
'''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			):
 lowerCamelCase				       =IFPipeline
 lowerCamelCase				       =TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
 lowerCamelCase				       =TEXT_TO_IMAGE_BATCH_PARAMS
 lowerCamelCase				       =PipelineTesterMixin.required_optional_params - {'''latents'''}
 def 		lowercase_(   self			:    Optional[Any]       ):



   """simple docstring"""





   return self._get_dummy_components()
 def 		lowercase_(   self			:    List[Any]				,   lowerCamelCase			:    Tuple				,   lowerCamelCase			:    List[Any]=0       ):



   """simple docstring"""





   if str(lowerCamelCase       ).startswith("""mps"""       ):
     __A		:     Optional[Any]												=		torch.manual_seed(lowerCamelCase       )
   else:
     __A		:     List[str]												=		torch.Generator(device=lowerCamelCase       ).manual_seed(lowerCamelCase       )

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

   return inputs
 def 		lowercase_(   self			:    str       ):



   """simple docstring"""





   self._test_save_load_optional_components()
 @unittest.skipIf(torch_device != """cuda"""				,   reason="""float16 requires CUDA"""       )
 def 		lowercase_(   self			:    Union[str, Any]       ):



   """simple docstring"""





   super().test_save_load_floataa(expected_max_diff=1e-1       )
 def 		lowercase_(   self			:    Any       ):



   """simple docstring"""





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



   """simple docstring"""





   self._test_save_load_local()
 def 		lowercase_(   self			:    Tuple       ):



   """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 		lowercase_(   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 		lowercase_(   self			:    int       ):



   """simple docstring"""





   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()
 def 		lowercase_(   self			:    Union[str, Any]       ):



   """simple docstring"""





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

   __A		:     Optional[int]												=		IFSuperResolutionPipeline.from_pretrained(
       """DeepFloyd/IF-II-L-v1.0"""				,   variant="""fp16"""				,   torch_dtype=torch.floataa				,   text_encoder=lowerCamelCase				,   tokenizer=lowerCamelCase       )

   # pre compute text embeddings and remove T5 to save memory

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

   __A							,					__A		:     int												=		pipe_a.encode_prompt("""anime turtle"""				,   device="""cuda"""       )

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

   __A		:     Optional[int]												=		None
   __A		:     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(lowerCamelCase				,   lowerCamelCase				,   lowerCamelCase				,   lowerCamelCase       )

   pipe_a.remove_all_hooks()
   pipe_a.remove_all_hooks()

   # img2img

   __A		:     int												=		IFImgaImgPipeline(**pipe_a.components       )
   __A		:     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(lowerCamelCase				,   lowerCamelCase				,   lowerCamelCase				,   lowerCamelCase       )

   pipe_a.remove_all_hooks()
   pipe_a.remove_all_hooks()

   # inpainting

   __A		:     List[str]												=		IFInpaintingPipeline(**pipe_a.components       )
   __A		:     List[Any]												=		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(lowerCamelCase				,   lowerCamelCase				,   lowerCamelCase				,   lowerCamelCase       )
 def 		lowercase_(   self			:    List[Any]				,   lowerCamelCase			:    Optional[Any]				,   lowerCamelCase			:    str				,   lowerCamelCase			:    Optional[int]				,   lowerCamelCase			:    List[Any]       ):



   """simple docstring"""





   _start_torch_memory_measurement()

   __A		:     List[Any]												=		torch.Generator(device="""cpu"""       ).manual_seed(0       )
   __A		:     Dict												=		pipe_a(
       prompt_embeds=lowerCamelCase				,   negative_prompt_embeds=lowerCamelCase				,   num_inference_steps=2				,   generator=lowerCamelCase				,   output_type="""np"""				,   )

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

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

   __A		:     Union[str, Any]												=		torch.cuda.max_memory_allocated()
   assert mem_bytes < 13 * 10**9

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

   # pipeline 2

   _start_torch_memory_measurement()

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

   __A		:     List[Any]												=		floats_tensor((1, 3, 64, 64)				,   rng=random.Random(0       )       ).to(lowerCamelCase       )

   __A		:     Dict												=		pipe_a(
       prompt_embeds=lowerCamelCase				,   negative_prompt_embeds=lowerCamelCase				,   image=lowerCamelCase				,   generator=lowerCamelCase				,   num_inference_steps=2				,   output_type="""np"""				,   )

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

   assert image.shape == (2_56, 2_56, 3)

   __A		:     Dict												=		torch.cuda.max_memory_allocated()
   assert mem_bytes < 4 * 10**9

   __A		:     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(lowerCamelCase				,   lowerCamelCase       )
 def 		lowercase_(   self			:    int				,   lowerCamelCase			:    Optional[Any]				,   lowerCamelCase			:    List[str]				,   lowerCamelCase			:    str				,   lowerCamelCase			:    int       ):



   """simple docstring"""





   _start_torch_memory_measurement()

   __A		:     Any												=		floats_tensor((1, 3, 64, 64)				,   rng=random.Random(0       )       ).to(lowerCamelCase       )

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

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

   __A		:     Tuple												=		output.images[0]

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

   __A		:     Tuple												=		torch.cuda.max_memory_allocated()
   assert mem_bytes < 10 * 10**9

   __A		:     List[Any]												=		load_numpy(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy"""       )
   assert_mean_pixel_difference(lowerCamelCase				,   lowerCamelCase       )

   # pipeline 2

   _start_torch_memory_measurement()

   __A		:     List[str]												=		torch.Generator(device="""cpu"""       ).manual_seed(0       )

   __A		:     Union[str, Any]												=		floats_tensor((1, 3, 2_56, 2_56)				,   rng=random.Random(0       )       ).to(lowerCamelCase       )
   __A		:     str												=		floats_tensor((1, 3, 64, 64)				,   rng=random.Random(0       )       ).to(lowerCamelCase       )

   __A		:     Any												=		pipe_a(
       prompt_embeds=lowerCamelCase				,   negative_prompt_embeds=lowerCamelCase				,   image=lowerCamelCase				,   original_image=lowerCamelCase				,   generator=lowerCamelCase				,   num_inference_steps=2				,   output_type="""np"""				,   )

   __A		:     Union[str, Any]												=		output.images[0]

   assert image.shape == (2_56, 2_56, 3)

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

   __A		:     List[str]												=		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(lowerCamelCase				,   lowerCamelCase       )






 def 		lowercase_(   self			:    int				,   lowerCamelCase			:    int				,   lowerCamelCase			:    str				,   lowerCamelCase			:    List[str]				,   lowerCamelCase			:    Union[str, Any]       ):



   """simple docstring"""





   _start_torch_memory_measurement()

   __A		:     Optional[Any]												=		floats_tensor((1, 3, 64, 64)				,   rng=random.Random(0       )       ).to(lowerCamelCase       )
   __A		:     int												=		floats_tensor((1, 3, 64, 64)				,   rng=random.Random(1       )       ).to(lowerCamelCase       )

   __A		:     List[str]												=		torch.Generator(device="""cpu"""       ).manual_seed(0       )
   __A		:     Dict												=		pipe_a(
       prompt_embeds=lowerCamelCase				,   negative_prompt_embeds=lowerCamelCase				,   image=lowerCamelCase				,   mask_image=lowerCamelCase				,   num_inference_steps=2				,   generator=lowerCamelCase				,   output_type="""np"""				,   )

   __A		:     List[str]												=		output.images[0]

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

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

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

   # pipeline 2

   _start_torch_memory_measurement()

   __A		:     int												=		torch.Generator(device="""cpu"""       ).manual_seed(0       )

   __A		:     Dict												=		floats_tensor((1, 3, 64, 64)				,   rng=random.Random(0       )       ).to(lowerCamelCase       )
   __A		:     Optional[int]												=		floats_tensor((1, 3, 2_56, 2_56)				,   rng=random.Random(0       )       ).to(lowerCamelCase       )
   __A		:     Optional[Any]												=		floats_tensor((1, 3, 2_56, 2_56)				,   rng=random.Random(1       )       ).to(lowerCamelCase       )

   __A		:     Any												=		pipe_a(
       prompt_embeds=lowerCamelCase				,   negative_prompt_embeds=lowerCamelCase				,   image=lowerCamelCase				,   mask_image=lowerCamelCase				,   original_image=lowerCamelCase				,   generator=lowerCamelCase				,   num_inference_steps=2				,   output_type="""np"""				,   )

   __A		:     Union[str, Any]												=		output.images[0]

   assert image.shape == (2_56, 2_56, 3)

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

   __A		:     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(lowerCamelCase				,   lowerCamelCase       )







def        A_     (						)				->       Tuple:





  """simple docstring"""

  torch.cuda.empty_cache()
  torch.cuda.reset_max_memory_allocated()
  torch.cuda.reset_peak_memory_stats()





'''simple docstring'''
from __future__ import annotations







def        A_     (						__SCREAMING_SNAKE_CASE						:  list[int] )				->       int:





  """simple docstring"""

  if not nums:
    return 0
  __A		:     List[Any]												=		nums[0]
  __A		:     Union[str, Any]												=		0
  for num in nums[1:]:
    __A							,					__A		:     Union[str, Any]												=		(
        max_excluding + num,
        max(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ),
    )
  return max(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
     import doctest

     doctest.testmod()



