'''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  _UpperCamelCase (     _a	:	Dict				,     _a	:	Any			):




    """simple docstring"""



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







@pytest.mark.integration
def  _UpperCamelCase (     _a	:	Tuple			):




    """simple docstring"""



    __UpperCamelCase	:	int		    =  _TestCommandArgs(dataset=_a				,     all_configs=_a				,     save_infos=_a			)
    __UpperCamelCase	:	Any		    =  TestCommand(*_a			)
    test_command.run()
    __UpperCamelCase	:	List[str]		    =  os.path.join(_a				,     'README.md'			)
    assert os.path.exists(_a			)
    __UpperCamelCase	:	Optional[Any]		    =  DatasetInfosDict.from_directory(_a			)
    __UpperCamelCase	:	str		    =  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_3_5_1_5_6_3,
                        'num_examples': 1_0_0_0_0,
                    },
                    {
                        'name': 'validation',
                        'num_bytes': 2_3_8_4_1_8,
                        'num_examples': 1_0_0_0,
                    },
                ]				,     download_size=3_9_4_0_6_8_0				,     dataset_size=2_5_8_9_9_8_1				,     )
        }			)
    assert dataset_infos.keys() == expected_dataset_infos.keys()
    for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
        __UpperCamelCase    ,					__UpperCamelCase	:	List[Any]		    =  getattr(dataset_infos['default']				,     _a			), getattr(expected_dataset_infos['default']				,     _a			)
        if key == "num_bytes":
            assert is_apercent_close(_a				,     _a			)
        elif key == "splits":
            assert list(_a			) == list(_a			)
            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




'''simple docstring'''
from ...processing_utils import ProcessorMixin






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						'''SpeechT5FeatureExtractor'''
     SCREAMING_SNAKE_CASE__		   =						'''SpeechT5Tokenizer'''







     def __init__(		self     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         super().__init__(_lowerCamelCase     ,			_lowerCamelCase    )







     def __call__(		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         __UpperCamelCase	:	Union[str, Any]		    =  kwargs.pop('audio'     ,			_lowerCamelCase    )
         __UpperCamelCase	:	Tuple		    =  kwargs.pop('text'     ,			_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  kwargs.pop('text_target'     ,			_lowerCamelCase    )
         __UpperCamelCase	:	List[Any]		    =  kwargs.pop('audio_target'     ,			_lowerCamelCase    )
         __UpperCamelCase	:	Any		    =  kwargs.pop('sampling_rate'     ,			_lowerCamelCase    )

         if audio is not None and text is not None:
             raise ValueError(
                 'Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?'    )
         if audio_target is not None and text_target is not None:
             raise ValueError(
                 'Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?'    )
         if audio is None and audio_target is None and text is None and text_target is None:
             raise ValueError(
                 'You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.'    )

         if audio is not None:
             __UpperCamelCase	:	Dict		    =  self.feature_extractor(_lowerCamelCase     ,			*_lowerCamelCase     ,			sampling_rate=_lowerCamelCase     ,			**_lowerCamelCase    )
         elif text is not None:
             __UpperCamelCase	:	int		    =  self.tokenizer(_lowerCamelCase     ,			**_lowerCamelCase    )
         else:
             __UpperCamelCase	:	List[str]		    =  None

         if audio_target is not None:
             __UpperCamelCase	:	Dict		    =  self.feature_extractor(audio_target=_lowerCamelCase     ,			*_lowerCamelCase     ,			sampling_rate=_lowerCamelCase     ,			**_lowerCamelCase    )
             __UpperCamelCase	:	Optional[int]		    =  targets['input_values']
         elif text_target is not None:
             __UpperCamelCase	:	Any		    =  self.tokenizer(_lowerCamelCase     ,			**_lowerCamelCase    )
             __UpperCamelCase	:	Optional[int]		    =  targets['input_ids']
         else:
             __UpperCamelCase	:	List[str]		    =  None

         if inputs is None:
             return targets

         if targets is not None:
             __UpperCamelCase	:	List[Any]		    =  labels

             __UpperCamelCase	:	Any		    =  targets.get('attention_mask'    )
             if decoder_attention_mask is not None:
                 __UpperCamelCase	:	int		    =  decoder_attention_mask

         return inputs







     def lowerCAmelCase     (		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         __UpperCamelCase	:	Optional[Any]		    =  kwargs.pop('input_values'     ,			_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  kwargs.pop('input_ids'     ,			_lowerCamelCase    )
         __UpperCamelCase	:	List[Any]		    =  kwargs.pop('labels'     ,			_lowerCamelCase    )

         if input_values is not None and input_ids is not None:
             raise ValueError('Cannot process both `input_values` and `input_ids` inputs.'    )
         if input_values is None and input_ids is None and labels is None:
             raise ValueError(
                 'You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.'    )

         if input_values is not None:
             __UpperCamelCase	:	Optional[int]		    =  self.feature_extractor.pad(_lowerCamelCase     ,			*_lowerCamelCase     ,			**_lowerCamelCase    )
         elif input_ids is not None:
             __UpperCamelCase	:	int		    =  self.tokenizer.pad(_lowerCamelCase     ,			**_lowerCamelCase    )
         else:
             __UpperCamelCase	:	str		    =  None

         if labels is not None:
             if "input_ids" in labels or (isinstance(_lowerCamelCase     ,			_lowerCamelCase    ) and "input_ids" in labels[0]):
                 __UpperCamelCase	:	Dict		    =  self.tokenizer.pad(_lowerCamelCase     ,			**_lowerCamelCase    )
                 __UpperCamelCase	:	str		    =  targets['input_ids']
             else:
                 __UpperCamelCase	:	int		    =  self.feature_extractor.feature_size
                 __UpperCamelCase	:	Any		    =  self.feature_extractor.num_mel_bins
                 __UpperCamelCase	:	Dict		    =  self.feature_extractor.pad(_lowerCamelCase     ,			*_lowerCamelCase     ,			**_lowerCamelCase    )
                 __UpperCamelCase	:	List[str]		    =  feature_size_hack
                 __UpperCamelCase	:	Tuple		    =  targets['input_values']
         else:
             __UpperCamelCase	:	List[str]		    =  None

         if inputs is None:
             return targets

         if targets is not None:
             __UpperCamelCase	:	Optional[int]		    =  labels

             __UpperCamelCase	:	Union[str, Any]		    =  targets.get('attention_mask'    )
             if decoder_attention_mask is not None:
                 __UpperCamelCase	:	Optional[Any]		    =  decoder_attention_mask

         return inputs







     def lowerCAmelCase     (		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         return self.tokenizer.batch_decode(*_lowerCamelCase     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         return self.tokenizer.decode(*_lowerCamelCase     ,			**_lowerCamelCase    )



