





'''simple docstring'''
import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 def __init__(       self       :							str							,	__A       :							str = "▁"							,	__A       :							bool = True							,	__A       :							Union[str, AddedToken] = "<unk>"							,	__A       :							Union[str, AddedToken] = "</s>"							,	__A       :							Union[str, AddedToken] = "<pad>"							,	):
        __UpperCamelCase       =  {
            'pad': {'id': 0, 'token': pad_token},
            'eos': {'id': 1, 'token': eos_token},
            'unk': {'id': 2, 'token': unk_token},
        }

        __UpperCamelCase       =  [None] * len(self.special_tokens	)
        for token_dict in self.special_tokens.values():
               __UpperCamelCase       =  token_dict['token']

        __UpperCamelCase       =  Tokenizer(Unigram()	)

        __UpperCamelCase       =  normalizers.Sequence(
            [
                normalizers.Nmt(),
                normalizers.NFKC(),
                normalizers.Replace(Regex(' {2,}'	)							,	' '	),
                normalizers.Lowercase(),
            ]	)
        __UpperCamelCase       =  pre_tokenizers.Sequence(
            [
                pre_tokenizers.Metaspace(replacement=__A							,	add_prefix_space=__A	),
                pre_tokenizers.Digits(individual_digits=__A	),
                pre_tokenizers.Punctuation(),
            ]	)
        __UpperCamelCase       =  decoders.Metaspace(replacement=__A							,	add_prefix_space=__A	)

        __UpperCamelCase       =  TemplateProcessing(
            single=f'''$A {self.special_tokens['eos']['token']}'''							,	special_tokens=[(self.special_tokens['eos']['token'], self.special_tokens['eos']['id'])]							,	)

        __UpperCamelCase       =  {
            'model': 'SentencePieceUnigram',
            'replacement': replacement,
            'add_prefix_space': add_prefix_space,
        }

        super().__init__(__A							,	__A	)






 def        _lowerCamelCase					(       self       :							List[Any]							,	__A       :							Union[str, List[str]]							,	__A       :							int = 8_0_0_0							,	__A       :							bool = True							,	):
        __UpperCamelCase       =  trainers.UnigramTrainer(
            vocab_size=__A							,	special_tokens=self.special_tokens_list							,	show_progress=__A							,	)

        if isinstance(__A							,	__A	):
               __UpperCamelCase       =  [files]
        self._tokenizer.train(__A							,	trainer=__A	)

        self.add_unk_id()






 def        _lowerCamelCase					(       self       :							List[str]							,	__A       :							Union[Iterator[str], Iterator[Iterator[str]]]							,	__A       :							int = 8_0_0_0							,	__A       :							bool = True							,	):
        __UpperCamelCase       =  trainers.UnigramTrainer(
            vocab_size=__A							,	special_tokens=self.special_tokens_list							,	show_progress=__A							,	)

        self._tokenizer.train_from_iterator(__A							,	trainer=__A	)

        self.add_unk_id()






 def        _lowerCamelCase					(       self       :							Dict	):
        __UpperCamelCase       =  json.loads(self._tokenizer.to_str()	)

        __UpperCamelCase       =  self.special_tokens['unk']['id']

        __UpperCamelCase       =  Tokenizer.from_str(json.dumps(__A	)	)







'''simple docstring'''
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Any					=["image_processor", "tokenizer"]
 SCREAMING_SNAKE_CASE_	:					List[Any]					="BlipImageProcessor"
 SCREAMING_SNAKE_CASE_	:					Optional[int]					=("BertTokenizer", "BertTokenizerFast")






 def __init__(       self       :							Dict							,	__A       :							Optional[int]							,	__A       :							List[Any]	):
        __UpperCamelCase       =  False
        super().__init__(__A							,	__A	)
        __UpperCamelCase       =  self.image_processor






 def __call__(       self       :							List[Any]							,	__A       :							ImageInput = None							,	__A       :							Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,	__A       :							bool = True							,	__A       :							Union[bool, str, PaddingStrategy] = False							,	__A       :							Union[bool, str, TruncationStrategy] = None							,	__A       :							Optional[int] = None							,	__A       :							int = 0							,	__A       :							Optional[int] = None							,	__A       :							Optional[bool] = None							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							bool = True							,	__A       :							Optional[Union[str, TensorType]] = None							,	**__A       :							List[Any]							,	):
        if images is None and text is None:
               raise ValueError('You have to specify either images or text.'	)

        # Get only text
        if images is None:
               __UpperCamelCase       =  self.tokenizer
               __UpperCamelCase       =  self.tokenizer(
                   text=__A							,	add_special_tokens=__A							,	padding=__A							,	truncation=__A							,	max_length=__A							,	stride=__A							,	pad_to_multiple_of=__A							,	return_attention_mask=__A							,	return_overflowing_tokens=__A							,	return_special_tokens_mask=__A							,	return_offsets_mapping=__A							,	return_token_type_ids=__A							,	return_length=__A							,	verbose=__A							,	return_tensors=__A							,	**__A							,	)
               return text_encoding

        # add pixel_values
        __UpperCamelCase       =  self.image_processor(__A							,	return_tensors=__A	)

        if text is not None:
               __UpperCamelCase       =  self.tokenizer(
                   text=__A							,	add_special_tokens=__A							,	padding=__A							,	truncation=__A							,	max_length=__A							,	stride=__A							,	pad_to_multiple_of=__A							,	return_attention_mask=__A							,	return_overflowing_tokens=__A							,	return_special_tokens_mask=__A							,	return_offsets_mapping=__A							,	return_token_type_ids=__A							,	return_length=__A							,	verbose=__A							,	return_tensors=__A							,	**__A							,	)
        else:
               __UpperCamelCase       =  None

        if text_encoding is not None:
               encoding_image_processor.update(__A	)

        return encoding_image_processor






 def        _lowerCamelCase					(       self       :							List[Any]							,	*__A       :							Dict							,	**__A       :							Optional[int]	):
        return self.tokenizer.batch_decode(*__A							,	**__A	)






 def        _lowerCamelCase					(       self       :							List[Any]							,	*__A       :							List[str]							,	**__A       :							Dict	):
        return self.tokenizer.decode(*__A							,	**__A	)






 @property
 def        _lowerCamelCase					(       self       :							Tuple	):
        __UpperCamelCase       =  self.tokenizer.model_input_names
        __UpperCamelCase       =  self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)
