





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__    (	_UpperCAmelCase  ):
						A_						:    List[Any]							=   'char'
						A_						:    List[Any]							=   'bpe'
						A_						:    Dict							=   'wp'






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

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
						A_						:    Tuple							=   ['image_processor', 'char_tokenizer']
						A_						:    Union[str, Any]							=   'ViTImageProcessor'
						A_						:    Optional[int]							=   'MgpstrTokenizer'


						def __init__(self	:				List[Any]    ,      a__	:				List[Any]=None    ,      a__	:				Optional[int]=None    ,      **a__	:				List[Any]				):





									"""simple docstring"""



									__snake_case								=       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__    ,      )
												__snake_case								=       kwargs.pop('''feature_extractor'''				)

									__snake_case								=       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`.'''				)

									__snake_case								=       tokenizer
									__snake_case								=       AutoTokenizer.from_pretrained('''gpt2'''				)
									__snake_case								=       AutoTokenizer.from_pretrained('''bert-base-uncased'''				)

									super().__init__(a__    ,      a__				)


						def __call__(self	:				Optional[Any]    ,      a__	:				Union[str, Any]=None    ,      a__	:				Any=None    ,      a__	:				Optional[Any]=None    ,      **a__	:				int				):





									"""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:
												__snake_case								=       self.image_processor(a__    ,      return_tensors=a__    ,      **a__				)
									if text is not None:
												__snake_case								=       self.char_tokenizer(a__    ,      return_tensors=a__    ,      **a__				)

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


						def        a			(self	:				Any    ,      a__	:				int				):





									"""simple docstring"""



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

									__snake_case	,     __snake_case								=       self._decode_helper(a__    ,      '''char'''				)
									__snake_case	,     __snake_case								=       self._decode_helper(a__    ,      '''bpe'''				)
									__snake_case	,     __snake_case								=       self._decode_helper(a__    ,      '''wp'''				)

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

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


						def        a			(self	:				List[Any]    ,      a__	:				Optional[int]    ,      a__	:				str				):





									"""simple docstring"""



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

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

									for index in range(a__				):
												__snake_case								=       preds_str[index].find(a__				)
												__snake_case								=       preds_str[index][:pred_eos]
												__snake_case								=       preds_index[index].cpu().tolist()
												__snake_case								=       pred_index.index(a__				) if eos_token in pred_index else -1
												__snake_case								=       preds_max_prob[index][: pred_eos_index + 1]
												__snake_case								=       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        a			(self	:				List[Any]    ,      a__	:				List[Any]				):





									"""simple docstring"""



									__snake_case								=       [seq.replace(''' '''    ,      ''''''				) for seq in self.char_tokenizer.batch_decode(a__				)]
									return decode_strs


						def        a			(self	:				Any    ,      a__	:				Optional[int]				):





									"""simple docstring"""



									return self.bpe_tokenizer.batch_decode(a__				)


						def        a			(self	:				Optional[Any]    ,      a__	:				str				):





									"""simple docstring"""



									__snake_case								=       [seq.replace(''' '''    ,      ''''''				) for seq in self.wp_tokenizer.batch_decode(a__				)]
									return decode_strs








from pathlib import Path

import fire






def lowerCamelCase__   (							snake_case_     :  str	,				snake_case_     :  str	,				snake_case_     :  int	)				->    str:
   __snake_case								=       Path(snake_case_	)
   __snake_case								=       Path(snake_case_	)
   dest_dir.mkdir(exist_ok=snake_case_	)
   for path in src_dir.iterdir():
      __snake_case								=       [x.rstrip() for x in list(path.open().readlines()	)][:n]
      __snake_case								=       dest_dir.joinpath(path.name	)
      print(snake_case_	)
      dest_path.open('''w'''	).write('''\n'''.join(snake_case_	)	)


if __name__ == "__main__":
    fire.Fire(minify)

