


'''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      lowerCamelCase_  (      __a ):
      lowerCAmelCase__							=	'char'
      lowerCAmelCase__							=	'bpe'
      lowerCAmelCase__							=	'wp'

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




class      lowerCamelCase_  (      __a ):
      lowerCAmelCase__							=	['image_processor', 'char_tokenizer']
      lowerCAmelCase__							=	'ViTImageProcessor'
      lowerCAmelCase__							=	'MgpstrTokenizer'






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


          '''simple docstring'''


          UpperCAmelCase__     :						Union[str, Any]						=							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		,		)
              UpperCAmelCase__     :						Optional[int]						=							kwargs.pop('''feature_extractor'''					)

          UpperCAmelCase__     :						str						=							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__     :						Dict						=							tokenizer
          UpperCAmelCase__     :						List[Any]						=							AutoTokenizer.from_pretrained('''gpt2'''					)
          UpperCAmelCase__     :						Optional[Any]						=							AutoTokenizer.from_pretrained('''bert-base-uncased'''					)

          super().__init__(_A		,		_A					)






      def __call__(   self							:       Dict		,		_A							:       Any=None		,		_A							:       Tuple=None		,		_A							:       Dict=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:
              UpperCAmelCase__     :						Any						=							self.image_processor(_A		,		return_tensors=_A		,		**_A					)
          if text is not None:
              UpperCAmelCase__     :						Any						=							self.char_tokenizer(_A		,		return_tensors=_A		,		**_A					)

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






      def lowercase_				(   self							:       Optional[int]		,		_A							:       List[Any]					):


          '''simple docstring'''


          UpperCAmelCase__		,		UpperCAmelCase__		,		UpperCAmelCase__     :						List[str]						=							sequences
          UpperCAmelCase__     :						Union[str, Any]						=							char_preds.size(0					)

          UpperCAmelCase__		,		UpperCAmelCase__     :						Optional[Any]						=							self._decode_helper(_A		,		'''char'''					)
          UpperCAmelCase__		,		UpperCAmelCase__     :						Union[str, Any]						=							self._decode_helper(_A		,		'''bpe'''					)
          UpperCAmelCase__		,		UpperCAmelCase__     :						List[Any]						=							self._decode_helper(_A		,		'''wp'''					)

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

          UpperCAmelCase__     :						Any						=							{}
          UpperCAmelCase__     :						Optional[int]						=							final_strs
          UpperCAmelCase__     :						Dict						=							final_scores
          UpperCAmelCase__     :						Optional[Any]						=							char_strs
          UpperCAmelCase__     :						List[Any]						=							bpe_strs
          UpperCAmelCase__     :						Dict						=							wp_strs
          return out






      def lowercase_				(   self							:       str		,		_A							:       Union[str, Any]		,		_A							:       Dict					):


          '''simple docstring'''


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

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

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


          '''simple docstring'''


          UpperCAmelCase__     :						Dict						=							[seq.replace(''' '''		,		''''''					) for seq in self.char_tokenizer.batch_decode(_A					)]
          return decode_strs






      def lowercase_				(   self							:       str		,		_A							:       List[Any]					):


          '''simple docstring'''


          return self.bpe_tokenizer.batch_decode(_A					)






      def lowercase_				(   self							:       Union[str, Any]		,		_A							:       List[str]					):


          '''simple docstring'''


          UpperCAmelCase__     :						Any						=							[seq.replace(''' '''		,		''''''					) for seq in self.wp_tokenizer.batch_decode(_A					)]
          return decode_strs

'''simple docstring'''






import pytest
import requests

from datasets.utils.file_utils import http_head

from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline





@pytest.mark.integration
def  _SCREAMING_SNAKE_CASE		(      ):
     with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT				):
          with pytest.raises(__snake_case				):
               requests.request('GET'   ,		'https://huggingface.co'				)
          with pytest.raises(requests.exceptions.ConnectTimeout				):
               requests.request('GET'   ,		'https://huggingface.co'   ,		timeout=1.0				)





@pytest.mark.integration
def  _SCREAMING_SNAKE_CASE		(      ):
     with offline(OfflineSimulationMode.CONNECTION_FAILS				):
          with pytest.raises(requests.exceptions.ConnectionError				):
               requests.request('GET'   ,		'https://huggingface.co'				)





def  _SCREAMING_SNAKE_CASE		(      ):
     with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1				):
          with pytest.raises(__snake_case				):
               http_head('https://huggingface.co'				)



