
"""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    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'char'
		lowercase__      =				'bpe'
		lowercase__      =				'wp'





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






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				['image_processor', 'char_tokenizer']
		lowercase__      =				'ViTImageProcessor'
		lowercase__      =				'MgpstrTokenizer'



		def __init__( self    ,		__a=None    ,		__a=None    ,		**__a)	->	Optional[int]:






							'''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)	->	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										=  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 						UpperCAmelCase							( self    ,		__a)	->	Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase										=  sequences
							_UpperCamelCase										=  char_preds.size(0)

							_UpperCamelCase			,  _UpperCamelCase										=  self._decode_helper(__a    ,		'''char''')
							_UpperCamelCase			,  _UpperCamelCase										=  self._decode_helper(__a    ,		'''bpe''')
							_UpperCamelCase			,  _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 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	Union[str, Any]:






							'''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_02
												_UpperCamelCase										=  '''[SEP]'''
							else:
												raise ValueError(F'''Format {format} is not supported.''')

							_UpperCamelCase			,  _UpperCamelCase										=  [], []
							_UpperCamelCase										=  pred_logits.size(0)
							_UpperCamelCase										=  pred_logits.size(1)
							_UpperCamelCase			,  _UpperCamelCase										=  pred_logits.topk(1    ,		dim=-1    ,		largest=__a    ,		sorted=__a)
							_UpperCamelCase										=  preds_index.view(-1    ,		__a)[:, 1:]
							_UpperCamelCase										=  decoder(__a)
							_UpperCamelCase			,  _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 						UpperCAmelCase							( self    ,		__a)	->	Tuple:






							'''simple docstring'''
							_UpperCamelCase										=  [seq.replace(''' '''    ,		'''''') for seq in self.char_tokenizer.batch_decode(__a)]
							return decode_strs



		def 						UpperCAmelCase							( self    ,		__a)	->	int:






							'''simple docstring'''
							return self.bpe_tokenizer.batch_decode(__a)




		def 						UpperCAmelCase							( self    ,		__a)	->	Optional[int]:






							'''simple docstring'''
							_UpperCamelCase										=  [seq.replace(''' '''    ,		'''''') for seq in self.wp_tokenizer.batch_decode(__a)]
							return decode_strs








"""simple docstring"""





import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


_a														=		namedtuple(
    """_TestCommandArgs""",
    [
        """dataset""",
        """name""",
        """cache_dir""",
        """data_dir""",
        """all_configs""",
        """save_infos""",
        """ignore_verifications""",
        """force_redownload""",
        """clear_cache""",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		Optional[int]:


					"""simple docstring"""





					return (abs(source - target     ) / target) < 0.01



@pytest.mark.integration
def 				lowerCamelCase__		(      __snake_case     )							->		Dict:


					"""simple docstring"""





					_UpperCamelCase										=  _TestCommandArgs(dataset=__snake_case,					all_configs=__snake_case,					save_infos=__snake_case     )
					_UpperCamelCase										=  TestCommand(*__snake_case     )
					test_command.run()
					_UpperCamelCase										=  os.path.join(__snake_case,					'''README.md'''     )
					assert os.path.exists(__snake_case     )
					_UpperCamelCase										=  DatasetInfosDict.from_directory(__snake_case     )
					_UpperCamelCase										=  DatasetInfosDict(
					    {
					        '''default''': DatasetInfo(
					            features=Features(
					                {
					                    '''tokens''': Sequence(Value('''string'''     )     ),
					                    '''ner_tags''': Sequence(
					                        ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']     )     ),
					                    '''langs''': Sequence(Value('''string'''     )     ),
					                    '''spans''': Sequence(Value('''string'''     )     ),
					                }     ),					splits=[
					                {
					                    '''name''': '''train''',
					                    '''num_bytes''': 2_35_15_63,
					                    '''num_examples''': 1_00_00,
					                },
					                {
					                    '''name''': '''validation''',
					                    '''num_bytes''': 23_84_18,
					                    '''num_examples''': 10_00,
					                },
					            ],					download_size=3_94_06_80,					dataset_size=2_58_99_81,					)
					    }     )
					assert dataset_infos.keys() == expected_dataset_infos.keys()
					for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
										_UpperCamelCase			,  _UpperCamelCase										=  getattr(dataset_infos['''default'''],					__snake_case     ), getattr(expected_dataset_infos['''default'''],					__snake_case     )
										if key == "num_bytes":
															assert is_apercent_close(__snake_case,					__snake_case     )
										elif key == "splits":
															assert list(__snake_case     ) == list(__snake_case     )
															for split in result:
																				assert result[split].name == expected[split].name
																				assert result[split].num_examples == expected[split].num_examples
																				assert is_apercent_close(result[split].num_bytes,					expected[split].num_bytes     )
										else:
															result == expected






