






'''simple docstring'''



from ..utils import DummyObject, requires_backends
class 							__SCREAMING_SNAKE_CASE       (							metaclass=_lowerCAmelCase     ):
      __a  					=["flax"]




      def __init__(  self ,   *lowerCamelCase ,   **lowerCamelCase      )							->Union[str, Any]:



         '''simple docstring'''
         requires_backends(self ,   ['flax']      )




      @classmethod
      def  __UpperCamelCase							(  cls ,   *lowerCamelCase ,   **lowerCamelCase      )							->List[Any]:



         '''simple docstring'''
         requires_backends(cls ,   ['flax']      )






      @classmethod
      def  __UpperCamelCase							(  cls ,   *lowerCamelCase ,   **lowerCamelCase      )							->List[str]:



         '''simple docstring'''
         requires_backends(cls ,   ['flax']      )







class 							__SCREAMING_SNAKE_CASE       (							metaclass=_lowerCAmelCase     ):
      __a  					=["flax"]




      def __init__(  self ,   *lowerCamelCase ,   **lowerCamelCase      )							->List[str]:



         '''simple docstring'''
         requires_backends(self ,   ['flax']      )




      @classmethod
      def  __UpperCamelCase							(  cls ,   *lowerCamelCase ,   **lowerCamelCase      )							->int:



         '''simple docstring'''
         requires_backends(cls ,   ['flax']      )






      @classmethod
      def  __UpperCamelCase							(  cls ,   *lowerCamelCase ,   **lowerCamelCase      )							->Union[str, Any]:



         '''simple docstring'''
         requires_backends(cls ,   ['flax']      )







class 							__SCREAMING_SNAKE_CASE       (							metaclass=_lowerCAmelCase     ):
      __a  					=["flax"]




      def __init__(  self ,   *lowerCamelCase ,   **lowerCamelCase      )							->Union[str, Any]:



         '''simple docstring'''
         requires_backends(self ,   ['flax']      )




      @classmethod
      def  __UpperCamelCase							(  cls ,   *lowerCamelCase ,   **lowerCamelCase      )							->Optional[int]:



         '''simple docstring'''
         requires_backends(cls ,   ['flax']      )






      @classmethod
      def  __UpperCamelCase							(  cls ,   *lowerCamelCase ,   **lowerCamelCase      )							->Union[str, Any]:



         '''simple docstring'''
         requires_backends(cls ,   ['flax']      )







class 							__SCREAMING_SNAKE_CASE       (							metaclass=_lowerCAmelCase     ):
      __a  					=["flax"]




      def __init__(  self ,   *lowerCamelCase ,   **lowerCamelCase      )							->Optional[int]:



         '''simple docstring'''
         requires_backends(self ,   ['flax']      )




      @classmethod
      def  __UpperCamelCase							(  cls ,   *lowerCamelCase ,   **lowerCamelCase      )							->Tuple:



         '''simple docstring'''
         requires_backends(cls ,   ['flax']      )






      @classmethod
      def  __UpperCamelCase							(  cls ,   *lowerCamelCase ,   **lowerCamelCase      )							->Union[str, Any]:



         '''simple docstring'''
         requires_backends(cls ,   ['flax']      )







class 							__SCREAMING_SNAKE_CASE       (							metaclass=_lowerCAmelCase     ):
      __a  					=["flax"]




      def __init__(  self ,   *lowerCamelCase ,   **lowerCamelCase      )							->Any:



         '''simple docstring'''
         requires_backends(self ,   ['flax']      )




      @classmethod
      def  __UpperCamelCase							(  cls ,   *lowerCamelCase ,   **lowerCamelCase      )							->str:



         '''simple docstring'''
         requires_backends(cls ,   ['flax']      )






      @classmethod
      def  __UpperCamelCase							(  cls ,   *lowerCamelCase ,   **lowerCamelCase      )							->List[Any]:



         '''simple docstring'''
         requires_backends(cls ,   ['flax']      )







class 							__SCREAMING_SNAKE_CASE       (							metaclass=_lowerCAmelCase     ):
      __a  					=["flax"]




      def __init__(  self ,   *lowerCamelCase ,   **lowerCamelCase      )							->Optional[int]:



         '''simple docstring'''
         requires_backends(self ,   ['flax']      )




      @classmethod
      def  __UpperCamelCase							(  cls ,   *lowerCamelCase ,   **lowerCamelCase      )							->Optional[Any]:



         '''simple docstring'''
         requires_backends(cls ,   ['flax']      )






      @classmethod
      def  __UpperCamelCase							(  cls ,   *lowerCamelCase ,   **lowerCamelCase      )							->Dict:



         '''simple docstring'''
         requires_backends(cls ,   ['flax']      )







class 							__SCREAMING_SNAKE_CASE       (							metaclass=_lowerCAmelCase     ):
      __a  					=["flax"]




      def __init__(  self ,   *lowerCamelCase ,   **lowerCamelCase      )							->Optional[int]:



         '''simple docstring'''
         requires_backends(self ,   ['flax']      )




      @classmethod
      def  __UpperCamelCase							(  cls ,   *lowerCamelCase ,   **lowerCamelCase      )							->str:



         '''simple docstring'''
         requires_backends(cls ,   ['flax']      )






      @classmethod
      def  __UpperCamelCase							(  cls ,   *lowerCamelCase ,   **lowerCamelCase      )							->Any:



         '''simple docstring'''
         requires_backends(cls ,   ['flax']      )







class 							__SCREAMING_SNAKE_CASE       (							metaclass=_lowerCAmelCase     ):
      __a  					=["flax"]




      def __init__(  self ,   *lowerCamelCase ,   **lowerCamelCase      )							->Any:



         '''simple docstring'''
         requires_backends(self ,   ['flax']      )




      @classmethod
      def  __UpperCamelCase							(  cls ,   *lowerCamelCase ,   **lowerCamelCase      )							->Dict:



         '''simple docstring'''
         requires_backends(cls ,   ['flax']      )






      @classmethod
      def  __UpperCamelCase							(  cls ,   *lowerCamelCase ,   **lowerCamelCase      )							->List[str]:



         '''simple docstring'''
         requires_backends(cls ,   ['flax']      )







class 							__SCREAMING_SNAKE_CASE       (							metaclass=_lowerCAmelCase     ):
      __a  					=["flax"]




      def __init__(  self ,   *lowerCamelCase ,   **lowerCamelCase      )							->Union[str, Any]:



         '''simple docstring'''
         requires_backends(self ,   ['flax']      )




      @classmethod
      def  __UpperCamelCase							(  cls ,   *lowerCamelCase ,   **lowerCamelCase      )							->Union[str, Any]:



         '''simple docstring'''
         requires_backends(cls ,   ['flax']      )






      @classmethod
      def  __UpperCamelCase							(  cls ,   *lowerCamelCase ,   **lowerCamelCase      )							->List[Any]:



         '''simple docstring'''
         requires_backends(cls ,   ['flax']      )







class 							__SCREAMING_SNAKE_CASE       (							metaclass=_lowerCAmelCase     ):
      __a  					=["flax"]




      def __init__(  self ,   *lowerCamelCase ,   **lowerCamelCase      )							->Tuple:



         '''simple docstring'''
         requires_backends(self ,   ['flax']      )




      @classmethod
      def  __UpperCamelCase							(  cls ,   *lowerCamelCase ,   **lowerCamelCase      )							->int:



         '''simple docstring'''
         requires_backends(cls ,   ['flax']      )






      @classmethod
      def  __UpperCamelCase							(  cls ,   *lowerCamelCase ,   **lowerCamelCase      )							->List[str]:



         '''simple docstring'''
         requires_backends(cls ,   ['flax']      )







class 							__SCREAMING_SNAKE_CASE       (							metaclass=_lowerCAmelCase     ):
      __a  					=["flax"]




      def __init__(  self ,   *lowerCamelCase ,   **lowerCamelCase      )							->Optional[Any]:



         '''simple docstring'''
         requires_backends(self ,   ['flax']      )




      @classmethod
      def  __UpperCamelCase							(  cls ,   *lowerCamelCase ,   **lowerCamelCase      )							->Union[str, Any]:



         '''simple docstring'''
         requires_backends(cls ,   ['flax']      )






      @classmethod
      def  __UpperCamelCase							(  cls ,   *lowerCamelCase ,   **lowerCamelCase      )							->Tuple:



         '''simple docstring'''
         requires_backends(cls ,   ['flax']      )







class 							__SCREAMING_SNAKE_CASE       (							metaclass=_lowerCAmelCase     ):
      __a  					=["flax"]




      def __init__(  self ,   *lowerCamelCase ,   **lowerCamelCase      )							->Union[str, Any]:



         '''simple docstring'''
         requires_backends(self ,   ['flax']      )




      @classmethod
      def  __UpperCamelCase							(  cls ,   *lowerCamelCase ,   **lowerCamelCase      )							->int:



         '''simple docstring'''
         requires_backends(cls ,   ['flax']      )






      @classmethod
      def  __UpperCamelCase							(  cls ,   *lowerCamelCase ,   **lowerCamelCase      )							->int:



         '''simple docstring'''
         requires_backends(cls ,   ['flax']      )







class 							__SCREAMING_SNAKE_CASE       (							metaclass=_lowerCAmelCase     ):
      __a  					=["flax"]




      def __init__(  self ,   *lowerCamelCase ,   **lowerCamelCase      )							->Optional[int]:



         '''simple docstring'''
         requires_backends(self ,   ['flax']      )




      @classmethod
      def  __UpperCamelCase							(  cls ,   *lowerCamelCase ,   **lowerCamelCase      )							->List[Any]:



         '''simple docstring'''
         requires_backends(cls ,   ['flax']      )






      @classmethod
      def  __UpperCamelCase							(  cls ,   *lowerCamelCase ,   **lowerCamelCase      )							->Optional[Any]:



         '''simple docstring'''
         requires_backends(cls ,   ['flax']      )







'''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 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase      ,      _lowerCAmelCase      ,      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      )							->int:



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




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase=0      )							->Tuple:



         '''simple docstring'''
         if str(lowerCamelCase      ).startswith('mps'      ):
            __a				     =    torch.manual_seed(lowerCamelCase      )
         else:
            __a				     =    torch.Generator(device=lowerCamelCase      ).manual_seed(lowerCamelCase      )

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

         return inputs




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



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




      @unittest.skipIf(torch_device != 'cuda' ,   reason='float16 requires CUDA'      )
      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''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  __UpperCamelCase							(  self      )							->Optional[int]:



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




      def  __UpperCamelCase							(  self      )							->Any:



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




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



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







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




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



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




      def  __UpperCamelCase							(  self      )							->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=lowerCamelCase ,   tokenizer=lowerCamelCase      )

         # 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(lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )

         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(lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )

         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(lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->List[Any]:



         '''simple docstring'''
         # pipeline 1

         _start_torch_memory_measurement()

         __a				     =    torch.Generator(device='cpu'      ).manual_seed(0      )
         __a				     =    pipe_a(
             prompt_embeds=lowerCamelCase ,   negative_prompt_embeds=lowerCamelCase ,   num_inference_steps=2 ,   generator=lowerCamelCase ,   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(lowerCamelCase ,   lowerCamelCase      )

         # 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(lowerCamelCase      )

         __a				     =    pipe_a(
             prompt_embeds=lowerCamelCase ,   negative_prompt_embeds=lowerCamelCase ,   image=lowerCamelCase ,   generator=lowerCamelCase ,   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(lowerCamelCase ,   lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->Any:



         '''simple docstring'''
         # pipeline 1

         _start_torch_memory_measurement()

         __a				     =    floats_tensor((1, 3, 64, 64) ,   rng=random.Random(0      )      ).to(lowerCamelCase      )

         __a				     =    torch.Generator(device='cpu'      ).manual_seed(0      )

         __a				     =    pipe_a(
             prompt_embeds=lowerCamelCase ,   negative_prompt_embeds=lowerCamelCase ,   image=lowerCamelCase ,   num_inference_steps=2 ,   generator=lowerCamelCase ,   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(lowerCamelCase ,   lowerCamelCase      )

         # 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(lowerCamelCase      )
         __a				     =    floats_tensor((1, 3, 64, 64) ,   rng=random.Random(0      )      ).to(lowerCamelCase      )

         __a				     =    pipe_a(
             prompt_embeds=lowerCamelCase ,   negative_prompt_embeds=lowerCamelCase ,   image=lowerCamelCase ,   original_image=lowerCamelCase ,   generator=lowerCamelCase ,   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(lowerCamelCase ,   lowerCamelCase      )






      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->Optional[int]:



         '''simple docstring'''
         # pipeline 1

         _start_torch_memory_measurement()

         __a				     =    floats_tensor((1, 3, 64, 64) ,   rng=random.Random(0      )      ).to(lowerCamelCase      )
         __a				     =    floats_tensor((1, 3, 64, 64) ,   rng=random.Random(1      )      ).to(lowerCamelCase      )

         __a				     =    torch.Generator(device='cpu'      ).manual_seed(0      )
         __a				     =    pipe_a(
             prompt_embeds=lowerCamelCase ,   negative_prompt_embeds=lowerCamelCase ,   image=lowerCamelCase ,   mask_image=lowerCamelCase ,   num_inference_steps=2 ,   generator=lowerCamelCase ,   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(lowerCamelCase ,   lowerCamelCase      )

         # 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(lowerCamelCase      )
         __a				     =    floats_tensor((1, 3, 256, 256) ,   rng=random.Random(0      )      ).to(lowerCamelCase      )
         __a				     =    floats_tensor((1, 3, 256, 256) ,   rng=random.Random(1      )      ).to(lowerCamelCase      )

         __a				     =    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				     =    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(lowerCamelCase ,   lowerCamelCase      )







def    __UpperCAmelCase	(				)  ->			Dict:

   """simple docstring"""
   torch.cuda.empty_cache()
   torch.cuda.reset_max_memory_allocated()
   torch.cuda.reset_peak_memory_stats()