





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

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


__lowerCAmelCase :Optional[int]     =    logging.get_logger(__name__)


__lowerCAmelCase :Dict     =    {
    'vocab_file': 'vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
    'merges_file': 'merges.txt',
}

__lowerCAmelCase :Optional[Any]     =    {
    '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'
        ),
    },
}

__lowerCAmelCase :Optional[int]     =    '</w>'
__lowerCAmelCase :Tuple     =    '@@ '







def    A				(							UpperCAmelCase				):
					_snake_case       :					Optional[int]				       =   set()
					_snake_case       :					Optional[int]				       =   word[0]
					for char in word[1:]:
										pairs.add((prev_char, char)				)
										_snake_case       :					Optional[int]				       =   char
					return pairs


# Speech2Text2 has no max input length
__lowerCAmelCase :Tuple     =    {'facebook/s2t-wav2vec2-large-en-de': 10_24}






class        _a(    __A    ):
		lowerCamelCase__     :str   				=		VOCAB_FILES_NAMES
		lowerCamelCase__     :Dict   				=		PRETRAINED_VOCAB_FILES_MAP
		lowerCamelCase__     :Optional[int]   				=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCamelCase__     :Optional[int]   				=		['input_ids', 'attention_mask']







		def __init__(    self ,						__snake_case ,						__snake_case="<s>" ,						__snake_case="<pad>" ,						__snake_case="</s>" ,						__snake_case="<unk>" ,						__snake_case=False ,						__snake_case=None ,						**__snake_case ,						)      ->      int:


							'''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 ,						)

							_snake_case       :					Any				       =   do_lower_case

							with open(__snake_case ,						encoding="utf-8"				) as vocab_handle:
												_snake_case       :					Optional[int]				       =   json.load(__snake_case				)
							_snake_case       :					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."""				)

												_snake_case       :					Tuple				       =   None
												_snake_case       :					int				       =   None
							else:
												with open(__snake_case ,						encoding="utf-8"				) as merges_handle:
																	_snake_case       :					Dict				       =   merges_handle.read().split("\n"				)[:-1]

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







		@property
		def        lowercase       (    self				)      ->      int:


							'''simple docstring'''

							return len(self.decoder				)







		def        lowercase       (    self				)      ->      Dict:


							'''simple docstring'''

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







		def        lowercase       (    self ,						__snake_case				)      ->      List[str]:


							'''simple docstring'''

							_snake_case       :					List[str]				       =   tuple(token[:-1]				) + (token[-1] + BPE_TOKEN_MERGES,)
							if token in self.cache:
												return self.cache[token]
							_snake_case       :					Dict				       =   get_pairs(__snake_case				)

							if not pairs:
												return token

							while True:
												_snake_case       :					Union[str, Any]				       =   min(__snake_case ,						key=lambda __snake_case				: self.bpe_ranks.get(__snake_case ,						float("inf"				)				)				)
												if bigram not in self.bpe_ranks:
																	break
												_snake_case       , _snake_case       :					Union[str, Any]				       =   bigram
												_snake_case       :					Union[str, Any]				       =   []
												_snake_case       :					List[str]				       =   0
												while i < len(__snake_case				):
																	try:
																						_snake_case       :					int				       =   word.index(__snake_case ,						__snake_case				)
																	except ValueError:
																						new_word.extend(word[i:]				)
																						break
																	else:
																						new_word.extend(word[i:j]				)
																						_snake_case       :					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
												_snake_case       :					List[Any]				       =   tuple(__snake_case				)
												_snake_case       :					List[str]				       =   new_word
												if len(__snake_case				) == 1:
																	break
												else:
																	_snake_case       :					Optional[int]				       =   get_pairs(__snake_case				)
							_snake_case       :					Union[str, Any]				       =   " ".join(__snake_case				)
							if word == "\n  " + BPE_TOKEN_MERGES:
												_snake_case       :					Tuple				       =   "\n" + BPE_TOKEN_MERGES

							if word.endswith(__snake_case				):
												_snake_case       :					Optional[int]				       =   word.replace(__snake_case ,						""				)

							_snake_case       :					Tuple				       =   word.replace(" " ,						__snake_case				)
							_snake_case       :					Optional[Any]				       =   word
							return word







		def        lowercase       (    self ,						__snake_case				)      ->      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:
												_snake_case       :					List[str]				       =   text.lower()

							_snake_case       :					Optional[Any]				       =   text.split()

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

							return split_tokens







		def        lowercase       (    self ,						__snake_case				)      ->      int:


							'''simple docstring'''

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







		def        lowercase       (    self ,						__snake_case				)      ->      str:


							'''simple docstring'''

							_snake_case       :					Dict				       =   self.decoder.get(__snake_case ,						self.unk_token				)
							return result







		def        lowercase       (    self ,						__snake_case				)      ->      str:


							'''simple docstring'''

							_snake_case       :					Any				       =   " ".join(__snake_case				)

							# make sure @@ tokens are concatenated
							_snake_case       :					Optional[Any]				       =   "".join(string.split(__snake_case				)				)

							return string







		def        lowercase       (    self ,						__snake_case ,						__snake_case = None				)      ->      Tuple[str]:


							'''simple docstring'''

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

							_snake_case       :					Tuple				       =   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 __snake_case				: 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!"				)
																						_snake_case       :					List[str]				       =   token_index
																	writer.write(" ".join(__snake_case				) + "\n"				)
																	index += 1

							return (vocab_file, merges_file)






import warnings

from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor


__lowerCAmelCase :str     =    logging.get_logger(__name__)






class        _a(    __A    ):







		def __init__(    self ,						*__snake_case ,						**__snake_case				)      ->      None:


							'''simple docstring'''

							warnings.warn(
							    "The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
							    " Please use VideoMAEImageProcessor instead." ,						__snake_case ,						)
							super().__init__(*__snake_case ,						**__snake_case				)