






'''simple docstring'''



import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput
class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      def __init__(			self      :		Any  ,   UpperCAmelCase      :		List[str]  ,   UpperCAmelCase      :		str  ,   UpperCAmelCase      :		str		)	->	Dict:


       '''simple docstring'''

       lowercase       : Any						=dataset
       lowercase       : Union[str, Any]						=process
       lowercase       : str						=params






      def __len__(			self      :		Any		)	->	Dict:


       '''simple docstring'''

       return len(self.dataset		)






      def __getitem__(			self      :		Optional[int]  ,   UpperCAmelCase      :		Optional[int]		)	->	List[str]:


       '''simple docstring'''

       lowercase       : Dict						=self.dataset[i]
       lowercase       : Optional[Any]						=self.process(UpperCAmelCase  ,   **self.params		)
       return processed





class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      def __init__(			self      :		List[Any]  ,   UpperCAmelCase      :		List[str]  ,   UpperCAmelCase      :		Union[str, Any]  ,   UpperCAmelCase      :		List[Any]  ,   UpperCAmelCase      :		Optional[int]=None		)	->	List[Any]:


       '''simple docstring'''

       lowercase       : Dict						=loader
       lowercase       : List[Any]						=infer
       lowercase       : Tuple						=params
       if loader_batch_size == 1:
        # Let's spare some time by deactivating altogether
        lowercase       : Any						=None
       lowercase       : int						=loader_batch_size

       # Internal bookkeeping
       lowercase       : Tuple						=None
       lowercase       : Tuple						=None






      def __len__(			self      :		List[Any]		)	->	Dict:


       '''simple docstring'''

       return len(self.loader		)






      def __iter__(			self      :		str		)	->	List[str]:


       '''simple docstring'''

       lowercase       : str						=iter(self.loader		)
       return self






      def 					A__   (			self      :		str		)	->	Union[str, Any]:


       '''simple docstring'''

       if isinstance(self._loader_batch_data  ,   torch.Tensor		):
        # Batch data is simple tensor, just fetch the slice
        lowercase       : int						=self._loader_batch_data[self._loader_batch_index]
       else:
        # Batch data is assumed to be BaseModelOutput (or dict)
        lowercase       : str						={}
        for k, element in self._loader_batch_data.items():
         if isinstance(UpperCAmelCase  ,   UpperCAmelCase		):
          # Convert ModelOutput to tuple first
          lowercase       : str						=element.to_tuple()
          if isinstance(element[0]  ,   torch.Tensor		):
           lowercase       : Any						=tuple(el[self._loader_batch_index].unsqueeze(0		) for el in element		)
          elif isinstance(element[0]  ,   np.ndarray		):
           lowercase       : Optional[int]						=tuple(np.expand_dims(el[self._loader_batch_index]  ,   0		) for el in element		)
          continue
         if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(UpperCAmelCase  ,   UpperCAmelCase		):
          # Those are stored as lists of tensors so need specific unbatching.
          if isinstance(element[0]  ,   torch.Tensor		):
           lowercase       : Optional[int]						=tuple(el[self._loader_batch_index].unsqueeze(0		) for el in element		)
          elif isinstance(element[0]  ,   np.ndarray		):
           lowercase       : Optional[Any]						=tuple(np.expand_dims(el[self._loader_batch_index]  ,   0		) for el in element		)
          continue
         if element is None:
          # This can happen for optional data that get passed around
          lowercase       : Any						=None
         elif isinstance(element[self._loader_batch_index]  ,   torch.Tensor		):
          # Take correct batch data, but make it looked like batch_size=1
          # For compatibility with other methods within transformers

          lowercase       : Optional[Any]						=element[self._loader_batch_index].unsqueeze(0		)
         elif isinstance(element[self._loader_batch_index]  ,   np.ndarray		):
          # Take correct batch data, but make it looked like batch_size=1
          # For compatibility with other methods within transformers
          lowercase       : Any						=np.expand_dims(element[self._loader_batch_index]  ,   0		)
         else:
          # This is typically a list, so no need to `unsqueeze`.
          lowercase       : List[Any]						=element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
        lowercase       : Dict						=self._loader_batch_data.__class__(UpperCAmelCase		)
       self._loader_batch_index += 1
       return result






      def 					A__   (			self      :		Tuple		)	->	Dict:


       '''simple docstring'''

       if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
        # We are currently unrolling a batch so we just need to return
        # the current item within a batch
        return self.loader_batch_item()

       # We're out of items within a batch
       lowercase       : Any						=next(self.iterator		)
       lowercase       : Optional[int]						=self.infer(UpperCAmelCase  ,   **self.params		)
       # We now have a batch of "inferred things".
       if self.loader_batch_size is not None:
        # Try to infer the size of the batch
        if isinstance(UpperCAmelCase  ,   torch.Tensor		):
         lowercase       : Optional[Any]						=processed
        else:
         lowercase       : Optional[int]						=list(processed.keys()		)[0]
         lowercase       : Any						=processed[key]
        if isinstance(UpperCAmelCase  ,   UpperCAmelCase		):
         lowercase       : Optional[int]						=len(UpperCAmelCase		)
        else:
         lowercase       : Dict						=first_tensor.shape[0]
        if 0 < observed_batch_size < self.loader_batch_size:
         # could be last batch so we can't unroll as many
         # elements.
         lowercase       : List[str]						=observed_batch_size
        # Setting internal index to unwrap the batch
        lowercase       : Dict						=processed
        lowercase       : Any						=0
        return self.loader_batch_item()
       else:
        # We're not unrolling batches
        return processed





class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      def __init__(			self      :		Dict  ,   UpperCAmelCase      :		Optional[Any]  ,   UpperCAmelCase      :		int  ,   UpperCAmelCase      :		Tuple  ,   UpperCAmelCase      :		str=None		)	->	Optional[Any]:


       '''simple docstring'''

       super().__init__(UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase		)






      def __iter__(			self      :		Optional[int]		)	->	str:


       '''simple docstring'''

       lowercase       : int						=iter(self.loader		)
       lowercase       : str						=None
       return self






      def 					A__   (			self      :		Dict		)	->	int:


       '''simple docstring'''

       if self.subiterator is None:
        lowercase       : Optional[Any]						=self.infer(next(self.iterator		)  ,   **self.params		)
       try:
        # Try to return next item
        lowercase       : int						=next(self.subiterator		)
       except StopIteration:
        # When a preprocess iterator ends, we can start lookig at the next item
        # ChunkIterator will keep feeding until ALL elements of iterator
        # all have created their subiterator and have been iterating against.
        #
        # Another way to look at it, is we're basically flattening lists of lists
        # into a single list, but with generators
        lowercase       : Optional[int]						=self.infer(next(self.iterator		)  ,   **self.params		)
        lowercase       : List[str]						=next(self.subiterator		)
       return processed





class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      def __iter__(			self      :		Optional[int]		)	->	List[str]:


       '''simple docstring'''

       lowercase       : str						=iter(self.loader		)
       return self






      def 					A__   (			self      :		int		)	->	Tuple:


       '''simple docstring'''

       lowercase       : Union[str, Any]						=False
       lowercase       : List[str]						=[]
       if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
        while self._loader_batch_index < self.loader_batch_size:
         lowercase       : Dict						=self.loader_batch_item()
         lowercase       : int						=item.pop('''is_last'''		)
         accumulator.append(UpperCAmelCase		)
         if is_last:
          return accumulator

       while not is_last:
        lowercase       : List[Any]						=self.infer(next(self.iterator		)  ,   **self.params		)
        if self.loader_batch_size is not None:
         if isinstance(UpperCAmelCase  ,   torch.Tensor		):
          lowercase       : Optional[int]						=processed
         else:
          lowercase       : int						=list(processed.keys()		)[0]
          lowercase       : int						=processed[key]
         if isinstance(UpperCAmelCase  ,   UpperCAmelCase		):
          lowercase       : Union[str, Any]						=len(UpperCAmelCase		)
         else:
          lowercase       : List[Any]						=first_tensor.shape[0]
         if 0 < observed_batch_size < self.loader_batch_size:
          # could be last batch so we can't unroll as many
          # elements.
          lowercase       : List[Any]						=observed_batch_size
         lowercase       : Any						=processed
         lowercase       : List[Any]						=0
         while self._loader_batch_index < self.loader_batch_size:
          lowercase       : Optional[int]						=self.loader_batch_item()
          lowercase       : str						=item.pop('''is_last'''		)
          accumulator.append(UpperCAmelCase		)
          if is_last:
           return accumulator
        else:
         lowercase       : int						=processed
         lowercase       : int						=item.pop('''is_last'''		)
         accumulator.append(UpperCAmelCase		)
       return accumulator





class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      def __init__(			self      :		Optional[Any]  ,   UpperCAmelCase      :		Dataset  ,   UpperCAmelCase      :		str		)	->	Dict:


       '''simple docstring'''

       lowercase       : Union[str, Any]						=dataset
       lowercase       : Dict						=key






      def __len__(			self      :		Dict		)	->	Optional[Any]:


       '''simple docstring'''

       return len(self.dataset		)






      def __getitem__(			self      :		List[str]  ,   UpperCAmelCase      :		Optional[int]		)	->	Optional[int]:


       '''simple docstring'''

       return self.dataset[i][self.key]





class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      def __init__(			self      :		Union[str, Any]  ,   UpperCAmelCase      :		Dataset  ,   UpperCAmelCase      :		str  ,   UpperCAmelCase      :		str		)	->	int:


       '''simple docstring'''

       lowercase       : int						=dataset
       lowercase       : Union[str, Any]						=keya
       lowercase       : Any						=keya






      def __len__(			self      :		Union[str, Any]		)	->	Any:


       '''simple docstring'''

       return len(self.dataset		)






      def __getitem__(			self      :		Dict  ,   UpperCAmelCase      :		Tuple		)	->	int:


       '''simple docstring'''

       return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}











'''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_       (     __A					):




      """simple docstring"""




      UpperCamelCase_             =      '''char'''
      UpperCamelCase_             =      '''bpe'''
      UpperCamelCase_             =      '''wp'''





SCREAMING_SNAKE_CASE							=						(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class UpperCAmelCase_       (     __A					):




      """simple docstring"""




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






      def __init__(			self      :		int  ,   UpperCAmelCase      :		Tuple=None  ,   UpperCAmelCase      :		Dict=None  ,   **UpperCAmelCase      :		Any		)	->	List[str]:


       '''simple docstring'''

       lowercase       : 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.'''  ,   UpperCAmelCase  ,   )
        lowercase       : int						=kwargs.pop('''feature_extractor'''		)

       lowercase       : 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`.'''		)

       lowercase       : Any						=tokenizer
       lowercase       : Dict						=AutoTokenizer.from_pretrained('''gpt2'''		)
       lowercase       : Optional[int]						=AutoTokenizer.from_pretrained('''bert-base-uncased'''		)

       super().__init__(UpperCAmelCase  ,   UpperCAmelCase		)






      def __call__(			self      :		List[Any]  ,   UpperCAmelCase      :		Union[str, Any]=None  ,   UpperCAmelCase      :		List[str]=None  ,   UpperCAmelCase      :		Dict=None  ,   **UpperCAmelCase      :		Union[str, Any]		)	->	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:
        lowercase       : str						=self.image_processor(UpperCAmelCase  ,   return_tensors=UpperCAmelCase  ,   **UpperCAmelCase		)
       if text is not None:
        lowercase       : Any						=self.char_tokenizer(UpperCAmelCase  ,   return_tensors=UpperCAmelCase  ,   **UpperCAmelCase		)

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






      def 					A__   (			self      :		List[Any]  ,   UpperCAmelCase      :		Optional[Any]		)	->	Optional[Any]:


       '''simple docstring'''

       lowercase	,		lowercase	,		lowercase       : Union[str, Any]						=sequences
       lowercase       : Dict						=char_preds.size(0		)

       lowercase	,		lowercase       : Any						=self._decode_helper(UpperCAmelCase  ,   '''char'''		)
       lowercase	,		lowercase       : int						=self._decode_helper(UpperCAmelCase  ,   '''bpe'''		)
       lowercase	,		lowercase       : Optional[Any]						=self._decode_helper(UpperCAmelCase  ,   '''wp'''		)

       lowercase       : Dict						=[]
       lowercase       : List[Any]						=[]
       for i in range(UpperCAmelCase		):
        lowercase       : List[str]						=[char_scores[i], bpe_scores[i], wp_scores[i]]
        lowercase       : Union[str, Any]						=[char_strs[i], bpe_strs[i], wp_strs[i]]
        lowercase       : List[str]						=scores.index(max(UpperCAmelCase		)		)
        final_strs.append(strs[max_score_index]		)
        final_scores.append(scores[max_score_index]		)

       lowercase       : List[Any]						={}
       lowercase       : Optional[Any]						=final_strs
       lowercase       : Optional[Any]						=final_scores
       lowercase       : str						=char_strs
       lowercase       : Tuple						=bpe_strs
       lowercase       : List[str]						=wp_strs
       return out






      def 					A__   (			self      :		str  ,   UpperCAmelCase      :		Dict  ,   UpperCAmelCase      :		Union[str, Any]		)	->	Optional[int]:


       '''simple docstring'''

       if format == DecodeType.CHARACTER:
        lowercase       : Union[str, Any]						=self.char_decode
        lowercase       : List[str]						=1
        lowercase       : Tuple						='''[s]'''
       elif format == DecodeType.BPE:
        lowercase       : Union[str, Any]						=self.bpe_decode
        lowercase       : Optional[int]						=2
        lowercase       : Optional[Any]						='''#'''
       elif format == DecodeType.WORDPIECE:
        lowercase       : List[str]						=self.wp_decode
        lowercase       : Union[str, Any]						=102
        lowercase       : int						='''[SEP]'''
       else:
        raise ValueError(f'Format {format} is not supported.'		)

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

       for index in range(UpperCAmelCase		):
        lowercase       : List[Any]						=preds_str[index].find(UpperCAmelCase		)
        lowercase       : Any						=preds_str[index][:pred_eos]
        lowercase       : Any						=preds_index[index].cpu().tolist()
        lowercase       : Optional[int]						=pred_index.index(UpperCAmelCase		) if eos_token in pred_index else -1
        lowercase       : int						=preds_max_prob[index][: pred_eos_index + 1]
        lowercase       : Tuple						=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 					A__   (			self      :		List[str]  ,   UpperCAmelCase      :		Optional[int]		)	->	Dict:


       '''simple docstring'''

       lowercase       : Union[str, Any]						=[seq.replace(''' '''  ,   ''''''		) for seq in self.char_tokenizer.batch_decode(UpperCAmelCase		)]
       return decode_strs






      def 					A__   (			self      :		Optional[int]  ,   UpperCAmelCase      :		Optional[Any]		)	->	str:


       '''simple docstring'''

       return self.bpe_tokenizer.batch_decode(UpperCAmelCase		)






      def 					A__   (			self      :		Tuple  ,   UpperCAmelCase      :		Dict		)	->	List[str]:


       '''simple docstring'''

       lowercase       : str						=[seq.replace(''' '''  ,   ''''''		) for seq in self.wp_tokenizer.batch_decode(UpperCAmelCase		)]
       return decode_strs



