


'''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__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    "char"
     __magic_name__    			=    "bpe"
     __magic_name__    			=    "wp"
lowerCAmelCase    :     List[str]					=						(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






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







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

            '''simple docstring'''

            _lowerCAmelCase    :							Tuple   			=       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    :							Optional[Any]   			=       kwargs.pop('feature_extractor'   )

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

            super().__init__(snake_case__      ,	snake_case__   )







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

            '''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    :							str   			=       self.image_processor(snake_case__      ,	return_tensors=snake_case__      ,	**snake_case__   )
            if text is not None:
                   _lowerCAmelCase    :							List[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    :							int   			=       encodings['input_ids']
                   return inputs







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase    :							List[str]   			=       sequences
            _lowerCAmelCase    :							Dict   			=       char_preds.size(0   )

            _lowerCAmelCase			,     _lowerCAmelCase    :							Union[str, Any]   			=       self._decode_helper(snake_case__      ,	'char'   )
            _lowerCAmelCase			,     _lowerCAmelCase    :							Optional[int]   			=       self._decode_helper(snake_case__      ,	'bpe'   )
            _lowerCAmelCase			,     _lowerCAmelCase    :							Union[str, Any]   			=       self._decode_helper(snake_case__      ,	'wp'   )

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

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







     def a	( self      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

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

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

            for index in range(snake_case__   ):
                   _lowerCAmelCase    :							List[str]   			=       preds_str[index].find(snake_case__   )
                   _lowerCAmelCase    :							Optional[Any]   			=       preds_str[index][:pred_eos]
                   _lowerCAmelCase    :							Optional[Any]   			=       preds_index[index].cpu().tolist()
                   _lowerCAmelCase    :							int   			=       pred_index.index(snake_case__   ) if eos_token in pred_index else -1
                   _lowerCAmelCase    :							Dict   			=       preds_max_prob[index][: pred_eos_index + 1]
                   _lowerCAmelCase    :							List[Any]   			=       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 a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       [seq.replace(' '      ,	''   ) for seq in self.char_tokenizer.batch_decode(snake_case__   )]
            return decode_strs







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            return self.bpe_tokenizer.batch_decode(snake_case__   )







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       [seq.replace(' '      ,	''   ) for seq in self.wp_tokenizer.batch_decode(snake_case__   )]
            return decode_strs





'''simple docstring'''







import pytest

import datasets


# Import fixture modules as plugins
lowerCAmelCase    :     List[str]					=						["""tests.fixtures.files""", """tests.fixtures.hub""", """tests.fixtures.fsspec"""]






def 			lowercase				(_A   ,						_A					):







       """simple docstring"""







       for item in items:
              if any(marker in item.keywords for marker in ['integration', 'unit']					):
                     continue
              item.add_marker(pytest.mark.unit					)






def 			lowercase				(_A					):







       """simple docstring"""







       config.addinivalue_line('markers'   ,						'torchaudio_latest: mark test to run with torchaudio>=0.12'					)






@pytest.fixture(autouse=_A					)
def 			lowercase				(_A   ,						_A					):







       """simple docstring"""







       _lowerCAmelCase    :							str   			=       tmp_path_factory.getbasetemp() / 'cache'
       _lowerCAmelCase    :							Dict   			=       test_hf_cache_home / 'datasets'
       _lowerCAmelCase    :							List[Any]   			=       test_hf_cache_home / 'metrics'
       _lowerCAmelCase    :							List[Any]   			=       test_hf_cache_home / 'modules'
       monkeypatch.setattr('datasets.config.HF_DATASETS_CACHE'   ,						str(_A					)					)
       monkeypatch.setattr('datasets.config.HF_METRICS_CACHE'   ,						str(_A					)					)
       monkeypatch.setattr('datasets.config.HF_MODULES_CACHE'   ,						str(_A					)					)
       _lowerCAmelCase    :							Dict   			=       test_hf_datasets_cache / 'downloads'
       monkeypatch.setattr('datasets.config.DOWNLOADED_DATASETS_PATH'   ,						str(_A					)					)
       _lowerCAmelCase    :							Union[str, Any]   			=       test_hf_datasets_cache / 'downloads' / 'extracted'
       monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH'   ,						str(_A					)					)






@pytest.fixture(autouse=_A   ,						scope='session'					)
def 			lowercase				():







       """simple docstring"""







       datasets.disable_progress_bar()






@pytest.fixture(autouse=_A					)
def 			lowercase				(_A					):







       """simple docstring"""







       monkeypatch.setattr('datasets.config.HF_UPDATE_DOWNLOAD_COUNTS'   ,						_A					)






@pytest.fixture
def 			lowercase				(_A					):







       """simple docstring"""







       monkeypatch.setattr('sqlalchemy.util.deprecations.SILENCE_UBER_WARNING'   ,						_A					)

