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

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


lowerCamelCase__          =		logging.get_logger(__name__)


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

lowerCamelCase__          =		{
    '''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__          =		'''</w>'''
lowerCamelCase__          =		'''@@ '''
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		Optional[int]:
			lowerCAmelCase__      :      List[str]         =		set()
			lowerCAmelCase__      :      Any         =		word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)     )
						lowerCAmelCase__      :      int         =		char
			return pairs


# Speech2Text2 has no max input length
lowerCamelCase__          =		{'''facebook/s2t-wav2vec2-large-en-de''': 1024}







class      A__							(     A__	):
				lowercase			    =  VOCAB_FILES_NAMES
				lowercase			    =  PRETRAINED_VOCAB_FILES_MAP
				lowercase			    =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowercase			    =  ['''input_ids''', '''attention_mask''']






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






							'''simple docstring'''







							super().__init__(
							    unk_token=a    ,     bos_token=a    ,     eos_token=a    ,     pad_token=a    ,     do_lower_case=a    ,     **a    ,     )

							lowerCAmelCase__      :      Tuple         =		do_lower_case

							with open(a    ,     encoding='utf-8'   ) as vocab_handle:
										lowerCAmelCase__      :      int         =		json.load(a   )
							lowerCAmelCase__      :      Union[str, 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.'''   )

										lowerCAmelCase__      :      List[str]         =		None
										lowerCAmelCase__      :      Union[str, Any]         =		None
							else:
										with open(a    ,     encoding='utf-8'   ) as merges_handle:
													lowerCAmelCase__      :      str         =		merges_handle.read().split('\n'   )[:-1]

										lowerCAmelCase__      :      Any         =		[tuple(merge.split()[:2]   ) for merge in merges]
										lowerCAmelCase__      :      Dict         =		dict(zip(a    ,     range(len(a   )   )   )   )
										lowerCAmelCase__      :      Optional[int]         =		{}






				@property
				def 							_lowerCamelCase		(						self					:      str   ):






							'''simple docstring'''







							return len(self.decoder   )






				def 							_lowerCamelCase		(						self					:      Dict   ):






							'''simple docstring'''







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






				def 							_lowerCamelCase		(						self					:      str    ,     a					:      str   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Any         =		tuple(token[:-1]   ) + (token[-1] + BPE_TOKEN_MERGES,)
							if token in self.cache:
										return self.cache[token]
							lowerCAmelCase__      :      Union[str, Any]         =		get_pairs(a   )

							if not pairs:
										return token

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

													if word[i] == first and i < len(a   ) - 1 and word[i + 1] == second:
																new_word.append(first + second   )
																i += 2
													else:
																new_word.append(word[i]   )
																i += 1
										lowerCAmelCase__      :      Dict         =		tuple(a   )
										lowerCAmelCase__      :      Optional[Any]         =		new_word
										if len(a   ) == 1:
													break
										else:
													lowerCAmelCase__      :      Optional[Any]         =		get_pairs(a   )
							lowerCAmelCase__      :      Dict         =		" ".join(a   )
							if word == "\n  " + BPE_TOKEN_MERGES:
										lowerCAmelCase__      :      Dict         =		"\n" + BPE_TOKEN_MERGES

							if word.endswith(a   ):
										lowerCAmelCase__      :      Union[str, Any]         =		word.replace(a    ,     ''   )

							lowerCAmelCase__      :      Optional[int]         =		word.replace(' '    ,     a   )
							lowerCAmelCase__      :      Tuple         =		word
							return word






				def 							_lowerCamelCase		(						self					:      Any    ,     a					:      Dict   ):






							'''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:
										lowerCAmelCase__      :      Union[str, Any]         =		text.lower()

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

							lowerCAmelCase__      :      int         =		[]
							for token in text:
										if token:
													split_tokens.extend(list(self.bpe(a   ).split(' '   )   )   )

							return split_tokens






				def 							_lowerCamelCase		(						self					:      Tuple    ,     a					:      str   ):






							'''simple docstring'''







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






				def 							_lowerCamelCase		(						self					:      Optional[int]    ,     a					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		self.decoder.get(a    ,     self.unk_token   )
							return result






				def 							_lowerCamelCase		(						self					:      Dict    ,     a					:      List[str]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Tuple         =		" ".join(a   )

							# make sure @@ tokens are concatenated
							lowerCAmelCase__      :      str         =		"".join(string.split(a   )   )

							return string






				def 							_lowerCamelCase		(						self					:      Optional[Any]    ,     a					:      str    ,     a					:      Optional[str] = None   ):






							'''simple docstring'''







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

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

							lowerCAmelCase__      :      List[str]         =		0
							if self.bpe_ranks is None:
										return (vocab_file,)

							with open(a    ,     'w'    ,     encoding='utf-8'   ) as writer:
										for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,     key=lambda a   : 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!'   )
																lowerCAmelCase__      :      int         =		token_index
													writer.write(' '.join(a   ) + '\n'   )
													index += 1

							return (vocab_file, merges_file)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase	:							Tuple				={
    '''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
    '''tokenization_luke''': ['''LukeTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase	:							Union[str, Any]				=[
     '''LUKE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''LukeForEntityClassification''',
     '''LukeForEntityPairClassification''',
     '''LukeForEntitySpanClassification''',
     '''LukeForMultipleChoice''',
     '''LukeForQuestionAnswering''',
     '''LukeForSequenceClassification''',
     '''LukeForTokenClassification''',
     '''LukeForMaskedLM''',
     '''LukeModel''',
     '''LukePreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
 from .tokenization_luke import LukeTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_luke import (
      LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
      LukeForEntityClassification,
      LukeForEntityPairClassification,
      LukeForEntitySpanClassification,
      LukeForMaskedLM,
      LukeForMultipleChoice,
      LukeForQuestionAnswering,
      LukeForSequenceClassification,
      LukeForTokenClassification,
      LukeModel,
      LukePreTrainedModel,
  )

else:
 import sys

 lowerCamelCase	:							Dict				=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)