"""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_							:							Optional[Any]				 =		(30, 487)
UpperCamelCase_							:							List[str]				 =		(36, 1024)
UpperCamelCase_							:							Optional[Any]				 =		(100, 100)
UpperCamelCase_							:							Union[str, Any]				 =		100

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

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


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


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


@get_duration
def 	a__   (		UpperCAmelCase__							:      List[str]		,      UpperCAmelCase__							:      Union[str, Any]      )->						Any:
						UpperCAmelCase__       						=		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__   (		UpperCAmelCase__							:      Dict		,      UpperCAmelCase__							:      int      )->						List[str]:
						UpperCAmelCase__       						=		1_0
						UpperCAmelCase__       						=		datasets.Dataset.from_file(
						    filename=os.path.join(UpperCAmelCase__		,      """beta.arrow"""      )		,      info=datasets.DatasetInfo(features=UpperCAmelCase__      )      )
						for i in range(0		,      len(UpperCAmelCase__      )		,      UpperCAmelCase__      ):
												UpperCAmelCase__       						=		dataset[i : i + batch_size]


@get_duration
def 	a__   (		UpperCAmelCase__							:      Optional[Any]		,      UpperCAmelCase__							:      List[str]      )->						Union[str, Any]:
						UpperCAmelCase__       						=		1_0
						UpperCAmelCase__       						=		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__      ):
												UpperCAmelCase__       						=		dataset[i : i + batch_size]


@get_duration
def 	a__   (		UpperCAmelCase__							:      str		,      UpperCAmelCase__							:      Optional[int]      )->						Optional[int]:
						UpperCAmelCase__       						=		datasets.Dataset.from_file(
						    filename=os.path.join(UpperCAmelCase__		,      """beta.arrow"""      )		,      info=datasets.DatasetInfo(features=UpperCAmelCase__      )      )
						for col in feats:
												UpperCAmelCase__       						=		dataset[col]


@get_duration
def 	a__   (		UpperCAmelCase__							:      str		,      UpperCAmelCase__							:      Tuple      )->						Union[str, Any]:
						UpperCAmelCase__       						=		datasets.Dataset.from_file(
						    filename=os.path.join(UpperCAmelCase__		,      """beta.arrow"""      )		,      info=datasets.DatasetInfo(features=UpperCAmelCase__      )      )
						dataset.set_format("""numpy"""      )
						for col in feats:
												UpperCAmelCase__       						=		dataset[col]


def 	a__   (		)->						int:
						UpperCAmelCase__       						=		{}
						UpperCAmelCase__       						=		(
						    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:
												UpperCAmelCase__       						=		datasets.Features({"""image""": ArrayaD(UpperCAmelCase__		,      dtype="""float32"""      )}      )
												UpperCAmelCase__       						=		generate_examples(features=UpperCAmelCase__		,      num_examples=UpperCAmelCase__      )
												UpperCAmelCase__       						=		write(UpperCAmelCase__		,      UpperCAmelCase__		,      UpperCAmelCase__      )
												for read_func in read_functions:
																		UpperCAmelCase__       						=		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])}
												# )
												UpperCAmelCase__       						=		datasets.Features({"""image""": datasets.Sequence(datasets.Sequence(datasets.Value("""float32"""      )      )      )}      )
												UpperCAmelCase__       						=		generate_examples(
												    features=UpperCAmelCase__		,      num_examples=UpperCAmelCase__		,      seq_shapes={"""image""": SPEED_TEST_SHAPE}      )
												UpperCAmelCase__       						=		write(UpperCAmelCase__		,      UpperCAmelCase__		,      UpperCAmelCase__      )
												for read_func in read_functions:
																		UpperCAmelCase__       						=		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])}
												# )
												UpperCAmelCase__       						=		datasets.Features({"""image""": datasets.Sequence(datasets.Value("""float32"""      )      )}      )
												UpperCAmelCase__       						=		generate_examples(
												    features=UpperCAmelCase__		,      num_examples=UpperCAmelCase__		,      seq_shapes={"""image""": [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}		,      )
												UpperCAmelCase__       						=		write(UpperCAmelCase__		,      UpperCAmelCase__		,      UpperCAmelCase__      )
												for read_func in read_functions:
																		UpperCAmelCase__       						=		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 (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
						if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.27.0""")):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import (
						    AudioLDMPipeline,
						)
else:
						from .pipeline_audioldm import AudioLDMPipeline

