





"""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


_snake_case	:						Dict					=					(30, 487)
_snake_case	:						Union[str, Any]					=					(36, 1024)
_snake_case	:						Optional[int]					=					(100, 100)
_snake_case	:						Dict					=					100

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

_snake_case	:						str					=					os.path.split(__file__)
_snake_case	:						List[Any]					=					os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))






@get_duration
def    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase			):
							with ArrowWriter(features=UpperCamelCase   ,    path=os.path.join(UpperCamelCase   ,    "beta.arrow"			)			) as writer:
														for key, record in dummy_data:
																					A						=    my_features.encode_example(UpperCamelCase			)
																					writer.write(UpperCamelCase			)
														A						=    writer.finalize()






@get_duration
def    A__							(     UpperCamelCase   ,    UpperCamelCase			):
							A						=    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   ,    UpperCamelCase			):
							A						=    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   ,    UpperCamelCase			):
							A						=    10
							A						=    datasets.Dataset.from_file(
							    filename=os.path.join(UpperCamelCase   ,    "beta.arrow"			)   ,    info=datasets.DatasetInfo(features=UpperCamelCase			)			)
							for i in range(0   ,    len(UpperCamelCase			)   ,    UpperCamelCase			):
														A						=    dataset[i : i + batch_size]






@get_duration
def    A__							(     UpperCamelCase   ,    UpperCamelCase			):
							A						=    10
							A						=    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			):
														A						=    dataset[i : i + batch_size]






@get_duration
def    A__							(     UpperCamelCase   ,    UpperCamelCase			):
							A						=    datasets.Dataset.from_file(
							    filename=os.path.join(UpperCamelCase   ,    "beta.arrow"			)   ,    info=datasets.DatasetInfo(features=UpperCamelCase			)			)
							for col in feats:
														A						=    dataset[col]






@get_duration
def    A__							(     UpperCamelCase   ,    UpperCamelCase			):
							A						=    datasets.Dataset.from_file(
							    filename=os.path.join(UpperCamelCase   ,    "beta.arrow"			)   ,    info=datasets.DatasetInfo(features=UpperCamelCase			)			)
							dataset.set_format("numpy"			)
							for col in feats:
														A						=    dataset[col]






def    A__							(     ):
							A						=    {}
							A						=    (
							    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						=    datasets.Features({"image": ArrayaD(UpperCamelCase   ,    dtype="float32"			)}			)
														A						=    generate_examples(features=UpperCamelCase   ,    num_examples=UpperCamelCase			)
														A						=    write(UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase			)
														for read_func in read_functions:
																					A						=    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])}
														# )
														A						=    datasets.Features({"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"			)			)			)}			)
														A						=    generate_examples(
														    features=UpperCamelCase   ,    num_examples=UpperCamelCase   ,    seq_shapes={"image": SPEED_TEST_SHAPE}			)
														A						=    write(UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase			)
														for read_func in read_functions:
																					A						=    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])}
														# )
														A						=    datasets.Features({"image": datasets.Sequence(datasets.Value("float32"			)			)}			)
														A						=    generate_examples(
														    features=UpperCamelCase   ,    num_examples=UpperCamelCase   ,    seq_shapes={"image": [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}   ,    )
														A						=    write(UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase			)
														for read_func in read_functions:
																					A						=    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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_A      :     Optional[Any]       	={
    '''configuration_realm''': ['''REALM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RealmConfig'''],
    '''tokenization_realm''': ['''RealmTokenizer'''],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A      :     str       	=['''RealmTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A      :     List[Any]       	=[
			    '''REALM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''RealmEmbedder''',
			    '''RealmForOpenQA''',
			    '''RealmKnowledgeAugEncoder''',
			    '''RealmPreTrainedModel''',
			    '''RealmReader''',
			    '''RealmScorer''',
			    '''load_tf_weights_in_realm''',
			]
			_A      :     Union[str, Any]       	=['''RealmRetriever''']


if TYPE_CHECKING:
			from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
			from .tokenization_realm import RealmTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_realm import RealmTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_realm import (
						    REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    RealmEmbedder,
						    RealmForOpenQA,
						    RealmKnowledgeAugEncoder,
						    RealmPreTrainedModel,
						    RealmReader,
						    RealmScorer,
						    load_tf_weights_in_realm,
						)
						from .retrieval_realm import RealmRetriever


else:
			import sys

			_A      :     int       	=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

