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		( __UpperCAmelCase			):
      snake_case__    :     Optional[int]													=			'''char'''
      snake_case__    :     List[Any]													=			'''bpe'''
      snake_case__    :     Dict													=			'''wp'''






__lowerCAmelCase					: Any        =(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)





class 				_A		( __UpperCAmelCase			):
      snake_case__    :     Any													=			['''image_processor''', '''char_tokenizer''']
      snake_case__    :     int													=			'''ViTImageProcessor'''
      snake_case__    :     Union[str, Any]													=			'''MgpstrTokenizer'''

      def __init__(		self	, __lowerCAmelCase=None	, __lowerCAmelCase=None	, **__lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			None
            if "feature_extractor" in kwargs:
                  warnings.warn(
                      """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                      """ instead."""	, __SCREAMING_SNAKE_CASE	, )
                  lowercase														=			kwargs.pop("""feature_extractor"""		)

            lowercase														=			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`."""		)

            lowercase														=			tokenizer
            lowercase														=			AutoTokenizer.from_pretrained("""gpt2"""		)
            lowercase														=			AutoTokenizer.from_pretrained("""bert-base-uncased"""		)

            super().__init__(__SCREAMING_SNAKE_CASE	, __SCREAMING_SNAKE_CASE		)

      def __call__(		self	, __lowerCAmelCase=None	, __lowerCAmelCase=None	, __lowerCAmelCase=None	, **__lowerCAmelCase		):







            """simple docstring"""



            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:
                  lowercase														=			self.image_processor(__SCREAMING_SNAKE_CASE	, return_tensors=__SCREAMING_SNAKE_CASE	, **__SCREAMING_SNAKE_CASE		)
            if text is not None:
                  lowercase														=			self.char_tokenizer(__SCREAMING_SNAKE_CASE	, return_tensors=__SCREAMING_SNAKE_CASE	, **__SCREAMING_SNAKE_CASE		)

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

      def        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase							,							lowercase							,							lowercase														=			sequences
            lowercase														=			char_preds.size(0		)

            lowercase							,							lowercase														=			self._decode_helper(__SCREAMING_SNAKE_CASE	, """char"""		)
            lowercase							,							lowercase														=			self._decode_helper(__SCREAMING_SNAKE_CASE	, """bpe"""		)
            lowercase							,							lowercase														=			self._decode_helper(__SCREAMING_SNAKE_CASE	, """wp"""		)

            lowercase														=			[]
            lowercase														=			[]
            for i in range(__SCREAMING_SNAKE_CASE		):
                  lowercase														=			[char_scores[i], bpe_scores[i], wp_scores[i]]
                  lowercase														=			[char_strs[i], bpe_strs[i], wp_strs[i]]
                  lowercase														=			scores.index(max(__SCREAMING_SNAKE_CASE		)		)
                  final_strs.append(strs[max_score_index]		)
                  final_scores.append(scores[max_score_index]		)

            lowercase														=			{}
            lowercase														=			final_strs
            lowercase														=			final_scores
            lowercase														=			char_strs
            lowercase														=			bpe_strs
            lowercase														=			wp_strs
            return out

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            if format == DecodeType.CHARACTER:
                  lowercase														=			self.char_decode
                  lowercase														=			1
                  lowercase														=			"""[s]"""
            elif format == DecodeType.BPE:
                  lowercase														=			self.bpe_decode
                  lowercase														=			2
                  lowercase														=			"""#"""
            elif format == DecodeType.WORDPIECE:
                  lowercase														=			self.wp_decode
                  lowercase														=			102
                  lowercase														=			"""[SEP]"""
            else:
                  raise ValueError(f'Format {format} is not supported.'		)

            lowercase							,							lowercase														=			[], []
            lowercase														=			pred_logits.size(0		)
            lowercase														=			pred_logits.size(1		)
            lowercase							,							lowercase														=			pred_logits.topk(1	, dim=-1	, largest=__SCREAMING_SNAKE_CASE	, sorted=__SCREAMING_SNAKE_CASE		)
            lowercase														=			preds_index.view(-1	, __SCREAMING_SNAKE_CASE		)[:, 1:]
            lowercase														=			decoder(__SCREAMING_SNAKE_CASE		)
            lowercase							,							lowercase														=			torch.nn.functional.softmax(__SCREAMING_SNAKE_CASE	, dim=2		).max(dim=2		)
            lowercase														=			preds_max_prob[:, 1:]

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

            return dec_strs, conf_scores

      def        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			[seq.replace(""" """	, """"""		) for seq in self.char_tokenizer.batch_decode(__SCREAMING_SNAKE_CASE		)]
            return decode_strs

      def        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



            return self.bpe_tokenizer.batch_decode(__SCREAMING_SNAKE_CASE		)







      def        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			[seq.replace(""" """	, """"""		) for seq in self.wp_tokenizer.batch_decode(__SCREAMING_SNAKE_CASE		)]
            return decode_strs




"""simple docstring"""






from __future__ import annotations


def     UpperCAmelCase__ (       lowerCAmelCase__  :list[float]      ,	lowerCAmelCase__  :list[float]	)				->					float:
      '''simple docstring'''


      lowercase														=			sorted(numsa + numsa	)
      lowercase							,							lowercase														=			divmod(len(lowerCAmelCase__	)      ,	2	)
      if mod == 1:
            return all_numbers[div]
      else:
            return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       __lowerCAmelCase					: List[Any]        =[float(x) for x in input("""Enter the elements of first array: """).split()]
       __lowerCAmelCase					: List[Any]        =[float(x) for x in input("""Enter the elements of second array: """).split()]
       print(F"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")



