
"""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 			_SCREAMING_SNAKE_CASE					(			a__							):
			UpperCAmelCase_  :List[str]				 =						"""char"""
			UpperCAmelCase_  :Optional[Any]				 =						"""bpe"""
			UpperCAmelCase_  :int				 =						"""wp"""




__UpperCAmelCase       =  (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)




class 			_SCREAMING_SNAKE_CASE					(			a__							):
			UpperCAmelCase_  :Any				 =						["""image_processor""", """char_tokenizer"""]
			UpperCAmelCase_  :int				 =						"""ViTImageProcessor"""
			UpperCAmelCase_  :Any				 =						"""MgpstrTokenizer"""






			def __init__(					self       ,   __A=None       ,   __A=None       ,   **__A       )			->     Optional[Any]:
						lowerCAmelCase_    :Dict			     =  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__       ,   )
									lowerCAmelCase_    :Optional[int]			     =  kwargs.pop("""feature_extractor"""       )

						lowerCAmelCase_    :Optional[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`."""       )

						lowerCAmelCase_    :Tuple			     =  tokenizer
						lowerCAmelCase_    :int			     =  AutoTokenizer.from_pretrained("""gpt2"""       )
						lowerCAmelCase_    :Tuple			     =  AutoTokenizer.from_pretrained("""bert-base-uncased"""       )

						super().__init__(lowerCAmelCase__       ,   lowerCAmelCase__       )






			def __call__(					self       ,   __A=None       ,   __A=None       ,   __A=None       ,   **__A       )			->     Optional[int]:
						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:
									lowerCAmelCase_    :Dict			     =  self.image_processor(lowerCAmelCase__       ,   return_tensors=lowerCAmelCase__       ,   **lowerCAmelCase__       )
						if text is not None:
									lowerCAmelCase_    :Tuple			     =  self.char_tokenizer(lowerCAmelCase__       ,   return_tensors=lowerCAmelCase__       ,   **lowerCAmelCase__       )

						if text is None:
									return inputs
						elif images is None:
									return encodings
						else:
									lowerCAmelCase_    :Tuple			     =  encodings["input_ids"]
									return inputs






			def  __lowerCAmelCase (					self       ,   __A       )			->     Dict:
						lowerCAmelCase_    :str			     =  sequences
						lowerCAmelCase_    :int			     =  char_preds.size(0       )

						lowerCAmelCase_    :Union[str, Any]			     =  self._decode_helper(lowerCAmelCase__       ,   """char"""       )
						lowerCAmelCase_    :Dict			     =  self._decode_helper(lowerCAmelCase__       ,   """bpe"""       )
						lowerCAmelCase_    :Any			     =  self._decode_helper(lowerCAmelCase__       ,   """wp"""       )

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

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






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

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

						for index in range(lowerCAmelCase__       ):
									lowerCAmelCase_    :Optional[int]			     =  preds_str[index].find(lowerCAmelCase__       )
									lowerCAmelCase_    :List[Any]			     =  preds_str[index][:pred_eos]
									lowerCAmelCase_    :Optional[int]			     =  preds_index[index].cpu().tolist()
									lowerCAmelCase_    :Any			     =  pred_index.index(lowerCAmelCase__       ) if eos_token in pred_index else -1
									lowerCAmelCase_    :Dict			     =  preds_max_prob[index][: pred_eos_index + 1]
									lowerCAmelCase_    :List[str]			     =  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  __lowerCAmelCase (					self       ,   __A       )			->     str:
						lowerCAmelCase_    :List[Any]			     =  [seq.replace(""" """       ,   """"""       ) for seq in self.char_tokenizer.batch_decode(lowerCAmelCase__       )]
						return decode_strs






			def  __lowerCAmelCase (					self       ,   __A       )			->     str:
						return self.bpe_tokenizer.batch_decode(lowerCAmelCase__       )






			def  __lowerCAmelCase (					self       ,   __A       )			->     Dict:
						lowerCAmelCase_    :Optional[int]			     =  [seq.replace(""" """       ,   """"""       ) for seq in self.wp_tokenizer.batch_decode(lowerCAmelCase__       )]
						return decode_strs













"""simple docstring"""




a						: Any     	=			{
    '''a''': '''AAAAA''',
    '''b''': '''AAAAB''',
    '''c''': '''AAABA''',
    '''d''': '''AAABB''',
    '''e''': '''AABAA''',
    '''f''': '''AABAB''',
    '''g''': '''AABBA''',
    '''h''': '''AABBB''',
    '''i''': '''ABAAA''',
    '''j''': '''BBBAA''',
    '''k''': '''ABAAB''',
    '''l''': '''ABABA''',
    '''m''': '''ABABB''',
    '''n''': '''ABBAA''',
    '''o''': '''ABBAB''',
    '''p''': '''ABBBA''',
    '''q''': '''ABBBB''',
    '''r''': '''BAAAA''',
    '''s''': '''BAAAB''',
    '''t''': '''BAABA''',
    '''u''': '''BAABB''',
    '''v''': '''BBBAB''',
    '''w''': '''BABAA''',
    '''x''': '''BABAB''',
    '''y''': '''BABBA''',
    '''z''': '''BABBB''',
    ''' ''': ''' ''',
}


a						: List[Any]     	=			{value: key for key, value in encode_dict.items()}






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str					) ->str:






	'''simple docstring'''






	a      :		int	=				""
	for letter in word.lower():
		if letter.isalpha() or letter == " ":
			encoded += encode_dict[letter]
		else:
			raise Exception("encode() accepts only letters of the alphabet and spaces"					)
	return encoded






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str					) ->str:






	'''simple docstring'''






	if set(_lowercase					) - {"A", "B", " "} != set():
		raise Exception("decode() accepts only 'A', 'B' and spaces"					)
	a      :		Optional[Any]	=				""
	for word in coded.split():
		while len(_lowercase					) != 0:
			decoded += decode_dict[word[:5]]
			a      :		List[Any]	=				word[5:]
		decoded += " "
	return decoded.strip()


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

		testmod()


