




'''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    __lowercase						(  __magic_name__       ,							__magic_name__       ,							unittest.TestCase ):
   _a							=      IFPipeline
   _a							=      TEXT_TO_IMAGE_PARAMS - {"""width""", """height""", """latents"""}
   _a							=      TEXT_TO_IMAGE_BATCH_PARAMS
   _a							=      PipelineTesterMixin.required_optional_params - {"""latents"""}







   def 		UpperCamelCase__			(	self      )				->    List[str]:
     return self._get_dummy_components()







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase=0      )				->    str:
     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 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     self._test_save_load_optional_components()







   @unittest.skipIf(torch_device != 'cuda'      ,							reason='float16 requires CUDA'      )
   def 		UpperCamelCase__			(	self      )				->    List[Any]:
     # 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 		UpperCamelCase__			(	self      )				->    Optional[int]:
     self._test_attention_slicing_forward_pass(expected_max_diff=1e-2      )







   def 		UpperCamelCase__			(	self      )				->    int:
     self._test_save_load_local()







   def 		UpperCamelCase__			(	self      )				->    List[Any]:
     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 		UpperCamelCase__			(	self      )				->    Dict:
     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3      )





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







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







   def 		UpperCamelCase__			(	self      )				->    List[Any]:
     # 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 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )				->    Any:
     # 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 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )				->    Tuple:
     # 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 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )				->    str:
     # 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 						SCREAMING_SNAKE_CASE       (      ):
  torch.cuda.empty_cache()
  torch.cuda.reset_max_memory_allocated()
  torch.cuda.reset_peak_memory_stats()









'''simple docstring'''


def 						SCREAMING_SNAKE_CASE       (      a_	:							int = 10**12			):
  __a			=					1
  __a			=					0

  __a			=					1
  __a			=					1

  while numerator <= 2 * min_total - 1:
    prev_numerator += 2 * numerator
    numerator += 2 * prev_numerator

    prev_denominator += 2 * denominator
    denominator += 2 * prev_denominator

  return (denominator + 1) // 2


if __name__ == "__main__":
  print(f"""{solution() = }""")



