
import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


lowerCamelCase_    :			Tuple    =	5_00_00
lowerCamelCase_    :			Union[str, Any]    =	50_00

lowerCamelCase_ ,							lowerCamelCase_    :			List[Any]    =	os.path.split(__file__)
lowerCamelCase_    :			Optional[int]    =	os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))







@get_duration
def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						str:
				for i in range(__lowerCAmelCase    ):
								UpperCamelCase_:				Optional[Any]						=    dataset[i]







@get_duration
def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						Optional[Any]:
				for i in range(0  ,     len(__lowerCAmelCase    )  ,     __lowerCAmelCase    ):
								UpperCamelCase_:				Any						=    dataset[i : i + batch_size]







@get_duration
def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						Optional[Any]:
				with dataset.formatted_as(type=__lowerCAmelCase    ):
								for i in range(__lowerCAmelCase    ):
												UpperCamelCase_:				str						=    dataset[i]







@get_duration
def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						List[str]:
				with dataset.formatted_as(type=__lowerCAmelCase    ):
								for i in range(0  ,     __lowerCAmelCase  ,     __lowerCAmelCase    ):
												UpperCamelCase_:				Optional[Any]						=    dataset[i : i + batch_size]







def 							A__       (    )	->						List[str]:
				UpperCamelCase_:				str						=    {"""num examples""": SPEED_TEST_N_EXAMPLES}
				UpperCamelCase_:				Optional[int]						=    [
				    (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}),
				]

				UpperCamelCase_:				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"""    )
								UpperCamelCase_:				List[Any]						=    datasets.Features(
								    {"""list""": datasets.Sequence(datasets.Value("""float32"""    )    ), """numbers""": datasets.Value("""float32"""    )}    )
								UpperCamelCase_:				Union[str, Any]						=    generate_example_dataset(
								    os.path.join(__lowerCAmelCase  ,     """dataset.arrow"""    )  ,     __lowerCAmelCase  ,     num_examples=__lowerCAmelCase  ,     seq_shapes={"""list""": (1_00,)}  ,     )
								print("""first set of iterations"""    )
								for func, kwargs in functions:
												print(func.__name__  ,     str(__lowerCAmelCase    )    )
												UpperCamelCase_:				Optional[Any]						=    func(__lowerCAmelCase  ,     **__lowerCAmelCase    )

								print("""shuffling dataset"""    )
								UpperCamelCase_:				int						=    dataset.shuffle()
								print("""Second set of iterations (after shuffling"""    )
								for func, kwargs in functions_shuffled:
												print("""shuffled """  ,     func.__name__  ,     str(__lowerCAmelCase    )    )
												UpperCamelCase_:				Optional[int]						=    func(
												    __lowerCAmelCase  ,     **__lowerCAmelCase    )

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


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













'''simple docstring'''




from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ..utils.dummy_pt_objects import *  # noqa F403
else:
 from .scheduling_consistency_models import CMStochasticIterativeScheduler
 from .scheduling_ddim import DDIMScheduler
 from .scheduling_ddim_inverse import DDIMInverseScheduler
 from .scheduling_ddim_parallel import DDIMParallelScheduler
 from .scheduling_ddpm import DDPMScheduler
 from .scheduling_ddpm_parallel import DDPMParallelScheduler
 from .scheduling_deis_multistep import DEISMultistepScheduler
 from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
 from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
 from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
 from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
 from .scheduling_euler_discrete import EulerDiscreteScheduler
 from .scheduling_heun_discrete import HeunDiscreteScheduler
 from .scheduling_ipndm import IPNDMScheduler
 from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
 from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
 from .scheduling_karras_ve import KarrasVeScheduler
 from .scheduling_pndm import PNDMScheduler
 from .scheduling_repaint import RePaintScheduler
 from .scheduling_sde_ve import ScoreSdeVeScheduler
 from .scheduling_sde_vp import ScoreSdeVpScheduler
 from .scheduling_unclip import UnCLIPScheduler
 from .scheduling_unipc_multistep import UniPCMultistepScheduler
 from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
 from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ..utils.dummy_flax_objects import *  # noqa F403
else:
 from .scheduling_ddim_flax import FlaxDDIMScheduler
 from .scheduling_ddpm_flax import FlaxDDPMScheduler
 from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
 from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
 from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
 from .scheduling_pndm_flax import FlaxPNDMScheduler
 from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
 from .scheduling_utils_flax import (
     FlaxKarrasDiffusionSchedulers,
     FlaxSchedulerMixin,
     FlaxSchedulerOutput,
     broadcast_to_shape_from_left,
 )


try:
 if not (is_torch_available() and is_scipy_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ..utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
 from .scheduling_lms_discrete import LMSDiscreteScheduler

try:
 if not (is_torch_available() and is_torchsde_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ..utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
 from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler

