






'''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    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 __a			:						Optional[Any]      =						'''char'''
 __a			:						Optional[int]      =						'''bpe'''
 __a			:						Optional[int]      =						'''wp'''






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




class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 __a			:						Tuple      =						['''image_processor''', '''char_tokenizer''']
 __a			:						Any      =						'''ViTImageProcessor'''
 __a			:						Any      =						'''MgpstrTokenizer'''
 def __init__(							self			,     __lowerCAmelCase=None			,     __lowerCAmelCase=None			,     **__lowerCAmelCase ):
       UpperCamelCase_				:   Optional[int]					    =							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			,     )
             UpperCamelCase_				:   Any					    =							kwargs.pop("""feature_extractor""" )

       UpperCamelCase_				:   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`.""" )

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

       super().__init__(__lowerCAmelCase			,     __lowerCAmelCase )
 def __call__(							self			,     __lowerCAmelCase=None			,     __lowerCAmelCase=None			,     __lowerCAmelCase=None			,     **__lowerCAmelCase ):
       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:
             UpperCamelCase_				:   List[str]					    =							self.image_processor(__lowerCAmelCase			,     return_tensors=__lowerCAmelCase			,     **__lowerCAmelCase )
       if text is not None:
             UpperCamelCase_				:   Optional[int]					    =							self.char_tokenizer(__lowerCAmelCase			,     return_tensors=__lowerCAmelCase			,     **__lowerCAmelCase )

       if text is None:
             return inputs
       elif images is None:
             return encodings
       else:
             UpperCamelCase_				:   Optional[Any]					    =							encodings["""input_ids"""]
             return inputs
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   List[Any]					    =							sequences
       UpperCamelCase_				:   Any					    =							char_preds.size(0 )

       UpperCamelCase_				:   Any					    =							self._decode_helper(__lowerCAmelCase			,     """char""" )
       UpperCamelCase_				:   Optional[Any]					    =							self._decode_helper(__lowerCAmelCase			,     """bpe""" )
       UpperCamelCase_				:   Dict					    =							self._decode_helper(__lowerCAmelCase			,     """wp""" )

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

       UpperCamelCase_				:   Optional[Any]					    =							{}
       UpperCamelCase_				:   Tuple					    =							final_strs
       UpperCamelCase_				:   Optional[int]					    =							final_scores
       UpperCamelCase_				:   str					    =							char_strs
       UpperCamelCase_				:   List[Any]					    =							bpe_strs
       UpperCamelCase_				:   Union[str, Any]					    =							wp_strs
       return out
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       if format == DecodeType.CHARACTER:
             UpperCamelCase_				:   List[Any]					    =							self.char_decode
             UpperCamelCase_				:   str					    =							1
             UpperCamelCase_				:   List[Any]					    =							"""[s]"""
       elif format == DecodeType.BPE:
             UpperCamelCase_				:   Any					    =							self.bpe_decode
             UpperCamelCase_				:   Union[str, Any]					    =							2
             UpperCamelCase_				:   Tuple					    =							"""#"""
       elif format == DecodeType.WORDPIECE:
             UpperCamelCase_				:   Dict					    =							self.wp_decode
             UpperCamelCase_				:   Tuple					    =							1_02
             UpperCamelCase_				:   Tuple					    =							"""[SEP]"""
       else:
             raise ValueError(F"Format {format} is not supported." )

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

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





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Dict					    =							[seq.replace(""" """			,     """""" ) for seq in self.wp_tokenizer.batch_decode(__lowerCAmelCase )]
       return decode_strs














'''simple docstring'''

# Lint as: python3
import itertools
import os
import re


UpperCamelCase							=re.compile(R"([A-Z]+)([A-Z][a-z])")
UpperCamelCase							=re.compile(R"([a-z\d])([A-Z])")

UpperCamelCase							=re.compile(R"(?<!_)_(?!_)")
UpperCamelCase							=re.compile(R"(_{2,})")

UpperCamelCase							=R"^\w+(\.\w+)*$"

UpperCamelCase							=R"<>:/\|?*"



def 							snake_case					(     a_							: str	)       ->				int:





      """simple docstring"""

      UpperCamelCase_				:   str					    =							_uppercase_uppercase_re.sub(r"""\1_\2"""    ,   a_	)
      UpperCamelCase_				:   List[Any]					    =							_lowercase_uppercase_re.sub(r"""\1_\2"""    ,   a_	)
      return name.lower()



def 							snake_case					(     a_							: Any	)       ->				Optional[Any]:





      """simple docstring"""

      UpperCamelCase_				:   Optional[Any]					    =							_single_underscore_re.split(a_	)
      UpperCamelCase_				:   Tuple					    =							[_multiple_underscores_re.split(a_	) for n in name]
      return "".join(n.capitalize() for n in itertools.chain.from_iterable(a_	) if n != """"""	)



def 							snake_case					(     a_							: Optional[int]	)       ->				str:





      """simple docstring"""

      if os.path.basename(a_	) != name:
            raise ValueError(f"Should be a dataset name, not a path: {name}"	)
      return camelcase_to_snakecase(a_	)



def 							snake_case					(     a_							: List[Any]    ,   a_							: Optional[int]	)       ->				Optional[Any]:





      """simple docstring"""

      if os.path.basename(a_	) != name:
            raise ValueError(f"Should be a dataset name, not a path: {name}"	)
      if not re.match(_split_re    ,   a_	):
            raise ValueError(f"Split name should match '{_split_re}'' but got '{split}'."	)
      return f"{filename_prefix_for_name(a_	)}-{split}"



def 							snake_case					(     a_							: Optional[Any]    ,   a_							: int    ,   a_							: List[Any]    ,   a_							: Union[str, Any]=None	)       ->				Dict:





      """simple docstring"""

      UpperCamelCase_				:   Optional[int]					    =							filename_prefix_for_split(a_    ,   a_	)
      if filetype_suffix:
            prefix += f".{filetype_suffix}"
      UpperCamelCase_				:   str					    =							os.path.join(a_    ,   a_	)
      return f"{filepath}*"



def 							snake_case					(     a_							: Tuple    ,   a_							: Any    ,   a_							: Optional[int]    ,   a_							: Union[str, Any]=None    ,   a_							: Optional[Any]=None	)       ->				List[Any]:





      """simple docstring"""

      UpperCamelCase_				:   Dict					    =							filename_prefix_for_split(a_    ,   a_	)
      UpperCamelCase_				:   Dict					    =							os.path.join(a_    ,   a_	)

      if shard_lengths:
            UpperCamelCase_				:   Optional[int]					    =							len(a_	)
            UpperCamelCase_				:   Tuple					    =							[f"{prefix}-{shard_id:05d}-of-{num_shards:05d}" for shard_id in range(a_	)]
            if filetype_suffix:
                  UpperCamelCase_				:   Tuple					    =							[filename + f".{filetype_suffix}" for filename in filenames]
            return filenames
      else:
            UpperCamelCase_				:   Dict					    =							prefix
            if filetype_suffix:
                  filename += f".{filetype_suffix}"
            return [filename]






