"""simple docstring"""






import inspect

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
				import jax
@require_flax
class   UpperCAmelCase__				:


	"""simple docstring"""







	def       A    (			self )    ->	List[Any]:
							a_		,					a_							:			Optional[int]     		=					self.prepare_init_args_and_inputs_for_common()

							a_							:			Union[str, Any]     		=					self.model_class(**_SCREAMING_SNAKE_CASE )
							a_							:			Tuple     		=					model.init(inputs_dict["prng_key"]       ,     inputs_dict["sample"] )
							jax.lax.stop_gradient(_SCREAMING_SNAKE_CASE )

							a_							:			Optional[int]     		=					model.apply(_SCREAMING_SNAKE_CASE       ,     inputs_dict["sample"] )

							if isinstance(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE ):
													a_							:			str     		=					output.sample

							self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
							a_							:			List[Any]     		=					inputs_dict["sample"].shape
							self.assertEqual(output.shape       ,     _SCREAMING_SNAKE_CASE       ,     "Input and output shapes do not match" )







	def       A    (			self )    ->	List[str]:
							a_		,					a_							:			List[Any]     		=					self.prepare_init_args_and_inputs_for_common()

							a_							:			List[str]     		=					1_6
							a_							:			Optional[Any]     		=					(1_6, 3_2)

							a_							:			Optional[int]     		=					self.model_class(**_SCREAMING_SNAKE_CASE )
							a_							:			int     		=					model.init(inputs_dict["prng_key"]       ,     inputs_dict["sample"] )
							jax.lax.stop_gradient(_SCREAMING_SNAKE_CASE )

							a_							:			Dict     		=					model.apply(_SCREAMING_SNAKE_CASE       ,     inputs_dict["sample"] )

							if isinstance(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE ):
													a_							:			str     		=					output.sample

							self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
							a_							:			Optional[int]     		=					inputs_dict["sample"].shape
							self.assertEqual(output.shape       ,     _SCREAMING_SNAKE_CASE       ,     "Input and output shapes do not match" )







	def       A    (			self )    ->	Dict:
							a_							:			List[Any]     		=					"kwargs" in inspect.signature(self.model_class.__init__ ).parameters
							a_							:			List[Any]     		=					len(self.model_class._deprecated_kwargs ) > 0

							if has_kwarg_in_model_class and not has_deprecated_kwarg:
													raise ValueError(
													    f'''{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs'''
													    " under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are"
													    " no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ="
													    " [<deprecated_argument>]`" )

							if not has_kwarg_in_model_class and has_deprecated_kwarg:
													raise ValueError(
													    f'''{self.model_class} doesn\'t have `**kwargs` in its __init__ method but has defined deprecated kwargs'''
													    " under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to"
													    f''' {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument'''
													    " from `_deprecated_kwargs = [<deprecated_argument>]`" )





"""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     					=     (30, 4_87)
UpperCamelCase     					=     (36, 10_24)
UpperCamelCase     					=     (1_00, 1_00)
UpperCamelCase     					=     1_00

UpperCamelCase     					=     datasets.Features(
    {'text': ArrayaD(SHAPE_TEST_1, dtype='float32'), 'image': ArrayaD(SHAPE_TEST_2, dtype='float32')}
)

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

@get_duration
def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:Tuple		,			_SCREAMING_SNAKE_CASE	:str		,			_SCREAMING_SNAKE_CASE	:Tuple  )				-> Any:
						with ArrowWriter(features=_SCREAMING_SNAKE_CASE		,			path=os.path.join(_SCREAMING_SNAKE_CASE		,			"beta.arrow"  )  ) as writer:
												for key, record in dummy_data:
																		a_							:			int     		=					my_features.encode_example(_SCREAMING_SNAKE_CASE  )
																		writer.write(_SCREAMING_SNAKE_CASE  )
												a_		,					a_							:			str     		=					writer.finalize()

@get_duration
def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:List[str]		,			_SCREAMING_SNAKE_CASE	:Any  )				-> Tuple:
						a_							:			Dict     		=					datasets.Dataset.from_file(
						    filename=os.path.join(_SCREAMING_SNAKE_CASE		,			"beta.arrow"  )		,			info=datasets.DatasetInfo(features=_SCREAMING_SNAKE_CASE  )  )
						for _ in dataset:
												pass

@get_duration
def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:str		,			_SCREAMING_SNAKE_CASE	:Dict  )				-> Optional[Any]:
						a_							:			List[str]     		=					datasets.Dataset.from_file(
						    filename=os.path.join(_SCREAMING_SNAKE_CASE		,			"beta.arrow"  )		,			info=datasets.DatasetInfo(features=_SCREAMING_SNAKE_CASE  )  )
						dataset.set_format("numpy"  )
						for _ in dataset:
												pass

@get_duration
def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:List[Any]		,			_SCREAMING_SNAKE_CASE	:List[str]  )				-> List[str]:
						a_							:			Optional[Any]     		=					10
						a_							:			Optional[int]     		=					datasets.Dataset.from_file(
						    filename=os.path.join(_SCREAMING_SNAKE_CASE		,			"beta.arrow"  )		,			info=datasets.DatasetInfo(features=_SCREAMING_SNAKE_CASE  )  )
						for i in range(0		,			len(_SCREAMING_SNAKE_CASE  )		,			_SCREAMING_SNAKE_CASE  ):
												a_							:			List[str]     		=					dataset[i : i + batch_size]

@get_duration
def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:Optional[int]		,			_SCREAMING_SNAKE_CASE	:List[str]  )				-> Optional[int]:
						a_							:			List[str]     		=					10
						a_							:			int     		=					datasets.Dataset.from_file(
						    filename=os.path.join(_SCREAMING_SNAKE_CASE		,			"beta.arrow"  )		,			info=datasets.DatasetInfo(features=_SCREAMING_SNAKE_CASE  )  )
						dataset.set_format("numpy"  )
						for i in range(0		,			len(_SCREAMING_SNAKE_CASE  )		,			_SCREAMING_SNAKE_CASE  ):
												a_							:			str     		=					dataset[i : i + batch_size]

@get_duration
def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:List[str]		,			_SCREAMING_SNAKE_CASE	:Optional[Any]  )				-> Dict:
						a_							:			List[str]     		=					datasets.Dataset.from_file(
						    filename=os.path.join(_SCREAMING_SNAKE_CASE		,			"beta.arrow"  )		,			info=datasets.DatasetInfo(features=_SCREAMING_SNAKE_CASE  )  )
						for col in feats:
												a_							:			List[Any]     		=					dataset[col]

@get_duration
def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:str		,			_SCREAMING_SNAKE_CASE	:Optional[Any]  )				-> Dict:
						a_							:			Any     		=					datasets.Dataset.from_file(
						    filename=os.path.join(_SCREAMING_SNAKE_CASE		,			"beta.arrow"  )		,			info=datasets.DatasetInfo(features=_SCREAMING_SNAKE_CASE  )  )
						dataset.set_format("numpy"  )
						for col in feats:
												a_							:			Any     		=					dataset[col]

def  lowerCAmelCase_      ()				-> Optional[Any]:
						a_							:			List[Any]     		=					{}
						a_							:			str     		=					(
						    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:
												a_							:			Optional[int]     		=					datasets.Features({"image": ArrayaD(_SCREAMING_SNAKE_CASE		,			dtype="float32"  )}  )
												a_							:			Optional[Any]     		=					generate_examples(features=_SCREAMING_SNAKE_CASE		,			num_examples=_SCREAMING_SNAKE_CASE  )
												a_							:			List[str]     		=					write(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )
												for read_func in read_functions:
																		a_							:			Optional[int]     		=					read_func(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )

						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])}
												# )
												a_							:			Union[str, Any]     		=					datasets.Features({"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"  )  )  )}  )
												a_							:			List[str]     		=					generate_examples(
												    features=_SCREAMING_SNAKE_CASE		,			num_examples=_SCREAMING_SNAKE_CASE		,			seq_shapes={"image": SPEED_TEST_SHAPE}  )
												a_							:			str     		=					write(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )
												for read_func in read_functions:
																		a_							:			List[str]     		=					read_func(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )

						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])}
												# )
												a_							:			int     		=					datasets.Features({"image": datasets.Sequence(datasets.Value("float32"  )  )}  )
												a_							:			str     		=					generate_examples(
												    features=_SCREAMING_SNAKE_CASE		,			num_examples=_SCREAMING_SNAKE_CASE		,			seq_shapes={"image": [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}		,			)
												a_							:			List[Any]     		=					write(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )
												for read_func in read_functions:
																		a_							:			str     		=					read_func(_SCREAMING_SNAKE_CASE		,			_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_array_xd()




