'''simple docstring'''




import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation

__snake_case   :      List[str]						    =  logging.get_logger(__name__)

__snake_case   :      Any						    =  {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

__snake_case   :      Any						    =  {
    'vocab_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/vocab.json',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/vocab.json',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/vocab.json',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/vocab.json',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/vocab.json',
    },
    'merges_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/merges.txt',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/merges.txt',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/merges.txt',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/merges.txt',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/merges.txt',
    },
}

__snake_case   :      Union[str, Any]						    =  {
    'gpt2': 1_024,
    'gpt2-medium': 1_024,
    'gpt2-large': 1_024,
    'gpt2-xl': 1_024,
    'distilgpt2': 1_024,
}





@lru_cache()
def      _UpperCAmelCase    (							)	->      Dict:
    A_     =   (
        list(range(ord('''!'''   ),   ord('''~'''   ) + 1   )   ) + list(range(ord('''¡'''   ),   ord('''¬'''   ) + 1   )   ) + list(range(ord('''®'''   ),   ord('''ÿ'''   ) + 1   )   )
    )
    A_     =   bs[:]
    A_     =   0
    for b in range(2**8   ):
        if b not in bs:
            bs.append(_UpperCamelCase   )
            cs.append(2**8 + n   )
            n += 1
    A_     =   [chr(_UpperCamelCase   ) for n in cs]
    return dict(zip(_UpperCamelCase,   _UpperCamelCase   )   )





def      _UpperCAmelCase    (							_UpperCamelCase    :	List[Any]   )	->      int:
    A_     =   set()
    A_     =   word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)   )
        A_     =   char
    return pairs




class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       __lowercase					:					Dict    =				VOCAB_FILES_NAMES
       __lowercase					:					str    =				PRETRAINED_VOCAB_FILES_MAP
       __lowercase					:					Dict    =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __lowercase					:					Any    =				['input_ids', 'attention_mask']

       def __init__(      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE="replace"    ,     _SCREAMING_SNAKE_CASE="<|endoftext|>"    ,     _SCREAMING_SNAKE_CASE="<|endoftext|>"    ,     _SCREAMING_SNAKE_CASE="<|endoftext|>"    ,     _SCREAMING_SNAKE_CASE=None    ,     _SCREAMING_SNAKE_CASE=False    ,     _SCREAMING_SNAKE_CASE=False    ,     **_SCREAMING_SNAKE_CASE    ,     )		->     Any:
           A_     =   AddedToken(_SCREAMING_SNAKE_CASE    ,     lstrip=_SCREAMING_SNAKE_CASE    ,     rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE ) else bos_token
           A_     =   AddedToken(_SCREAMING_SNAKE_CASE    ,     lstrip=_SCREAMING_SNAKE_CASE    ,     rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE ) else eos_token
           A_     =   AddedToken(_SCREAMING_SNAKE_CASE    ,     lstrip=_SCREAMING_SNAKE_CASE    ,     rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE ) else unk_token
           A_     =   AddedToken(_SCREAMING_SNAKE_CASE    ,     lstrip=_SCREAMING_SNAKE_CASE    ,     rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE ) else pad_token
           super().__init__(
               errors=_SCREAMING_SNAKE_CASE    ,     unk_token=_SCREAMING_SNAKE_CASE    ,     bos_token=_SCREAMING_SNAKE_CASE    ,     eos_token=_SCREAMING_SNAKE_CASE    ,     pad_token=_SCREAMING_SNAKE_CASE    ,     add_prefix_space=_SCREAMING_SNAKE_CASE    ,     add_bos_token=_SCREAMING_SNAKE_CASE    ,     **_SCREAMING_SNAKE_CASE    ,     )
           A_     =   add_bos_token

           with open(_SCREAMING_SNAKE_CASE    ,     encoding='''utf-8''' ) as vocab_handle:
               A_     =   json.load(_SCREAMING_SNAKE_CASE )
           A_     =   {v: k for k, v in self.encoder.items()}
           A_     =   errors  # how to handle errors in decoding
           A_     =   bytes_to_unicode()
           A_     =   {v: k for k, v in self.byte_encoder.items()}
           with open(_SCREAMING_SNAKE_CASE    ,     encoding='''utf-8''' ) as merges_handle:
               A_     =   merges_handle.read().split('''\n''' )[1:-1]
           A_     =   [tuple(merge.split() ) for merge in bpe_merges]
           A_     =   dict(zip(_SCREAMING_SNAKE_CASE    ,     range(len(_SCREAMING_SNAKE_CASE ) ) ) )
           A_     =   {}
           A_     =   add_prefix_space

           # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
           A_     =   re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''' )

       @property
       def 					__A       (      self )		->     Optional[Any]:
           return len(self.encoder )

       def 					__A       (      self )		->     Optional[Any]:
           return dict(self.encoder    ,     **self.added_tokens_encoder )

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE )		->     Any:
           if token in self.cache:
               return self.cache[token]
           A_     =   tuple(_SCREAMING_SNAKE_CASE )
           A_     =   get_pairs(_SCREAMING_SNAKE_CASE )

           if not pairs:
               return token

           while True:
               A_     =   min(_SCREAMING_SNAKE_CASE    ,     key=lambda _SCREAMING_SNAKE_CASE : self.bpe_ranks.get(_SCREAMING_SNAKE_CASE    ,     float('''inf''' ) ) )
               if bigram not in self.bpe_ranks:
                   break
               A_   ,A_     =   bigram
               A_     =   []
               A_     =   0
               while i < len(_SCREAMING_SNAKE_CASE ):
                   try:
                       A_     =   word.index(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )
                   except ValueError:
                       new_word.extend(word[i:] )
                       break
                   else:
                       new_word.extend(word[i:j] )
                       A_     =   j

                   if word[i] == first and i < len(_SCREAMING_SNAKE_CASE ) - 1 and word[i + 1] == second:
                       new_word.append(first + second )
                       i += 2
                   else:
                       new_word.append(word[i] )
                       i += 1
               A_     =   tuple(_SCREAMING_SNAKE_CASE )
               A_     =   new_word
               if len(_SCREAMING_SNAKE_CASE ) == 1:
                   break
               else:
                   A_     =   get_pairs(_SCREAMING_SNAKE_CASE )
           A_     =   ''' '''.join(_SCREAMING_SNAKE_CASE )
           A_     =   word
           return word

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE=None )		->     Any:
           if self.add_bos_token:
               A_     =   [self.bos_token_id]
           else:
               A_     =   []

           A_     =   bos_token_ids + token_ids_a

           if token_ids_a is None:
               return output

           return output + bos_token_ids + token_ids_a

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE = None    ,     _SCREAMING_SNAKE_CASE = False )		->     List[int]:
           if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=_SCREAMING_SNAKE_CASE    ,     token_ids_a=_SCREAMING_SNAKE_CASE    ,     already_has_special_tokens=_SCREAMING_SNAKE_CASE )

           if not self.add_bos_token:
               return super().get_special_tokens_mask(
                   token_ids_a=_SCREAMING_SNAKE_CASE    ,     token_ids_a=_SCREAMING_SNAKE_CASE    ,     already_has_special_tokens=_SCREAMING_SNAKE_CASE )

           if token_ids_a is None:
               return [1] + ([0] * len(_SCREAMING_SNAKE_CASE ))
           return [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE ))

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE )		->     Dict:
           A_     =   []
           for token in re.findall(self.pat    ,     _SCREAMING_SNAKE_CASE ):
               A_     =   ''''''.join(
                   self.byte_encoder[b] for b in token.encode('''utf-8''' ) )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
               bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_SCREAMING_SNAKE_CASE ).split(''' ''' ) )
           return bpe_tokens

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE )		->     Optional[int]:
           return self.encoder.get(_SCREAMING_SNAKE_CASE    ,     self.encoder.get(self.unk_token ) )

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE )		->     str:
           return self.decoder.get(_SCREAMING_SNAKE_CASE )

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE )		->     List[Any]:
           A_     =   ''''''.join(_SCREAMING_SNAKE_CASE )
           A_     =   bytearray([self.byte_decoder[c] for c in text] ).decode('''utf-8'''    ,     errors=self.errors )
           return text

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE = None )		->     Tuple[str]:
           if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
               logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
               return
           A_     =   os.path.join(
               _SCREAMING_SNAKE_CASE    ,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
           A_     =   os.path.join(
               _SCREAMING_SNAKE_CASE    ,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )

           with open(_SCREAMING_SNAKE_CASE    ,     '''w'''    ,     encoding='''utf-8''' ) as f:
               f.write(json.dumps(self.encoder    ,     indent=2    ,     sort_keys=_SCREAMING_SNAKE_CASE    ,     ensure_ascii=_SCREAMING_SNAKE_CASE ) + '''\n''' )

           A_     =   0
           with open(_SCREAMING_SNAKE_CASE    ,     '''w'''    ,     encoding='''utf-8''' ) as writer:
               writer.write('''#version: 0.2\n''' )
               for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,     key=lambda _SCREAMING_SNAKE_CASE : kv[1] ):
                   if index != token_index:
                       logger.warning(
                           F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                           ''' Please check that the tokenizer is not corrupted!''' )
                       A_     =   token_index
                   writer.write(''' '''.join(_SCREAMING_SNAKE_CASE ) + '''\n''' )
                   index += 1

           return vocab_file, merge_file

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE=False    ,     **_SCREAMING_SNAKE_CASE )		->     Dict:
           A_     =   kwargs.pop('''add_prefix_space'''    ,     self.add_prefix_space )
           if is_split_into_words or add_prefix_space:
               A_     =   ''' ''' + text
           return (text, kwargs)

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE )		->     List[int]:
           A_     =   []
           for is_user, text in conversation.iter_texts():
               input_ids.extend(self.encode(_SCREAMING_SNAKE_CASE    ,     add_special_tokens=_SCREAMING_SNAKE_CASE ) + [self.eos_token_id] )
           if len(_SCREAMING_SNAKE_CASE ) > self.model_max_length:
               A_     =   input_ids[-self.model_max_length :]
           return input_ids







import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE						:    Dict     = logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						:    List[Any]     = ['UtteranceLevel']
@torch.no_grad()
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase					,			__lowercase						)       ->       Tuple:

						'''simple docstring'''



						_snake_case						:     Optional[int]				=    torch.load(__lowercase					,			map_location="cpu"						)
						if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
												raise NotImplementedError(F"""The supported s3prl models are {SUPPORTED_MODELS}"""						)

						_snake_case						:     List[str]				=    checkpoint["Downstream"]

						_snake_case						:     List[str]				=    HubertConfig.from_pretrained(__lowercase						)
						_snake_case						:     Union[str, Any]				=    HubertForSequenceClassification.from_pretrained(__lowercase					,			config=__lowercase						)
						_snake_case						:     Union[str, Any]				=    WavaVecaFeatureExtractor.from_pretrained(
						    __lowercase					,			return_attention_mask=__lowercase					,			do_normalize=__lowercase						)

						if hf_congfig.use_weighted_layer_sum:
												_snake_case						:     Optional[int]				=    checkpoint["Featurizer"]["weights"]

						_snake_case						:     str				=    downstream_dict["projector.weight"]
						_snake_case						:     Tuple				=    downstream_dict["projector.bias"]
						_snake_case						:     List[str]				=    downstream_dict["model.post_net.linear.weight"]
						_snake_case						:     str				=    downstream_dict["model.post_net.linear.bias"]

						hf_feature_extractor.save_pretrained(__lowercase						)
						hf_model.save_pretrained(__lowercase						)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:    Optional[int]     = argparse.ArgumentParser()
				parser.add_argument(
				    '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
				)
				parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
				parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
				parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
				__SCREAMING_SNAKE_CASE						:    str     = parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)