
'''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			(					__UpperCAmelCase      ):
		a				:       int							=       """config.json"""




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


class       lowerCAmelCase			(					__UpperCAmelCase      ):
		a				:       int							=       """config.json"""




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


class       lowerCAmelCase			(					__UpperCAmelCase      ):
		a				:       List[Any]							=       """config.json"""




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


class       lowerCAmelCase			(					__UpperCAmelCase      ):
		a				:       Optional[Any]							=       """config.json"""




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


class       lowerCAmelCase			(					unittest.TestCase      ):




		def      lowercase							(					self   ):
								with self.assertRaises(UpperCamelCase   ):
														ConfigMixin.load_config("dummy_path"   )




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			SampleObject()
								_SCREAMING_SNAKE_CASE					=			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
								_SCREAMING_SNAKE_CASE					=			SampleObject(_name_or_path="lalala"   )
								_SCREAMING_SNAKE_CASE					=			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
								_SCREAMING_SNAKE_CASE					=			SampleObject(c=6   )
								_SCREAMING_SNAKE_CASE					=			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.
								_SCREAMING_SNAKE_CASE					=			SampleObject(1       ,    c=6   )
								_SCREAMING_SNAKE_CASE					=			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      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			SampleObject()
								_SCREAMING_SNAKE_CASE					=			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(UpperCamelCase   )
														_SCREAMING_SNAKE_CASE					=			SampleObject.from_config(SampleObject.load_config(UpperCamelCase   )   )
														_SCREAMING_SNAKE_CASE					=			new_obj.config

								# unfreeze configs
								_SCREAMING_SNAKE_CASE					=			dict(UpperCamelCase   )
								_SCREAMING_SNAKE_CASE					=			dict(UpperCamelCase   )

								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      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			logging.get_logger("diffusers.configuration_utils"   )
								# 30 for warning
								logger.setLevel(30   )

								with CaptureLogger(UpperCamelCase   ) as cap_logger:
														_SCREAMING_SNAKE_CASE					=			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      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			logging.get_logger("diffusers.configuration_utils"   )
								# 30 for warning
								logger.setLevel(30   )

								with CaptureLogger(UpperCamelCase   ) as cap_logger:
														_SCREAMING_SNAKE_CASE					=			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      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			logging.get_logger("diffusers.configuration_utils"   )
								# 30 for warning
								logger.setLevel(30   )

								with CaptureLogger(UpperCamelCase   ) as cap_logger:
														_SCREAMING_SNAKE_CASE					=			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      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			logging.get_logger("diffusers.configuration_utils"   )
								# 30 for warning
								logger.setLevel(30   )

								with CaptureLogger(UpperCamelCase   ) as cap_logger:
														_SCREAMING_SNAKE_CASE					=			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      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			logging.get_logger("diffusers.configuration_utils"   )
								# 30 for warning
								logger.setLevel(30   )

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

								with CaptureLogger(UpperCamelCase   ) as cap_logger_a:
														_SCREAMING_SNAKE_CASE					=			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      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			logging.get_logger("diffusers.configuration_utils"   )
								# 30 for warning
								logger.setLevel(30   )

								with CaptureLogger(UpperCamelCase   ) as cap_logger:
														_SCREAMING_SNAKE_CASE					=			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      lowercase							(					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],

								_SCREAMING_SNAKE_CASE					=			SampleObject()

								_SCREAMING_SNAKE_CASE					=			{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(UpperCamelCase   )

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

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

								config.config._use_default_values.pop()
								_SCREAMING_SNAKE_CASE					=			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]

'''simple docstring'''







from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class       lowerCAmelCase			(					__UpperCAmelCase      ):




		def __init__(					self       ,    UpperCamelCase       ,    UpperCamelCase   ):
								super().__init__()
								self.register_modules(unet=UpperCamelCase       ,    scheduler=UpperCamelCase   )




		@torch.no_grad()
		def __call__(					self       ,    UpperCamelCase = 1       ,    UpperCamelCase = None       ,    UpperCamelCase = 1_000       ,    UpperCamelCase = "pil"       ,    UpperCamelCase = True       ,    ):
								# Sample gaussian noise to begin loop
								if isinstance(self.unet.config.sample_size       ,    UpperCamelCase   ):
														_SCREAMING_SNAKE_CASE					=			(
														    batch_size,
														    self.unet.config.in_channels,
														    self.unet.config.sample_size,
														    self.unet.config.sample_size,
														)
								else:
														_SCREAMING_SNAKE_CASE					=			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

								if self.device.type == "mps":
														# randn does not work reproducibly on mps
														_SCREAMING_SNAKE_CASE					=			randn_tensor(UpperCamelCase       ,    generator=UpperCamelCase   )
														_SCREAMING_SNAKE_CASE					=			image.to(self.device   )
								else:
														_SCREAMING_SNAKE_CASE					=			randn_tensor(UpperCamelCase       ,    generator=UpperCamelCase       ,    device=self.device   )

								# set step values
								self.scheduler.set_timesteps(UpperCamelCase   )

								for t in self.progress_bar(self.scheduler.timesteps   ):
														# 1. predict noise model_output
														_SCREAMING_SNAKE_CASE					=			self.unet(UpperCamelCase       ,    UpperCamelCase   ).sample

														# 2. compute previous image: x_t -> x_t-1
														_SCREAMING_SNAKE_CASE					=			self.scheduler.step(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    generator=UpperCamelCase   ).prev_sample

								_SCREAMING_SNAKE_CASE					=			(image / 2 + 0.5).clamp(0       ,    1   )
								_SCREAMING_SNAKE_CASE					=			image.cpu().permute(0       ,    2       ,    3       ,    1   ).numpy()
								if output_type == "pil":
														_SCREAMING_SNAKE_CASE					=			self.numpy_to_pil(UpperCamelCase   )

								if not return_dict:
														return (image,)

								return ImagePipelineOutput(images=UpperCamelCase   )