




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    ):




    """simple docstring"""

    snake_case__					=      IFImgaImgPipeline
    snake_case__					=      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
    snake_case__					=      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    snake_case__					=      PipelineTesterMixin.required_optional_params - {"latents"}


    def    a      (       self     :     Tuple   )      ->       Optional[int]:
         return self._get_dummy_components()


    def    a      (       self     :     List[str]   ,	SCREAMING_SNAKE_CASE__     :     Any   ,	SCREAMING_SNAKE_CASE__     :     Any=0   )      ->       Optional[Any]:
         if str(SCREAMING_SNAKE_CASE__   ).startswith("mps"   ):
              lowerCAmelCase__		= torch.manual_seed(SCREAMING_SNAKE_CASE__   )
         else:
              lowerCAmelCase__		= torch.Generator(device=SCREAMING_SNAKE_CASE__   ).manual_seed(SCREAMING_SNAKE_CASE__   )

         lowerCAmelCase__		= floats_tensor((1, 3, 32, 32)   ,	rng=random.Random(SCREAMING_SNAKE_CASE__   )   ).to(SCREAMING_SNAKE_CASE__   )

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

         return inputs


    def    a      (       self     :     Optional[int]   )      ->       int:
         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    a      (       self     :     Optional[Any]   )      ->       List[str]:
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3   )


    @unittest.skipIf(torch_device != "cuda"   ,	reason="float16 requires CUDA"   )
    def    a      (       self     :     Optional[int]   )      ->       Optional[int]:
         # 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    a      (       self     :     str   )      ->       int:
         super().test_floataa_inference(expected_max_diff=1e-1   )


    def    a      (       self     :     List[Any]   )      ->       Tuple:
         self._test_attention_slicing_forward_pass(expected_max_diff=1e-2   )


    def    a      (       self     :     int   )      ->       Union[str, Any]:
         self._test_save_load_local()


    def    a      (       self     :     List[Any]   )      ->       Tuple:
         self._test_inference_batch_single_identical(
             expected_max_diff=1e-2   ,	)






# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class  __lowerCamelCase       (    UpperCamelCase__    ):




    """simple docstring"""

    def __init__(       self     :     Union[str, Any]   ,	SCREAMING_SNAKE_CASE__     :     Union[str, Any]   ,	SCREAMING_SNAKE_CASE__     :     Any   )      ->       Union[str, Any]:
         super().__init__()
         self.register_modules(unet=SCREAMING_SNAKE_CASE__   ,	scheduler=SCREAMING_SNAKE_CASE__   )


    @torch.no_grad()
    def __call__(       self     :     Dict   ,	SCREAMING_SNAKE_CASE__     :     int = 1   ,	SCREAMING_SNAKE_CASE__     :     Optional[torch.Generator] = None   ,	SCREAMING_SNAKE_CASE__     :     int = 50   ,	SCREAMING_SNAKE_CASE__     :     Optional[str] = "pil"   ,	SCREAMING_SNAKE_CASE__     :     bool = True   ,	**SCREAMING_SNAKE_CASE__     :     Union[str, Any]   ,	)      ->       Union[ImagePipelineOutput, Tuple]:
         lowerCAmelCase__		= torch.randn(
             (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,	generator=SCREAMING_SNAKE_CASE__   ,	)
         lowerCAmelCase__		= image.to(self.device   )

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

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

              # 2. predict previous mean of image x_t-1 and add variance depending on eta
              # eta corresponds to η in paper and should be between [0, 1]
              # do x_t -> x_t-1
              lowerCAmelCase__		= self.scheduler.step(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ).prev_sample

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

         if not return_dict:
              return (image,), "This is a local test"

         return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__   ), "This is a local test"
