

'''simple docstring'''







import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin


@skip_mps
class    A__ ( _snake_case  ,  _snake_case  ,  unittest.TestCase     ):
    lowercase		   =						IFPipeline
    lowercase		   =						TEXT_TO_IMAGE_PARAMS - {"width", "height", "latents"}
    lowercase		   =						TEXT_TO_IMAGE_BATCH_PARAMS
    lowercase		   =						PipelineTesterMixin.required_optional_params - {"latents"}



    def    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           return self._get_dummy_components()



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__=0    )  -> List[str]:





           '''simple docstring'''
           if str(UpperCamelCase__    ).startswith("""mps"""    ):
                  A_		   = torch.manual_seed(UpperCamelCase__    )
           else:
                  A_		   = torch.Generator(device=UpperCamelCase__    ).manual_seed(UpperCamelCase__    )

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

           return inputs



    def    snake_case_     (   self    )  -> Union[str, Any]:





           '''simple docstring'''
           self._test_save_load_optional_components()



    @unittest.skipIf(torch_device != """cuda"""						,			reason="""float16 requires CUDA"""    )
    def    snake_case_     (   self    )  -> Tuple:





           '''simple docstring'''
           # 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    )



    def    snake_case_     (   self    )  -> int:





           '''simple docstring'''
           self._test_attention_slicing_forward_pass(expected_max_diff=1e-2    )



    def    snake_case_     (   self    )  -> int:





           '''simple docstring'''
           self._test_save_load_local()



    def    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           self._test_inference_batch_single_identical(
               expected_max_diff=1e-2						,			)







    @unittest.skipIf(
        torch_device != """cuda""" or not is_xformers_available()						,			reason="""XFormers attention is only available with CUDA and `xformers` installed"""						,			)
    def    snake_case_     (   self    )  -> List[Any]:





           '''simple docstring'''
           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3    )


@slow
@require_torch_gpu
class    A__ ( unittest.TestCase     ):



    def    snake_case_     (   self    )  -> List[str]:





           '''simple docstring'''
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()



    def    snake_case_     (   self    )  -> Optional[Any]:





           '''simple docstring'''
           # if

           A_		   = IFPipeline.from_pretrained("""DeepFloyd/IF-I-XL-v1.0"""						,			variant="""fp16"""						,			torch_dtype=torch.floataa    )

           A_		   = IFSuperResolutionPipeline.from_pretrained(
               """DeepFloyd/IF-II-L-v1.0"""						,			variant="""fp16"""						,			torch_dtype=torch.floataa						,			text_encoder=UpperCamelCase__						,			tokenizer=UpperCamelCase__    )

           # pre compute text embeddings and remove T5 to save memory

           pipe_a.text_encoder.to("""cuda"""    )

           A_   ,     A_		   = pipe_a.encode_prompt("""anime turtle"""						,			device="""cuda"""    )

           del pipe_a.tokenizer
           del pipe_a.text_encoder
           gc.collect()

           A_		   = None
           A_		   = None

           pipe_a.enable_model_cpu_offload()
           pipe_a.enable_model_cpu_offload()

           pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()    )
           pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()    )

           self._test_if(UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__    )

           pipe_a.remove_all_hooks()
           pipe_a.remove_all_hooks()

           # img2img

           A_		   = IFImgaImgPipeline(**pipe_a.components    )
           A_		   = IFImgaImgSuperResolutionPipeline(**pipe_a.components    )

           pipe_a.enable_model_cpu_offload()
           pipe_a.enable_model_cpu_offload()

           pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()    )
           pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()    )

           self._test_if_imgaimg(UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__    )

           pipe_a.remove_all_hooks()
           pipe_a.remove_all_hooks()

           # inpainting

           A_		   = IFInpaintingPipeline(**pipe_a.components    )
           A_		   = IFInpaintingSuperResolutionPipeline(**pipe_a.components    )

           pipe_a.enable_model_cpu_offload()
           pipe_a.enable_model_cpu_offload()

           pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()    )
           pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()    )

           self._test_if_inpainting(UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__    )



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__    )  -> Tuple:





           '''simple docstring'''
           # pipeline 1

           _start_torch_memory_measurement()

           A_		   = torch.Generator(device="""cpu"""    ).manual_seed(0    )
           A_		   = pipe_a(
               prompt_embeds=UpperCamelCase__						,			negative_prompt_embeds=UpperCamelCase__						,			num_inference_steps=2						,			generator=UpperCamelCase__						,			output_type="""np"""						,			)

           A_		   = output.images[0]

           assert image.shape == (64, 64, 3)

           A_		   = torch.cuda.max_memory_allocated()
           assert mem_bytes < 13 * 10**9

           A_		   = load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy"""    )
           assert_mean_pixel_difference(UpperCamelCase__						,			UpperCamelCase__    )

           # pipeline 2

           _start_torch_memory_measurement()

           A_		   = torch.Generator(device="""cpu"""    ).manual_seed(0    )

           A_		   = floats_tensor((1, 3, 64, 64)						,			rng=random.Random(0    )    ).to(UpperCamelCase__    )

           A_		   = pipe_a(
               prompt_embeds=UpperCamelCase__						,			negative_prompt_embeds=UpperCamelCase__						,			image=UpperCamelCase__						,			generator=UpperCamelCase__						,			num_inference_steps=2						,			output_type="""np"""						,			)

           A_		   = output.images[0]

           assert image.shape == (256, 256, 3)

           A_		   = torch.cuda.max_memory_allocated()
           assert mem_bytes < 4 * 10**9

           A_		   = load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy"""    )
           assert_mean_pixel_difference(UpperCamelCase__						,			UpperCamelCase__    )



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__    )  -> Dict:





           '''simple docstring'''
           # pipeline 1

           _start_torch_memory_measurement()

           A_		   = floats_tensor((1, 3, 64, 64)						,			rng=random.Random(0    )    ).to(UpperCamelCase__    )

           A_		   = torch.Generator(device="""cpu"""    ).manual_seed(0    )

           A_		   = pipe_a(
               prompt_embeds=UpperCamelCase__						,			negative_prompt_embeds=UpperCamelCase__						,			image=UpperCamelCase__						,			num_inference_steps=2						,			generator=UpperCamelCase__						,			output_type="""np"""						,			)

           A_		   = output.images[0]

           assert image.shape == (64, 64, 3)

           A_		   = torch.cuda.max_memory_allocated()
           assert mem_bytes < 10 * 10**9

           A_		   = load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy"""    )
           assert_mean_pixel_difference(UpperCamelCase__						,			UpperCamelCase__    )

           # pipeline 2

           _start_torch_memory_measurement()

           A_		   = torch.Generator(device="""cpu"""    ).manual_seed(0    )

           A_		   = floats_tensor((1, 3, 256, 256)						,			rng=random.Random(0    )    ).to(UpperCamelCase__    )
           A_		   = floats_tensor((1, 3, 64, 64)						,			rng=random.Random(0    )    ).to(UpperCamelCase__    )

           A_		   = pipe_a(
               prompt_embeds=UpperCamelCase__						,			negative_prompt_embeds=UpperCamelCase__						,			image=UpperCamelCase__						,			original_image=UpperCamelCase__						,			generator=UpperCamelCase__						,			num_inference_steps=2						,			output_type="""np"""						,			)

           A_		   = output.images[0]

           assert image.shape == (256, 256, 3)

           A_		   = torch.cuda.max_memory_allocated()
           assert mem_bytes < 4 * 10**9

           A_		   = load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy"""    )
           assert_mean_pixel_difference(UpperCamelCase__						,			UpperCamelCase__    )







    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__    )  -> List[Any]:





           '''simple docstring'''
           # pipeline 1

           _start_torch_memory_measurement()

           A_		   = floats_tensor((1, 3, 64, 64)						,			rng=random.Random(0    )    ).to(UpperCamelCase__    )
           A_		   = floats_tensor((1, 3, 64, 64)						,			rng=random.Random(1    )    ).to(UpperCamelCase__    )

           A_		   = torch.Generator(device="""cpu"""    ).manual_seed(0    )
           A_		   = pipe_a(
               prompt_embeds=UpperCamelCase__						,			negative_prompt_embeds=UpperCamelCase__						,			image=UpperCamelCase__						,			mask_image=UpperCamelCase__						,			num_inference_steps=2						,			generator=UpperCamelCase__						,			output_type="""np"""						,			)

           A_		   = output.images[0]

           assert image.shape == (64, 64, 3)

           A_		   = torch.cuda.max_memory_allocated()
           assert mem_bytes < 10 * 10**9

           A_		   = load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy"""    )
           assert_mean_pixel_difference(UpperCamelCase__						,			UpperCamelCase__    )

           # pipeline 2

           _start_torch_memory_measurement()

           A_		   = torch.Generator(device="""cpu"""    ).manual_seed(0    )

           A_		   = floats_tensor((1, 3, 64, 64)						,			rng=random.Random(0    )    ).to(UpperCamelCase__    )
           A_		   = floats_tensor((1, 3, 256, 256)						,			rng=random.Random(0    )    ).to(UpperCamelCase__    )
           A_		   = floats_tensor((1, 3, 256, 256)						,			rng=random.Random(1    )    ).to(UpperCamelCase__    )

           A_		   = pipe_a(
               prompt_embeds=UpperCamelCase__						,			negative_prompt_embeds=UpperCamelCase__						,			image=UpperCamelCase__						,			mask_image=UpperCamelCase__						,			original_image=UpperCamelCase__						,			generator=UpperCamelCase__						,			num_inference_steps=2						,			output_type="""np"""						,			)

           A_		   = output.images[0]

           assert image.shape == (256, 256, 3)

           A_		   = torch.cuda.max_memory_allocated()
           assert mem_bytes < 4 * 10**9

           A_		   = load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy"""    )
           assert_mean_pixel_difference(UpperCamelCase__						,			UpperCamelCase__    )

def  UpperCAmelCase__      ( )     ->     Dict:
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()



'''simple docstring'''







from functools import lru_cache
@lru_cache
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     int:
       if num < 0:
              raise ValueError("""Number should not be negative."""			)

       return 1 if num in (0, 1) else num * factorial(num - 1			)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
