
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


_UpperCAmelCase      :				Dict          = logging.get_logger(__name__)

_UpperCAmelCase      :				Optional[Any]          = {"vocab_file": "vocab.txt", "emoji_file": "emoji.json"}

_UpperCAmelCase      :				Optional[Any]          = {
    "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",
    },
}

_UpperCAmelCase      :				List[Any]          = {
    "abeja/gpt-neox-japanese-2.7b": 2_048,
}


def    A (      lowercase					, lowercase				)     ->						Any:
  '''simple docstring'''

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

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

  return vocab, raw_vocab, ids_to_tokens, emoji


class        lowercase      (	__lowerCamelCase     ):
  __lowercase : Optional[int]			  =       VOCAB_FILES_NAMES
  __lowercase : int			  =       PRETRAINED_VOCAB_FILES_MAP
  __lowercase : Optional[Any]			  =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowercase : Dict			  =       ['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=UpperCamelCase_       ,   pad_token=UpperCamelCase_       ,   bos_token=UpperCamelCase_       ,   eos_token=UpperCamelCase_       ,   do_clean_text=UpperCamelCase_       ,   **UpperCamelCase_       ,   )
    if not os.path.isfile(UpperCamelCase_ ):
      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(UpperCamelCase_ ):
      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)`' )
    UpperCamelCase						=  do_clean_text
    UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase						=  load_vocab_and_emoji(UpperCamelCase_       ,   UpperCamelCase_ )
    UpperCamelCase						=  SubWordJapaneseTokenizer(
        vocab=self.vocab       ,   ids_to_tokens=self.ids_to_tokens       ,   emoji=self.emoji )



  @property
  def __UpperCamelCase				(				self )						->      str:
    """simple docstring"""
    # self.vocab contains support for character fluctuation unique to Japanese, and has a large number of vocab
    return len(self.raw_vocab )



  def __UpperCamelCase				(				self )						->      str:
    """simple docstring"""
    return dict(self.raw_vocab       ,   **self.added_tokens_encoder )



  def __UpperCamelCase				(				self       ,   A_ )						->      List[Any]:
    """simple docstring"""
    return self.subword_tokenizer.tokenize(UpperCamelCase_       ,   clean=self.do_clean_text )



  def __UpperCamelCase				(				self       ,   A_ )						->      Any:
    """simple docstring"""
    return self.vocab.get(UpperCamelCase_       ,   self.vocab.get(self.unk_token ) )



  def __UpperCamelCase				(				self       ,   A_ )						->      Optional[Any]:
    """simple docstring"""
    return self.subword_tokenizer.convert_id_to_token(UpperCamelCase_ )



  def __UpperCamelCase				(				self       ,   A_ )						->      List[str]:
    """simple docstring"""
    UpperCamelCase						=  ''.join(UpperCamelCase_ ).strip()
    return out_string



  def __UpperCamelCase				(				self       ,   A_ )						->      Any:
    """simple docstring"""
    UpperCamelCase						=  []
    for is_user, text in conversation.iter_texts():
      input_ids.extend(self.encode(UpperCamelCase_       ,   add_special_tokens=UpperCamelCase_ ) + [self.eos_token_id] )

    if len(UpperCamelCase_ ) > self.model_max_length:
      UpperCamelCase						=  input_ids[-self.model_max_length :]
    return input_ids



  def __UpperCamelCase				(				self       ,   A_       ,   A_ = None )						->      List[str]:
    """simple docstring"""
    UpperCamelCase						=  0
    if os.path.isdir(UpperCamelCase_ ):
      UpperCamelCase						=  os.path.join(
          UpperCamelCase_       ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
      UpperCamelCase						=  os.path.join(
          UpperCamelCase_       ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['emoji_file'] )
    else:
      UpperCamelCase						=  (
          (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['vocab_file']
      )
      UpperCamelCase						=  (
          (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['emoji_file']
      )
    with open(UpperCamelCase_       ,   '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!' )
          UpperCamelCase						=  token_index
        writer.write(','.join(UpperCamelCase_ ) + '\n' )
        index += 1
    with open(UpperCamelCase_       ,   'w'       ,   encoding='utf-8' ) as writer:
      json.dump(self.emoji       ,   UpperCamelCase_ )
    return vocab_file, emoji_file






class        lowercase      (	__lowerCamelCase     ):



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



  def __len__(				self )						->      Tuple:
    """simple docstring"""
    return len(self.ids_to_tokens )



  def __UpperCamelCase				(				self       ,   A_ )						->      Optional[Any]:
    """simple docstring"""
    UpperCamelCase						=  self.content_repattera.sub('<URL>'       ,   UpperCamelCase_ )
    UpperCamelCase						=  self.content_repattera.sub('<EMAIL>'       ,   UpperCamelCase_ )
    UpperCamelCase						=  self.content_repattera.sub('<TEL>'       ,   UpperCamelCase_ )
    UpperCamelCase						=  self.content_repattera.sub('<DATE>'       ,   UpperCamelCase_ )
    UpperCamelCase						=  self.content_repattera.sub('<DATE>'       ,   UpperCamelCase_ )
    UpperCamelCase						=  self.content_repattera.sub('<PRICE>'       ,   UpperCamelCase_ )
    UpperCamelCase						=  content.translate(self.content_transa )
    while "<BLOCK><BLOCK>" in content:
      UpperCamelCase						=  content.replace('<BLOCK><BLOCK>'       ,   '<BLOCK>' )
    return content



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

    def check_simbol(A_ ):
      UpperCamelCase						=  x.encode()
      if len(UpperCamelCase_ ) == 1 and len(UpperCamelCase_ ) == 2:
        UpperCamelCase						=  (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_ ):
      UpperCamelCase						=  x.encode()
      if len(UpperCamelCase_ ) == 1 and len(UpperCamelCase_ ) == 3:
        UpperCamelCase						=  (int(e[0] ) << 16) + (int(e[1] ) << 8) + int(e[2] )
        if c >= 0XE28_080 and c <= 0XE2B_07F:
          return True
      return False

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



  def __UpperCamelCase				(				self       ,   A_       ,   A_="\n" )						->      Any:
    """simple docstring"""
    UpperCamelCase						=  []
    UpperCamelCase						=  []
    UpperCamelCase						=  self.ids_to_tokens[index][0]
    if word[:6] == "<|byte" and word[-2:] == "|>":
      byte_tokens.append(int(word[6:-2] ) )
    else:
      if len(UpperCamelCase_ ) > 0:
        words.append(bytearray(UpperCamelCase_ ).decode('utf-8'       ,   errors='replace' ) )
        UpperCamelCase						=  []
      if word[:7] == "<|emoji" and word[-2:] == "|>":
        words.append(self.emoji['emoji_inv'][word] )
      elif word == "<SP>":
        words.append(' ' )
      elif word == "<BR>":
        words.append(UpperCamelCase_ )
      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(UpperCamelCase_ )
    if len(UpperCamelCase_ ) > 0:
      words.append(bytearray(UpperCamelCase_ ).decode('utf-8'       ,   errors='replace' ) )
    UpperCamelCase						=  ''.join(UpperCamelCase_ )
    return text


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_UpperCAmelCase      :				Tuple          = logging.get_logger(__name__)

_UpperCAmelCase      :				Tuple          = {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.linear_k": "encoder.layers.*.self_attn.linear_k",
    "self_attn.linear_v": "encoder.layers.*.self_attn.linear_v",
    "self_attn.linear_q": "encoder.layers.*.self_attn.linear_q",
    "self_attn.pos_bias_u": "encoder.layers.*.self_attn.pos_bias_u",
    "self_attn.pos_bias_v": "encoder.layers.*.self_attn.pos_bias_v",
    "self_attn.linear_out": "encoder.layers.*.self_attn.linear_out",
    "self_attn.linear_pos": "encoder.layers.*.self_attn.linear_pos",
    "self_attn.rotary_emb": "encoder.embed_positions",
    "self_attn_layer_norm": "encoder.layers.*.self_attn_layer_norm",
    "conv_module.pointwise_conv1": "encoder.layers.*.conv_module.pointwise_conv1",
    "conv_module.pointwise_conv2": "encoder.layers.*.conv_module.pointwise_conv2",
    "conv_module.depthwise_conv": "encoder.layers.*.conv_module.depthwise_conv",
    "conv_module.batch_norm": "encoder.layers.*.conv_module.batch_norm",
    "conv_module.layer_norm": "encoder.layers.*.conv_module.layer_norm",
    "ffn1.w_1": "encoder.layers.*.ffn1.intermediate_dense",
    "ffn1.w_2": "encoder.layers.*.ffn1.output_dense",
    "ffn1.layer_norm": "encoder.layers.*.ffn1_layer_norm",
    "ffn2.w_1": "encoder.layers.*.ffn2.intermediate_dense",
    "ffn2.w_2": "encoder.layers.*.ffn2.output_dense",
    "ffn2.layer_norm": "encoder.layers.*.ffn2_layer_norm",
    "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": "lm_head",
    "mask_emb": "masked_spec_embed",
}
_UpperCAmelCase      :				Any          = [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]


def    A (      lowercase					, lowercase					, lowercase					, lowercase					, lowercase				)     ->						Dict:
		'''simple docstring'''

		for attribute in key.split('.'				):
				UpperCamelCase						=  getattr(lowercase					, lowercase				)

		if weight_type is not None:
				UpperCamelCase						=  getattr(lowercase					, lowercase				).shape
		else:
				UpperCamelCase						=  hf_pointer.shape

		if hf_shape != value.shape:
				raise ValueError(
				    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						=  value
		elif weight_type == "weight_g":
				UpperCamelCase						=  value
		elif weight_type == "weight_v":
				UpperCamelCase						=  value
		elif weight_type == "bias":
				UpperCamelCase						=  value
		elif weight_type == "running_mean":
				UpperCamelCase						=  value
		elif weight_type == "running_var":
				UpperCamelCase						=  value
		elif weight_type == "num_batches_tracked":
				UpperCamelCase						=  value
		elif weight_type == "inv_freq":
				UpperCamelCase						=  value
		else:
				UpperCamelCase						=  value

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


def    A (      lowercase					, lowercase					, lowercase				)     ->						Any:
		'''simple docstring'''

		UpperCamelCase						=  []
		UpperCamelCase						=  fairseq_model.state_dict()

		UpperCamelCase						=  hf_model.wavaveca_conformer.feature_extractor

		for name, value in fairseq_dict.items():
				UpperCamelCase						=  False
				if "conv_layers" in name:
						load_conv_layer(
						    lowercase					, lowercase					, lowercase					, lowercase					, hf_model.config.feat_extract_norm == 'group'					, )
						UpperCamelCase						=  True
				else:
						for key, mapped_key in MAPPING.items():
								UpperCamelCase						=  'wav2vec2_conformer.' + 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						=  True
										if "*" in mapped_key:
												UpperCamelCase						=  name.split(lowercase				)[0].split('.'				)[-2]
												UpperCamelCase						=  mapped_key.replace('*'					, lowercase				)
										if "pos_bias_u" in name:
												UpperCamelCase						=  None
										elif "pos_bias_v" in name:
												UpperCamelCase						=  None
										elif "weight_g" in name:
												UpperCamelCase						=  'weight_g'
										elif "weight_v" in name:
												UpperCamelCase						=  'weight_v'
										elif "bias" in name:
												UpperCamelCase						=  'bias'
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												UpperCamelCase						=  'weight'
										elif "running_mean" in name:
												UpperCamelCase						=  'running_mean'
										elif "inv_freq" in name:
												UpperCamelCase						=  'inv_freq'
										elif "running_var" in name:
												UpperCamelCase						=  'running_var'
										elif "num_batches_tracked" in name:
												UpperCamelCase						=  'num_batches_tracked'
										else:
												UpperCamelCase						=  None
										set_recursively(lowercase					, lowercase					, lowercase					, lowercase					, lowercase				)
								continue
				if not is_used:
						unused_weights.append(lowercase				)

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


def    A (      lowercase					, lowercase					, lowercase					, lowercase					, lowercase				)     ->						Optional[int]:
		'''simple docstring'''

		UpperCamelCase						=  full_name.split('conv_layers.'				)[-1]
		UpperCamelCase						=  name.split('.'				)
		UpperCamelCase						=  int(items[0]				)
		UpperCamelCase						=  int(items[1]				)

		if type_id == 0:
				if "bias" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
								raise ValueError(
								    f'''{full_name} has size {value.shape}, but'''
								    f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''				)
						UpperCamelCase						=  value
						logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''				)
				elif "weight" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
								raise ValueError(
								    f'''{full_name} has size {value.shape}, but'''
								    f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''				)
						UpperCamelCase						=  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:
						if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
								raise ValueError(
								    f'''{full_name} has size {value.shape}, but'''
								    f''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''				)
						UpperCamelCase						=  value
						logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
				elif "weight" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
								raise ValueError(
								    f'''{full_name} has size {value.shape}, but'''
								    f''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''				)
						UpperCamelCase						=  value
						logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
		else:
				unused_weights.append(lowercase				)


@torch.no_grad()
def    A (      lowercase					, lowercase					, lowercase=None					, lowercase=None					, lowercase=True				)     ->						int:
		'''simple docstring'''

		if config_path is not None:
				UpperCamelCase						=  WavaVecaConformerConfig.from_pretrained(lowercase					, hidden_act='swish'				)
		else:
				UpperCamelCase						=  WavaVecaConformerConfig()

		if "rope" in checkpoint_path:
				UpperCamelCase						=  'rotary'

		if is_finetuned:
				if dict_path:
						UpperCamelCase						=  Dictionary.load(lowercase				)

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

						# fairseq has the <pad> and <s> switched
						UpperCamelCase						=  0
						UpperCamelCase						=  1
						with open(lowercase					, 'w'					, encoding='utf-8'				) as vocab_handle:
								json.dump(lowercase					, lowercase				)
						UpperCamelCase						=  WavaVecaCTCTokenizer(
						    lowercase					, 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=lowercase					, )
						UpperCamelCase						=  True if config.feat_extract_norm == 'layer' else False
						UpperCamelCase						=  WavaVecaFeatureExtractor(
						    feature_size=1					, sampling_rate=16_000					, padding_value=0					, do_normalize=lowercase					, return_attention_mask=lowercase					, )
						UpperCamelCase						=  WavaVecaProcessor(feature_extractor=lowercase					, tokenizer=lowercase				)
						processor.save_pretrained(lowercase				)

				UpperCamelCase						=  WavaVecaConformerForCTC(lowercase				)
		else:
				UpperCamelCase						=  WavaVecaConformerForPreTraining(lowercase				)

		if is_finetuned:
				UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase						=  fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]					, arg_overrides={'data': '/'.join(dict_path.split('/'				)[:-1]				)}				)
		else:
				UpperCamelCase						=  argparse.Namespace(task='audio_pretraining'				)
				UpperCamelCase						=  fairseq.tasks.setup_task(lowercase				)

				UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase						=  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					, task=lowercase				)

		UpperCamelCase						=  model[0].eval()

		recursively_load_weights(lowercase					, lowercase					, not is_finetuned				)

		hf_wavavec.save_pretrained(lowercase				)


if __name__ == "__main__":
					_UpperCAmelCase      :				Tuple          = 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"
					)
					_UpperCAmelCase      :				Dict          = parser.parse_args()
					convert_wavaveca_conformer_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)
