
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       =					logging.get_logger(__name__)


UpperCAmelCase       =					{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

UpperCAmelCase       =					{
    """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       =					{"""facebook/blenderbot_small-90M""": 512}






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE				):
	lowercase	    =       set()
	lowercase	    =       word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)				)
		lowercase	    =       char

	lowercase	    =       set(UpperCamelCase__				)
	return pairs







class 			A_  (     lowercase_  ):




	'''simple docstring'''


	_UpperCamelCase	:	Optional[Any]									=			VOCAB_FILES_NAMES
	_UpperCamelCase	:	Any									=			PRETRAINED_VOCAB_FILES_MAP
	_UpperCamelCase	:	str									=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_UpperCamelCase	:	Optional[int]									=			['''input_ids''', '''attention_mask''']





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

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





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





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





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):
		if token in self.cache:
			return self.cache[token]
		lowercase	    =       re.sub('([.,!?()])'						,       r' \1'						,       snake_case			)
		lowercase	    =       re.sub('(\')'						,       r' \1 '						,       snake_case			)
		lowercase	    =       re.sub(r'\s{2,}'						,       ' '						,       snake_case			)
		if "\n" in token:
			lowercase	    =       token.replace('\n'						,       ' __newln__'			)

		lowercase	    =       token.split(' '			)
		lowercase	    =       []
		for token in tokens:
			if not len(snake_case			):
				continue

			lowercase	    =       token.lower()
			lowercase	    =       tuple(snake_case			)
			lowercase	    =       tuple(list(word[:-1]			) + [word[-1] + '</w>']			)
			lowercase	    =       get_pairs(snake_case			)

			if not pairs:
				words.append(snake_case			)
				continue

			while True:
				lowercase	    =       min(snake_case						,       key=lambda snake_case			: self.bpe_ranks.get(snake_case						,       float('inf'			)			)			)
				if bigram not in self.bpe_ranks:
					break
				lowercase    ,   lowercase	    =       bigram
				lowercase	    =       []
				lowercase	    =       0

				while i < len(snake_case			):
					try:
						lowercase	    =       word.index(snake_case						,       snake_case			)
						new_word.extend(word[i:j]			)
						lowercase	    =       j
					except ValueError:
						new_word.extend(word[i:]			)
						break

					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
				lowercase	    =       tuple(snake_case			)
				lowercase	    =       new_word
				if len(snake_case			) == 1:
					break
				else:
					lowercase	    =       get_pairs(snake_case			)
			lowercase	    =       '@@ '.join(snake_case			)
			lowercase	    =       word[:-4]

			lowercase	    =       word
			words.append(snake_case			)
		return " ".join(snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):
		lowercase	    =       []

		lowercase	    =       re.findall(r'\S+\n?'						,       snake_case			)

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





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):
		lowercase	    =       token.lower()
		return self.encoder.get(snake_case						,       self.encoder.get(self.unk_token			)			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):
		return self.decoder.get(snake_case						,       self.unk_token			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):
		lowercase	    =       ' '.join(snake_case			).replace('@@ '						,       ''			).strip()
		return out_string





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

		lowercase	    =       0
		with open(snake_case						,       '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 snake_case			: 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!'			)
					lowercase	    =       token_index
				writer.write(' '.join(snake_case			) + '\n'			)
				index += 1

		return vocab_file, merge_file




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase						:  int									=			{
    """configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase						:  str									=			[
					    """LILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """LiltForQuestionAnswering""",
					    """LiltForSequenceClassification""",
					    """LiltForTokenClassification""",
					    """LiltModel""",
					    """LiltPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_lilt import (
										    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LiltForQuestionAnswering,
										    LiltForSequenceClassification,
										    LiltForTokenClassification,
										    LiltModel,
										    LiltPreTrainedModel,
										)

else:
					import sys

					_UpperCAmelCase						:  Dict									=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




