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__	:						List[str]						=      """char"""
    a__	:						str						=      """bpe"""
    a__	:						Union[str, Any]						=      """wp"""
__lowercase							 =					(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






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







    def __init__(	self , __lowercase=None , __lowercase=None , **__lowercase)       ->	Any:
        __UpperCamelCase     :Tuple			  =		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     :Any			  =		kwargs.pop('''feature_extractor''')

        __UpperCamelCase     :List[str]			  =		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     :Optional[Any]			  =		tokenizer
        __UpperCamelCase     :Any			  =		AutoTokenizer.from_pretrained('''gpt2''')
        __UpperCamelCase     :List[Any]			  =		AutoTokenizer.from_pretrained('''bert-base-uncased''')

        super().__init__(__lowercase , __lowercase)







    def __call__(	self , __lowercase=None , __lowercase=None , __lowercase=None , **__lowercase)       ->	Tuple:
        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     :Union[str, Any]			  =		self.image_processor(__lowercase , return_tensors=__lowercase , **__lowercase)
        if text is not None:
            __UpperCamelCase     :Dict			  =		self.char_tokenizer(__lowercase , return_tensors=__lowercase , **__lowercase)

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







    def 			UpperCamelCase__   (	self , __lowercase)       ->	Any:
        __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :Any			  =		sequences
        __UpperCamelCase     :Dict			  =		char_preds.size(0)

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

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

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







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

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

        for index in range(__lowercase):
            __UpperCamelCase     :int			  =		preds_str[index].find(__lowercase)
            __UpperCamelCase     :Union[str, Any]			  =		preds_str[index][:pred_eos]
            __UpperCamelCase     :int			  =		preds_index[index].cpu().tolist()
            __UpperCamelCase     :str			  =		pred_index.index(__lowercase) if eos_token in pred_index else -1
            __UpperCamelCase     :Optional[int]			  =		preds_max_prob[index][: pred_eos_index + 1]
            __UpperCamelCase     :str			  =		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     :Optional[int]			  =		[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)       ->	Tuple:
        __UpperCamelCase     :Optional[int]			  =		[seq.replace(''' ''' , '''''') for seq in self.wp_tokenizer.batch_decode(__lowercase)]
        return decode_strs









import math




def    snake_case_     (  snake_case			,							snake_case     )  ->				float:
       return math.pow(snake_case			,							2     ) - a




def    snake_case_     (  snake_case     )  ->				float:
       return 2 * x




def    snake_case_     (  snake_case     )  ->				float:
       lowercase__:   Dict  			=   2.0

       while start <= a:
              lowercase__:   str  			=   math.pow(snake_case			,							2     )

       return start




def    snake_case_     (  snake_case			,							snake_case = 99_99			,							snake_case = 0.0_0_0_0_0_0_0_0_0_0_0_0_0_1     )  ->				float:

       if a < 0:
              raise ValueError('math domain error'     )

       lowercase__:   Tuple  			=   get_initial_point(snake_case     )

       for _ in range(snake_case     ):
              lowercase__:   List[Any]  			=   value
              lowercase__:   Any  			=   value - fx(snake_case			,							snake_case     ) / fx_derivative(snake_case     )
              if abs(prev_value - value     ) < tolerance:
                     return value

       return value


if __name__ == "__main__":
 from doctest import testmod

 testmod()


