




"""simple docstring"""


from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
     if not (is_transformers_available() and is_torch_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
     from .multicontrolnet import MultiControlNetModel
     from .pipeline_controlnet import StableDiffusionControlNetPipeline
     from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
     from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
     from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline







"""simple docstring"""






import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__snake_case							:   Optional[int]      =    50_000
__snake_case							:   Dict      =    5_000

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


@get_duration
def 			_lowercase				(  __snake_case					,__snake_case	)						->   Dict:
			for i in range(__snake_case	):
						__lowerCAmelCase      :  Union[str, Any]												=       dataset[i]


@get_duration
def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case	)						->   Dict:
			for i in range(0					,len(__snake_case	)					,__snake_case	):
						__lowerCAmelCase      :  List[str]												=       dataset[i : i + batch_size]


@get_duration
def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case	)						->   Dict:
			with dataset.formatted_as(type=__snake_case	):
						for i in range(__snake_case	):
									__lowerCAmelCase      :  Union[str, Any]												=       dataset[i]


@get_duration
def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case					,__snake_case	)						->   str:
			with dataset.formatted_as(type=__snake_case	):
						for i in range(0					,__snake_case					,__snake_case	):
									__lowerCAmelCase      :  Optional[int]												=       dataset[i : i + batch_size]


def 			_lowercase				(  )						->   Union[str, Any]:
			__lowerCAmelCase      :  Optional[int]												=       {"num examples": SPEED_TEST_N_EXAMPLES}
			__lowerCAmelCase      :  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": 100}),
			    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1_000}),
			    (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": 1_000}),
			]

			__lowerCAmelCase      :  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": 100}),
			    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1_000}),
			    (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": 1_000}),
			]
			with tempfile.TemporaryDirectory() as tmp_dir:
						print("generating dataset"	)
						__lowerCAmelCase      :  int												=       datasets.Features(
						    {"list": datasets.Sequence(datasets.Value("float32"	)	), "numbers": datasets.Value("float32"	)}	)
						__lowerCAmelCase      :  str												=       generate_example_dataset(
						    os.path.join(__snake_case					,"dataset.arrow"	)					,__snake_case					,num_examples=__snake_case					,seq_shapes={"list": (100,)}					,)
						print("first set of iterations"	)
						for func, kwargs in functions:
									print(func.__name__					,str(__snake_case	)	)
									__lowerCAmelCase      :  str												=       func(__snake_case					,**__snake_case	)

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

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


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