import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
    import torch


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    a__     :						Any      =				"""char"""
    a__     :						Any      =				"""bpe"""
    a__     :						Dict      =				"""wp"""
__lowercase										= (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    a__     :						Tuple      =				["""image_processor""", """char_tokenizer"""]
    a__     :						List[Any]      =				"""ViTImageProcessor"""
    a__     :						Optional[Any]      =				"""MgpstrTokenizer"""







    def __init__(     self			,			__lowercase=None			,			__lowercase=None			,			**__lowercase)	->     List[str]:
        __UpperCamelCase					:List[str]        =		None
        if "feature_extractor" in kwargs:
            warnings.warn(
                '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                ''' instead.'''			,			__lowercase			,			)
            __UpperCamelCase					:int        =		kwargs.pop('''feature_extractor''')

        __UpperCamelCase					:int        =		image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
            raise ValueError('''You need to specify an `image_processor`.''')
        if tokenizer is None:
            raise ValueError('''You need to specify a `tokenizer`.''')

        __UpperCamelCase					:str        =		tokenizer
        __UpperCamelCase					:Dict        =		AutoTokenizer.from_pretrained('''gpt2''')
        __UpperCamelCase					:Any        =		AutoTokenizer.from_pretrained('''bert-base-uncased''')

        super().__init__(__lowercase			,			__lowercase)







    def __call__(     self			,			__lowercase=None			,			__lowercase=None			,			__lowercase=None			,			**__lowercase)	->     Union[str, Any]:
        if images is None and text is None:
            raise ValueError('''You need to specify either an `images` or `text` input to process.''')

        if images is not None:
            __UpperCamelCase					:str        =		self.image_processor(__lowercase			,			return_tensors=__lowercase			,			**__lowercase)
        if text is not None:
            __UpperCamelCase					:Union[str, Any]        =		self.char_tokenizer(__lowercase			,			return_tensors=__lowercase			,			**__lowercase)

        if text is None:
            return inputs
        elif images is None:
            return encodings
        else:
            __UpperCamelCase					:Optional[int]        =		encodings['''input_ids''']
            return inputs







    def       UpperCamelCase__       (     self			,			__lowercase)	->     List[str]:
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Tuple        =		sequences
        __UpperCamelCase					:Union[str, Any]        =		char_preds.size(0)

        __UpperCamelCase  ,	__UpperCamelCase					:str        =		self._decode_helper(__lowercase			,			'''char''')
        __UpperCamelCase  ,	__UpperCamelCase					:Dict        =		self._decode_helper(__lowercase			,			'''bpe''')
        __UpperCamelCase  ,	__UpperCamelCase					:Optional[int]        =		self._decode_helper(__lowercase			,			'''wp''')

        __UpperCamelCase					:List[str]        =		[]
        __UpperCamelCase					:Optional[Any]        =		[]
        for i in range(__lowercase):
            __UpperCamelCase					:Optional[Any]        =		[char_scores[i], bpe_scores[i], wp_scores[i]]
            __UpperCamelCase					:Optional[Any]        =		[char_strs[i], bpe_strs[i], wp_strs[i]]
            __UpperCamelCase					:int        =		scores.index(max(__lowercase))
            final_strs.append(strs[max_score_index])
            final_scores.append(scores[max_score_index])

        __UpperCamelCase					:int        =		{}
        __UpperCamelCase					:List[Any]        =		final_strs
        __UpperCamelCase					:Any        =		final_scores
        __UpperCamelCase					:Optional[int]        =		char_strs
        __UpperCamelCase					:Union[str, Any]        =		bpe_strs
        __UpperCamelCase					:Any        =		wp_strs
        return out







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase)	->     Optional[int]:
        if format == DecodeType.CHARACTER:
            __UpperCamelCase					:int        =		self.char_decode
            __UpperCamelCase					:int        =		1
            __UpperCamelCase					:int        =		'''[s]'''
        elif format == DecodeType.BPE:
            __UpperCamelCase					:Dict        =		self.bpe_decode
            __UpperCamelCase					:Union[str, Any]        =		2
            __UpperCamelCase					:List[Any]        =		'''#'''
        elif format == DecodeType.WORDPIECE:
            __UpperCamelCase					:int        =		self.wp_decode
            __UpperCamelCase					:Any        =		102
            __UpperCamelCase					:Any        =		'''[SEP]'''
        else:
            raise ValueError(f"""Format {format} is not supported.""")

        __UpperCamelCase  ,	__UpperCamelCase					:Tuple        =		[], []
        __UpperCamelCase					:str        =		pred_logits.size(0)
        __UpperCamelCase					:str        =		pred_logits.size(1)
        __UpperCamelCase  ,	__UpperCamelCase					:Union[str, Any]        =		pred_logits.topk(1			,			dim=-1			,			largest=__lowercase			,			sorted=__lowercase)
        __UpperCamelCase					:List[Any]        =		preds_index.view(-1			,			__lowercase)[:, 1:]
        __UpperCamelCase					:Optional[int]        =		decoder(__lowercase)
        __UpperCamelCase  ,	__UpperCamelCase					:List[Any]        =		torch.nn.functional.softmax(__lowercase			,			dim=2).max(dim=2)
        __UpperCamelCase					:str        =		preds_max_prob[:, 1:]

        for index in range(__lowercase):
            __UpperCamelCase					:Tuple        =		preds_str[index].find(__lowercase)
            __UpperCamelCase					:List[Any]        =		preds_str[index][:pred_eos]
            __UpperCamelCase					:Tuple        =		preds_index[index].cpu().tolist()
            __UpperCamelCase					:Optional[int]        =		pred_index.index(__lowercase) if eos_token in pred_index else -1
            __UpperCamelCase					:Tuple        =		preds_max_prob[index][: pred_eos_index + 1]
            __UpperCamelCase					:Dict        =		pred_max_prob.cumprod(dim=0)[-1] if pred_max_prob.nelement() != 0 else 0.0
            dec_strs.append(__lowercase)
            conf_scores.append(__lowercase)

        return dec_strs, conf_scores







    def       UpperCamelCase__       (     self			,			__lowercase)	->     Any:
        __UpperCamelCase					:str        =		[seq.replace(''' '''			,			'''''') for seq in self.char_tokenizer.batch_decode(__lowercase)]
        return decode_strs







    def       UpperCamelCase__       (     self			,			__lowercase)	->     Optional[int]:
        return self.bpe_tokenizer.batch_decode(__lowercase)







    def       UpperCamelCase__       (     self			,			__lowercase)	->     str:
        __UpperCamelCase					:str        =		[seq.replace(''' '''			,			'''''') for seq in self.wp_tokenizer.batch_decode(__lowercase)]
        return decode_strs






from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


__lowercase										= logging.get_logger(__name__)

__lowercase										= {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__lowercase										= {
    '''vocab_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
    },
}

__lowercase										= {'''allegro/herbert-base-cased''': 514}
__lowercase										= {}


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    a__     :						Tuple      =				VOCAB_FILES_NAMES
    a__     :						Dict      =				PRETRAINED_VOCAB_FILES_MAP
    a__     :						Tuple      =				PRETRAINED_INIT_CONFIGURATION
    a__     :						Optional[Any]      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__     :						Union[str, Any]      =				HerbertTokenizer







    def __init__(     self			,			__lowercase=None			,			__lowercase=None			,			__lowercase=None			,			__lowercase="<s>"			,			__lowercase="<unk>"			,			__lowercase="<pad>"			,			__lowercase="<mask>"			,			__lowercase="</s>"			,			**__lowercase			,			)	->     Optional[Any]:
        super().__init__(
            __lowercase			,			__lowercase			,			tokenizer_file=__lowercase			,			cls_token=__lowercase			,			unk_token=__lowercase			,			pad_token=__lowercase			,			mask_token=__lowercase			,			sep_token=__lowercase			,			**__lowercase			,			)







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = None)	->     List[int]:
        __UpperCamelCase					:List[str]        =		[self.cls_token_id]
        __UpperCamelCase					:Tuple        =		[self.sep_token_id]
        if token_ids_a is None:
            return cls + token_ids_a + sep

        return cls + token_ids_a + sep + token_ids_a + sep







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = None			,			__lowercase = False)	->     List[int]:
        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] + ([0] * len(__lowercase)) + [1]







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = None)	->     List[int]:
        __UpperCamelCase					:Optional[Any]        =		[self.sep_token_id]
        __UpperCamelCase					:int        =		[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) * [0] + len(token_ids_a + sep) * [1]







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = None)	->     Tuple[str]:
        __UpperCamelCase					:Optional[int]        =		self._tokenizer.model.save(__lowercase			,			name=__lowercase)
        return tuple(__lowercase)





