


'''simple docstring'''




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 					a			(				snake_case__  ):
 '''simple docstring'''




 __lowerCAmelCase						: Tuple							  =		"""char"""
 __lowerCAmelCase						: List[Any]							  =		"""bpe"""
 __lowerCAmelCase						: Optional[int]							  =		"""wp"""





UpperCAmelCase_      :		Optional[Any]          =		(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)







class 					a			(				snake_case__  ):
 '''simple docstring'''




 __lowerCAmelCase						: int							  =		["""image_processor""", """char_tokenizer"""]
 __lowerCAmelCase						: Tuple							  =		"""ViTImageProcessor"""
 __lowerCAmelCase						: Optional[int]							  =		"""MgpstrTokenizer"""




 def __init__(    self		,      lowerCamelCase_=None		,      lowerCamelCase_=None		,      **lowerCamelCase_      )      ->    str:
    _a					:  Any						=  None
    if "feature_extractor" in kwargs:
       warnings.warn(
           'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
           ' instead.'		,      lowerCamelCase_		,      )
       _a					:  Tuple						=  kwargs.pop('feature_extractor'      )

    _a					:  Union[str, Any]						=  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`.'      )

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

    super().__init__(lowerCamelCase_		,      lowerCamelCase_      )




 def __call__(    self		,      lowerCamelCase_=None		,      lowerCamelCase_=None		,      lowerCamelCase_=None		,      **lowerCamelCase_      )      ->    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:
       _a					:  Union[str, Any]						=  self.image_processor(lowerCamelCase_		,      return_tensors=lowerCamelCase_		,      **lowerCamelCase_      )
    if text is not None:
       _a					:  Optional[int]						=  self.char_tokenizer(lowerCamelCase_		,      return_tensors=lowerCamelCase_		,      **lowerCamelCase_      )

    if text is None:
       return inputs
    elif images is None:
       return encodings
    else:
       _a					:  List[str]						=  encodings['input_ids']
       return inputs




 def   __UpperCamelCase   (    self		,      lowerCamelCase_      )      ->    List[str]:
    _a		,						_a		,						_a					:  Dict						=  sequences
    _a					:  Optional[Any]						=  char_preds.size(0      )

    _a		,						_a					:  Any						=  self._decode_helper(lowerCamelCase_		,      'char'      )
    _a		,						_a					:  Union[str, Any]						=  self._decode_helper(lowerCamelCase_		,      'bpe'      )
    _a		,						_a					:  List[Any]						=  self._decode_helper(lowerCamelCase_		,      'wp'      )

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

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




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_      )      ->    List[str]:
    if format == DecodeType.CHARACTER:
       _a					:  int						=  self.char_decode
       _a					:  Optional[int]						=  1
       _a					:  List[str]						=  '[s]'
    elif format == DecodeType.BPE:
       _a					:  Optional[Any]						=  self.bpe_decode
       _a					:  Optional[int]						=  2
       _a					:  Union[str, Any]						=  '#'
    elif format == DecodeType.WORDPIECE:
       _a					:  Union[str, Any]						=  self.wp_decode
       _a					:  Optional[int]						=  1_0_2
       _a					:  Optional[int]						=  '[SEP]'
    else:
       raise ValueError(F'''Format {format} is not supported.'''      )

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

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

    return dec_strs, conf_scores




 def   __UpperCamelCase   (    self		,      lowerCamelCase_      )      ->    List[str]:
    _a					:  Optional[int]						=  [seq.replace(' '		,      ''      ) for seq in self.char_tokenizer.batch_decode(lowerCamelCase_      )]
    return decode_strs




 def   __UpperCamelCase   (    self		,      lowerCamelCase_      )      ->    Tuple:
    return self.bpe_tokenizer.batch_decode(lowerCamelCase_      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_      )      ->    List[Any]:
    _a					:  Optional[Any]						=  [seq.replace(' '		,      ''      ) for seq in self.wp_tokenizer.batch_decode(lowerCamelCase_      )]
    return decode_strs






'''simple docstring'''




def    UpperCAmelCase_  (       A		,	A      ):
   '''simple docstring'''





   return "\n".join(
       f'''{number} * {i} = {number * i}''' for i in range(1		,	number_of_terms + 1      )      )


if __name__ == "__main__":
 print(multiplication_table(number=5, number_of_terms=10))


