





'''simple docstring'''







import json
import os
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


snake_case_   :				Union[str, Any]		=     logging.get_logger(__name__)


snake_case_   :				Optional[int]		=     {
    'vocab_file': 'vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
    'merges_file': 'merges.txt',
}

snake_case_   :				Tuple		=     {
    'vocab_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'
        ),
    },
    'tokenizer_config_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'
        ),
    },
    'merges_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'
        ),
    },
}

snake_case_   :				int		=     '</w>'
snake_case_   :				Dict		=     '@@ '





def 				lowerCamelCase_					(     SCREAMING_SNAKE_CASE__					:	Union[str, Any]      )       ->		Any:
						UpperCAmelCase_	:					Any     =     set()
						UpperCAmelCase_	:					List[Any]     =     word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)      )
												UpperCAmelCase_	:					int     =     char
						return pairs


# Speech2Text2 has no max input length
snake_case_   :				Optional[Any]		=     {'facebook/s2t-wav2vec2-large-en-de': 10_24}


class     __a					(lowerCamelCase   ):
							__a    :      List[Any]  	=				VOCAB_FILES_NAMES
							__a    :      Dict  	=				PRETRAINED_VOCAB_FILES_MAP
							__a    :      List[Any]  	=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__a    :      Tuple  	=				["input_ids", "attention_mask"]

							def __init__(							self							:   int				,   __magic_name__							:   Optional[Any]				,   __magic_name__							:   Union[str, Any]="<s>"				,   __magic_name__							:   Union[str, Any]="<pad>"				,   __magic_name__							:   Union[str, Any]="</s>"				,   __magic_name__							:   Tuple="<unk>"				,   __magic_name__							:   str=False				,   __magic_name__							:   int=None				,   **__magic_name__							:   int				,   )    ->				List[Any]:







													"""simple docstring"""
													super().__init__(
													    unk_token=_snake_case				,   bos_token=_snake_case				,   eos_token=_snake_case				,   pad_token=_snake_case				,   do_lower_case=_snake_case				,   **_snake_case				,   )

													UpperCAmelCase_	:					List[str]     =     do_lower_case

													with open(_snake_case				,   encoding='''utf-8'''      ) as vocab_handle:
																			UpperCAmelCase_	:					Optional[int]     =     json.load(_snake_case      )
													UpperCAmelCase_	:					List[Any]     =     {v: k for k, v in self.encoder.items()}

													if merges_file is None:
																			logger.info(F"""No merges files provided. {self.__class__.__name__} can only be used for decoding."""      )

																			UpperCAmelCase_	:					int     =     None
																			UpperCAmelCase_	:					Any     =     None
													else:
																			with open(_snake_case				,   encoding='''utf-8'''      ) as merges_handle:
																									UpperCAmelCase_	:					List[Any]     =     merges_handle.read().split('''\n'''      )[:-1]

																			UpperCAmelCase_	:					str     =     [tuple(merge.split()[:2]      ) for merge in merges]
																			UpperCAmelCase_	:					List[Any]     =     dict(zip(_snake_case				,   range(len(_snake_case      )      )      )      )
																			UpperCAmelCase_	:					Union[str, Any]     =     {}

							@property
							def 			UpperCAmelCase__		(							self							:   Dict      )    ->				int:







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

							def 			UpperCAmelCase__		(							self							:   Dict      )    ->				Dict:







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

							def 			UpperCAmelCase__		(							self							:   str				,   __magic_name__							:   List[Any]      )    ->				Any:







													"""simple docstring"""
													UpperCAmelCase_	:					int     =     tuple(token[:-1]      ) + (token[-1] + BPE_TOKEN_MERGES,)
													if token in self.cache:
																			return self.cache[token]
													UpperCAmelCase_	:					Tuple     =     get_pairs(_snake_case      )

													if not pairs:
																			return token

													while True:
																			UpperCAmelCase_	:					Tuple     =     min(_snake_case				,   key=lambda __magic_name__      : self.bpe_ranks.get(_snake_case				,   float('''inf'''      )      )      )
																			if bigram not in self.bpe_ranks:
																									break
																			UpperCAmelCase_  ,				UpperCAmelCase_	:					Union[str, Any]     =     bigram
																			UpperCAmelCase_	:					Union[str, Any]     =     []
																			UpperCAmelCase_	:					Union[str, Any]     =     0
																			while i < len(_snake_case      ):
																									try:
																															UpperCAmelCase_	:					Tuple     =     word.index(_snake_case				,   _snake_case      )
																									except ValueError:
																															new_word.extend(word[i:]      )
																															break
																									else:
																															new_word.extend(word[i:j]      )
																															UpperCAmelCase_	:					int     =     j

																									if word[i] == first and i < len(_snake_case      ) - 1 and word[i + 1] == second:
																															new_word.append(first + second      )
																															i += 2
																									else:
																															new_word.append(word[i]      )
																															i += 1
																			UpperCAmelCase_	:					Optional[Any]     =     tuple(_snake_case      )
																			UpperCAmelCase_	:					Union[str, Any]     =     new_word
																			if len(_snake_case      ) == 1:
																									break
																			else:
																									UpperCAmelCase_	:					List[Any]     =     get_pairs(_snake_case      )
													UpperCAmelCase_	:					str     =     ''' '''.join(_snake_case      )
													if word == "\n  " + BPE_TOKEN_MERGES:
																			UpperCAmelCase_	:					Optional[Any]     =     '''\n''' + BPE_TOKEN_MERGES

													if word.endswith(_snake_case      ):
																			UpperCAmelCase_	:					List[Any]     =     word.replace(_snake_case				,   ''''''      )

													UpperCAmelCase_	:					Dict     =     word.replace(''' '''				,   _snake_case      )
													UpperCAmelCase_	:					str     =     word
													return word

							def 			UpperCAmelCase__		(							self							:   Optional[Any]				,   __magic_name__							:   Optional[int]      )    ->				Optional[Any]:







													"""simple docstring"""




													if self.bpe_ranks is None:
																			raise ValueError(
																			    '''This tokenizer was instantiated without a `merges.txt` file, so'''
																			    ''' that it can only be used for decoding, not for encoding.'''
																			    '''Make sure to provide `merges.txt` file at instantiation to enable '''
																			    '''encoding.'''      )

													if self.do_lower_case:
																			UpperCAmelCase_	:					List[str]     =     text.lower()

													UpperCAmelCase_	:					List[Any]     =     text.split()

													UpperCAmelCase_	:					Any     =     []
													for token in text:
																			if token:
																									split_tokens.extend(list(self.bpe(_snake_case      ).split(''' '''      )      )      )

													return split_tokens

							def 			UpperCAmelCase__		(							self							:   List[Any]				,   __magic_name__							:   List[Any]      )    ->				int:







													"""simple docstring"""
													return self.encoder.get(_snake_case				,   self.encoder.get(self.unk_token      )      )

							def 			UpperCAmelCase__		(							self							:   Optional[Any]				,   __magic_name__							:   List[str]      )    ->				str:







													"""simple docstring"""
													UpperCAmelCase_	:					Any     =     self.decoder.get(_snake_case				,   self.unk_token      )
													return result

							def 			UpperCAmelCase__		(							self							:   Tuple				,   __magic_name__							:   Optional[Any]      )    ->				str:







													"""simple docstring"""
													UpperCAmelCase_	:					Union[str, Any]     =     ''' '''.join(_snake_case      )

													# make sure @@ tokens are concatenated
													UpperCAmelCase_	:					int     =     ''''''.join(string.split(_snake_case      )      )

													return string



							def 			UpperCAmelCase__		(							self							:   Union[str, Any]				,   __magic_name__							:   List[str]				,   __magic_name__							:   Dict = None      )    ->				Tuple[str]:







													"""simple docstring"""




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

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

													UpperCAmelCase_	:					Dict     =     0
													if self.bpe_ranks is None:
																			return (vocab_file,)

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

													return (vocab_file, merges_file)







import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save


def     __lowerCAmelCase			(			a__						,    a__						,    a__=1024						,    a__=1024						,    a__=False						,    **a__  )       ->  Optional[Any]:
				__a										=    AutoTokenizer.from_pretrained(a__  )
				__a										=    SeqaSeqDataset(a__						,    a__						,    a__						,    a__						,    type_path='''train'''						,    **a__  )
				__a										=    tok.pad_token_id

				def get_lens(a__  ):
								__a										=    tqdm(
								    DataLoader(a__						,    batch_size=512						,    num_workers=8						,    shuffle=a__						,    collate_fn=ds.collate_fn  )						,    desc=str(ds.len_file  )						,    )
								__a										=    []
								for batch in dl:
												__a										=    batch['''input_ids'''].ne(a__  ).sum(1  ).tolist()
												__a										=    batch['''labels'''].ne(a__  ).sum(1  ).tolist()
												if consider_target:
																for src, tgt in zip(a__						,    a__  ):
																				max_lens.append(max(a__						,    a__  )  )
												else:
																max_lens.extend(a__  )
								return max_lens

				__a										=    get_lens(a__  )
				__a										=    SeqaSeqDataset(a__						,    a__						,    a__						,    a__						,    type_path='''val'''						,    **a__  )
				__a										=    get_lens(a__  )
				pickle_save(a__						,    train_ds.len_file  )
				pickle_save(a__						,    val_ds.len_file  )


if __name__ == "__main__":
					fire.Fire(save_len_file)