
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase :       Optional[Any]     					=    logging.get_logger(__name__)


_lowerCamelCase :       Tuple     					=    {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

# See all MVP models at https://huggingface.co/models?filter=mvp
_lowerCamelCase :       str     					=    {
    '''vocab_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json''',
    },
    '''added_tokens.json''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json''',
    },
    '''merges_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt''',
    },
}

_lowerCamelCase :       int     					=    {
    '''RUCAIBox/mvp''': 1_024,
}






@lru_cache()
def  a_	( )				->						Optional[int]:
			_snake_case						=    (
			    list(range(ord('!'				) ,      ord('~'				) + 1				)				) + list(range(ord('¡'				) ,      ord('¬'				) + 1				)				) + list(range(ord('®'				) ,      ord('ÿ'				) + 1				)				)
			)
			_snake_case						=    bs[:]
			_snake_case						=    0
			for b in range(2**8				):
						if b not in bs:
									bs.append(__lowercase				)
									cs.append(2**8 + n				)
									n += 1
			_snake_case						=    [chr(__lowercase				) for n in cs]
			return dict(zip(__lowercase ,      __lowercase				)				)






def  a_	( __lowercase     :			Any				)				->						List[str]:
			_snake_case						=    set()
			_snake_case						=    word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)				)
						_snake_case						=    char
			return pairs




class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	str					     =      VOCAB_FILES_NAMES
	_UpperCAmelCase :	Union[str, Any]					     =      PRETRAINED_VOCAB_FILES_MAP
	_UpperCAmelCase :	Dict					     =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_UpperCAmelCase :	str					     =      ["input_ids", "attention_mask"]



	def __init__(						self			:					Optional[Any]    ,						lowercase			:					List[str]    ,						lowercase			:					str    ,						lowercase			:					str="replace"    ,						lowercase			:					Optional[int]="<s>"    ,						lowercase			:					List[Any]="</s>"    ,						lowercase			:					Union[str, Any]="</s>"    ,						lowercase			:					Union[str, Any]="<s>"    ,						lowercase			:					List[str]="<unk>"    ,						lowercase			:					str="<pad>"    ,						lowercase			:					Tuple="<mask>"    ,						lowercase			:					List[Any]=False    ,						**lowercase			:					Optional[Any]    ,						):


				'''simple docstring'''


				_snake_case						=    AddedToken(lowercase    ,						lstrip=lowercase    ,						rstrip=lowercase			) if isinstance(lowercase    ,						lowercase			) else bos_token
				_snake_case						=    AddedToken(lowercase    ,						lstrip=lowercase    ,						rstrip=lowercase			) if isinstance(lowercase    ,						lowercase			) else eos_token
				_snake_case						=    AddedToken(lowercase    ,						lstrip=lowercase    ,						rstrip=lowercase			) if isinstance(lowercase    ,						lowercase			) else sep_token
				_snake_case						=    AddedToken(lowercase    ,						lstrip=lowercase    ,						rstrip=lowercase			) if isinstance(lowercase    ,						lowercase			) else cls_token
				_snake_case						=    AddedToken(lowercase    ,						lstrip=lowercase    ,						rstrip=lowercase			) if isinstance(lowercase    ,						lowercase			) else unk_token
				_snake_case						=    AddedToken(lowercase    ,						lstrip=lowercase    ,						rstrip=lowercase			) if isinstance(lowercase    ,						lowercase			) else pad_token

				# Mask token behave like a normal word, i.e. include the space before it
				_snake_case						=    AddedToken(lowercase    ,						lstrip=lowercase    ,						rstrip=lowercase			) if isinstance(lowercase    ,						lowercase			) else mask_token

				super().__init__(
				    errors=lowercase    ,						bos_token=lowercase    ,						eos_token=lowercase    ,						unk_token=lowercase    ,						sep_token=lowercase    ,						cls_token=lowercase    ,						pad_token=lowercase    ,						mask_token=lowercase    ,						add_prefix_space=lowercase    ,						**lowercase    ,						)
				with open(lowercase    ,						encoding='utf-8'			) as vocab_handle:
							_snake_case						=    json.load(lowercase			)
				_snake_case						=    {v: k for k, v in self.encoder.items()}
				_snake_case						=    errors  # how to handle errors in decoding
				_snake_case						=    bytes_to_unicode()
				_snake_case						=    {v: k for k, v in self.byte_encoder.items()}
				with open(lowercase    ,						encoding='utf-8'			) as merges_handle:
							_snake_case						=    merges_handle.read().split('\n'			)[1:-1]
				_snake_case						=    [tuple(merge.split()			) for merge in bpe_merges]
				_snake_case						=    dict(zip(lowercase    ,						range(len(lowercase			)			)			)			)
				_snake_case						=    {}
				_snake_case						=    add_prefix_space

				# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
				_snake_case						=    re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'			)



	@property
	def 				A							(						self			:					List[str]			):


				'''simple docstring'''


				return len(self.encoder			)



	def 				A							(						self			:					List[str]			):


				'''simple docstring'''


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



	def 				A							(						self			:					str    ,						lowercase			:					Tuple			):


				'''simple docstring'''


				if token in self.cache:
							return self.cache[token]
				_snake_case						=    tuple(lowercase			)
				_snake_case						=    get_pairs(lowercase			)

				if not pairs:
							return token

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

										if word[i] == first and i < len(lowercase			) - 1 and word[i + 1] == second:
													new_word.append(first + second			)
													i += 2
										else:
													new_word.append(word[i]			)
													i += 1
							_snake_case						=    tuple(lowercase			)
							_snake_case						=    new_word
							if len(lowercase			) == 1:
										break
							else:
										_snake_case						=    get_pairs(lowercase			)
				_snake_case						=    ' '.join(lowercase			)
				_snake_case						=    word
				return word



	def 				A							(						self			:					Optional[int]    ,						lowercase			:					Any			):


				'''simple docstring'''


				_snake_case						=    []
				for token in re.findall(self.pat    ,						lowercase			):
							_snake_case						=    ''.join(
							    self.byte_encoder[b] for b in token.encode('utf-8'			)			)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
							bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowercase			).split(' '			)			)
				return bpe_tokens



	def 				A							(						self			:					Dict    ,						lowercase			:					List[str]			):


				'''simple docstring'''


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



	def 				A							(						self			:					Optional[int]    ,						lowercase			:					Optional[int]			):


				'''simple docstring'''


				return self.decoder.get(lowercase			)



	def 				A							(						self			:					Union[str, Any]    ,						lowercase			:					List[Any]			):


				'''simple docstring'''


				_snake_case						=    ''.join(lowercase			)
				_snake_case						=    bytearray([self.byte_decoder[c] for c in text]			).decode('utf-8'    ,						errors=self.errors			)
				return text



	def 				A							(						self			:					Tuple    ,						lowercase			:					str    ,						lowercase			:					Optional[str] = None			):


				'''simple docstring'''


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

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

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

				return vocab_file, merge_file



	def 				A							(						self			:					Optional[Any]    ,						lowercase			:					List[int]    ,						lowercase			:					Optional[List[int]] = None			):


				'''simple docstring'''


				if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				_snake_case						=    [self.cls_token_id]
				_snake_case						=    [self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep



	def 				A							(						self			:					Dict    ,						lowercase			:					List[int]    ,						lowercase			:					Optional[List[int]] = None    ,						lowercase			:					bool = False			):


				'''simple docstring'''


				if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=lowercase    ,						token_ids_a=lowercase    ,						already_has_special_tokens=lowercase			)

				if token_ids_a is None:
							return [1] + ([0] * len(lowercase			)) + [1]
				return [1] + ([0] * len(lowercase			)) + [1, 1] + ([0] * len(lowercase			)) + [1]



	def 				A							(						self			:					int    ,						lowercase			:					List[int]    ,						lowercase			:					Optional[List[int]] = None			):


				'''simple docstring'''


				_snake_case						=    [self.sep_token_id]
				_snake_case						=    [self.cls_token_id]

				if token_ids_a is None:
							return len(cls + token_ids_a + sep			) * [0]
				return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]





	def 				A							(						self			:					Tuple    ,						lowercase			:					Dict    ,						lowercase			:					List[str]=False    ,						**lowercase			:					Optional[Any]			):


				'''simple docstring'''


				_snake_case						=    kwargs.pop('add_prefix_space'    ,						self.add_prefix_space			)
				if (is_split_into_words or add_prefix_space) and (len(lowercase			) > 0 and not text[0].isspace()):
							_snake_case						=    ' ' + text
				return (text, kwargs)

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	int					     =      ["image_processor", "tokenizer"]
	_UpperCAmelCase :	Tuple					     =      "EfficientNetImageProcessor"
	_UpperCAmelCase :	Optional[Any]					     =      ("BertTokenizer", "BertTokenizerFast")



	def __init__(						self			:					Tuple    ,						lowercase			:					List[Any]    ,						lowercase			:					Optional[int]			):


				'''simple docstring'''


				super().__init__(lowercase    ,						lowercase			)



	def __call__(						self			:					Union[str, Any]    ,						lowercase			:					Dict=None    ,						lowercase			:					Optional[int]=None    ,						lowercase			:					List[Any]="max_length"    ,						lowercase			:					Optional[int]=64    ,						lowercase			:					int=None    ,						**lowercase			:					Tuple			):


				'''simple docstring'''


				if text is None and images is None:
							raise ValueError('You have to specify either text or images. Both cannot be none.'			)

				if text is not None:
							_snake_case						=    self.tokenizer(
							    lowercase    ,						padding=lowercase    ,						max_length=lowercase    ,						return_tensors=lowercase    ,						**lowercase			)

				if images is not None:
							_snake_case						=    self.image_processor(lowercase    ,						return_tensors=lowercase    ,						**lowercase			)

				if text is not None and images is not None:
							_snake_case						=    image_features.pixel_values
							return encoding
				elif text is not None:
							return encoding
				else:
							return BatchEncoding(data=dict(**lowercase			)    ,						tensor_type=lowercase			)



	def 				A							(						self			:					Any    ,						*lowercase			:					List[str]    ,						**lowercase			:					Optional[Any]			):


				'''simple docstring'''


				return self.tokenizer.batch_decode(*lowercase    ,						**lowercase			)



	def 				A							(						self			:					List[Any]    ,						*lowercase			:					Union[str, Any]    ,						**lowercase			:					Tuple			):


				'''simple docstring'''


				return self.tokenizer.decode(*lowercase    ,						**lowercase			)





	@property
	def 				A							(						self			:					List[str]			):


				'''simple docstring'''


				_snake_case						=    self.tokenizer.model_input_names
				_snake_case						=    self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)