






'''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    _UpperCAmelCase			(     snake_case_						):




   """simple docstring"""

   snake_case    				=						'''char'''
   snake_case    				=						'''bpe'''
   snake_case    				=						'''wp'''





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

class    _UpperCAmelCase			(     snake_case_						):




   """simple docstring"""

   snake_case    				=						['''image_processor''', '''char_tokenizer''']
   snake_case    				=						'''ViTImageProcessor'''
   snake_case    				=						'''MgpstrTokenizer'''

   def __init__(		self :	List[str]      ,      __UpperCAmelCase :	Optional[int]=None      ,      __UpperCAmelCase :	Tuple=None      ,      **__UpperCAmelCase :	str		):





         '''simple docstring'''





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

         _A			     =	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`."		)

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

         super().__init__(__UpperCAmelCase      ,      __UpperCAmelCase		)

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

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

   def 						lowerCAmelCase (		self :	Optional[int]      ,      __UpperCAmelCase :	List[Any]		):





         '''simple docstring'''





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

         _A     ,	_A			     =	self._decode_helper(__UpperCAmelCase      ,      "char"		)
         _A     ,	_A			     =	self._decode_helper(__UpperCAmelCase      ,      "bpe"		)
         _A     ,	_A			     =	self._decode_helper(__UpperCAmelCase      ,      "wp"		)

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

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

   def 						lowerCAmelCase (		self :	Tuple      ,      __UpperCAmelCase :	Tuple      ,      __UpperCAmelCase :	List[str]		):





         '''simple docstring'''





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

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

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

         return dec_strs, conf_scores

   def 						lowerCAmelCase (		self :	Dict      ,      __UpperCAmelCase :	List[Any]		):





         '''simple docstring'''





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

   def 						lowerCAmelCase (		self :	Dict      ,      __UpperCAmelCase :	List[str]		):





         '''simple docstring'''





         return self.bpe_tokenizer.batch_decode(__UpperCAmelCase		)

   def 						lowerCAmelCase (		self :	Optional[int]      ,      __UpperCAmelCase :	Union[str, Any]		):





         '''simple docstring'''





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










'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_UpperCamelCase											=						{
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=						[
       'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'BioGptForCausalLM',
       'BioGptForTokenClassification',
       'BioGptForSequenceClassification',
       'BioGptModel',
       'BioGptPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
   from .tokenization_biogpt import BioGptTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_biogpt import (
          BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          BioGptForCausalLM,
          BioGptForSequenceClassification,
          BioGptForTokenClassification,
          BioGptModel,
          BioGptPreTrainedModel,
      )


else:
   import sys

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






