

import tempfile
import unittest

from diffusers import (
    DDIMScheduler,
    DDPMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    PNDMScheduler,
    logging,
)
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils.testing_utils import CaptureLogger





class      snake_case_      (					__UpperCamelCase				):


							"""simple docstring"""





							__UpperCAmelCase      ="config.json"




							@register_to_config
							def __init__(			self       ,    _A=2       ,    _A=5       ,    _A=(2, 5)       ,    _A="for diffusion"       ,    _A=[1, 3]       ,    ):
												pass





class      snake_case_      (					__UpperCamelCase				):


							"""simple docstring"""





							__UpperCAmelCase      ="config.json"




							@register_to_config
							def __init__(			self       ,    _A=2       ,    _A=5       ,    _A=(2, 5)       ,    _A="for diffusion"       ,    _A=[1, 3]       ,    ):
												pass





class      snake_case_      (					__UpperCamelCase				):


							"""simple docstring"""





							__UpperCAmelCase      ="config.json"




							@register_to_config
							def __init__(			self       ,    _A=2       ,    _A=5       ,    _A=(2, 5)       ,    _A="for diffusion"       ,    _A=[1, 3]       ,    _A=[1, 3]       ,    ):
												pass





class      snake_case_      (					__UpperCamelCase				):


							"""simple docstring"""





							__UpperCAmelCase      ="config.json"




							@register_to_config
							def __init__(			self       ,    _A=2       ,    _A=5       ,    _A=(2, 5)       ,    _A="for diffusion"       ,    _A=[1, 5]       ,    _A=[5, 4]       ,    ):
												pass





class      snake_case_      (					unittest.TestCase				):


							"""simple docstring"""





							def 							A__	(			self				):
												with self.assertRaises(_lowerCAmelCase				):
																	ConfigMixin.load_config('dummy_path'				)




							def 							A__	(			self				):
												__lowerCAmelCase       =				SampleObject()
												__lowerCAmelCase       =				obj.config
												assert config["a"] == 2
												assert config["b"] == 5
												assert config["c"] == (2, 5)
												assert config["d"] == "for diffusion"
												assert config["e"] == [1, 3]

												# init ignore private arguments
												__lowerCAmelCase       =				SampleObject(_name_or_path='lalala'				)
												__lowerCAmelCase       =				obj.config
												assert config["a"] == 2
												assert config["b"] == 5
												assert config["c"] == (2, 5)
												assert config["d"] == "for diffusion"
												assert config["e"] == [1, 3]

												# can override default
												__lowerCAmelCase       =				SampleObject(c=6				)
												__lowerCAmelCase       =				obj.config
												assert config["a"] == 2
												assert config["b"] == 5
												assert config["c"] == 6
												assert config["d"] == "for diffusion"
												assert config["e"] == [1, 3]

												# can use positional arguments.
												__lowerCAmelCase       =				SampleObject(1       ,    c=6				)
												__lowerCAmelCase       =				obj.config
												assert config["a"] == 1
												assert config["b"] == 5
												assert config["c"] == 6
												assert config["d"] == "for diffusion"
												assert config["e"] == [1, 3]




							def 							A__	(			self				):
												__lowerCAmelCase       =				SampleObject()
												__lowerCAmelCase       =				obj.config

												assert config["a"] == 2
												assert config["b"] == 5
												assert config["c"] == (2, 5)
												assert config["d"] == "for diffusion"
												assert config["e"] == [1, 3]

												with tempfile.TemporaryDirectory() as tmpdirname:
																	obj.save_config(_lowerCAmelCase				)
																	__lowerCAmelCase       =				SampleObject.from_config(SampleObject.load_config(_lowerCAmelCase				)				)
																	__lowerCAmelCase       =				new_obj.config

												# unfreeze configs
												__lowerCAmelCase       =				dict(_lowerCAmelCase				)
												__lowerCAmelCase       =				dict(_lowerCAmelCase				)

												assert config.pop('c'				) == (2, 5)  # instantiated as tuple
												assert new_config.pop('c'				) == [2, 5]  # saved & loaded as list because of json
												config.pop('_use_default_values'				)
												assert config == new_config




							def 							A__	(			self				):
												__lowerCAmelCase       =				logging.get_logger('diffusers.configuration_utils'				)
												# 30 for warning
												logger.setLevel(3_0				)

												with CaptureLogger(_lowerCAmelCase				) as cap_logger:
																	__lowerCAmelCase       =				DDIMScheduler.from_pretrained(
																	    'hf-internal-testing/tiny-stable-diffusion-torch'       ,    subfolder='scheduler'				)

												assert ddim.__class__ == DDIMScheduler
												# no warning should be thrown
												assert cap_logger.out == ""




							def 							A__	(			self				):
												__lowerCAmelCase       =				logging.get_logger('diffusers.configuration_utils'				)
												# 30 for warning
												logger.setLevel(3_0				)

												with CaptureLogger(_lowerCAmelCase				) as cap_logger:
																	__lowerCAmelCase       =				EulerDiscreteScheduler.from_pretrained(
																	    'hf-internal-testing/tiny-stable-diffusion-torch'       ,    subfolder='scheduler'				)

												assert euler.__class__ == EulerDiscreteScheduler
												# no warning should be thrown
												assert cap_logger.out == ""




							def 							A__	(			self				):
												__lowerCAmelCase       =				logging.get_logger('diffusers.configuration_utils'				)
												# 30 for warning
												logger.setLevel(3_0				)

												with CaptureLogger(_lowerCAmelCase				) as cap_logger:
																	__lowerCAmelCase       =				EulerAncestralDiscreteScheduler.from_pretrained(
																	    'hf-internal-testing/tiny-stable-diffusion-torch'       ,    subfolder='scheduler'				)

												assert euler.__class__ == EulerAncestralDiscreteScheduler
												# no warning should be thrown
												assert cap_logger.out == ""




							def 							A__	(			self				):
												__lowerCAmelCase       =				logging.get_logger('diffusers.configuration_utils'				)
												# 30 for warning
												logger.setLevel(3_0				)

												with CaptureLogger(_lowerCAmelCase				) as cap_logger:
																	__lowerCAmelCase       =				PNDMScheduler.from_pretrained(
																	    'hf-internal-testing/tiny-stable-diffusion-torch'       ,    subfolder='scheduler'				)

												assert pndm.__class__ == PNDMScheduler
												# no warning should be thrown
												assert cap_logger.out == ""




							def 							A__	(			self				):
												__lowerCAmelCase       =				logging.get_logger('diffusers.configuration_utils'				)
												# 30 for warning
												logger.setLevel(3_0				)

												with CaptureLogger(_lowerCAmelCase				) as cap_logger:
																	__lowerCAmelCase       =				DDPMScheduler.from_pretrained(
																	    'hf-internal-testing/tiny-stable-diffusion-torch'       ,    subfolder='scheduler'       ,    prediction_type='sample'       ,    beta_end=8       ,    )

												with CaptureLogger(_lowerCAmelCase				) as cap_logger_a:
																	__lowerCAmelCase       =				DDPMScheduler.from_pretrained('google/ddpm-celebahq-256'       ,    beta_start=8_8				)

												assert ddpm.__class__ == DDPMScheduler
												assert ddpm.config.prediction_type == "sample"
												assert ddpm.config.beta_end == 8
												assert ddpm_a.config.beta_start == 8_8

												# no warning should be thrown
												assert cap_logger.out == ""
												assert cap_logger_a.out == ""




							def 							A__	(			self				):
												__lowerCAmelCase       =				logging.get_logger('diffusers.configuration_utils'				)
												# 30 for warning
												logger.setLevel(3_0				)

												with CaptureLogger(_lowerCAmelCase				) as cap_logger:
																	__lowerCAmelCase       =				DPMSolverMultistepScheduler.from_pretrained(
																	    'hf-internal-testing/tiny-stable-diffusion-torch'       ,    subfolder='scheduler'				)

												assert dpm.__class__ == DPMSolverMultistepScheduler
												# no warning should be thrown
												assert cap_logger.out == ""




							def 							A__	(			self				):
												# let's first save a config that should be in the form
												#    a=2,
												#    b=5,
												#    c=(2, 5),
												#    d="for diffusion",
												#    e=[1, 3],

												__lowerCAmelCase       =				SampleObject()

												__lowerCAmelCase       =				{k: v for k, v in config.config.items() if not k.startswith('_'				)}

												# make sure that default config has all keys in `_use_default_values`
												assert set(config_dict.keys()				) == set(config.config._use_default_values				)

												with tempfile.TemporaryDirectory() as tmpdirname:
																	config.save_config(_lowerCAmelCase				)

																	# now loading it with SampleObject2 should put f into `_use_default_values`
																	__lowerCAmelCase       =				SampleObjecta.from_config(_lowerCAmelCase				)

																	assert "f" in config._use_default_values
																	assert config.f == [1, 3]

												# now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
												# **BECAUSE** it is part of `config._use_default_values`
												__lowerCAmelCase       =				SampleObjecta.from_config(config.config				)
												assert new_config.f == [5, 4]

												config.config._use_default_values.pop()
												__lowerCAmelCase       =				SampleObjecta.from_config(config.config				)
												assert new_config_a.f == [1, 3]

												# Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
												assert new_config_a.e == [1, 3]







import math
from timeit import timeit







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					int   ):







    '''simple docstring'''
    __snake_case       :				Optional[Any] 			=    0
    __snake_case       :				Union[str, Any] 			=    abs(__SCREAMING_SNAKE_CASE   )
    while True:
        __snake_case       :				List[str] 			=    n // 1_0
        digits += 1
        if n == 0:
            break
    return digits







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					int   ):







    '''simple docstring'''
    return 1 if n == 0 else math.floor(math.log(abs(__SCREAMING_SNAKE_CASE   )		,							1_0   ) + 1   )







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					int   ):







    '''simple docstring'''
    return len(str(abs(__SCREAMING_SNAKE_CASE   )   )   )







def 					__lowerCAmelCase				(		):







    '''simple docstring'''
    from collections.abc import Callable

    def benchmark_a_function(__SCREAMING_SNAKE_CASE       :					Callable		,							__SCREAMING_SNAKE_CASE       :					int   ) -> None:
        __snake_case       :				Tuple 			=    F'''{func.__name__}({value})'''
        __snake_case       :				Union[str, Any] 			=    timeit(F'''__main__.{call}'''		,							setup="""import __main__"""   )
        print(F'''{call}: {func(__SCREAMING_SNAKE_CASE   )} -- {timing} seconds'''   )

    for value in (2_6_2_1_4_4, 1_1_2_5_8_9_9_9_0_6_8_4_2_6_2_4, 1_2_6_7_6_5_0_6_0_0_2_2_8_2_2_9_4_0_1_4_9_6_7_0_3_2_0_5_3_7_6):
        for func in (num_digits, num_digits_fast, num_digits_faster):
            benchmark_a_function(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )
        print()


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     benchmark()



