





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_						(					_lowerCamelCase   ):
					lowerCAmelCase_							=		'''char'''
					lowerCAmelCase_							=		'''bpe'''
					lowerCAmelCase_							=		'''wp'''





UpperCAmelCase_ 							=       (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class      UpperCamelCase_						(					_lowerCamelCase   ):
					lowerCAmelCase_							=		['''image_processor''', '''char_tokenizer''']
					lowerCAmelCase_							=		'''ViTImageProcessor'''
					lowerCAmelCase_							=		'''MgpstrTokenizer'''




					def __init__(						self			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  **lowerCAmelCase_					)						->							str:
										_snake_case													=			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_			,  )
															_snake_case													=			kwargs.pop('feature_extractor'					)

										_snake_case													=			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`.'					)

										_snake_case													=			tokenizer
										_snake_case													=			AutoTokenizer.from_pretrained('gpt2'					)
										_snake_case													=			AutoTokenizer.from_pretrained('bert-base-uncased'					)

										super().__init__(lowerCAmelCase_			,  lowerCAmelCase_					)




					def __call__(						self			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  **lowerCAmelCase_					)						->							str:
										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:
															_snake_case													=			self.image_processor(lowerCAmelCase_			,  return_tensors=lowerCAmelCase_			,  **lowerCAmelCase_					)
										if text is not None:
															_snake_case													=			self.char_tokenizer(lowerCAmelCase_			,  return_tensors=lowerCAmelCase_			,  **lowerCAmelCase_					)

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




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							int:
										_snake_case							,       _snake_case							,       _snake_case													=			sequences
										_snake_case													=			char_preds.size(0					)

										_snake_case							,       _snake_case													=			self._decode_helper(lowerCAmelCase_			,  'char'					)
										_snake_case							,       _snake_case													=			self._decode_helper(lowerCAmelCase_			,  'bpe'					)
										_snake_case							,       _snake_case													=			self._decode_helper(lowerCAmelCase_			,  'wp'					)

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

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




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Optional[Any]:
										if format == DecodeType.CHARACTER:
															_snake_case													=			self.char_decode
															_snake_case													=			1
															_snake_case													=			'[s]'
										elif format == DecodeType.BPE:
															_snake_case													=			self.bpe_decode
															_snake_case													=			2
															_snake_case													=			'#'
										elif format == DecodeType.WORDPIECE:
															_snake_case													=			self.wp_decode
															_snake_case													=			102
															_snake_case													=			'[SEP]'
										else:
															raise ValueError(F'''Format {format} is not supported.'''					)

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

										for index in range(lowerCAmelCase_					):
															_snake_case													=			preds_str[index].find(lowerCAmelCase_					)
															_snake_case													=			preds_str[index][:pred_eos]
															_snake_case													=			preds_index[index].cpu().tolist()
															_snake_case													=			pred_index.index(lowerCAmelCase_					) if eos_token in pred_index else -1
															_snake_case													=			preds_max_prob[index][: pred_eos_index + 1]
															_snake_case													=			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 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							List[str]:
										_snake_case													=			[seq.replace(' '			,  ''					) for seq in self.char_tokenizer.batch_decode(lowerCAmelCase_					)]
										return decode_strs




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Optional[int]:
										return self.bpe_tokenizer.batch_decode(lowerCAmelCase_					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							int:
										_snake_case													=			[seq.replace(' '			,  ''					) for seq in self.wp_tokenizer.batch_decode(lowerCAmelCase_					)]
										return decode_strs










import warnings

from ...utils import logging
from .image_processing_flava import FlavaImageProcessor


UpperCAmelCase_ 							=       logging.get_logger(__name__)
class      UpperCamelCase_						(					_lowerCamelCase   ):




					def __init__(						self			,  *lowerCAmelCase_			,  **lowerCAmelCase_					)						->							None:
										warnings.warn(
										    'The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
										    ' use FlavaImageProcessor instead.'			,  lowerCAmelCase_			,  )
										super().__init__(*lowerCAmelCase_			,  **lowerCAmelCase_					)



