





'''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 		lowerCamelCase  (      _A      ,   _A      ,   unittest.TestCase       ):
	snake_case_      =   IFPipeline
	snake_case_      =   TEXT_TO_IMAGE_PARAMS - {"width", "height", "latents"}
	snake_case_      =   TEXT_TO_IMAGE_BATCH_PARAMS
	snake_case_      =   PipelineTesterMixin.required_optional_params - {"latents"}



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



	def   _lowerCamelCase    (   self		,      a_		,      a_=0	):
			if str(a_	).startswith("mps"	):
					lowerCAmelCase     :    Dict						=   torch.manual_seed(a_	)
			else:
					lowerCAmelCase     :    Optional[int]						=   torch.Generator(device=a_	).manual_seed(a_	)

			lowerCAmelCase     :    List[Any]						=   {
			    "prompt": "A painting of a squirrel eating a burger",
			    "generator": generator,
			    "num_inference_steps": 2,
			    "output_type": "numpy",
			}

			return inputs



	def   _lowerCamelCase    (   self	):
			self._test_save_load_optional_components()



	@unittest.skipIf(torch_device != "cuda"		,      reason="float16 requires CUDA"	)
	def   _lowerCamelCase    (   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   _lowerCamelCase    (   self	):
			self._test_attention_slicing_forward_pass(expected_max_diff=1e-2	)



	def   _lowerCamelCase    (   self	):
			self._test_save_load_local()



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






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



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



	def   _lowerCamelCase    (   self	):
			# if

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

			lowerCAmelCase     :    Optional[Any]						=   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     :    Optional[int]						=   pipe_a.encode_prompt("anime turtle"		,      device="cuda"	)

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

			lowerCAmelCase     :    str						=   None
			lowerCAmelCase     :    List[str]						=   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     :    List[str]						=   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     :    List[Any]						=   IFInpaintingPipeline(**pipe_a.components	)
			lowerCAmelCase     :    Optional[int]						=   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   _lowerCamelCase    (   self		,      a_		,      a_		,      a_		,      a_	):
			# pipeline 1

			_start_torch_memory_measurement()

			lowerCAmelCase     :    int						=   torch.Generator(device="cpu"	).manual_seed(0	)
			lowerCAmelCase     :    Optional[Any]						=   pipe_a(
			    prompt_embeds=a_		,      negative_prompt_embeds=a_		,      num_inference_steps=2		,      generator=a_		,      output_type="np"		,      )

			lowerCAmelCase     :    Optional[Any]						=   output.images[0]

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

			lowerCAmelCase     :    Dict						=   torch.cuda.max_memory_allocated()
			assert mem_bytes < 13 * 10**9

			lowerCAmelCase     :    Optional[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     :    int						=   torch.Generator(device="cpu"	).manual_seed(0	)

			lowerCAmelCase     :    int						=   floats_tensor((1, 3, 64, 64)		,      rng=random.Random(0	)	).to(a_	)

			lowerCAmelCase     :    List[Any]						=   pipe_a(
			    prompt_embeds=a_		,      negative_prompt_embeds=a_		,      image=a_		,      generator=a_		,      num_inference_steps=2		,      output_type="np"		,      )

			lowerCAmelCase     :    List[Any]						=   output.images[0]

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

			lowerCAmelCase     :    Union[str, Any]						=   torch.cuda.max_memory_allocated()
			assert mem_bytes < 4 * 10**9

			lowerCAmelCase     :    Optional[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   _lowerCamelCase    (   self		,      a_		,      a_		,      a_		,      a_	):
			# pipeline 1

			_start_torch_memory_measurement()

			lowerCAmelCase     :    Tuple						=   floats_tensor((1, 3, 64, 64)		,      rng=random.Random(0	)	).to(a_	)

			lowerCAmelCase     :    Any						=   torch.Generator(device="cpu"	).manual_seed(0	)

			lowerCAmelCase     :    Any						=   pipe_a(
			    prompt_embeds=a_		,      negative_prompt_embeds=a_		,      image=a_		,      num_inference_steps=2		,      generator=a_		,      output_type="np"		,      )

			lowerCAmelCase     :    Tuple						=   output.images[0]

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

			lowerCAmelCase     :    Union[str, Any]						=   torch.cuda.max_memory_allocated()
			assert mem_bytes < 10 * 10**9

			lowerCAmelCase     :    Any						=   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     :    Optional[int]						=   torch.Generator(device="cpu"	).manual_seed(0	)

			lowerCAmelCase     :    int						=   floats_tensor((1, 3, 256, 256)		,      rng=random.Random(0	)	).to(a_	)
			lowerCAmelCase     :    Union[str, Any]						=   floats_tensor((1, 3, 64, 64)		,      rng=random.Random(0	)	).to(a_	)

			lowerCAmelCase     :    Optional[Any]						=   pipe_a(
			    prompt_embeds=a_		,      negative_prompt_embeds=a_		,      image=a_		,      original_image=a_		,      generator=a_		,      num_inference_steps=2		,      output_type="np"		,      )

			lowerCAmelCase     :    Dict						=   output.images[0]

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

			lowerCAmelCase     :    str						=   torch.cuda.max_memory_allocated()
			assert mem_bytes < 4 * 10**9

			lowerCAmelCase     :    Dict						=   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   _lowerCamelCase    (   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     :    Optional[int]						=   floats_tensor((1, 3, 64, 64)		,      rng=random.Random(1	)	).to(a_	)

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

			lowerCAmelCase     :    Tuple						=   output.images[0]

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

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

			lowerCAmelCase     :    Any						=   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     :    Union[str, Any]						=   floats_tensor((1, 3, 256, 256)		,      rng=random.Random(0	)	).to(a_	)
			lowerCAmelCase     :    str						=   floats_tensor((1, 3, 256, 256)		,      rng=random.Random(1	)	).to(a_	)

			lowerCAmelCase     :    List[Any]						=   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     :    Tuple						=   torch.cuda.max_memory_allocated()
			assert mem_bytes < 4 * 10**9

			lowerCAmelCase     :    str						=   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      __A						(							):
		torch.cuda.empty_cache()
		torch.cuda.reset_max_memory_allocated()
		torch.cuda.reset_peak_memory_stats()










'''simple docstring'''






def      __A						(							a_						:  int	):
		assert (
		    isinstance(a_      ,a_	) and number_of_steps > 0
		), f'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
		if number_of_steps == 1:
				return 1
		lowerCAmelCase	,		lowerCAmelCase     :    int						=   1, 1
		for _ in range(number_of_steps - 1	):
				lowerCAmelCase	,		lowerCAmelCase     :    Union[str, Any]						=   current + previous, current
		return current


if __name__ == "__main__":
					import doctest

					doctest.testmod()



