"""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

__lowercase      					=							logging.get_logger(__name__)

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

__lowercase      					=							{
    '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',
    },
}

__lowercase      					=							{
    'gpt2': 1_024,
    'gpt2-medium': 1_024,
    'gpt2-large': 1_024,
    'gpt2-xl': 1_024,
    'distilgpt2': 1_024,
}


@lru_cache()
def 					lowerCAmelCase       ():
							"""simple docstring"""


							__UpperCamelCase		     =(
							    list(range(ord('''!'''       )				, ord('''~'''       ) + 1       )       ) + list(range(ord('''¡'''       )				, ord('''¬'''       ) + 1       )       ) + list(range(ord('''®'''       )				, ord('''ÿ'''       ) + 1       )       )
							)
							__UpperCamelCase		     =bs[:]
							__UpperCamelCase		     =0
							for b in range(2**8       ):
														if b not in bs:
																					bs.append(__lowercase       )
																					cs.append(2**8 + n       )
																					n += 1
							__UpperCamelCase		     =[chr(__lowercase       ) for n in cs]
							return dict(zip(__lowercase				, __lowercase       )       )


def 					lowerCAmelCase       (__UpperCamelCase				:  Dict       ):
							"""simple docstring"""


							__UpperCamelCase		     =set()
							__UpperCamelCase		     =word[0]
							for char in word[1:]:
														pairs.add((prev_char, char)       )
														__UpperCamelCase		     =char
							return pairs






class 						_lowercase      (		__snake_case	):



						"""simple docstring"""




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





						def __init__(							self				:  int			,      UpperCamelCase__				:  Tuple			,      UpperCamelCase__				:  int			,      UpperCamelCase__				:  Tuple="replace"			,      UpperCamelCase__				:  List[Any]="<|endoftext|>"			,      UpperCamelCase__				:  Optional[int]="<|endoftext|>"			,      UpperCamelCase__				:  Optional[Any]="<|endoftext|>"			,      UpperCamelCase__				:  Optional[int]=None			,      UpperCamelCase__				:  Optional[Any]=False			,      UpperCamelCase__				:  Dict=False			,      **UpperCamelCase__				:  Any			,      )							->							Any:
													'''simple docstring'''



													__UpperCamelCase		     =AddedToken(lowercase_			,      lstrip=lowercase_			,      rstrip=lowercase_							) if isinstance(lowercase_			,      lowercase_							) else bos_token
													__UpperCamelCase		     =AddedToken(lowercase_			,      lstrip=lowercase_			,      rstrip=lowercase_							) if isinstance(lowercase_			,      lowercase_							) else eos_token
													__UpperCamelCase		     =AddedToken(lowercase_			,      lstrip=lowercase_			,      rstrip=lowercase_							) if isinstance(lowercase_			,      lowercase_							) else unk_token
													__UpperCamelCase		     =AddedToken(lowercase_			,      lstrip=lowercase_			,      rstrip=lowercase_							) if isinstance(lowercase_			,      lowercase_							) else pad_token
													super().__init__(
													    errors=lowercase_			,      unk_token=lowercase_			,      bos_token=lowercase_			,      eos_token=lowercase_			,      pad_token=lowercase_			,      add_prefix_space=lowercase_			,      add_bos_token=lowercase_			,      **lowercase_			,      )
													__UpperCamelCase		     =add_bos_token

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

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





						@property
						def    UpperCAmelCase_      (							self				:  Tuple							)							->							List[Any]:
													'''simple docstring'''



													return len(self.encoder							)





						def    UpperCAmelCase_      (							self				:  Optional[Any]							)							->							Dict:
													'''simple docstring'''



													return dict(self.encoder			,      **self.added_tokens_encoder							)





						def    UpperCAmelCase_      (							self				:  Any			,      UpperCamelCase__				:  Tuple							)							->							Union[str, Any]:
													'''simple docstring'''



													if token in self.cache:
																				return self.cache[token]
													__UpperCamelCase		     =tuple(lowercase_							)
													__UpperCamelCase		     =get_pairs(lowercase_							)

													if not pairs:
																				return token

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

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





						def    UpperCAmelCase_      (							self				:  Optional[int]			,      UpperCamelCase__				:  List[str]			,      UpperCamelCase__				:  List[Any]=None							)							->							List[str]:
													'''simple docstring'''



													if self.add_bos_token:
																				__UpperCamelCase		     =[self.bos_token_id]
													else:
																				__UpperCamelCase		     =[]

													__UpperCamelCase		     =bos_token_ids + token_ids_a

													if token_ids_a is None:
																				return output

													return output + bos_token_ids + token_ids_a





						def    UpperCAmelCase_      (							self				:  Optional[int]			,      UpperCamelCase__				:  str			,      UpperCamelCase__				:  List[str] = None			,      UpperCamelCase__				:  Optional[int] = False							)							->							Dict:
													'''simple docstring'''



													if already_has_special_tokens:
																				return super().get_special_tokens_mask(
																				    token_ids_a=lowercase_			,      token_ids_a=lowercase_			,      already_has_special_tokens=lowercase_							)

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

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





						def    UpperCAmelCase_      (							self				:  Tuple			,      UpperCamelCase__				:  Dict							)							->							int:
													'''simple docstring'''



													__UpperCamelCase		     =[]
													for token in re.findall(self.pat			,      lowercase_							):
																				__UpperCamelCase		     ="".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(lowercase_							).split(''' '''							)							)
													return bpe_tokens





						def    UpperCAmelCase_      (							self				:  List[Any]			,      UpperCamelCase__				:  Dict							)							->							List[str]:
													'''simple docstring'''



													return self.encoder.get(lowercase_			,      self.encoder.get(self.unk_token							)							)





						def    UpperCAmelCase_      (							self				:  Union[str, Any]			,      UpperCamelCase__				:  Optional[Any]							)							->							List[Any]:
													'''simple docstring'''



													return self.decoder.get(lowercase_							)





						def    UpperCAmelCase_      (							self				:  Any			,      UpperCamelCase__				:  Optional[int]							)							->							int:
													'''simple docstring'''



													__UpperCamelCase		     ="".join(lowercase_							)
													__UpperCamelCase		     =bytearray([self.byte_decoder[c] for c in text]							).decode('''utf-8'''			,      errors=self.errors							)
													return text





						def    UpperCAmelCase_      (							self				:  List[str]			,      UpperCamelCase__				:  Tuple			,      UpperCamelCase__				:  int = None							)							->							int:
													'''simple docstring'''



													if not os.path.isdir(lowercase_							):
																				logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
																				return
													__UpperCamelCase		     =os.path.join(
													    lowercase_			,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)
													__UpperCamelCase		     =os.path.join(
													    lowercase_			,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']							)

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

													__UpperCamelCase		     =0
													with open(lowercase_			,      '''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 UpperCamelCase__							: 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!'''							)
																																		__UpperCamelCase		     =token_index
																											writer.write(''' '''.join(lowercase_							) + '''\n'''							)
																											index += 1

													return vocab_file, merge_file





						def    UpperCAmelCase_      (							self				:  str			,      UpperCamelCase__				:  Dict			,      UpperCamelCase__				:  Optional[int]=False			,      **UpperCamelCase__				:  Tuple							)							->							Tuple:
													'''simple docstring'''



													__UpperCamelCase		     =kwargs.pop('''add_prefix_space'''			,      self.add_prefix_space							)
													if is_split_into_words or add_prefix_space:
																				__UpperCamelCase		     =" " + text
													return (text, kwargs)





						def    UpperCAmelCase_      (							self				:  Any			,      UpperCamelCase__				:  Union[str, Any]							)							->							Tuple:
													'''simple docstring'''



													__UpperCamelCase		     =[]
													for is_user, text in conversation.iter_texts():
																				input_ids.extend(self.encode(lowercase_			,      add_special_tokens=lowercase_							) + [self.eos_token_id]							)
													if len(lowercase_							) > self.model_max_length:
																				__UpperCamelCase		     =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)