"""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							(				SCREAMING_SNAKE_CASE_					,			SCREAMING_SNAKE_CASE_					,			unittest.TestCase	):
     _UpperCamelCase							:    List[str]	      =	IFPipeline
     _UpperCamelCase							:    List[Any]	      =	TEXT_TO_IMAGE_PARAMS - {"width", "height", "latents"}
     _UpperCamelCase							:    Any	      =	TEXT_TO_IMAGE_BATCH_PARAMS
     _UpperCamelCase							:    int	      =	PipelineTesterMixin.required_optional_params - {"latents"}






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






     def 					__A						(	self       ,  a__       ,  a__=0						):
       if str(a__						).startswith("""mps"""						):
         _lowerCAmelCase		: str						    =  torch.manual_seed(a__						)
       else:
         _lowerCAmelCase		: Optional[int]						    =  torch.Generator(device=a__						).manual_seed(a__						)

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

       return inputs






     def 					__A						(	self						):
       self._test_save_load_optional_components()






     @unittest.skipIf(torch_device != """cuda"""       ,  reason="""float16 requires CUDA"""						)
     def 					__A						(	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						)






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






     def 					__A						(	self						):
       self._test_save_load_local()






     def 					__A						(	self						):
       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 					__A						(	self						):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3						)







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






     def 					__A						(	self						):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






     def 					__A						(	self						):
       # if

       _lowerCAmelCase		: Union[str, Any]						    =  IFPipeline.from_pretrained("""DeepFloyd/IF-I-XL-v1.0"""       ,  variant="""fp16"""       ,  torch_dtype=torch.floataa						)

       _lowerCAmelCase		: List[str]						    =  IFSuperResolutionPipeline.from_pretrained(
           """DeepFloyd/IF-II-L-v1.0"""       ,  variant="""fp16"""       ,  torch_dtype=torch.floataa       ,  text_encoder=a__       ,  tokenizer=a__						)

       # pre compute text embeddings and remove T5 to save memory

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

       _lowerCAmelCase    ,			_lowerCAmelCase		: Any						    =  pipe_a.encode_prompt("""anime turtle"""       ,  device="""cuda"""						)

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

       _lowerCAmelCase		: Union[str, Any]						    =  None
       _lowerCAmelCase		: List[Any]						    =  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(a__       ,  a__       ,  a__       ,  a__						)

       pipe_a.remove_all_hooks()
       pipe_a.remove_all_hooks()

       # img2img

       _lowerCAmelCase		: Any						    =  IFImgaImgPipeline(**pipe_a.components						)
       _lowerCAmelCase		: Optional[int]						    =  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(a__       ,  a__       ,  a__       ,  a__						)

       pipe_a.remove_all_hooks()
       pipe_a.remove_all_hooks()

       # inpainting

       _lowerCAmelCase		: Any						    =  IFInpaintingPipeline(**pipe_a.components						)
       _lowerCAmelCase		: Tuple						    =  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(a__       ,  a__       ,  a__       ,  a__						)






     def 					__A						(	self       ,  a__       ,  a__       ,  a__       ,  a__						):
       # pipeline 1

       _start_torch_memory_measurement()

       _lowerCAmelCase		: Any						    =  torch.Generator(device="""cpu"""						).manual_seed(0						)
       _lowerCAmelCase		: Any						    =  pipe_a(
           prompt_embeds=a__       ,  negative_prompt_embeds=a__       ,  num_inference_steps=2       ,  generator=a__       ,  output_type="""np"""       ,  )

       _lowerCAmelCase		: Any						    =  output.images[0]

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

       _lowerCAmelCase		: List[str]						    =  torch.cuda.max_memory_allocated()
       assert mem_bytes < 13 * 10**9

       _lowerCAmelCase		: List[Any]						    =  load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy"""						)
       assert_mean_pixel_difference(a__       ,  a__						)

       # pipeline 2

       _start_torch_memory_measurement()

       _lowerCAmelCase		: List[Any]						    =  torch.Generator(device="""cpu"""						).manual_seed(0						)

       _lowerCAmelCase		: str						    =  floats_tensor((1, 3, 64, 64)       ,  rng=random.Random(0						)						).to(a__						)

       _lowerCAmelCase		: int						    =  pipe_a(
           prompt_embeds=a__       ,  negative_prompt_embeds=a__       ,  image=a__       ,  generator=a__       ,  num_inference_steps=2       ,  output_type="""np"""       ,  )

       _lowerCAmelCase		: Any						    =  output.images[0]

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

       _lowerCAmelCase		: Optional[Any]						    =  torch.cuda.max_memory_allocated()
       assert mem_bytes < 4 * 10**9

       _lowerCAmelCase		: Union[str, Any]						    =  load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy"""						)
       assert_mean_pixel_difference(a__       ,  a__						)






     def 					__A						(	self       ,  a__       ,  a__       ,  a__       ,  a__						):
       # pipeline 1

       _start_torch_memory_measurement()

       _lowerCAmelCase		: List[str]						    =  floats_tensor((1, 3, 64, 64)       ,  rng=random.Random(0						)						).to(a__						)

       _lowerCAmelCase		: str						    =  torch.Generator(device="""cpu"""						).manual_seed(0						)

       _lowerCAmelCase		: Tuple						    =  pipe_a(
           prompt_embeds=a__       ,  negative_prompt_embeds=a__       ,  image=a__       ,  num_inference_steps=2       ,  generator=a__       ,  output_type="""np"""       ,  )

       _lowerCAmelCase		: Optional[int]						    =  output.images[0]

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

       _lowerCAmelCase		: str						    =  torch.cuda.max_memory_allocated()
       assert mem_bytes < 10 * 10**9

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

       # pipeline 2

       _start_torch_memory_measurement()

       _lowerCAmelCase		: str						    =  torch.Generator(device="""cpu"""						).manual_seed(0						)

       _lowerCAmelCase		: Tuple						    =  floats_tensor((1, 3, 256, 256)       ,  rng=random.Random(0						)						).to(a__						)
       _lowerCAmelCase		: Dict						    =  floats_tensor((1, 3, 64, 64)       ,  rng=random.Random(0						)						).to(a__						)

       _lowerCAmelCase		: str						    =  pipe_a(
           prompt_embeds=a__       ,  negative_prompt_embeds=a__       ,  image=a__       ,  original_image=a__       ,  generator=a__       ,  num_inference_steps=2       ,  output_type="""np"""       ,  )

       _lowerCAmelCase		: Any						    =  output.images[0]

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

       _lowerCAmelCase		: List[str]						    =  torch.cuda.max_memory_allocated()
       assert mem_bytes < 4 * 10**9

       _lowerCAmelCase		: Union[str, Any]						    =  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(a__       ,  a__						)






     def 					__A						(	self       ,  a__       ,  a__       ,  a__       ,  a__						):
       # pipeline 1

       _start_torch_memory_measurement()

       _lowerCAmelCase		: Any						    =  floats_tensor((1, 3, 64, 64)       ,  rng=random.Random(0						)						).to(a__						)
       _lowerCAmelCase		: Optional[Any]						    =  floats_tensor((1, 3, 64, 64)       ,  rng=random.Random(1						)						).to(a__						)

       _lowerCAmelCase		: Optional[Any]						    =  torch.Generator(device="""cpu"""						).manual_seed(0						)
       _lowerCAmelCase		: str						    =  pipe_a(
           prompt_embeds=a__       ,  negative_prompt_embeds=a__       ,  image=a__       ,  mask_image=a__       ,  num_inference_steps=2       ,  generator=a__       ,  output_type="""np"""       ,  )

       _lowerCAmelCase		: str						    =  output.images[0]

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

       _lowerCAmelCase		: str						    =  torch.cuda.max_memory_allocated()
       assert mem_bytes < 10 * 10**9

       _lowerCAmelCase		: List[str]						    =  load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy"""						)
       assert_mean_pixel_difference(a__       ,  a__						)

       # pipeline 2

       _start_torch_memory_measurement()

       _lowerCAmelCase		: Tuple						    =  torch.Generator(device="""cpu"""						).manual_seed(0						)

       _lowerCAmelCase		: Optional[int]						    =  floats_tensor((1, 3, 64, 64)       ,  rng=random.Random(0						)						).to(a__						)
       _lowerCAmelCase		: Tuple						    =  floats_tensor((1, 3, 256, 256)       ,  rng=random.Random(0						)						).to(a__						)
       _lowerCAmelCase		: int						    =  floats_tensor((1, 3, 256, 256)       ,  rng=random.Random(1						)						).to(a__						)

       _lowerCAmelCase		: Dict						    =  pipe_a(
           prompt_embeds=a__       ,  negative_prompt_embeds=a__       ,  image=a__       ,  mask_image=a__       ,  original_image=a__       ,  generator=a__       ,  num_inference_steps=2       ,  output_type="""np"""       ,  )

       _lowerCAmelCase		: int						    =  output.images[0]

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

       _lowerCAmelCase		: List[str]						    =  torch.cuda.max_memory_allocated()
       assert mem_bytes < 4 * 10**9

       _lowerCAmelCase		: Optional[int]						    =  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(a__       ,  a__						)







def 							SCREAMING_SNAKE_CASE  (    ) ->   str:
  torch.cuda.empty_cache()
  torch.cuda.reset_max_memory_allocated()
  torch.cuda.reset_peak_memory_stats()





"""simple docstring"""




import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
    ' StableDiffusionInpaintPipeline` instead.'
)




