
'''simple docstring'''







import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class       lowerCAmelCase			(					__UpperCAmelCase		,  __UpperCAmelCase		,  unittest.TestCase      ):
		a				:       Union[str, Any]							=       IFImgaImgPipeline
		a				:       Union[str, Any]							=       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
		a				:       List[str]							=       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
		a				:       int							=       PipelineTesterMixin.required_optional_params - {"""latents"""}




		def      lowercase							(					self   ):
								return self._get_dummy_components()




		def      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase=0   ):
								if str(UpperCamelCase   ).startswith("mps"   ):
														_SCREAMING_SNAKE_CASE					=			torch.manual_seed(UpperCamelCase   )
								else:
														_SCREAMING_SNAKE_CASE					=			torch.Generator(device=UpperCamelCase   ).manual_seed(UpperCamelCase   )

								_SCREAMING_SNAKE_CASE					=			floats_tensor((1, 3, 32, 32)       ,    rng=random.Random(UpperCamelCase   )   ).to(UpperCamelCase   )

								_SCREAMING_SNAKE_CASE					=			{
								    "prompt": "A painting of a squirrel eating a burger",
								    "image": image,
								    "generator": generator,
								    "num_inference_steps": 2,
								    "output_type": "numpy",
								}

								return inputs




		def      lowercase							(					self   ):
								self._test_save_load_optional_components()




		@unittest.skipIf(
		    torch_device != "cuda" or not is_xformers_available()       ,    reason="XFormers attention is only available with CUDA and `xformers` installed"       ,    )
		def      lowercase							(					self   ):
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3   )




		@unittest.skipIf(torch_device != "cuda"       ,    reason="float16 requires CUDA"   )
		def      lowercase							(					self   ):
								# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
								super().test_save_load_floataa(expected_max_diff=1e-1   )




		@unittest.skipIf(torch_device != "cuda"       ,    reason="float16 requires CUDA"   )
		def      lowercase							(					self   ):
								super().test_floataa_inference(expected_max_diff=1e-1   )




		def      lowercase							(					self   ):
								self._test_attention_slicing_forward_pass(expected_max_diff=1e-2   )




		def      lowercase							(					self   ):
								self._test_save_load_local()




		def      lowercase							(					self   ):
								self._test_inference_batch_single_identical(
								    expected_max_diff=1e-2       ,    )

'''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   )