

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 			=		logging.get_logger(__name__)

_UpperCamelCase 			=		{"vocab_file": "vocab.txt", "emoji_file": "emoji.json"}

_UpperCamelCase 			=		{
    "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 			=		{
    "abeja/gpt-neox-japanese-2.7b": 2048,
}







def 		_lowercase (     lowercase__   ,	lowercase__						):
      with open(lowercase__   ,	'''r'''   ,	encoding='''utf-8'''						) as f:
            __lowerCAmelCase					:						int					=					json.loads(f.read()						)

      __lowerCAmelCase					:						List[Any]					=					collections.OrderedDict()
      __lowerCAmelCase					:						List[Any]					=					collections.OrderedDict()
      __lowerCAmelCase					:						int					=					collections.OrderedDict()
      with open(lowercase__   ,	'''r'''   ,	encoding='''utf-8'''						) as f:
            __lowerCAmelCase					:						Optional[int]					=					f.readlines()
      __lowerCAmelCase					:						str					=					[[t.rstrip('''\n'''						)] if (t == ''',''' or ''',''' not in t) else t.rstrip('''\n'''						).split(''','''						) for t in token]
      for idx, b in enumerate(lowercase__						):
            __lowerCAmelCase					:						List[str]					=					b
            __lowerCAmelCase					:						Tuple					=					idx
            for wd in b:
                  __lowerCAmelCase					:						List[str]					=					idx

      return vocab, raw_vocab, ids_to_tokens, emoji







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  VOCAB_FILES_NAMES
 _UpperCamelCase			  =  PRETRAINED_VOCAB_FILES_MAP
 _UpperCamelCase			  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _UpperCamelCase			  =  ["""input_ids""", """attention_mask"""]
 def __init__(							self      ,							A_      ,							A_      ,							A_="<|endoftext|>"      ,							A_="<|endoftext|>"      ,							A_="<|startoftext|>"      ,							A_="<|endoftext|>"      ,							A_=False      ,							**A_      ,							)					->Any:



       '''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					:						Optional[Any]					=					do_clean_text
       __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase					:						Union[str, Any]					=					load_vocab_and_emoji(A_      ,							A_					)
       __lowerCAmelCase					:						int					=					SubWordJapaneseTokenizer(
           vocab=self.vocab      ,							ids_to_tokens=self.ids_to_tokens      ,							emoji=self.emoji					)
 @property
 def 	UpperCamelCase__  (							self					)					->List[str]:



       '''simple docstring'''





       return len(self.raw_vocab					)
 def 	UpperCamelCase__  (							self					)					->List[str]:



       '''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_					)					->Optional[int]:



       '''simple docstring'''





       return self.vocab.get(A_      ,							self.vocab.get(self.unk_token					)					)
 def 	UpperCamelCase__  (							self      ,							A_					)					->Any:



       '''simple docstring'''





       return self.subword_tokenizer.convert_id_to_token(A_					)
 def 	UpperCamelCase__  (							self      ,							A_					)					->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Dict					=					''''''.join(A_					).strip()
       return out_string
 def 	UpperCamelCase__  (							self      ,							A_					)					->List[int]:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[int]					=					[]
       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					:						Optional[Any]					=					input_ids[-self.model_max_length :]
       return input_ids





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



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[int]					=					0
       if os.path.isdir(A_					):
             __lowerCAmelCase					:						Any					=					os.path.join(
                 A_      ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)
             __lowerCAmelCase					:						Any					=					os.path.join(
                 A_      ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''emoji_file''']					)
       else:
             __lowerCAmelCase					:						int					=					(
                 (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory + VOCAB_FILES_NAMES['''vocab_file''']
             )
             __lowerCAmelCase					:						List[Any]					=					(
                 (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					:						int					=					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   __lowercase      (_UpperCAmelCase ):
 def __init__(							self      ,							A_      ,							A_      ,							A_					)					->Union[str, Any]:



       '''simple docstring'''





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



       '''simple docstring'''





       return len(self.ids_to_tokens					)
 def 	UpperCamelCase__  (							self      ,							A_					)					->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					self.content_repattera.sub('''<URL>'''      ,							A_					)
       __lowerCAmelCase					:						List[Any]					=					self.content_repattera.sub('''<EMAIL>'''      ,							A_					)
       __lowerCAmelCase					:						List[str]					=					self.content_repattera.sub('''<TEL>'''      ,							A_					)
       __lowerCAmelCase					:						str					=					self.content_repattera.sub('''<DATE>'''      ,							A_					)
       __lowerCAmelCase					:						int					=					self.content_repattera.sub('''<DATE>'''      ,							A_					)
       __lowerCAmelCase					:						Any					=					self.content_repattera.sub('''<PRICE>'''      ,							A_					)
       __lowerCAmelCase					:						List[Any]					=					content.translate(self.content_transa					)
       while "<BLOCK><BLOCK>" in content:
             __lowerCAmelCase					:						str					=					content.replace('''<BLOCK><BLOCK>'''      ,							'''<BLOCK>'''					)
       return content
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_=False					)					->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					text.replace(''' '''      ,							'''<SP>'''					)
       __lowerCAmelCase					:						List[Any]					=					text.replace('''　'''      ,							'''<SP>'''					)
       __lowerCAmelCase					:						Any					=					text.replace('''\r\n'''      ,							'''<BR>'''					)
       __lowerCAmelCase					:						str					=					text.replace('''\n'''      ,							'''<BR>'''					)
       __lowerCAmelCase					:						Union[str, Any]					=					text.replace('''\r'''      ,							'''<BR>'''					)
       __lowerCAmelCase					:						Union[str, Any]					=					text.replace('''\t'''      ,							'''<TAB>'''					)
       __lowerCAmelCase					:						Optional[Any]					=					text.replace('''—'''      ,							'''ー'''					)
       __lowerCAmelCase					:						List[Any]					=					text.replace('''−'''      ,							'''ー'''					)
       for k, v in self.emoji["emoji"].items():
             if k in text:
                   __lowerCAmelCase					:						List[Any]					=					text.replace(A_      ,							A_					)
       if clean:
             __lowerCAmelCase					:						List[Any]					=					self.clean_text(A_					)

       def check_simbol(A_					):
             __lowerCAmelCase					:						List[str]					=					x.encode()
             if len(A_					) == 1 and len(A_					) == 2:
                   __lowerCAmelCase					:						Optional[Any]					=					(int(e[0]					) << 8) + int(e[1]					)
                   if (
                       (c >= 0XC_2_A_1 and c <= 0XC_2_B_F)
                       or (c >= 0XC_7_8_0 and c <= 0XC_7_8_3)
                       or (c >= 0XC_A_B_9 and c <= 0XC_B_B_F)
                       or (c >= 0XC_C_8_0 and c <= 0XC_D_A_2)
                   ):
                         return True
             return False

       def checkuae(A_					):
             __lowerCAmelCase					:						int					=					x.encode()
             if len(A_					) == 1 and len(A_					) == 3:
                   __lowerCAmelCase					:						List[str]					=					(int(e[0]					) << 16) + (int(e[1]					) << 8) + int(e[2]					)
                   if c >= 0XE_2_8_0_8_0 and c <= 0XE_2_B_0_7_F:
                         return True
             return False

       __lowerCAmelCase					:						List[Any]					=					0
       __lowerCAmelCase					:						Union[str, Any]					=					[]
       while pos < len(A_					):
             __lowerCAmelCase					:						Optional[int]					=					min(len(A_					)      ,							pos + self.maxlen + 1					) if text[pos] == '''<''' else pos + 3
             __lowerCAmelCase					:						Tuple					=					[]  # (token_id, token, pos)
             for e in range(A_      ,							A_      ,							-1					):
                   __lowerCAmelCase					:						Optional[Any]					=					text[pos:e]
                   if wd in self.vocab:
                         if wd[0] == "<" and len(A_					) > 2:
                               __lowerCAmelCase					:						int					=					[(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					:						str					=					sorted(A_      ,							key=lambda A_					: x[0]					)[0]
                   result.append(A_					)
                   __lowerCAmelCase					:						Optional[int]					=					e
             else:
                   __lowerCAmelCase					:						List[Any]					=					pos + 1
                   __lowerCAmelCase					:						List[str]					=					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					:						str					=					end
       return result





 def 	UpperCamelCase__  (							self      ,							A_      ,							A_="\n"					)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						List[Any]					=					[]
       __lowerCAmelCase					:						Optional[int]					=					[]
       __lowerCAmelCase					:						Union[str, Any]					=					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					:						Optional[Any]					=					[]
             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					:						str					=					''''''.join(A_					)
       return text





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
_UpperCamelCase 			=		logging.get_logger(__name__)

_UpperCamelCase 			=		{
    "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",
    "adapter_layer": "encoder.layers.*.adapter_layer",
    "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",
    "pooling_layer.linear": "projector",
    "pooling_layer.projection": "classifier",
}
_UpperCamelCase 			=		[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]







def 		_lowercase (     lowercase__						):
      __lowerCAmelCase					:						List[str]					=					{}
      with open(lowercase__   ,	'''r'''						) as file:
            for line_number, line in enumerate(lowercase__						):
                  __lowerCAmelCase					:						Any					=					line.strip()
                  if line:
                        __lowerCAmelCase					:						Dict					=					line.split()
                        __lowerCAmelCase					:						str					=					line_number
                        __lowerCAmelCase					:						List[str]					=					words[0]
                        __lowerCAmelCase					:						Any					=					value
      return result







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__						):
      for attribute in key.split('''.'''						):
            __lowerCAmelCase					:						List[Any]					=					getattr(lowercase__   ,	lowercase__						)

      __lowerCAmelCase					:						Any					=					None
      for param_key in PARAM_MAPPING.keys():
            if full_name.endswith(lowercase__						):
                  __lowerCAmelCase					:						Tuple					=					PARAM_MAPPING[full_name.split('''.'''						)[-1]]
                  __lowerCAmelCase					:						List[Any]					=					'''param'''

      if weight_type is not None and weight_type != "param":
            __lowerCAmelCase					:						str					=					getattr(lowercase__   ,	lowercase__						).shape
      elif weight_type is not None and weight_type == "param":
            __lowerCAmelCase					:						Dict					=					hf_pointer
            for attribute in hf_param_name.split('''.'''						):
                  __lowerCAmelCase					:						Dict					=					getattr(lowercase__   ,	lowercase__						)
            __lowerCAmelCase					:						str					=					shape_pointer.shape

            # let's reduce dimension
            __lowerCAmelCase					:						Any					=					value[0]
      else:
            __lowerCAmelCase					:						str					=					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":
            __lowerCAmelCase					:						Union[str, Any]					=					value
      elif weight_type == "weight_g":
            __lowerCAmelCase					:						List[str]					=					value
      elif weight_type == "weight_v":
            __lowerCAmelCase					:						int					=					value
      elif weight_type == "bias":
            __lowerCAmelCase					:						Union[str, Any]					=					value
      elif weight_type == "param":
            for attribute in hf_param_name.split('''.'''						):
                  __lowerCAmelCase					:						Dict					=					getattr(lowercase__   ,	lowercase__						)
            __lowerCAmelCase					:						Tuple					=					value
      else:
            __lowerCAmelCase					:						Any					=					value

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







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						Any					=					None
      for param_key in PARAM_MAPPING.keys():
            if full_name.endswith(lowercase__						):
                  __lowerCAmelCase					:						str					=					PARAM_MAPPING[full_name.split('''.'''						)[-1]]
                  __lowerCAmelCase					:						int					=					'''param'''

      if weight_type is not None and weight_type != "param":
            __lowerCAmelCase					:						Tuple					=					'''.'''.join([key, weight_type]						)
      elif weight_type is not None and weight_type == "param":
            __lowerCAmelCase					:						List[str]					=					'''.'''.join([key, hf_param_name]						)
      else:
            __lowerCAmelCase					:						Optional[int]					=					key

      __lowerCAmelCase					:						Union[str, Any]					=					value if '''lm_head''' in full_key else value[0]


_UpperCamelCase 			=		{
    "W_a": "linear_1.weight",
    "W_b": "linear_2.weight",
    "b_a": "linear_1.bias",
    "b_b": "linear_2.bias",
    "ln_W": "norm.weight",
    "ln_b": "norm.bias",
}







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__=None   ,	lowercase__=None						):
      __lowerCAmelCase					:						Any					=					False
      for key, mapped_key in MAPPING.items():
            __lowerCAmelCase					:						Tuple					=					'''wav2vec2.''' + 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					:						Optional[Any]					=					True
                  if "*" in mapped_key:
                        __lowerCAmelCase					:						List[str]					=					name.split(lowercase__						)[0].split('''.'''						)[-2]
                        __lowerCAmelCase					:						Dict					=					mapped_key.replace('''*'''   ,	lowercase__						)
                  if "weight_g" in name:
                        __lowerCAmelCase					:						List[Any]					=					'''weight_g'''
                  elif "weight_v" in name:
                        __lowerCAmelCase					:						List[Any]					=					'''weight_v'''
                  elif "bias" in name:
                        __lowerCAmelCase					:						Any					=					'''bias'''
                  elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        __lowerCAmelCase					:						int					=					'''weight'''
                  else:
                        __lowerCAmelCase					:						Any					=					None
                  if hf_dict is not None:
                        rename_dict(lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__						)
                  else:
                        set_recursively(lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__						)
                  return is_used
      return is_used







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						List[str]					=					[]
      __lowerCAmelCase					:						Optional[Any]					=					fairseq_model.state_dict()

      __lowerCAmelCase					:						Tuple					=					hf_model.wavaveca.feature_extractor

      for name, value in fairseq_dict.items():
            __lowerCAmelCase					:						Any					=					False
            if "conv_layers" in name:
                  load_conv_layer(
                      lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__   ,	hf_model.config.feat_extract_norm == '''group'''   ,	)
                  __lowerCAmelCase					:						int					=					True
            else:
                  __lowerCAmelCase					:						Dict					=					load_wavaveca_layer(lowercase__   ,	lowercase__   ,	lowercase__						)
            if not is_used:
                  unused_weights.append(lowercase__						)

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







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						Any					=					full_name.split('''conv_layers.'''						)[-1]
      __lowerCAmelCase					:						List[str]					=					name.split('''.'''						)
      __lowerCAmelCase					:						Any					=					int(items[0]						)
      __lowerCAmelCase					:						str					=					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."""						)
                  __lowerCAmelCase					:						List[str]					=					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."""						)
                  __lowerCAmelCase					:						List[str]					=					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."""						)
                  __lowerCAmelCase					:						List[str]					=					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."""						)
                  __lowerCAmelCase					:						Optional[int]					=					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 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__=None   ,	lowercase__=None   ,	lowercase__=True   ,	lowercase__=False						):
      if config_path is not None:
            __lowerCAmelCase					:						Union[str, Any]					=					WavaVecaConfig.from_pretrained(lowercase__						)
      else:
            __lowerCAmelCase					:						Optional[int]					=					WavaVecaConfig()

      if is_seq_class:
            __lowerCAmelCase					:						Optional[Any]					=					read_txt_into_dict(lowercase__						)
            __lowerCAmelCase					:						int					=					idalabel
            __lowerCAmelCase					:						Optional[int]					=					WavaVecaForSequenceClassification(lowercase__						)
            __lowerCAmelCase					:						List[str]					=					WavaVecaFeatureExtractor(
                feature_size=1   ,	sampling_rate=1_6_0_0_0   ,	padding_value=0   ,	do_normalize=lowercase__   ,	return_attention_mask=lowercase__   ,	)
            feature_extractor.save_pretrained(lowercase__						)

      elif is_finetuned:
            if dict_path:
                  __lowerCAmelCase					:						List[str]					=					Dictionary.load(lowercase__						)

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

                  # fairseq has the <pad> and <s> switched
                  __lowerCAmelCase					:						List[str]					=					0
                  __lowerCAmelCase					:						int					=					1
                  with open(lowercase__   ,	'''w'''   ,	encoding='''utf-8'''						) as vocab_handle:
                        json.dump(lowercase__   ,	lowercase__						)
                  __lowerCAmelCase					:						Dict					=					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__   ,	)
                  __lowerCAmelCase					:						List[str]					=					True if config.feat_extract_norm == '''layer''' else False
                  __lowerCAmelCase					:						List[str]					=					WavaVecaFeatureExtractor(
                      feature_size=1   ,	sampling_rate=1_6_0_0_0   ,	padding_value=0   ,	do_normalize=lowercase__   ,	return_attention_mask=lowercase__   ,	)
                  __lowerCAmelCase					:						List[Any]					=					WavaVecaProcessor(feature_extractor=lowercase__   ,	tokenizer=lowercase__						)
                  processor.save_pretrained(lowercase__						)

            __lowerCAmelCase					:						str					=					WavaVecaForCTC(lowercase__						)
      else:
            __lowerCAmelCase					:						Any					=					WavaVecaForPreTraining(lowercase__						)

      if is_finetuned or is_seq_class:
            __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase					:						Optional[int]					=					fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]   ,	arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''						)[:-1]						)}						)
      else:
            __lowerCAmelCase					:						Union[str, Any]					=					argparse.Namespace(task='''audio_pretraining'''						)
            __lowerCAmelCase					:						str					=					fairseq.tasks.setup_task(lowercase__						)

            __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase					:						Dict					=					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   ,	task=lowercase__						)

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

      recursively_load_weights(lowercase__   ,	lowercase__   ,	not is_finetuned						)

      hf_wavavec.save_pretrained(lowercase__						)


if __name__ == "__main__":
 _UpperCamelCase 			=		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"
 )
 parser.add_argument(
     "--is_seq_class",
     action="store_true",
     help="Whether the model to convert is a fine-tuned sequence classification model or not",
 )
 _UpperCamelCase 			=		parser.parse_args()

 _UpperCamelCase 			=		not args.not_finetuned and not args.is_seq_class
 convert_wavaveca_checkpoint(
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.config_path,
     args.dict_path,
     is_finetuned,
     args.is_seq_class,
 )


