


'''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, 487)
__UpperCAmelCase    				=							(36, 1_024)
__UpperCAmelCase    				=							(100, 100)
__UpperCAmelCase    				=							100

__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 					_snake_case   (     A ,    A ,    A       )						->    Optional[int]:
							with ArrowWriter(features=A ,    path=os.path.join(A ,    '''beta.arrow'''       )       ) as writer:
														for key, record in dummy_data:
																					lowerCAmelCase__          =	my_features.encode_example(A       )
																					writer.write(A       )
														lowerCAmelCase__   ,					lowerCAmelCase__          =	writer.finalize()


@get_duration
def 					_snake_case   (     A ,    A       )						->    Tuple:
							lowerCAmelCase__          =	datasets.Dataset.from_file(
							    filename=os.path.join(A ,    '''beta.arrow'''       ) ,    info=datasets.DatasetInfo(features=A       )       )
							for _ in dataset:
														pass


@get_duration
def 					_snake_case   (     A ,    A       )						->    Any:
							lowerCAmelCase__          =	datasets.Dataset.from_file(
							    filename=os.path.join(A ,    '''beta.arrow'''       ) ,    info=datasets.DatasetInfo(features=A       )       )
							dataset.set_format('''numpy'''       )
							for _ in dataset:
														pass


@get_duration
def 					_snake_case   (     A ,    A       )						->    Any:
							lowerCAmelCase__          =	10
							lowerCAmelCase__          =	datasets.Dataset.from_file(
							    filename=os.path.join(A ,    '''beta.arrow'''       ) ,    info=datasets.DatasetInfo(features=A       )       )
							for i in range(0 ,    len(A       ) ,    A       ):
														lowerCAmelCase__          =	dataset[i : i + batch_size]


@get_duration
def 					_snake_case   (     A ,    A       )						->    Dict:
							lowerCAmelCase__          =	10
							lowerCAmelCase__          =	datasets.Dataset.from_file(
							    filename=os.path.join(A ,    '''beta.arrow'''       ) ,    info=datasets.DatasetInfo(features=A       )       )
							dataset.set_format('''numpy'''       )
							for i in range(0 ,    len(A       ) ,    A       ):
														lowerCAmelCase__          =	dataset[i : i + batch_size]


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


@get_duration
def 					_snake_case   (     A ,    A       )						->    List[str]:
							lowerCAmelCase__          =	datasets.Dataset.from_file(
							    filename=os.path.join(A ,    '''beta.arrow'''       ) ,    info=datasets.DatasetInfo(features=A       )       )
							dataset.set_format('''numpy'''       )
							for col in feats:
														lowerCAmelCase__          =	dataset[col]


def 					_snake_case   (     )						->    int:
							lowerCAmelCase__          =	{}
							lowerCAmelCase__          =	(
							    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:
														lowerCAmelCase__          =	datasets.Features({'''image''': ArrayaD(A ,    dtype='''float32'''       )}       )
														lowerCAmelCase__          =	generate_examples(features=A ,    num_examples=A       )
														lowerCAmelCase__          =	write(A ,    A ,    A       )
														for read_func in read_functions:
																					lowerCAmelCase__          =	read_func(A ,    A       )

							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])}
														# )
														lowerCAmelCase__          =	datasets.Features({'''image''': datasets.Sequence(datasets.Sequence(datasets.Value('''float32'''       )       )       )}       )
														lowerCAmelCase__          =	generate_examples(
														    features=A ,    num_examples=A ,    seq_shapes={'''image''': SPEED_TEST_SHAPE}       )
														lowerCAmelCase__          =	write(A ,    A ,    A       )
														for read_func in read_functions:
																					lowerCAmelCase__          =	read_func(A ,    A       )

							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])}
														# )
														lowerCAmelCase__          =	datasets.Features({'''image''': datasets.Sequence(datasets.Value('''float32'''       )       )}       )
														lowerCAmelCase__          =	generate_examples(
														    features=A ,    num_examples=A ,    seq_shapes={'''image''': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]} ,    )
														lowerCAmelCase__          =	write(A ,    A ,    A       )
														for read_func in read_functions:
																					lowerCAmelCase__          =	read_func(A ,    A       )

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


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



'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase    				=							{
    '''configuration_switch_transformers''': [
        '''SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SwitchTransformersConfig''',
        '''SwitchTransformersOnnxConfig''',
    ]
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase    				=							[
		    '''SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''SwitchTransformersEncoderModel''',
		    '''SwitchTransformersForConditionalGeneration''',
		    '''SwitchTransformersModel''',
		    '''SwitchTransformersPreTrainedModel''',
		    '''SwitchTransformersTop1Router''',
		    '''SwitchTransformersSparseMLP''',
		]


if TYPE_CHECKING:
		from .configuration_switch_transformers import (
		    SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    SwitchTransformersConfig,
		    SwitchTransformersOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_switch_transformers import (
				    SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SwitchTransformersEncoderModel,
				    SwitchTransformersForConditionalGeneration,
				    SwitchTransformersModel,
				    SwitchTransformersPreTrainedModel,
				    SwitchTransformersSparseMLP,
				    SwitchTransformersTopaRouter,
				)


else:
		import sys

		__UpperCAmelCase    				=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)