


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							(    __SCREAMING_SNAKE_CASE):
	_a  :  Dict				   =							"char"
	_a  :  List[Any]				   =							"bpe"
	_a  :  str				   =							"wp"
lowerCamelCase						=		(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)





class  _a							(    __SCREAMING_SNAKE_CASE):
	_a  :  List[str]				   =							["image_processor", "char_tokenizer"]
	_a  :  Optional[Any]				   =							"ViTImageProcessor"
	_a  :  str				   =							"MgpstrTokenizer"




	def __init__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		List[str]=None		, _SCREAMING_SNAKE_CASE						:		int=None		, **_SCREAMING_SNAKE_CASE						:		Tuple		)->	Any:
			lowerCAmelCase__       :      Optional[int]   					= None
			if "feature_extractor" in kwargs:
					warnings.warn(
					    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
					    ''' instead.'''		, _snake_case		, )
					lowerCAmelCase__       :      Any   					= kwargs.pop('''feature_extractor'''		)

			lowerCAmelCase__       :      List[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`.'''		)

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

			super().__init__(_snake_case		, _snake_case		)




	def __call__( self						:		int		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=None		, _SCREAMING_SNAKE_CASE						:		int=None		, _SCREAMING_SNAKE_CASE						:		Optional[int]=None		, **_SCREAMING_SNAKE_CASE						:		Dict		)->	Dict:
			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__       :      Dict   					= self.image_processor(_snake_case		, return_tensors=_snake_case		, **_snake_case		)
			if text is not None:
					lowerCAmelCase__       :      Any   					= self.char_tokenizer(_snake_case		, return_tensors=_snake_case		, **_snake_case		)

			if text is None:
					return inputs
			elif images is None:
					return encodings
			else:
					lowerCAmelCase__       :      Optional[int]   					= encodings["input_ids"]
					return inputs




	def 							UpperCAmelCase__( self						:		int		, _SCREAMING_SNAKE_CASE						:		List[str]		)->	List[str]:
			lowerCAmelCase__       :      Union[str, Any]   					= sequences
			lowerCAmelCase__       :      List[str]   					= char_preds.size(0		)

			lowerCAmelCase__       :      Union[str, Any]   					= self._decode_helper(_snake_case		, '''char'''		)
			lowerCAmelCase__       :      List[Any]   					= self._decode_helper(_snake_case		, '''bpe'''		)
			lowerCAmelCase__       :      Tuple   					= self._decode_helper(_snake_case		, '''wp'''		)

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

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




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

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

			for index in range(_snake_case		):
					lowerCAmelCase__       :      Union[str, Any]   					= preds_str[index].find(_snake_case		)
					lowerCAmelCase__       :      Dict   					= preds_str[index][:pred_eos]
					lowerCAmelCase__       :      Dict   					= preds_index[index].cpu().tolist()
					lowerCAmelCase__       :      Dict   					= pred_index.index(_snake_case		) if eos_token in pred_index else -1
					lowerCAmelCase__       :      Optional[int]   					= preds_max_prob[index][: pred_eos_index + 1]
					lowerCAmelCase__       :      List[str]   					= pred_max_prob.cumprod(dim=0		)[-1] if pred_max_prob.nelement() != 0 else 0.0
					dec_strs.append(_snake_case		)
					conf_scores.append(_snake_case		)

			return dec_strs, conf_scores




	def 							UpperCAmelCase__( self						:		Tuple		, _SCREAMING_SNAKE_CASE						:		List[str]		)->	Union[str, Any]:
			lowerCAmelCase__       :      Any   					= [seq.replace(''' '''		, ''''''		) for seq in self.char_tokenizer.batch_decode(_snake_case		)]
			return decode_strs




	def 							UpperCAmelCase__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		List[Any]		)->	List[Any]:
			return self.bpe_tokenizer.batch_decode(_snake_case		)




	def 							UpperCAmelCase__( self						:		Any		, _SCREAMING_SNAKE_CASE						:		List[str]		)->	Tuple:
			lowerCAmelCase__       :      Optional[Any]   					= [seq.replace(''' '''		, ''''''		) for seq in self.wp_tokenizer.batch_decode(_snake_case		)]
			return decode_strs








import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		def wrapper(*_a     ,       **_a	):
				lowerCAmelCase__       :      List[str]   					= timeit.default_timer()
				lowerCAmelCase__       :      List[Any]   					= func(*_a     ,       **_a	)
				lowerCAmelCase__       :      Any   					= timeit.default_timer() - starttime
				return delta

		lowerCAmelCase__       :      Any   					= func.__name__

		return wrapper






def 					lowerCamelCase_   (	_a     ,       _a=100     ,       _a=None	):




		"""simple docstring"""






		lowerCAmelCase__       :      str   					= []
		lowerCAmelCase__       :      str   					= seq_shapes or {}
		for i in range(_a	):
				lowerCAmelCase__       :      List[str]   					= {}
				for col_id, (k, v) in enumerate(features.items()	):
						if isinstance(_a     ,       _ArrayXD	):
								lowerCAmelCase__       :      List[str]   					= np.random.rand(*v.shape	).astype(v.dtype	)
						elif isinstance(_a     ,       datasets.Value	):
								if v.dtype == "string":
										lowerCAmelCase__       :      Dict   					= '''The small grey turtle was surprisingly fast when challenged.'''
								else:
										lowerCAmelCase__       :      Any   					= np.random.randint(10     ,       size=1	).astype(v.dtype	).item()
						elif isinstance(_a     ,       datasets.Sequence	):
								while isinstance(_a     ,       datasets.Sequence	):
										lowerCAmelCase__       :      Optional[int]   					= v.feature
								lowerCAmelCase__       :      str   					= seq_shapes[k]
								lowerCAmelCase__       :      Any   					= np.random.rand(*_a	).astype(v.dtype	)
						lowerCAmelCase__       :      int   					= data

				dummy_data.append((i, example)	)

		return dummy_data






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a=100     ,       _a=None	):




		"""simple docstring"""






		lowerCAmelCase__       :      Optional[Any]   					= generate_examples(_a     ,       num_examples=_a     ,       seq_shapes=_a	)

		with ArrowWriter(features=_a     ,       path=_a	) as writer:
				for key, record in dummy_data:
						lowerCAmelCase__       :      Optional[int]   					= features.encode_example(_a	)
						writer.write(_a	)

				lowerCAmelCase__					,     lowerCAmelCase__       :      List[str]   					= writer.finalize()

		if not num_final_examples == num_examples:
				raise ValueError(
				    f'Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.'	)

		lowerCAmelCase__       :      List[Any]   					= datasets.Dataset.from_file(filename=_a     ,       info=datasets.DatasetInfo(features=_a	)	)

		return dataset




