                 _lowerCamelCase			    =				x.encode()
                 if len(A_						) == 1 and len(A_						) == 2:
                       _lowerCamelCase			    =				(int(e[0]						) << 8) + int(e[1]						)
                       if (
                           (c >= 0xc_2a1 and c <= 0xc_2bf)
                           or (c >= 0xc_780 and c <= 0xc_783)
                           or (c >= 0xc_ab9 and c <= 0xc_bbf)
                           or (c >= 0xc_c80 and c <= 0xc_da2)
                       ):
                             return True
                 return False

           def checkuae(A_						):
                 _lowerCamelCase			    =				x.encode()
                 if len(A_						) == 1 and len(A_						) == 3:
                       _lowerCamelCase			    =				(int(e[0]						) << 16) + (int(e[1]						) << 8) + int(e[2]						)
                       if c >= 0xe28_080 and c <= 0xe2b_07f:
                             return True
                 return False

           _lowerCamelCase			    =				0
           _lowerCamelCase			    =				[]
           while pos < len(A_						):
                 _lowerCamelCase			    =				min(len(A_						)				,				pos + self.maxlen + 1						) if text[pos] == '''<''' else pos + 3
                 _lowerCamelCase			    =				[]  # (token_id, token, pos)
                 for e in range(A_				,				A_				,				-1						):
                       _lowerCamelCase			    =				text[pos:e]
                       if wd in self.vocab:
                             if wd[0] == "<" and len(A_						) > 2:
                                   _lowerCamelCase			    =				[(self.vocab[wd], wd, e)]
                                   break
                             else:
                                   candidates.append((self.vocab[wd], wd, e)						)
                 if len(A_						) > 0:
                       # the smallest token_id is adopted
                       _lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				sorted(A_				,				key=lambda A_						: x[0]						)[0]
                       result.append(A_						)
                       _lowerCamelCase			    =				e
                 else:
                       _lowerCamelCase			    =				pos + 1
                       _lowerCamelCase			    =				text[pos:end]
                       if check_simbol(A_						):
                             result.append('''<KIGOU>'''						)
                       elif checkuae(A_						):
                             result.append('''<U2000U2BFF>'''						)
                       else:
                             for i in wd.encode('''utf-8'''						):
                                   result.append('''<|byte%d|>''' % i						)
                       _lowerCamelCase			    =				end
           return result





     def 		UpperCamelCase_						(					self				,				A_				,				A_="\n"						) ->				Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase			    =				[]
           _lowerCamelCase			    =				[]
           _lowerCamelCase			    =				self.ids_to_tokens[index][0]
           if word[:6] == "<|byte" and word[-2:] == "|>":
                 byte_tokens.append(int(word[6:-2]						)						)
           else:
                 if len(A_						) > 0:
                       words.append(bytearray(A_						).decode('''utf-8'''				,				errors='''replace'''						)						)
                       _lowerCamelCase			    =				[]
                 if word[:7] == "<|emoji" and word[-2:] == "|>":
                       words.append(self.emoji['''emoji_inv'''][word]						)
                 elif word == "<SP>":
                       words.append(''' '''						)
                 elif word == "<BR>":
                       words.append(A_						)
                 elif word == "<TAB>":
                       words.append('''\t'''						)
                 elif word == "<BLOCK>":
                       words.append('''▀'''						)
                 elif word == "<KIGOU>":
                       words.append('''ǀ'''						)
                 elif word == "<U2000U2BFF>":
                       words.append('''‖'''						)
                 else:
                       words.append(A_						)
           if len(A_						) > 0:
                 words.append(bytearray(A_						).decode('''utf-8'''				,				errors='''replace'''						)						)
           _lowerCamelCase			    =				''''''.join(A_						)
           return text


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()
snake_case__				= logging.get_logger(__name__)

snake_case__				= {
    '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',
}
snake_case__				= [
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    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			    =				'''lm_head'''

            _lowerCamelCase			    =				getattr(__UpperCAmelCase ,						__UpperCAmelCase		)

      if weight_type is not None:
            _lowerCamelCase			    =				getattr(__UpperCAmelCase ,						__UpperCAmelCase		).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       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    List[str]:



      '''simple docstring'''

      _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(
                      __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						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(__UpperCAmelCase		)[0].split('''.'''		)[-2]
                                    _lowerCamelCase			    =				mapped_key.replace('''*''' ,						__UpperCAmelCase		)
                              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(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)
                        continue
            if not is_used:
                  unused_weights.append(__UpperCAmelCase		)

      logger.warning(F'Unused weights: {unused_weights}'		)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    int:



      '''simple docstring'''

      _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(__UpperCAmelCase		)
@torch.no_grad()
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase=None ,						__UpperCAmelCase=None ,						__UpperCAmelCase=True		)						->    Union[str, Any]:



      '''simple docstring'''

      if config_path is not None:
            _lowerCamelCase			    =				UniSpeechConfig.from_pretrained(__UpperCAmelCase		)
      else:
            _lowerCamelCase			    =				UniSpeechConfig()

      if is_finetuned:
            if dict_path:
                  _lowerCamelCase			    =				Dictionary.load_from_json(__UpperCAmelCase		)

                  # 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(__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		)
                  _lowerCamelCase			    =				target_dict.indices

                  # fairseq has the <pad> and <s> switched
                  _lowerCamelCase			    =				42
                  _lowerCamelCase			    =				43
                  with open(__UpperCAmelCase ,						'''w''' ,						encoding='''utf-8'''		) as vocab_handle:
                        json.dump(__UpperCAmelCase ,						__UpperCAmelCase		)
                  _lowerCamelCase			    =				WavaVecaPhonemeCTCTokenizer(
                      __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 ,						)
                  _lowerCamelCase			    =				True if config.feat_extract_norm == '''layer''' else False
                  _lowerCamelCase			    =				WavaVecaFeatureExtractor(
                      feature_size=1 ,						sampling_rate=1_6000 ,						padding_value=0 ,						do_normalize=__UpperCAmelCase ,						return_attention_mask=__UpperCAmelCase ,						)
                  _lowerCamelCase			    =				WavaVecaProcessor(feature_extractor=__UpperCAmelCase ,						tokenizer=__UpperCAmelCase		)
                  processor.save_pretrained(__UpperCAmelCase		)

            _lowerCamelCase			    =				UniSpeechForCTC(__UpperCAmelCase		)
      else:
            _lowerCamelCase			    =				UniSpeechForPreTraining(__UpperCAmelCase		)

      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(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)

      hf_unispeech.save_pretrained(__UpperCAmelCase		)


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