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

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


_SCREAMING_SNAKE_CASE    :      List[Any]          =			logging.get_logger(__name__)


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

_SCREAMING_SNAKE_CASE    :      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'''
        ),
    },
}

_SCREAMING_SNAKE_CASE    :      Optional[Any]          =			'''</w>'''
_SCREAMING_SNAKE_CASE    :      Union[str, Any]          =			'''@@ '''

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Optional[Any]       ) ->       Tuple:

				"""simple docstring"""







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


# Speech2Text2 has no max input length
_SCREAMING_SNAKE_CASE    :      int          =			{'''facebook/s2t-wav2vec2-large-en-de''': 1_0_2_4}







class  UpperCamelCase__		(    __a   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				VOCAB_FILES_NAMES
	_lowerCAmelCase							       =				PRETRAINED_VOCAB_FILES_MAP
	_lowerCAmelCase							       =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowerCAmelCase							       =				['''input_ids''', '''attention_mask''']





	def __init__(			self				,       UpperCamelCase__				,       UpperCamelCase__="<s>"				,       UpperCamelCase__="<pad>"				,       UpperCamelCase__="</s>"				,       UpperCamelCase__="<unk>"				,       UpperCamelCase__=False				,       UpperCamelCase__=None				,       **UpperCamelCase__				,       ):
					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__				,       )

					A__         =							do_lower_case

					with open(snake_case__				,       encoding='''utf-8'''		) as vocab_handle:
									A__         =							json.load(snake_case__		)
					A__         =							{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."		)

									A__         =							None
									A__         =							None
					else:
									with open(snake_case__				,       encoding='''utf-8'''		) as merges_handle:
													A__         =							merges_handle.read().split('''\n'''		)[:-1]

									A__         =							[tuple(merge.split()[:2]		) for merge in merges]
									A__         =							dict(zip(snake_case__				,       range(len(snake_case__		)		)		)		)
									A__         =							{}





	@property
	def   __snake_case				(			self		):
					return len(self.decoder		)





	def   __snake_case				(			self		):
					return dict(self.encoder				,       **self.added_tokens_encoder		)





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__         =							tuple(token[:-1]		) + (token[-1] + BPE_TOKEN_MERGES,)
					if token in self.cache:
									return self.cache[token]
					A__         =							get_pairs(snake_case__		)

					if not pairs:
									return token

					while True:
									A__         =							min(snake_case__				,       key=lambda UpperCamelCase__		: self.bpe_ranks.get(snake_case__				,       float('''inf'''		)		)		)
									if bigram not in self.bpe_ranks:
													break
									A__       ,							A__         =							bigram
									A__         =							[]
									A__         =							0
									while i < len(snake_case__		):
													try:
																	A__         =							word.index(snake_case__				,       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(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(snake_case__		)
									A__         =							new_word
									if len(snake_case__		) == 1:
													break
									else:
													A__         =							get_pairs(snake_case__		)
					A__         =							''' '''.join(snake_case__		)
					if word == "\n  " + BPE_TOKEN_MERGES:
									A__         =							'''\n''' + BPE_TOKEN_MERGES

					if word.endswith(snake_case__		):
									A__         =							word.replace(snake_case__				,       ''''''		)

					A__         =							word.replace(''' '''				,       snake_case__		)
					A__         =							word
					return word





	def   __snake_case				(			self				,       UpperCamelCase__		):

					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:
									A__         =							text.lower()

					A__         =							text.split()

					A__         =							[]
					for token in text:
									if token:
													split_tokens.extend(list(self.bpe(snake_case__		).split(''' '''		)		)		)

					return split_tokens





	def   __snake_case				(			self				,       UpperCamelCase__		):
					return self.encoder.get(snake_case__				,       self.encoder.get(self.unk_token		)		)





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__         =							self.decoder.get(snake_case__				,       self.unk_token		)
					return result





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__         =							''' '''.join(snake_case__		)

					# make sure @@ tokens are concatenated
					A__         =							''''''.join(string.split(snake_case__		)		)

					return string





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__ = None		):
					if not os.path.isdir(snake_case__		):
									logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
									return
					A__         =							os.path.join(
					    snake_case__				,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)
					A__         =							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'''		)

					A__         =							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 UpperCamelCase__		: 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!'''		)
																	A__         =							token_index
													writer.write(''' '''.join(snake_case__		) + '''\n'''		)
													index += 1

					return (vocab_file, merges_file)

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE    :      int          =			{
    'configuration_bert': ['BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BertConfig', 'BertOnnxConfig'],
    'tokenization_bert': ['BasicTokenizer', 'BertTokenizer', 'WordpieceTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE    :      Optional[Any]          =			['BertTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE    :      Union[str, Any]          =			[
					    'BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'BertForMaskedLM',
					    'BertForMultipleChoice',
					    'BertForNextSentencePrediction',
					    'BertForPreTraining',
					    'BertForQuestionAnswering',
					    'BertForSequenceClassification',
					    'BertForTokenClassification',
					    'BertLayer',
					    'BertLMHeadModel',
					    'BertModel',
					    'BertPreTrainedModel',
					    'load_tf_weights_in_bert',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE    :      Tuple          =			[
					    'TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFBertEmbeddings',
					    'TFBertForMaskedLM',
					    'TFBertForMultipleChoice',
					    'TFBertForNextSentencePrediction',
					    'TFBertForPreTraining',
					    'TFBertForQuestionAnswering',
					    'TFBertForSequenceClassification',
					    'TFBertForTokenClassification',
					    'TFBertLMHeadModel',
					    'TFBertMainLayer',
					    'TFBertModel',
					    'TFBertPreTrainedModel',
					]
try:
					if not is_tensorflow_text_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE    :      Dict          =			['TFBertTokenizer']

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE    :      Optional[int]          =			[
					    'FlaxBertForCausalLM',
					    'FlaxBertForMaskedLM',
					    'FlaxBertForMultipleChoice',
					    'FlaxBertForNextSentencePrediction',
					    'FlaxBertForPreTraining',
					    'FlaxBertForQuestionAnswering',
					    'FlaxBertForSequenceClassification',
					    'FlaxBertForTokenClassification',
					    'FlaxBertModel',
					    'FlaxBertPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
					from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_bert_fast import BertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_bert import (
										    BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BertForMaskedLM,
										    BertForMultipleChoice,
										    BertForNextSentencePrediction,
										    BertForPreTraining,
										    BertForQuestionAnswering,
										    BertForSequenceClassification,
										    BertForTokenClassification,
										    BertLayer,
										    BertLMHeadModel,
										    BertModel,
										    BertPreTrainedModel,
										    load_tf_weights_in_bert,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_bert import (
										    TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFBertEmbeddings,
										    TFBertForMaskedLM,
										    TFBertForMultipleChoice,
										    TFBertForNextSentencePrediction,
										    TFBertForPreTraining,
										    TFBertForQuestionAnswering,
										    TFBertForSequenceClassification,
										    TFBertForTokenClassification,
										    TFBertLMHeadModel,
										    TFBertMainLayer,
										    TFBertModel,
										    TFBertPreTrainedModel,
										)

					try:
										if not is_tensorflow_text_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_bert_tf import TFBertTokenizer

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_bert import (
										    FlaxBertForCausalLM,
										    FlaxBertForMaskedLM,
										    FlaxBertForMultipleChoice,
										    FlaxBertForNextSentencePrediction,
										    FlaxBertForPreTraining,
										    FlaxBertForQuestionAnswering,
										    FlaxBertForSequenceClassification,
										    FlaxBertForTokenClassification,
										    FlaxBertModel,
										    FlaxBertPreTrainedModel,
										)

else:
					import sys

					_SCREAMING_SNAKE_CASE    :      Union[str, Any]          =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)