






'''simple docstring'''







import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


UpperCamelCase_					=				500000

UpperCamelCase_					=				os.path.split(__file__)
UpperCamelCase_					=				os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))






@get_duration
def  lowerCAmelCase__		(					a_  :     datasets.Dataset			)   -> Optional[int]:
       UpperCAmelCase__	:   Optional[Any]												=	dataset.select(range(0			,						len(__SCREAMING_SNAKE_CASE			)			,						2			)			)






@get_duration
def  lowerCAmelCase__		(					a_  :     datasets.Dataset			)   -> Optional[int]:
       UpperCAmelCase__	:   Optional[Any]												=	dataset.sort('''numbers'''			)






@get_duration
def  lowerCAmelCase__		(					a_  :     datasets.Dataset			)   -> Any:
       UpperCAmelCase__	:   str												=	dataset.shuffle()






@get_duration
def  lowerCAmelCase__		(					a_  :     datasets.Dataset			)   -> Dict:
       UpperCAmelCase__	:   Optional[int]												=	dataset.train_test_split(0.1			)






@get_duration
def  lowerCAmelCase__		(					a_  :     datasets.Dataset			,						a_  :     Dict=1_0			)   -> Optional[int]:

       for shard_id in range(__SCREAMING_SNAKE_CASE			):
              UpperCAmelCase__	:   int												=	dataset.shard(__SCREAMING_SNAKE_CASE			,						__SCREAMING_SNAKE_CASE			)






def  lowerCAmelCase__		(					)   -> int:
       UpperCAmelCase__	:   Optional[Any]												=	{"""num examples""": SPEED_TEST_N_EXAMPLES}
       UpperCAmelCase__	:   List[str]												=	(select, sort, shuffle, train_test_split, shard)
       with tempfile.TemporaryDirectory() as tmp_dir:
              print('''generating dataset'''			)
              UpperCAmelCase__	:   List[str]												=	datasets.Features({'''text''': datasets.Value('''string'''			), '''numbers''': datasets.Value('''float32'''			)}			)
              UpperCAmelCase__	:   Union[str, Any]												=	generate_example_dataset(
                  os.path.join(__SCREAMING_SNAKE_CASE			,						'''dataset.arrow'''			)			,						__SCREAMING_SNAKE_CASE			,						num_examples=__SCREAMING_SNAKE_CASE			)
              print('''Functions'''			)
              for func in functions:
                     print(func.__name__			)
                     UpperCAmelCase__	:   Optional[int]												=	func(__SCREAMING_SNAKE_CASE			)

       with open(__SCREAMING_SNAKE_CASE			,						'''wb'''			) as f:
              f.write(json.dumps(__SCREAMING_SNAKE_CASE			).encode('''utf-8'''			)			)


if __name__ == "__main__":  # useful to run the profiler
  benchmark_indices_mapping()

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







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 lowerCamelCase												=['''image_processor''', '''tokenizer''']
 lowerCamelCase												='''EfficientNetImageProcessor'''
 lowerCamelCase												=('''BertTokenizer''', '''BertTokenizerFast''')
 def __init__( self      :     str					, lowerCamelCase      :     Optional[int]					, lowerCamelCase      :     Tuple							):



   """simple docstring"""





   super().__init__(lowerCamelCase					, lowerCamelCase							)
 def __call__( self      :     Tuple					, lowerCamelCase      :     str=None					, lowerCamelCase      :     Tuple=None					, lowerCamelCase      :     int="max_length"					, lowerCamelCase      :     int=64					, lowerCamelCase      :     Union[str, Any]=None					, **lowerCamelCase      :     Dict							):



   """simple docstring"""





   if text is None and images is None:
     raise ValueError("""You have to specify either text or images. Both cannot be none."""							)

   if text is not None:
     __A		:  str       				=		self.tokenizer(
         lowerCamelCase					, padding=lowerCamelCase					, max_length=lowerCamelCase					, return_tensors=lowerCamelCase					, **lowerCamelCase							)

   if images is not None:
     __A		:  Optional[Any]       				=		self.image_processor(lowerCamelCase					, return_tensors=lowerCamelCase					, **lowerCamelCase							)

   if text is not None and images is not None:
     __A		:  Optional[int]       				=		image_features.pixel_values
     return encoding
   elif text is not None:
     return encoding
   else:
     return BatchEncoding(data=dict(**lowerCamelCase							)					, tensor_type=lowerCamelCase							)
 def 		lowercase_( self      :     Any					, *lowerCamelCase      :     Optional[Any]					, **lowerCamelCase      :     Union[str, Any]							):



   """simple docstring"""





   return self.tokenizer.batch_decode(*lowerCamelCase					, **lowerCamelCase							)
 def 		lowercase_( self      :     Any					, *lowerCamelCase      :     List[Any]					, **lowerCamelCase      :     Union[str, Any]							):



   """simple docstring"""





   return self.tokenizer.decode(*lowerCamelCase					, **lowerCamelCase							)






 @property
 def 		lowercase_( self      :     int							):



   """simple docstring"""





   __A		:  Dict       				=		self.tokenizer.model_input_names
   __A		:  int       				=		self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)



