





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 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						"""char"""
			lowerCAmelCase_							     =						"""bpe"""
			lowerCAmelCase_							     =						"""wp"""



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






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						["""image_processor""", """char_tokenizer"""]
			lowerCAmelCase_							     =						"""ViTImageProcessor"""
			lowerCAmelCase_							     =						"""MgpstrTokenizer"""





			def __init__(    self       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase=None       ,  **__lowerCAmelCase							):



				'''simple docstring'''







				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.'''       ,  __lowerCAmelCase       ,  )
					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__(__lowerCAmelCase       ,  __lowerCAmelCase							)





			def __call__(    self       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase=None       ,  **__lowerCAmelCase							):



				'''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:
					lowerCamelCase__								=		self.image_processor(__lowerCAmelCase       ,  return_tensors=__lowerCAmelCase       ,  **__lowerCAmelCase							)
				if text is not None:
					lowerCamelCase__								=		self.char_tokenizer(__lowerCAmelCase       ,  return_tensors=__lowerCAmelCase       ,  **__lowerCAmelCase							)

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





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







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

				lowerCamelCase__				,	lowerCamelCase__								=		self._decode_helper(__lowerCAmelCase       ,  '''char'''							)
				lowerCamelCase__				,	lowerCamelCase__								=		self._decode_helper(__lowerCAmelCase       ,  '''bpe'''							)
				lowerCamelCase__				,	lowerCamelCase__								=		self._decode_helper(__lowerCAmelCase       ,  '''wp'''							)

				lowerCamelCase__								=		[]
				lowerCamelCase__								=		[]
				for i in range(__lowerCAmelCase							):
					lowerCamelCase__								=		[char_scores[i], bpe_scores[i], wp_scores[i]]
					lowerCamelCase__								=		[char_strs[i], bpe_strs[i], wp_strs[i]]
					lowerCamelCase__								=		scores.index(max(__lowerCAmelCase							)							)
					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       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				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_0_2
					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=__lowerCAmelCase       ,  sorted=__lowerCAmelCase							)
				lowerCamelCase__								=		preds_index.view(-1       ,  __lowerCAmelCase							)[:, 1:]
				lowerCamelCase__								=		decoder(__lowerCAmelCase							)
				lowerCamelCase__				,	lowerCamelCase__								=		torch.nn.functional.softmax(__lowerCAmelCase       ,  dim=2							).max(dim=2							)
				lowerCamelCase__								=		preds_max_prob[:, 1:]

				for index in range(__lowerCAmelCase							):
					lowerCamelCase__								=		preds_str[index].find(__lowerCAmelCase							)
					lowerCamelCase__								=		preds_str[index][:pred_eos]
					lowerCamelCase__								=		preds_index[index].cpu().tolist()
					lowerCamelCase__								=		pred_index.index(__lowerCAmelCase							) 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(__lowerCAmelCase							)
					conf_scores.append(__lowerCAmelCase							)

				return dec_strs, conf_scores





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		[seq.replace(''' '''       ,  ''''''							) for seq in self.char_tokenizer.batch_decode(__lowerCAmelCase							)]
				return decode_strs





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				return self.bpe_tokenizer.batch_decode(__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		[seq.replace(''' '''       ,  ''''''							) for seq in self.wp_tokenizer.batch_decode(__lowerCAmelCase							)]
				return decode_strs










import numpy as np

from transformers import Pipeline





def lowerCAmelCase__(__snake_case							)				->      Tuple:







	'''simple docstring'''







	lowerCamelCase__								=		np.max(__snake_case       ,axis=-1       ,keepdims=__snake_case							)
	lowerCamelCase__								=		np.exp(outputs - maxes							)
	return shifted_exp / shifted_exp.sum(axis=-1       ,keepdims=__snake_case							)






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			def 		__lowerCamelCase				(    self       ,  **__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		{}
				if "second_text" in kwargs:
					lowerCamelCase__								=		kwargs['''second_text''']
				return preprocess_kwargs, {}, {}





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase=None							):



				'''simple docstring'''







				return self.tokenizer(__lowerCAmelCase       ,  text_pair=__lowerCAmelCase       ,  return_tensors=self.framework							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				return self.model(**__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		model_outputs.logits[0].numpy()
				lowerCamelCase__								=		softmax(__lowerCAmelCase							)

				lowerCamelCase__								=		np.argmax(__lowerCAmelCase							)
				lowerCamelCase__								=		self.model.config.idalabel[best_class]
				lowerCamelCase__								=		probabilities[best_class].item()
				lowerCamelCase__								=		logits.tolist()
				return {"label": label, "score": score, "logits": logits}



