






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

import regex as re

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


_UpperCamelCase			:	List[str]								=logging.get_logger(__name__)


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

_UpperCamelCase			:	str								={
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

_UpperCamelCase			:	List[Any]								={'facebook/blenderbot_small-90M': 512}



def  a__ (__lowercase							:Union[str, Any]   )		-> str:
				_A	:				Optional[int]   =      set()
				_A	:				Optional[Any]   =      word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)   )
								_A	:				str   =      char

				_A	:				str   =      set(__lowercase   )
				return pairs






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Any     =   VOCAB_FILES_NAMES
		__snake_case      :       str     =   PRETRAINED_VOCAB_FILES_MAP
		__snake_case      :       int     =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__snake_case      :       str     =   ["input_ids", "attention_mask"]







		def __init__(					self       ,A__       ,A__       ,A__="__start__"       ,A__="__end__"       ,A__="__unk__"       ,A__="__null__"       ,**A__       ,):
						super().__init__(unk_token=A__       ,bos_token=A__       ,eos_token=A__       ,pad_token=A__       ,**A__		)

						with open(A__       ,encoding='''utf-8'''		) as vocab_handle:
										_A	:				Optional[int]   =      json.load(A__		)
						_A	:				Optional[int]   =      {v: k for k, v in self.encoder.items()}
						with open(A__       ,encoding='''utf-8'''		) as merges_handle:
										_A	:				Optional[int]   =      merges_handle.read().split('''\n'''		)[1:-1]
						_A	:				Optional[Any]   =      [tuple(merge.split()		) for merge in merges]
						_A	:				List[str]   =      dict(zip(A__       ,range(len(A__		)		)		)		)
						_A	:				int   =      {}







		@property
		def  A__      (					self		):
						return len(self.encoder		)







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







		def  A__      (					self       ,A__		):
						if token in self.cache:
										return self.cache[token]
						_A	:				Optional[int]   =      re.sub('''([.,!?()])'''       ,r''' \1'''       ,A__		)
						_A	:				List[Any]   =      re.sub('''(\')'''       ,r''' \1 '''       ,A__		)
						_A	:				int   =      re.sub(r'''\s{2,}'''       ,''' '''       ,A__		)
						if "\n" in token:
										_A	:				int   =      token.replace('''\n'''       ,''' __newln__'''		)

						_A	:				List[Any]   =      token.split(''' '''		)
						_A	:				int   =      []
						for token in tokens:
										if not len(A__		):
														continue

										_A	:				List[str]   =      token.lower()
										_A	:				Optional[int]   =      tuple(A__		)
										_A	:				Tuple   =      tuple(list(word[:-1]		) + [word[-1] + '''</w>''']		)
										_A	:				Dict   =      get_pairs(A__		)

										if not pairs:
														words.append(A__		)
														continue

										while True:
														_A	:				Optional[int]   =      min(A__       ,key=lambda A__		: self.bpe_ranks.get(A__       ,float('''inf'''		)		)		)
														if bigram not in self.bpe_ranks:
																		break
														_A  ,				_A	:				List[Any]   =      bigram
														_A	:				Dict   =      []
														_A	:				List[str]   =      0

														while i < len(A__		):
																		try:
																						_A	:				List[Any]   =      word.index(A__       ,A__		)
																						new_word.extend(word[i:j]		)
																						_A	:				Dict   =      j
																		except ValueError:
																						new_word.extend(word[i:]		)
																						break

																		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
														_A	:				int   =      tuple(A__		)
														_A	:				str   =      new_word
														if len(A__		) == 1:
																		break
														else:
																		_A	:				str   =      get_pairs(A__		)
										_A	:				Tuple   =      '''@@ '''.join(A__		)
										_A	:				List[str]   =      word[:-4]

										_A	:				str   =      word
										words.append(A__		)
						return " ".join(A__		)







		def  A__      (					self       ,A__		):
						_A	:				List[Any]   =      []

						_A	:				Optional[Any]   =      re.findall(r'''\S+\n?'''       ,A__		)

						for token in words:
										split_tokens.extend(list(self.bpe(A__		).split(''' '''		)		)		)
						return split_tokens







		def  A__      (					self       ,A__		):
						_A	:				Any   =      token.lower()
						return self.encoder.get(A__       ,self.encoder.get(self.unk_token		)		)







		def  A__      (					self       ,A__		):
						return self.decoder.get(A__       ,self.unk_token		)







		def  A__      (					self       ,A__		):
						_A	:				Optional[Any]   =      ''' '''.join(A__		).replace('''@@ '''       ,''''''		).strip()
						return out_string







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

						_A	:				Optional[int]   =      0
						with open(A__       ,'''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 A__		: 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!'''		)
																		_A	:				List[str]   =      token_index
														writer.write(''' '''.join(A__		) + '''\n'''		)
														index += 1

						return vocab_file, merge_file











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCamelCase			:	Any								={
    'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	Optional[Any]								=[
	    'MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'MegatronBertForCausalLM',
	    'MegatronBertForMaskedLM',
	    'MegatronBertForMultipleChoice',
	    'MegatronBertForNextSentencePrediction',
	    'MegatronBertForPreTraining',
	    'MegatronBertForQuestionAnswering',
	    'MegatronBertForSequenceClassification',
	    'MegatronBertForTokenClassification',
	    'MegatronBertModel',
	    'MegatronBertPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_megatron_bert import (
		    MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MegatronBertForCausalLM,
		    MegatronBertForMaskedLM,
		    MegatronBertForMultipleChoice,
		    MegatronBertForNextSentencePrediction,
		    MegatronBertForPreTraining,
		    MegatronBertForQuestionAnswering,
		    MegatronBertForSequenceClassification,
		    MegatronBertForTokenClassification,
		    MegatronBertModel,
		    MegatronBertPreTrainedModel,
		)

else:
	import sys

	_UpperCamelCase			:	Optional[int]								=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



