


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


@get_duration
def 					_snake_case   (     A ,    A ,    A       )						->    List[str]:



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


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


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


@get_duration
def 					_snake_case   (     A ,    A       )						->    Union[str, Any]:
							lowerCAmelCase__          =	10
							lowerCAmelCase__          =	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_       ):
														lowerCAmelCase__          =	dataset[i : i + batch_size]


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


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


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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case			:			Optional[int]			   =   {"""configuration_sew_d""": ["""SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWDConfig"""]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case			:			List[Any]			   =   [
					    """SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """SEWDForCTC""",
					    """SEWDForSequenceClassification""",
					    """SEWDModel""",
					    """SEWDPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_sew_d import (
										    SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SEWDForCTC,
										    SEWDForSequenceClassification,
										    SEWDModel,
										    SEWDPreTrainedModel,
										)


else:
					import sys

					__snake_case			:			Dict			   =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





