



import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				(EulerDiscreteScheduler,)
     UpperCamelCase__		    =				10




     def 		snake_case_		(				self ,						**a__):
         A__		 =					{
             '''num_train_timesteps''': 1_1_0_0,
             '''beta_start''': 0.0_0_0_1,
             '''beta_end''': 0.0_2,
             '''beta_schedule''': '''linear''',
         }

         config.update(**a__)
         return config




     def 		snake_case_		(				self):
         for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
             self.check_over_configs(num_train_timesteps=a__)




     def 		snake_case_		(				self):
         for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1] ,						[0.0_0_0_2, 0.0_0_2, 0.0_2]):
             self.check_over_configs(beta_start=a__ ,						beta_end=a__)




     def 		snake_case_		(				self):
         for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=a__)




     def 		snake_case_		(				self):
         for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=a__)




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**a__)

         scheduler.set_timesteps(self.num_inference_steps)

         A__		 =					torch.manual_seed(0)

         A__		 =					self.dummy_model()
         A__		 =					self.dummy_sample_deter * scheduler.init_noise_sigma
         A__		 =					sample.to(a__)

         for i, t in enumerate(scheduler.timesteps):
             A__		 =					scheduler.scale_model_input(a__ ,						a__)

             A__		 =					model(a__ ,						a__)

             A__		 =					scheduler.step(a__ ,						a__ ,						a__ ,						generator=a__)
             A__		 =					output.prev_sample

         A__		 =					torch.sum(torch.abs(a__))
         A__		 =					torch.mean(torch.abs(a__))

         assert abs(result_sum.item() - 1_0.0_8_0_7) < 1e-2
         assert abs(result_mean.item() - 0.0_1_3_1) < 1e-3




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config(prediction_type='''v_prediction''')
         A__		 =					scheduler_class(**a__)

         scheduler.set_timesteps(self.num_inference_steps)

         A__		 =					torch.manual_seed(0)

         A__		 =					self.dummy_model()
         A__		 =					self.dummy_sample_deter * scheduler.init_noise_sigma
         A__		 =					sample.to(a__)

         for i, t in enumerate(scheduler.timesteps):
             A__		 =					scheduler.scale_model_input(a__ ,						a__)

             A__		 =					model(a__ ,						a__)

             A__		 =					scheduler.step(a__ ,						a__ ,						a__ ,						generator=a__)
             A__		 =					output.prev_sample

         A__		 =					torch.sum(torch.abs(a__))
         A__		 =					torch.mean(torch.abs(a__))

         assert abs(result_sum.item() - 0.0_0_0_2) < 1e-2
         assert abs(result_mean.item() - 2.2_676e-06) < 1e-3




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**a__)

         scheduler.set_timesteps(self.num_inference_steps ,						device=a__)

         A__		 =					torch.manual_seed(0)

         A__		 =					self.dummy_model()
         A__		 =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         A__		 =					sample.to(a__)

         for t in scheduler.timesteps:
             A__		 =					scheduler.scale_model_input(a__ ,						a__)

             A__		 =					model(a__ ,						a__)

             A__		 =					scheduler.step(a__ ,						a__ ,						a__ ,						generator=a__)
             A__		 =					output.prev_sample

         A__		 =					torch.sum(torch.abs(a__))
         A__		 =					torch.mean(torch.abs(a__))

         assert abs(result_sum.item() - 1_0.0_8_0_7) < 1e-2
         assert abs(result_mean.item() - 0.0_1_3_1) < 1e-3




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**a__ ,						use_karras_sigmas=a__)

         scheduler.set_timesteps(self.num_inference_steps ,						device=a__)

         A__		 =					torch.manual_seed(0)

         A__		 =					self.dummy_model()
         A__		 =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         A__		 =					sample.to(a__)

         for t in scheduler.timesteps:
             A__		 =					scheduler.scale_model_input(a__ ,						a__)

             A__		 =					model(a__ ,						a__)

             A__		 =					scheduler.step(a__ ,						a__ ,						a__ ,						generator=a__)
             A__		 =					output.prev_sample

         A__		 =					torch.sum(torch.abs(a__))
         A__		 =					torch.mean(torch.abs(a__))

         assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9) < 1e-2
         assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3) < 1e-3





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			(       A__			,      A__			,      unittest.TestCase     ):
     UpperCamelCase__		    =				IFPipeline
     UpperCamelCase__		    =				TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
     UpperCamelCase__		    =				TEXT_TO_IMAGE_BATCH_PARAMS
     UpperCamelCase__		    =				PipelineTesterMixin.required_optional_params - {'''latents'''}




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




     def 		snake_case_		(				self ,						a__ ,						a__=0):
         if str(a__).startswith('''mps'''):
             A__		 =					torch.manual_seed(a__)
         else:
             A__		 =					torch.Generator(device=a__).manual_seed(a__)

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

         return inputs




     def 		snake_case_		(				self):
         self._test_save_load_optional_components()




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




     def 		snake_case_		(				self):
         self._test_save_load_local()




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







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




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




     def 		snake_case_		(				self):
         # 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=a__ ,						tokenizer=a__)

         # 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(a__ ,						a__ ,						a__ ,						a__)

         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(a__ ,						a__ ,						a__ ,						a__)

         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(a__ ,						a__ ,						a__ ,						a__)




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__):
         # pipeline 1

         _start_torch_memory_measurement()

         A__		 =					torch.Generator(device='''cpu''').manual_seed(0)
         A__		 =					pipe_a(
             prompt_embeds=a__ ,						negative_prompt_embeds=a__ ,						num_inference_steps=2 ,						generator=a__ ,						output_type='''np''' ,						)

         A__		 =					output.images[0]

         assert image.shape == (6_4, 6_4, 3)

         A__		 =					torch.cuda.max_memory_allocated()
         assert mem_bytes < 1_3 * 1_0**9

         A__		 =					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()

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

         A__		 =					floats_tensor((1, 3, 6_4, 6_4) ,						rng=random.Random(0)).to(a__)

         A__		 =					pipe_a(
             prompt_embeds=a__ ,						negative_prompt_embeds=a__ ,						image=a__ ,						generator=a__ ,						num_inference_steps=2 ,						output_type='''np''' ,						)

         A__		 =					output.images[0]

         assert image.shape == (2_5_6, 2_5_6, 3)

         A__		 =					torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 1_0**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(a__ ,						a__)




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__):
         # pipeline 1

         _start_torch_memory_measurement()

         A__		 =					floats_tensor((1, 3, 6_4, 6_4) ,						rng=random.Random(0)).to(a__)

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

         A__		 =					pipe_a(
             prompt_embeds=a__ ,						negative_prompt_embeds=a__ ,						image=a__ ,						num_inference_steps=2 ,						generator=a__ ,						output_type='''np''' ,						)

         A__		 =					output.images[0]

         assert image.shape == (6_4, 6_4, 3)

         A__		 =					torch.cuda.max_memory_allocated()
         assert mem_bytes < 1_0 * 1_0**9

         A__		 =					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()

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

         A__		 =					floats_tensor((1, 3, 2_5_6, 2_5_6) ,						rng=random.Random(0)).to(a__)
         A__		 =					floats_tensor((1, 3, 6_4, 6_4) ,						rng=random.Random(0)).to(a__)

         A__		 =					pipe_a(
             prompt_embeds=a__ ,						negative_prompt_embeds=a__ ,						image=a__ ,						original_image=a__ ,						generator=a__ ,						num_inference_steps=2 ,						output_type='''np''' ,						)

         A__		 =					output.images[0]

         assert image.shape == (2_5_6, 2_5_6, 3)

         A__		 =					torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 1_0**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(a__ ,						a__)




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__):
         # pipeline 1

         _start_torch_memory_measurement()

         A__		 =					floats_tensor((1, 3, 6_4, 6_4) ,						rng=random.Random(0)).to(a__)
         A__		 =					floats_tensor((1, 3, 6_4, 6_4) ,						rng=random.Random(1)).to(a__)

         A__		 =					torch.Generator(device='''cpu''').manual_seed(0)
         A__		 =					pipe_a(
             prompt_embeds=a__ ,						negative_prompt_embeds=a__ ,						image=a__ ,						mask_image=a__ ,						num_inference_steps=2 ,						generator=a__ ,						output_type='''np''' ,						)

         A__		 =					output.images[0]

         assert image.shape == (6_4, 6_4, 3)

         A__		 =					torch.cuda.max_memory_allocated()
         assert mem_bytes < 1_0 * 1_0**9

         A__		 =					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()

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

         A__		 =					floats_tensor((1, 3, 6_4, 6_4) ,						rng=random.Random(0)).to(a__)
         A__		 =					floats_tensor((1, 3, 2_5_6, 2_5_6) ,						rng=random.Random(0)).to(a__)
         A__		 =					floats_tensor((1, 3, 2_5_6, 2_5_6) ,						rng=random.Random(1)).to(a__)

         A__		 =					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''' ,						)

         A__		 =					output.images[0]

         assert image.shape == (2_5_6, 2_5_6, 3)

         A__		 =					torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 1_0**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(a__ ,						a__)







def 		lowerCAmelCase__    (					)->  List[Any]:
    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()
    torch.cuda.reset_peak_memory_stats()
