




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     lowerCamelCase__(					A_):
      UpperCAmelCase__      :	Optional[int]    		=   "char"
      UpperCAmelCase__      :	List[str]    		=   "bpe"
      UpperCAmelCase__      :	Tuple    		=   "wp"



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





class     lowerCamelCase__(					A_):
      UpperCAmelCase__      :	Optional[int]    		=   ["image_processor", "char_tokenizer"]
      UpperCAmelCase__      :	str    		=   "ViTImageProcessor"
      UpperCAmelCase__      :	Optional[Any]    		=   "MgpstrTokenizer"
      def __init__(				self: int    ,       UpperCamelCase_: str=None    ,       UpperCamelCase_: Tuple=None    ,       **UpperCamelCase_: Dict   ):
             __lowerCamelCase   =						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_    ,       )
                    __lowerCamelCase   =						kwargs.pop("""feature_extractor"""   )

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

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

             if text is None:
                    return inputs
             elif images is None:
                    return encodings
             else:
                    __lowerCamelCase   =						encodings["""input_ids"""]
                    return inputs
      def 			lowerCAmelCase__						(				self: Tuple    ,       UpperCamelCase_: Dict   ):
             __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase   =						sequences
             __lowerCamelCase   =						char_preds.size(0   )

             __lowerCamelCase,   __lowerCamelCase   =						self._decode_helper(A_    ,       """char"""   )
             __lowerCamelCase,   __lowerCamelCase   =						self._decode_helper(A_    ,       """bpe"""   )
             __lowerCamelCase,   __lowerCamelCase   =						self._decode_helper(A_    ,       """wp"""   )

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

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

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

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




      def 			lowerCAmelCase__						(				self: str    ,       UpperCamelCase_: int   ):
             __lowerCamelCase   =						[seq.replace(""" """    ,       """"""   ) for seq in self.wp_tokenizer.batch_decode(A_   )]
             return decode_strs





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A		       =						{'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'SEW_PRETRAINED_MODEL_ARCHIVE_LIST',
       'SEWForCTC',
       'SEWForSequenceClassification',
       'SEWModel',
       'SEWPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_sew import (
          SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
          SEWForCTC,
          SEWForSequenceClassification,
          SEWModel,
          SEWPreTrainedModel,
      )


else:
   import sys

   _A		       =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





