





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_       (__UpperCamelCase			):
	UpperCAmelCase__					=			'''char'''
	UpperCAmelCase__					=			'''bpe'''
	UpperCAmelCase__					=			'''wp'''
__A				:    Dict       =   (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)

class 	A_       (__UpperCamelCase			):
	UpperCAmelCase__					=			['''image_processor''', '''char_tokenizer''']
	UpperCAmelCase__					=			'''ViTImageProcessor'''
	UpperCAmelCase__					=			'''MgpstrTokenizer'''
	def __init__(    self							,					_A=None							,					_A=None							,					**_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				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   		=				kwargs.pop('''feature_extractor'''		)

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

				super().__init__(a_							,					a_		)
	def __call__(    self							,					_A=None							,					_A=None							,					_A=None							,					**_A		):






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

				if text is None:
							return inputs
				elif images is None:
							return encodings
				else:
							UpperCAmelCase   		=				encodings['''input_ids''']
							return inputs
	def      _lowercase				(    self							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				sequences
				UpperCAmelCase   		=				char_preds.size(0		)

				UpperCAmelCase   		=				self._decode_helper(a_							,					'''char'''		)
				UpperCAmelCase   		=				self._decode_helper(a_							,					'''bpe'''		)
				UpperCAmelCase   		=				self._decode_helper(a_							,					'''wp'''		)

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

				UpperCAmelCase   		=				{}
				UpperCAmelCase   		=				final_strs
				UpperCAmelCase   		=				final_scores
				UpperCAmelCase   		=				char_strs
				UpperCAmelCase   		=				bpe_strs
				UpperCAmelCase   		=				wp_strs
				return out
	def      _lowercase				(    self							,					_A							,					_A		):






				'''simple docstring'''







				if format == DecodeType.CHARACTER:
							UpperCAmelCase   		=				self.char_decode
							UpperCAmelCase   		=				1
							UpperCAmelCase   		=				'''[s]'''
				elif format == DecodeType.BPE:
							UpperCAmelCase   		=				self.bpe_decode
							UpperCAmelCase   		=				2
							UpperCAmelCase   		=				'''#'''
				elif format == DecodeType.WORDPIECE:
							UpperCAmelCase   		=				self.wp_decode
							UpperCAmelCase   		=				1_0_2
							UpperCAmelCase   		=				'''[SEP]'''
				else:
							raise ValueError(F"""Format {format} is not supported."""		)

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

				for index in range(a_		):
							UpperCAmelCase   		=				preds_str[index].find(a_		)
							UpperCAmelCase   		=				preds_str[index][:pred_eos]
							UpperCAmelCase   		=				preds_index[index].cpu().tolist()
							UpperCAmelCase   		=				pred_index.index(a_		) if eos_token in pred_index else -1
							UpperCAmelCase   		=				preds_max_prob[index][: pred_eos_index + 1]
							UpperCAmelCase   		=				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							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				[seq.replace(''' '''							,					''''''		) for seq in self.char_tokenizer.batch_decode(a_		)]
				return decode_strs
	def      _lowercase				(    self							,					_A		):






				'''simple docstring'''







				return self.bpe_tokenizer.batch_decode(a_		)







	def      _lowercase				(    self							,					_A		):






				'''simple docstring'''







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








import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format

class 		UpperCAmelCase__			:




			'''simple docstring'''






			UpperCamelCase									=       None




			def  snake_case__     (   self							: List[str] ):
					'''simple docstring'''





					__UpperCAmelCase    :     List[Any]													=	self.feature_extraction_class(**self.feat_extract_dict )
					__UpperCAmelCase    :     Optional[int]													=	json.loads(feat_extract.to_json_string() )
					for key, value in self.feat_extract_dict.items():
							self.assertEqual(obj[key]       ,       a_ )




			def  snake_case__     (   self							: Any ):
					'''simple docstring'''





					__UpperCAmelCase    :     str													=	self.feature_extraction_class(**self.feat_extract_dict )

					with tempfile.TemporaryDirectory() as tmpdirname:
							__UpperCAmelCase    :     Union[str, Any]													=	os.path.join(a_       ,       '''feat_extract.json''' )
							feat_extract_first.to_json_file(a_ )
							__UpperCAmelCase    :     Any													=	self.feature_extraction_class.from_json_file(a_ )

					self.assertEqual(feat_extract_second.to_dict()       ,       feat_extract_first.to_dict() )




			def  snake_case__     (   self							: Optional[int] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Tuple													=	self.feature_extraction_class(**self.feat_extract_dict )

					with tempfile.TemporaryDirectory() as tmpdirname:
							__UpperCAmelCase    :     List[str]													=	feat_extract_first.save_pretrained(a_ )[0]
							check_json_file_has_correct_format(a_ )
							__UpperCAmelCase    :     Optional[Any]													=	self.feature_extraction_class.from_pretrained(a_ )

					self.assertEqual(feat_extract_second.to_dict()       ,       feat_extract_first.to_dict() )




			def  snake_case__     (   self							: Dict ):
					'''simple docstring'''





					__UpperCAmelCase    :     int													=	self.feature_extraction_class()
					self.assertIsNotNone(a_ )





