"""simple docstring"""





import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


_UpperCamelCase       :						Union[str, Any]     =   (30, 4_87)
_UpperCamelCase       :						int     =   (36, 10_24)
_UpperCamelCase       :						Optional[Any]     =   (1_00, 1_00)
_UpperCamelCase       :						str     =   1_00

_UpperCamelCase       :						List[Any]     =   datasets.Features(
    {"text": ArrayaD(SHAPE_TEST_1, dtype="float32"), "image": ArrayaD(SHAPE_TEST_2, dtype="float32")}
)

_UpperCamelCase       :						str     =   os.path.split(__file__)
_UpperCamelCase       :						Optional[Any]     =   os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))





@get_duration
def     a_					(				_lowerCAmelCase  :			Dict       ,			_lowerCAmelCase  :			Dict       ,			_lowerCAmelCase  :			Tuple					):





       '''simple docstring'''

       with ArrowWriter(features=UpperCamelCase_       ,			path=os.path.join(UpperCamelCase_       ,			'beta.arrow'					)					) as writer:
              for key, record in dummy_data:
                     lowercase__      :		str          =     my_features.encode_example(UpperCamelCase_					)
                     writer.write(UpperCamelCase_					)
              lowercase__   ,				lowercase__      :		List[str]          =     writer.finalize()





@get_duration
def     a_					(				_lowerCAmelCase  :			int       ,			_lowerCAmelCase  :			Any					):





       '''simple docstring'''

       lowercase__      :		Union[str, Any]          =     datasets.Dataset.from_file(
           filename=os.path.join(UpperCamelCase_       ,			'beta.arrow'					)       ,			info=datasets.DatasetInfo(features=UpperCamelCase_					)					)
       for _ in dataset:
              pass





@get_duration
def     a_					(				_lowerCAmelCase  :			Tuple       ,			_lowerCAmelCase  :			str					):





       '''simple docstring'''

       lowercase__      :		str          =     datasets.Dataset.from_file(
           filename=os.path.join(UpperCamelCase_       ,			'beta.arrow'					)       ,			info=datasets.DatasetInfo(features=UpperCamelCase_					)					)
       dataset.set_format('numpy'					)
       for _ in dataset:
              pass





@get_duration
def     a_					(				_lowerCAmelCase  :			int       ,			_lowerCAmelCase  :			str					):





       '''simple docstring'''

       lowercase__      :		Union[str, Any]          =     10
       lowercase__      :		Union[str, Any]          =     datasets.Dataset.from_file(
           filename=os.path.join(UpperCamelCase_       ,			'beta.arrow'					)       ,			info=datasets.DatasetInfo(features=UpperCamelCase_					)					)
       for i in range(0       ,			len(UpperCamelCase_					)       ,			UpperCamelCase_					):
              lowercase__      :		str          =     dataset[i : i + batch_size]





@get_duration
def     a_					(				_lowerCAmelCase  :			Dict       ,			_lowerCAmelCase  :			str					):





       '''simple docstring'''

       lowercase__      :		Optional[Any]          =     10
       lowercase__      :		str          =     datasets.Dataset.from_file(
           filename=os.path.join(UpperCamelCase_       ,			'beta.arrow'					)       ,			info=datasets.DatasetInfo(features=UpperCamelCase_					)					)
       dataset.set_format('numpy'					)
       for i in range(0       ,			len(UpperCamelCase_					)       ,			UpperCamelCase_					):
              lowercase__      :		List[Any]          =     dataset[i : i + batch_size]





@get_duration
def     a_					(				_lowerCAmelCase  :			Optional[int]       ,			_lowerCAmelCase  :			Optional[int]					):





       '''simple docstring'''

       lowercase__      :		Tuple          =     datasets.Dataset.from_file(
           filename=os.path.join(UpperCamelCase_       ,			'beta.arrow'					)       ,			info=datasets.DatasetInfo(features=UpperCamelCase_					)					)
       for col in feats:
              lowercase__      :		Union[str, Any]          =     dataset[col]





@get_duration
def     a_					(				_lowerCAmelCase  :			str       ,			_lowerCAmelCase  :			str					):





       '''simple docstring'''

       lowercase__      :		List[Any]          =     datasets.Dataset.from_file(
           filename=os.path.join(UpperCamelCase_       ,			'beta.arrow'					)       ,			info=datasets.DatasetInfo(features=UpperCamelCase_					)					)
       dataset.set_format('numpy'					)
       for col in feats:
              lowercase__      :		Dict          =     dataset[col]





def     a_					(				):





       '''simple docstring'''

       lowercase__      :		List[Any]          =     {}
       lowercase__      :		Optional[Any]          =     (
           read_unformated,
           read_formatted_as_numpy,
           read_batch_unformated,
           read_batch_formatted_as_numpy,
           read_col_unformated,
           read_col_formatted_as_numpy,
       )
       with tempfile.TemporaryDirectory() as tmp_dir:
              lowercase__      :		str          =     datasets.Features({'image': ArrayaD(UpperCamelCase_       ,			dtype='float32'					)}					)
              lowercase__      :		Optional[int]          =     generate_examples(features=UpperCamelCase_       ,			num_examples=UpperCamelCase_					)
              lowercase__      :		Optional[Any]          =     write(UpperCamelCase_       ,			UpperCamelCase_       ,			UpperCamelCase_					)
              for read_func in read_functions:
                     lowercase__      :		str          =     read_func(UpperCamelCase_       ,			UpperCamelCase_					)

       with tempfile.TemporaryDirectory() as tmp_dir:
              # don't use fixed length for fair comparison
              # feats = datasets.Features(
              #     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
              # )
              lowercase__      :		Optional[Any]          =     datasets.Features({'image': datasets.Sequence(datasets.Sequence(datasets.Value('float32'					)					)					)}					)
              lowercase__      :		Dict          =     generate_examples(
                  features=UpperCamelCase_       ,			num_examples=UpperCamelCase_       ,			seq_shapes={'image': SPEED_TEST_SHAPE}					)
              lowercase__      :		Optional[Any]          =     write(UpperCamelCase_       ,			UpperCamelCase_       ,			UpperCamelCase_					)
              for read_func in read_functions:
                     lowercase__      :		int          =     read_func(UpperCamelCase_       ,			UpperCamelCase_					)

       with tempfile.TemporaryDirectory() as tmp_dir:
              # don't use fixed length for fair comparison
              # feats = datasets.Features(
              #     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
              # )
              lowercase__      :		int          =     datasets.Features({'image': datasets.Sequence(datasets.Value('float32'					)					)}					)
              lowercase__      :		Tuple          =     generate_examples(
                  features=UpperCamelCase_       ,			num_examples=UpperCamelCase_       ,			seq_shapes={'image': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}       ,			)
              lowercase__      :		str          =     write(UpperCamelCase_       ,			UpperCamelCase_       ,			UpperCamelCase_					)
              for read_func in read_functions:
                     lowercase__      :		Optional[Any]          =     read_func(UpperCamelCase_       ,			UpperCamelCase_					)

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


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


"""simple docstring"""





from ..utils import DummyObject, requires_backends


class 					UpperCAmelCase_     (   metaclass=_a):
      lowerCamelCase__		:   List[str]			  =  ["torch", "transformers", "k_diffusion"]


      def __init__(       self		,      *a		,      **a							)	-> Dict:
             requires_backends(self		,      ['torch', 'transformers', 'k_diffusion']							)


      @classmethod
      def 		_UpperCAmelCase	(       cls		,      *a		,      **a							)	-> Tuple:
             requires_backends(cls		,      ['torch', 'transformers', 'k_diffusion']							)


      @classmethod
      def 		_UpperCAmelCase	(       cls		,      *a		,      **a							)	-> Any:
             requires_backends(cls		,      ['torch', 'transformers', 'k_diffusion']							)

