




"""simple docstring"""

import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor
class 			lowerCamelCase__    (  unittest.TestCase			):


			@property
			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						int								=				1
					snake_case		:						Dict								=				3
					snake_case		:						int								=				8
					snake_case		:						int								=				8

					snake_case		:						List[str]								=				torch.rand((batch_size, num_channels, height, width)							)

					return sample


			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					if isinstance(image[0]				, PIL.Image.Image							):
							return np.stack([np.array(SCREAMING_SNAKE_CASE							) for i in image]				, axis=0							)
					elif isinstance(SCREAMING_SNAKE_CASE				, torch.Tensor							):
							return image.cpu().numpy().transpose(0				, 2				, 3				, 1							)
					return image


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Optional[int]								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)

					snake_case		:						Any								=				self.dummy_sample
					snake_case		:						Optional[Any]								=				self.to_np(SCREAMING_SNAKE_CASE							)

					for output_type in ["pt", "np", "pil"]:
							snake_case		:						Optional[Any]								=				image_processor.postprocess(
							    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type=SCREAMING_SNAKE_CASE				, )
							snake_case		:						Optional[Any]								=				self.to_np(SCREAMING_SNAKE_CASE							)
							snake_case		:						Any								=				(input_np * 255).round() if output_type == "pil" else input_np
							assert (
							    np.abs(in_np - out_np							).max() < 1E-6
							), F'''decoded output does not match input for output_type {output_type}'''


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Dict								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)
					snake_case		:						Tuple								=				self.dummy_sample.cpu().numpy().transpose(0				, 2				, 3				, 1							)

					for output_type in ["pt", "np", "pil"]:
							snake_case		:						Dict								=				image_processor.postprocess(image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type=SCREAMING_SNAKE_CASE							)

							snake_case		:						Union[str, Any]								=				self.to_np(SCREAMING_SNAKE_CASE							)
							snake_case		:						Tuple								=				(input_np * 255).round() if output_type == "pil" else input_np
							assert (
							    np.abs(in_np - out_np							).max() < 1E-6
							), F'''decoded output does not match input for output_type {output_type}'''


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Optional[Any]								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)

					snake_case		:						str								=				self.dummy_sample.cpu().numpy().transpose(0				, 2				, 3				, 1							)
					snake_case		:						int								=				image_processor.numpy_to_pil(SCREAMING_SNAKE_CASE							)

					for output_type in ["pt", "np", "pil"]:
							snake_case		:						Dict								=				image_processor.postprocess(image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type=SCREAMING_SNAKE_CASE							)
							for i, o in zip(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):
									snake_case		:						Dict								=				np.array(SCREAMING_SNAKE_CASE							)
									snake_case		:						Tuple								=				self.to_np(SCREAMING_SNAKE_CASE							) if output_type == "pil" else (self.to_np(SCREAMING_SNAKE_CASE							) * 255).round()
									assert (
									    np.abs(in_np - out_np							).max() < 1E-6
									), F'''decoded output does not match input for output_type {output_type}'''


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Tuple								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)

					snake_case		:						List[str]								=				self.dummy_sample
					snake_case		:						Optional[int]								=				input_pt_ad.squeeze(0							)

					snake_case		:						Any								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
					snake_case		:						Union[str, Any]								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )

					snake_case		:						Union[str, Any]								=				self.to_np(self.dummy_sample							)
					snake_case		:						Tuple								=				input_np_ad.squeeze(0							)

					snake_case		:						str								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
					snake_case		:						Union[str, Any]								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )

					assert np.abs(out_pt_ad - out_pt_ad							).max() < 1E-6
					assert np.abs(out_np_ad - out_np_ad							).max() < 1E-6



			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Optional[Any]								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)

					snake_case		:						Union[str, Any]								=				self.dummy_sample
					snake_case		:						List[str]								=				list(SCREAMING_SNAKE_CASE							)

					snake_case		:						str								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )

					snake_case		:						Any								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )

					snake_case		:						Dict								=				self.to_np(self.dummy_sample							)
					list(SCREAMING_SNAKE_CASE							)

					snake_case		:						str								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )

					snake_case		:						Union[str, Any]								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )

					assert np.abs(out_pt_ad - out_pt_list							).max() < 1E-6
					assert np.abs(out_np_ad - out_np_list							).max() < 1E-6







"""simple docstring"""

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 			lowerCamelCase__    (  lowerCamelCase_			):
			a__ :      int								=     """config.json"""


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



					"""simple docstring"""







					pass







class 			lowerCamelCase__    (  lowerCamelCase_			):
			a__ :      Dict								=     """config.json"""


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



					"""simple docstring"""







					pass







class 			lowerCamelCase__    (  lowerCamelCase_			):
			a__ :      Union[str, Any]								=     """config.json"""


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



					"""simple docstring"""







					pass







class 			lowerCamelCase__    (  lowerCamelCase_			):
			a__ :      Tuple								=     """config.json"""


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



					"""simple docstring"""







					pass







class 			lowerCamelCase__    (  unittest.TestCase			):


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					with self.assertRaises(SCREAMING_SNAKE_CASE							):
							ConfigMixin.load_config("dummy_path"							)


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Dict								=				SampleObject()
					snake_case		:						Dict								=				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
					snake_case		:						Dict								=				SampleObject(_name_or_path="lalala"							)
					snake_case		:						List[Any]								=				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
					snake_case		:						str								=				SampleObject(c=6							)
					snake_case		:						Any								=				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.
					snake_case		:						int								=				SampleObject(1				, c=6							)
					snake_case		:						Dict								=				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 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						str								=				SampleObject()
					snake_case		:						List[str]								=				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(SCREAMING_SNAKE_CASE							)
							snake_case		:						Union[str, Any]								=				SampleObject.from_config(SampleObject.load_config(SCREAMING_SNAKE_CASE							)							)
							snake_case		:						Union[str, Any]								=				new_obj.config

					# unfreeze configs
					snake_case		:						List[str]								=				dict(SCREAMING_SNAKE_CASE							)
					snake_case		:						Union[str, Any]								=				dict(SCREAMING_SNAKE_CASE							)

					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 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						List[Any]								=				logging.get_logger("diffusers.configuration_utils"							)
					# 30 for warning
					logger.setLevel(30							)

					with CaptureLogger(SCREAMING_SNAKE_CASE							) as cap_logger:
							snake_case		:						Any								=				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 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						int								=				logging.get_logger("diffusers.configuration_utils"							)
					# 30 for warning
					logger.setLevel(30							)

					with CaptureLogger(SCREAMING_SNAKE_CASE							) as cap_logger:
							snake_case		:						Optional[int]								=				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 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Any								=				logging.get_logger("diffusers.configuration_utils"							)
					# 30 for warning
					logger.setLevel(30							)

					with CaptureLogger(SCREAMING_SNAKE_CASE							) as cap_logger:
							snake_case		:						Union[str, Any]								=				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 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Union[str, Any]								=				logging.get_logger("diffusers.configuration_utils"							)
					# 30 for warning
					logger.setLevel(30							)

					with CaptureLogger(SCREAMING_SNAKE_CASE							) as cap_logger:
							snake_case		:						List[Any]								=				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 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						str								=				logging.get_logger("diffusers.configuration_utils"							)
					# 30 for warning
					logger.setLevel(30							)

					with CaptureLogger(SCREAMING_SNAKE_CASE							) as cap_logger:
							snake_case		:						List[Any]								=				DDPMScheduler.from_pretrained(
							    "hf-internal-testing/tiny-stable-diffusion-torch"				, subfolder="scheduler"				, prediction_type="sample"				, beta_end=8				, )

					with CaptureLogger(SCREAMING_SNAKE_CASE							) as cap_logger_a:
							snake_case		:						Optional[int]								=				DDPMScheduler.from_pretrained("google/ddpm-celebahq-256"				, beta_start=88							)

					assert ddpm.__class__ == DDPMScheduler
					assert ddpm.config.prediction_type == "sample"
					assert ddpm.config.beta_end == 8
					assert ddpm_a.config.beta_start == 88

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


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Dict								=				logging.get_logger("diffusers.configuration_utils"							)
					# 30 for warning
					logger.setLevel(30							)

					with CaptureLogger(SCREAMING_SNAKE_CASE							) as cap_logger:
							snake_case		:						Union[str, Any]								=				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 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Optional[Any]								=				SampleObject()

					snake_case		:						str								=				{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(SCREAMING_SNAKE_CASE							)

							# now loading it with SampleObject2 should put f into `_use_default_values`
							snake_case		:						Optional[int]								=				SampleObjecta.from_config(SCREAMING_SNAKE_CASE							)

							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`
					snake_case		:						Tuple								=				SampleObjecta.from_config(config.config							)
					assert new_config.f == [5, 4]

					config.config._use_default_values.pop()
					snake_case		:						List[Any]								=				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]

