

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 _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			lowerCamelCase__           ='char'
			lowerCamelCase__           ='bpe'
			lowerCamelCase__           ='wp'

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






class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			lowerCamelCase__           =['image_processor', 'char_tokenizer']
			lowerCamelCase__           ='ViTImageProcessor'
			lowerCamelCase__           ='MgpstrTokenizer'
			def __init__(			self							: str      ,      a							: Optional[int]=None      ,      a							: Tuple=None      ,      **a							: Union[str, Any]	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      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."      ,      a      ,      )
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       kwargs.pop("feature_extractor"	)

				SCREAMING_SNAKE_CASE   :      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`."	)

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       tokenizer
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       AutoTokenizer.from_pretrained("gpt2"	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       AutoTokenizer.from_pretrained("bert-base-uncased"	)

				super().__init__(a      ,      a	)
			def __call__(			self							: Union[str, Any]      ,      a							: str=None      ,      a							: List[Any]=None      ,      a							: List[str]=None      ,      **a							: Optional[int]	)							->       Union[str, Any]:

				"""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:
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.image_processor(a      ,      return_tensors=a      ,      **a	)
				if text is not None:
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.char_tokenizer(a      ,      return_tensors=a      ,      **a	)

				if text is None:
					return inputs
				elif images is None:
					return encodings
				else:
					SCREAMING_SNAKE_CASE   :      Tuple				     =       encodings["input_ids"]
					return inputs
			def 							__UpperCamelCase (			self							: List[str]      ,      a							: str	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Dict				     =       sequences
				SCREAMING_SNAKE_CASE   :      str				     =       char_preds.size(0	)

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Dict				     =       self._decode_helper(a      ,      "char"	)
				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Tuple				     =       self._decode_helper(a      ,      "bpe"	)
				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self._decode_helper(a      ,      "wp"	)

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

				SCREAMING_SNAKE_CASE   :      Dict				     =       {}
				SCREAMING_SNAKE_CASE   :      int				     =       final_strs
				SCREAMING_SNAKE_CASE   :      Any				     =       final_scores
				SCREAMING_SNAKE_CASE   :      Tuple				     =       char_strs
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       bpe_strs
				SCREAMING_SNAKE_CASE   :      List[str]				     =       wp_strs
				return out
			def 							__UpperCamelCase (			self							: List[Any]      ,      a							: Union[str, Any]      ,      a							: Optional[int]	)							->       Any:

				"""simple docstring"""

				if format == DecodeType.CHARACTER:
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.char_decode
					SCREAMING_SNAKE_CASE   :      Dict				     =       1
					SCREAMING_SNAKE_CASE   :      str				     =       "[s]"
				elif format == DecodeType.BPE:
					SCREAMING_SNAKE_CASE   :      List[str]				     =       self.bpe_decode
					SCREAMING_SNAKE_CASE   :      int				     =       2
					SCREAMING_SNAKE_CASE   :      Dict				     =       "#"
				elif format == DecodeType.WORDPIECE:
					SCREAMING_SNAKE_CASE   :      str				     =       self.wp_decode
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       102
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       "[SEP]"
				else:
					raise ValueError(F"Format {format} is not supported."	)

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

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

				return dec_strs, conf_scores
			def 							__UpperCamelCase (			self							: int      ,      a							: Optional[Any]	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      str				     =       [seq.replace(" "      ,      ""	) for seq in self.char_tokenizer.batch_decode(a	)]
				return decode_strs
			def 							__UpperCamelCase (			self							: Dict      ,      a							: Optional[int]	)							->       Any:

				"""simple docstring"""

				return self.bpe_tokenizer.batch_decode(a	)






			def 							__UpperCamelCase (			self							: List[Any]      ,      a							: Optional[int]	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       [seq.replace(" "      ,      ""	) for seq in self.wp_tokenizer.batch_decode(a	)]
				return decode_strs


import math

def  lowerCamelCase__ (					_a							,			_a):
	if (
	    not isinstance(_a							,			(int, float))
	    or power_factor < -1
	    or power_factor > 1
	):
		raise ValueError("power_factor must be a valid float value between -1 and 1.")
	return apparent_power * power_factor

def  lowerCamelCase__ (					_a							,			_a):
	if (
	    not isinstance(_a							,			(int, float))
	    or power_factor < -1
	    or power_factor > 1
	):
		raise ValueError("power_factor must be a valid float value between -1 and 1.")
	return apparent_power * math.sqrt(1 - power_factor**2)


if __name__ == "__main__":
			import doctest

			doctest.testmod()