

"""simple docstring"""





import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__UpperCamelCase    : List[Any]				 =						5_0_0_0_0
__UpperCamelCase    : Tuple				 =						5_0_0_0

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

@get_duration
def     __SCREAMING_SNAKE_CASE    ( A_		,	A_					):
	for i in range(A_					):
		lowerCAmelCase__      :		List[Any]    							=      dataset[i]

@get_duration
def     __SCREAMING_SNAKE_CASE    ( A_		,	A_		,	A_					):
	for i in range(0		,	len(A_					)		,	A_					):
		lowerCAmelCase__      :		Any    							=      dataset[i : i + batch_size]

@get_duration
def     __SCREAMING_SNAKE_CASE    ( A_		,	A_		,	A_					):
	with dataset.formatted_as(type=A_					):
		for i in range(A_					):
			lowerCAmelCase__      :		Optional[int]    							=      dataset[i]

@get_duration
def     __SCREAMING_SNAKE_CASE    ( A_		,	A_		,	A_		,	A_					):
	with dataset.formatted_as(type=A_					):
		for i in range(0		,	A_		,	A_					):
			lowerCAmelCase__      :		str    							=      dataset[i : i + batch_size]

def     __SCREAMING_SNAKE_CASE    ( ):
	lowerCAmelCase__      :		Tuple    							=      {'''num examples''': SPEED_TEST_N_EXAMPLES}
	lowerCAmelCase__      :		Union[str, Any]    							=      [
	    (read, {'''length''': SMALL_TEST}),
	    (read, {'''length''': SPEED_TEST_N_EXAMPLES}),
	    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 10}),
	    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_00}),
	    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 10_00}),
	    (read_formatted, {'''type''': '''numpy''', '''length''': SMALL_TEST}),
	    (read_formatted, {'''type''': '''pandas''', '''length''': SMALL_TEST}),
	    (read_formatted, {'''type''': '''torch''', '''length''': SMALL_TEST}),
	    (read_formatted, {'''type''': '''tensorflow''', '''length''': SMALL_TEST}),
	    (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 10}),
	    (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 10_00}),
	]

	lowerCAmelCase__      :		str    							=      [
	    (read, {'''length''': SMALL_TEST}),
	    (read, {'''length''': SPEED_TEST_N_EXAMPLES}),
	    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 10}),
	    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_00}),
	    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 10_00}),
	    (read_formatted, {'''type''': '''numpy''', '''length''': SMALL_TEST}),
	    (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 10}),
	    (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 10_00}),
	]
	with tempfile.TemporaryDirectory() as tmp_dir:
		print('''generating dataset'''					)
		lowerCAmelCase__      :		Union[str, Any]    							=      datasets.Features(
		    {'''list''': datasets.Sequence(datasets.Value('''float32'''					)					), '''numbers''': datasets.Value('''float32'''					)}					)
		lowerCAmelCase__      :		Any    							=      generate_example_dataset(
		    os.path.join(A_		,	'''dataset.arrow'''					)		,	A_		,	num_examples=A_		,	seq_shapes={'''list''': (1_00,)}		,	)
		print('''first set of iterations'''					)
		for func, kwargs in functions:
			print(func.__name__		,	str(A_					)					)
			lowerCAmelCase__      :		List[str]    							=      func(A_		,	**A_					)

		print('''shuffling dataset'''					)
		lowerCAmelCase__      :		List[Any]    							=      dataset.shuffle()
		print('''Second set of iterations (after shuffling'''					)
		for func, kwargs in functions_shuffled:
			print('''shuffled '''		,	func.__name__		,	str(A_					)					)
			lowerCAmelCase__      :		Tuple    							=      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_iterating()







"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase    : str				 =						{
    '''configuration_swinv2''': ['''SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Swinv2Config'''],
}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : List[Any]				 =						[
							    '''SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''Swinv2ForImageClassification''',
							    '''Swinv2ForMaskedImageModeling''',
							    '''Swinv2Model''',
							    '''Swinv2PreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_swinva import (
														    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SwinvaForImageClassification,
														    SwinvaForMaskedImageModeling,
														    SwinvaModel,
														    SwinvaPreTrainedModel,
														)


else:
							import sys

							__UpperCamelCase    : List[Any]				 =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




