





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 	UpperCamelCase__				(   lowerCAmelCase_							,   lowerCAmelCase_							,   unittest.TestCase     ):



     '''simple docstring'''

     __snake_case					:			int				=    IFPipeline
     __snake_case					:			Tuple				=    TEXT_TO_IMAGE_PARAMS - {"width", "height", "latents"}
     __snake_case					:			Union[str, Any]				=    TEXT_TO_IMAGE_BATCH_PARAMS
     __snake_case					:			Any				=    PipelineTesterMixin.required_optional_params - {"latents"}






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	str:




           '''simple docstring'''


           return self._get_dummy_components()






     def        SCREAMING_SNAKE_CASE__						(							self		:					int		,lowerCamelCase__		:					List[str]		,lowerCamelCase__		:					Optional[Any]=0 )  ->	Any:




           '''simple docstring'''


           if str(lowerCamelCase__ ).startswith("""mps""" ):
                 SCREAMING_SNAKE_CASE	=		torch.manual_seed(lowerCamelCase__ )
           else:
                 SCREAMING_SNAKE_CASE	=		torch.Generator(device=lowerCamelCase__ ).manual_seed(lowerCamelCase__ )

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

           return inputs






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	Union[str, Any]:




           '''simple docstring'''


           self._test_save_load_optional_components()






     @unittest.skipIf(torch_device != """cuda"""		,reason="""float16 requires CUDA""" )
     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	int:




           '''simple docstring'''


           super().test_save_load_floataa(expected_max_diff=1e-1 )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict )  ->	List[str]:




           '''simple docstring'''


           self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	Tuple:




           '''simple docstring'''


           self._test_save_load_local()






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




           '''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        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	List[Any]:




           '''simple docstring'''


           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )

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



     '''simple docstring'''

     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	Dict:




           '''simple docstring'''


           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any] )  ->	List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		IFPipeline.from_pretrained("""DeepFloyd/IF-I-XL-v1.0"""		,variant="""fp16"""		,torch_dtype=torch.floataa )

           SCREAMING_SNAKE_CASE	=		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""" )

           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		pipe_a.encode_prompt("""anime turtle"""		,device="""cuda""" )

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

           SCREAMING_SNAKE_CASE	=		None
           SCREAMING_SNAKE_CASE	=		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

           SCREAMING_SNAKE_CASE	=		IFImgaImgPipeline(**pipe_a.components )
           SCREAMING_SNAKE_CASE	=		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

           SCREAMING_SNAKE_CASE	=		IFInpaintingPipeline(**pipe_a.components )
           SCREAMING_SNAKE_CASE	=		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        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any]		,lowerCamelCase__		:					Dict		,lowerCamelCase__		:					int		,lowerCamelCase__		:					Optional[int]		,lowerCamelCase__		:					List[Any] )  ->	int:




           '''simple docstring'''


           _start_torch_memory_measurement()

           SCREAMING_SNAKE_CASE	=		torch.Generator(device="""cpu""" ).manual_seed(0 )
           SCREAMING_SNAKE_CASE	=		pipe_a(
               prompt_embeds=lowerCamelCase__		,negative_prompt_embeds=lowerCamelCase__		,num_inference_steps=2		,generator=lowerCamelCase__		,output_type="""np"""		,)

           SCREAMING_SNAKE_CASE	=		output.images[0]

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

           SCREAMING_SNAKE_CASE	=		torch.cuda.max_memory_allocated()
           assert mem_bytes < 13 * 10**9

           SCREAMING_SNAKE_CASE	=		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()

           SCREAMING_SNAKE_CASE	=		torch.Generator(device="""cpu""" ).manual_seed(0 )

           SCREAMING_SNAKE_CASE	=		floats_tensor((1, 3, 64, 64)		,rng=random.Random(0 ) ).to(lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		pipe_a(
               prompt_embeds=lowerCamelCase__		,negative_prompt_embeds=lowerCamelCase__		,image=lowerCamelCase__		,generator=lowerCamelCase__		,num_inference_steps=2		,output_type="""np"""		,)

           SCREAMING_SNAKE_CASE	=		output.images[0]

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

           SCREAMING_SNAKE_CASE	=		torch.cuda.max_memory_allocated()
           assert mem_bytes < 4 * 10**9

           SCREAMING_SNAKE_CASE	=		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        SCREAMING_SNAKE_CASE__						(							self		:					str		,lowerCamelCase__		:					Optional[int]		,lowerCamelCase__		:					Any		,lowerCamelCase__		:					Dict		,lowerCamelCase__		:					Optional[int] )  ->	Optional[int]:




           '''simple docstring'''


           _start_torch_memory_measurement()

           SCREAMING_SNAKE_CASE	=		floats_tensor((1, 3, 64, 64)		,rng=random.Random(0 ) ).to(lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		torch.Generator(device="""cpu""" ).manual_seed(0 )

           SCREAMING_SNAKE_CASE	=		pipe_a(
               prompt_embeds=lowerCamelCase__		,negative_prompt_embeds=lowerCamelCase__		,image=lowerCamelCase__		,num_inference_steps=2		,generator=lowerCamelCase__		,output_type="""np"""		,)

           SCREAMING_SNAKE_CASE	=		output.images[0]

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

           SCREAMING_SNAKE_CASE	=		torch.cuda.max_memory_allocated()
           assert mem_bytes < 10 * 10**9

           SCREAMING_SNAKE_CASE	=		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()

           SCREAMING_SNAKE_CASE	=		torch.Generator(device="""cpu""" ).manual_seed(0 )

           SCREAMING_SNAKE_CASE	=		floats_tensor((1, 3, 256, 256)		,rng=random.Random(0 ) ).to(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		floats_tensor((1, 3, 64, 64)		,rng=random.Random(0 ) ).to(lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		pipe_a(
               prompt_embeds=lowerCamelCase__		,negative_prompt_embeds=lowerCamelCase__		,image=lowerCamelCase__		,original_image=lowerCamelCase__		,generator=lowerCamelCase__		,num_inference_steps=2		,output_type="""np"""		,)

           SCREAMING_SNAKE_CASE	=		output.images[0]

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

           SCREAMING_SNAKE_CASE	=		torch.cuda.max_memory_allocated()
           assert mem_bytes < 4 * 10**9

           SCREAMING_SNAKE_CASE	=		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        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any]		,lowerCamelCase__		:					Tuple		,lowerCamelCase__		:					Dict		,lowerCamelCase__		:					Optional[int]		,lowerCamelCase__		:					Any )  ->	List[str]:




           '''simple docstring'''


           _start_torch_memory_measurement()

           SCREAMING_SNAKE_CASE	=		floats_tensor((1, 3, 64, 64)		,rng=random.Random(0 ) ).to(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		floats_tensor((1, 3, 64, 64)		,rng=random.Random(1 ) ).to(lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		torch.Generator(device="""cpu""" ).manual_seed(0 )
           SCREAMING_SNAKE_CASE	=		pipe_a(
               prompt_embeds=lowerCamelCase__		,negative_prompt_embeds=lowerCamelCase__		,image=lowerCamelCase__		,mask_image=lowerCamelCase__		,num_inference_steps=2		,generator=lowerCamelCase__		,output_type="""np"""		,)

           SCREAMING_SNAKE_CASE	=		output.images[0]

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

           SCREAMING_SNAKE_CASE	=		torch.cuda.max_memory_allocated()
           assert mem_bytes < 10 * 10**9

           SCREAMING_SNAKE_CASE	=		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()

           SCREAMING_SNAKE_CASE	=		torch.Generator(device="""cpu""" ).manual_seed(0 )

           SCREAMING_SNAKE_CASE	=		floats_tensor((1, 3, 64, 64)		,rng=random.Random(0 ) ).to(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		floats_tensor((1, 3, 256, 256)		,rng=random.Random(0 ) ).to(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		floats_tensor((1, 3, 256, 256)		,rng=random.Random(1 ) ).to(lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		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"""		,)

           SCREAMING_SNAKE_CASE	=		output.images[0]

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

           SCREAMING_SNAKE_CASE	=		torch.cuda.max_memory_allocated()
           assert mem_bytes < 4 * 10**9

           SCREAMING_SNAKE_CASE	=		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      __lowercase     (   )				->     Tuple:


      '''simple docstring'''


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









import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


SCREAMING_SNAKE_CASE_		     = logging.get_logger(__name__)

class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     def __init__(							self		:					int		,*lowerCamelCase__		:					int		,**lowerCamelCase__		:					List[Any] )  ->	None:




           '''simple docstring'''


           warnings.warn(
               """The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
               """ Please use DeformableDetrImageProcessor instead."""		,lowerCamelCase__		,)
           super().__init__(*lowerCamelCase__		,**lowerCamelCase__ )


