




"""simple docstring"""

from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml





class 							lowercase_			(	yaml.SafeLoader				):



 '''simple docstring'''

 def      lowerCAmelCase_				(					self			:			List[str]       ,				_UpperCAmelCase			:			List[Any]						):
       _A     =  [self.constructed_objects[key_node] for key_node, _ in node.value]
       _A     =  [tuple(_UpperCAmelCase						) if isinstance(_UpperCAmelCase       ,				_UpperCAmelCase						) else key for key in keys]
       _A     =  Counter(_UpperCAmelCase						)
       _A     =  [key for key in counter if counter[key] > 1]
       if duplicate_keys:
             raise TypeError(F'''Got duplicate yaml keys: {duplicate_keys}'''						)



 def      lowerCAmelCase_				(					self			:			Tuple       ,				_UpperCAmelCase			:			int       ,				_UpperCAmelCase			:			Union[str, Any]=False						):
       _A     =  super().construct_mapping(_UpperCAmelCase       ,				deep=_UpperCAmelCase						)
       self._check_no_duplicates_on_constructed_node(_UpperCAmelCase						)
       return mapping






def 		_snake_case	(   _snake_case				:   str				)       ->   Tuple[Optional[str], str]:






      '''simple docstring'''


      _A     =  list(readme_content.splitlines()				)
      if full_content and full_content[0] == "---" and "---" in full_content[1:]:
            _A     =  full_content[1:].index('---'				) + 1
            _A     =  '\n'.join(full_content[1:sep_idx]				)
            return yamlblock, "\n".join(full_content[sep_idx + 1 :]				)

      return None, "\n".join(_snake_case				)





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 # class attributes
 UpperCAmelCase       :	Union[str, Any]     =      {'''train_eval_index'''}  # train-eval-index in the YAML metadata

 @classmethod
 def      lowerCAmelCase_				(					cls			:			int       ,				_UpperCAmelCase			:			Path						):
       with open(_UpperCAmelCase       ,				encoding='utf-8'						) as readme_file:
             _A  ,       _A     =  _split_yaml_from_readme(readme_file.read()						)
       if yaml_string is not None:
             return cls.from_yaml_string(_UpperCAmelCase						)
       else:
             return cls()

 def      lowerCAmelCase_				(					self			:			str       ,				_UpperCAmelCase			:			Path						):
       if path.exists():
             with open(_UpperCAmelCase       ,				encoding='utf-8'						) as readme_file:
                   _A     =  readme_file.read()
       else:
             _A     =  None
       _A     =  self._to_readme(_UpperCAmelCase						)
       with open(_UpperCAmelCase       ,				'w'       ,				encoding='utf-8'						) as readme_file:
             readme_file.write(_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			int       ,				_UpperCAmelCase			:			Optional[str] = None						):
       if readme_content is not None:
             _A  ,       _A     =  _split_yaml_from_readme(_UpperCAmelCase						)
             _A     =  '---\n' + self.to_yaml_string() + '---\n' + content
       else:
             _A     =  '---\n' + self.to_yaml_string() + '---\n'
       return full_content

 @classmethod
 def      lowerCAmelCase_				(					cls			:			List[str]       ,				_UpperCAmelCase			:			str						):
       _A     =  yaml.load(_UpperCAmelCase       ,				Loader=_NoDuplicateSafeLoader						) or {}

       # Convert the YAML keys to DatasetMetadata fields
       _A     =  {
           (key.replace('-'       ,				'_'						) if key.replace('-'       ,				'_'						) in cls._FIELDS_WITH_DASHES else key): value
           for key, value in metadata_dict.items()
       }
       return cls(**_UpperCAmelCase						)



 def      lowerCAmelCase_				(					self			:			Dict						):
       return yaml.safe_dump(
           {
               (key.replace('_'       ,				'-'						) if key in self._FIELDS_WITH_DASHES else key): value
               for key, value in self.items()
           }       ,				sort_keys=_UpperCAmelCase       ,				allow_unicode=_UpperCAmelCase       ,				encoding='utf-8'       ,				).decode('utf-8'						)



a							      =     {
    '''image-classification''': [],
    '''translation''': [],
    '''image-segmentation''': [],
    '''fill-mask''': [],
    '''automatic-speech-recognition''': [],
    '''token-classification''': [],
    '''sentence-similarity''': [],
    '''audio-classification''': [],
    '''question-answering''': [],
    '''summarization''': [],
    '''zero-shot-classification''': [],
    '''table-to-text''': [],
    '''feature-extraction''': [],
    '''other''': [],
    '''multiple-choice''': [],
    '''text-classification''': [],
    '''text-to-image''': [],
    '''text2text-generation''': [],
    '''zero-shot-image-classification''': [],
    '''tabular-classification''': [],
    '''tabular-regression''': [],
    '''image-to-image''': [],
    '''tabular-to-text''': [],
    '''unconditional-image-generation''': [],
    '''text-retrieval''': [],
    '''text-to-speech''': [],
    '''object-detection''': [],
    '''audio-to-audio''': [],
    '''text-generation''': [],
    '''conversational''': [],
    '''table-question-answering''': [],
    '''visual-question-answering''': [],
    '''image-to-text''': [],
    '''reinforcement-learning''': [],
    '''voice-activity-detection''': [],
    '''time-series-forecasting''': [],
    '''document-question-answering''': [],
}


if __name__ == "__main__":
       from argparse import ArgumentParser

       a							      =     ArgumentParser(usage='''Validate the yaml metadata block of a README.md file.''')
       ap.add_argument('''readme_filepath''')
       a							      =     ap.parse_args()

       a							      =     Path(args.readme_filepath)
       a							      =     DatasetMetadata.from_readme(readme_filepath)
       print(dataset_metadata)
       dataset_metadata.to_readme(readme_filepath)





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
   from transformers import TvltImageProcessor

if is_speech_available():
   from transformers import TvltFeatureExtractor

from transformers import TvltProcessor
@require_torch
class 		__magic_name__						(unittest.TestCase				):





     '''simple docstring'''



     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       snake_case__						= '''ZinengTang/tvlt-base'''
       snake_case__						= tempfile.mkdtemp()

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		**_a:List[Any]   ):
       return TvltImageProcessor.from_pretrained(self.checkpoint     ,		**_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]     ,		**_a:Tuple   ):
       return TvltFeatureExtractor.from_pretrained(self.checkpoint     ,		**_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       shutil.rmtree(self.tmpdirname   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       snake_case__						= self.get_image_processor()
       snake_case__						= self.get_feature_extractor()

       snake_case__						= TvltProcessor(image_processor=_a     ,		feature_extractor=_a   )
       processor.save_pretrained(self.tmpdirname   )
       snake_case__						= TvltProcessor.from_pretrained(self.tmpdirname   )

       self.assertIsInstance(processor.feature_extractor     ,		_a   )
       self.assertIsInstance(processor.image_processor     ,		_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       snake_case__						= self.get_image_processor()
       snake_case__						= self.get_feature_extractor()

       snake_case__						= TvltProcessor(image_processor=_a     ,		feature_extractor=_a   )

       snake_case__						= np.ones([1_20_00]   )

       snake_case__						= feature_extractor(_a     ,		return_tensors='''np'''   )
       snake_case__						= processor(audio=_a     ,		return_tensors='''np'''   )

       for key in audio_dict.keys():
         self.assertAlmostEqual(audio_dict[key].sum()     ,		input_processor[key].sum()     ,		delta=1e-2   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       snake_case__						= self.get_image_processor()
       snake_case__						= self.get_feature_extractor()

       snake_case__						= TvltProcessor(image_processor=_a     ,		feature_extractor=_a   )

       snake_case__						= np.ones([3, 2_24, 2_24]   )

       snake_case__						= image_processor(_a     ,		return_tensors='''np'''   )
       snake_case__						= processor(images=_a     ,		return_tensors='''np'''   )

       for key in image_dict.keys():
         self.assertAlmostEqual(image_dict[key].sum()     ,		input_processor[key].sum()     ,		delta=1e-2   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       snake_case__						= self.get_image_processor()
       snake_case__						= self.get_feature_extractor()

       snake_case__						= TvltProcessor(image_processor=_a     ,		feature_extractor=_a   )

       snake_case__						= np.ones([1_20_00]   )
       snake_case__						= np.ones([3, 2_24, 2_24]   )

       snake_case__						= processor(audio=_a     ,		images=_a   )

       self.assertListEqual(list(inputs.keys()   )     ,		['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask''']   )

       # test if it raises when no input is passed
       with pytest.raises(_a   ):
         processor()







     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       snake_case__						= self.get_image_processor()
       snake_case__						= self.get_feature_extractor()

       snake_case__						= TvltProcessor(image_processor=_a     ,		feature_extractor=_a   )

       self.assertListEqual(
           processor.model_input_names     ,		image_processor.model_input_names + feature_extractor.model_input_names     ,		msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match'''     ,		)

