






'''simple docstring'''




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class       __lowercase					(__lowerCamelCase    ):
	_lowerCamelCase              =  (DDIMParallelScheduler,)
	_lowerCamelCase              =  (('''eta''', 0.0), ('''num_inference_steps''', 50))

	def      __UpperCamelCase							(      self   :						Optional[int]  ,					**UpperCAmelCase_   :						List[str]):
			UpperCamelCase__				:					List[str]					=					{
			    'num_train_timesteps': 1_000,
			    'beta_start': 0.00_01,
			    'beta_end': 0.02,
			    'beta_schedule': 'linear',
			    'clip_sample': True,
			}

			config.update(**UpperCAmelCase_)
			return config

	def      __UpperCamelCase							(      self   :						Optional[int]  ,					**UpperCAmelCase_   :						Optional[Any]):
			UpperCamelCase__				:					Optional[int]					=					self.scheduler_classes[0]
			UpperCamelCase__				:					List[Any]					=					self.get_scheduler_config(**UpperCAmelCase_)
			UpperCamelCase__				:					List[Any]					=					scheduler_class(**UpperCAmelCase_)

			UpperCamelCase__,	UpperCamelCase__				:					int					=					10, 0.0

			UpperCamelCase__				:					Optional[int]					=					self.dummy_model()
			UpperCamelCase__				:					Dict					=					self.dummy_sample_deter

			scheduler.set_timesteps(UpperCAmelCase_)

			for t in scheduler.timesteps:
					UpperCamelCase__				:					str					=					model(UpperCAmelCase_  ,					UpperCAmelCase_)
					UpperCamelCase__				:					Optional[int]					=					scheduler.step(UpperCAmelCase_  ,					UpperCAmelCase_  ,					UpperCAmelCase_  ,					UpperCAmelCase_).prev_sample

			return sample

	def      __UpperCamelCase							(      self   :						Optional[int]):
			for timesteps in [100, 500, 1_000]:
					self.check_over_configs(num_train_timesteps=UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						str):
			for steps_offset in [0, 1]:
					self.check_over_configs(steps_offset=UpperCAmelCase_)

			UpperCamelCase__				:					int					=					self.scheduler_classes[0]
			UpperCamelCase__				:					Union[str, Any]					=					self.get_scheduler_config(steps_offset=1)
			UpperCamelCase__				:					List[Any]					=					scheduler_class(**UpperCAmelCase_)
			scheduler.set_timesteps(5)
			assert torch.equal(scheduler.timesteps  ,					torch.LongTensor([801, 601, 401, 201, 1]))

	def      __UpperCamelCase							(      self   :						List[str]):
			for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]  ,					[0.0_02, 0.02, 0.2, 2]):
					self.check_over_configs(beta_start=UpperCAmelCase_  ,					beta_end=UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Optional[Any]):
			for schedule in ["linear", "squaredcos_cap_v2"]:
					self.check_over_configs(beta_schedule=UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						List[str]):
			for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(prediction_type=UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						int):
			for clip_sample in [True, False]:
					self.check_over_configs(clip_sample=UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						int):
			for timestep_spacing in ["trailing", "leading"]:
					self.check_over_configs(timestep_spacing=UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Optional[int]):
			for rescale_betas_zero_snr in [True, False]:
					self.check_over_configs(rescale_betas_zero_snr=UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Any):
			self.check_over_configs(thresholding=UpperCAmelCase_)
			for threshold in [0.5, 1.0, 2.0]:
					for prediction_type in ["epsilon", "v_prediction"]:
							self.check_over_configs(
							    thresholding=UpperCAmelCase_  ,					prediction_type=UpperCAmelCase_  ,					sample_max_value=UpperCAmelCase_  ,					)

	def      __UpperCamelCase							(      self   :						int):
			for t in [1, 10, 49]:
					self.check_over_forward(time_step=UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						int):
			for t, num_inference_steps in zip([1, 10, 50]  ,					[10, 50, 500]):
					self.check_over_forward(time_step=UpperCAmelCase_  ,					num_inference_steps=UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Optional[int]):
			for t, eta in zip([1, 10, 49]  ,					[0.0, 0.5, 1.0]):
					self.check_over_forward(time_step=UpperCAmelCase_  ,					eta=UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Optional[Any]):
			UpperCamelCase__				:					str					=					self.scheduler_classes[0]
			UpperCamelCase__				:					Union[str, Any]					=					self.get_scheduler_config()
			UpperCamelCase__				:					Dict					=					scheduler_class(**UpperCAmelCase_)

			assert torch.sum(torch.abs(scheduler._get_variance(0  ,					0) - 0.0)) < 1e-5
			assert torch.sum(torch.abs(scheduler._get_variance(420  ,					400) - 0.1_47_71)) < 1e-5
			assert torch.sum(torch.abs(scheduler._get_variance(980  ,					960) - 0.3_24_60)) < 1e-5
			assert torch.sum(torch.abs(scheduler._get_variance(0  ,					0) - 0.0)) < 1e-5
			assert torch.sum(torch.abs(scheduler._get_variance(487  ,					486) - 0.0_09_79)) < 1e-5
			assert torch.sum(torch.abs(scheduler._get_variance(999  ,					998) - 0.02)) < 1e-5

	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					Optional[Any]					=					self.scheduler_classes[0]
			UpperCamelCase__				:					List[Any]					=					self.get_scheduler_config()
			UpperCamelCase__				:					List[Any]					=					scheduler_class(**UpperCAmelCase_)

			UpperCamelCase__,	UpperCamelCase__				:					Any					=					10, 0.0
			scheduler.set_timesteps(UpperCAmelCase_)

			UpperCamelCase__				:					Optional[int]					=					self.dummy_model()
			UpperCamelCase__				:					Any					=					self.dummy_sample_deter
			UpperCamelCase__				:					Any					=					self.dummy_sample_deter + 0.1
			UpperCamelCase__				:					List[str]					=					self.dummy_sample_deter - 0.1

			UpperCamelCase__				:					int					=					samplea.shape[0]
			UpperCamelCase__				:					Any					=					torch.stack([samplea, samplea, samplea]  ,					dim=0)
			UpperCamelCase__				:					Any					=					torch.arange(UpperCAmelCase_)[0:3, None].repeat(1  ,					UpperCAmelCase_)

			UpperCamelCase__				:					Tuple					=					model(samples.flatten(0  ,					1)  ,					timesteps.flatten(0  ,					1))
			UpperCamelCase__				:					int					=					scheduler.batch_step_no_noise(UpperCAmelCase_  ,					timesteps.flatten(0  ,					1)  ,					samples.flatten(0  ,					1)  ,					UpperCAmelCase_)

			UpperCamelCase__				:					Dict					=					torch.sum(torch.abs(UpperCAmelCase_))
			UpperCamelCase__				:					List[Any]					=					torch.mean(torch.abs(UpperCAmelCase_))

			assert abs(result_sum.item() - 11_47.79_04) < 1e-2
			assert abs(result_mean.item() - 0.49_82) < 1e-3

	def      __UpperCamelCase							(      self   :						int):
			UpperCamelCase__				:					Tuple					=					self.full_loop()

			UpperCamelCase__				:					Optional[Any]					=					torch.sum(torch.abs(UpperCAmelCase_))
			UpperCamelCase__				:					List[str]					=					torch.mean(torch.abs(UpperCAmelCase_))

			assert abs(result_sum.item() - 1_72.00_67) < 1e-2
			assert abs(result_mean.item() - 0.22_39_67) < 1e-3

	def      __UpperCamelCase							(      self   :						Optional[int]):
			UpperCamelCase__				:					str					=					self.full_loop(prediction_type='v_prediction')

			UpperCamelCase__				:					str					=					torch.sum(torch.abs(UpperCAmelCase_))
			UpperCamelCase__				:					Any					=					torch.mean(torch.abs(UpperCAmelCase_))

			assert abs(result_sum.item() - 52.53_02) < 1e-2
			assert abs(result_mean.item() - 0.06_84) < 1e-3

	def      __UpperCamelCase							(      self   :						List[str]):
			# We specify different beta, so that the first alpha is 0.99
			UpperCamelCase__				:					Any					=					self.full_loop(set_alpha_to_one=UpperCAmelCase_  ,					beta_start=0.01)
			UpperCamelCase__				:					List[Any]					=					torch.sum(torch.abs(UpperCAmelCase_))
			UpperCamelCase__				:					Any					=					torch.mean(torch.abs(UpperCAmelCase_))

			assert abs(result_sum.item() - 1_49.82_95) < 1e-2
			assert abs(result_mean.item() - 0.19_51) < 1e-3





	def      __UpperCamelCase							(      self   :						Union[str, Any]):
			# We specify different beta, so that the first alpha is 0.99
			UpperCamelCase__				:					Any					=					self.full_loop(set_alpha_to_one=UpperCAmelCase_  ,					beta_start=0.01)
			UpperCamelCase__				:					Any					=					torch.sum(torch.abs(UpperCAmelCase_))
			UpperCamelCase__				:					Optional[int]					=					torch.mean(torch.abs(UpperCAmelCase_))

			assert abs(result_sum.item() - 1_49.07_84) < 1e-2
			assert abs(result_mean.item() - 0.19_41) < 1e-3












'''simple docstring'''




import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np







class       __lowercase					:

	def      __UpperCamelCase							(      self   :						Union[str, Any]):
			torch.manual_seed(0)
			UpperCamelCase__				:					Dict					=					TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5')

			torch.manual_seed(0)
			UpperCamelCase__				:					Union[str, Any]					=					AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5')

			torch.manual_seed(0)
			UpperCamelCase__				:					List[str]					=					UNetaDConditionModel(
			    sample_size=32  ,					layers_per_block=1  ,					block_out_channels=[32, 64]  ,					down_block_types=[
			        'ResnetDownsampleBlock2D',
			        'SimpleCrossAttnDownBlock2D',
			    ]  ,					mid_block_type='UNetMidBlock2DSimpleCrossAttn'  ,					up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']  ,					in_channels=3  ,					out_channels=6  ,					cross_attention_dim=32  ,					encoder_hid_dim=32  ,					attention_head_dim=8  ,					addition_embed_type='text'  ,					addition_embed_type_num_heads=2  ,					cross_attention_norm='group_norm'  ,					resnet_time_scale_shift='scale_shift'  ,					act_fn='gelu'  ,					)
			unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

			torch.manual_seed(0)
			UpperCamelCase__				:					Optional[Any]					=					DDPMScheduler(
			    num_train_timesteps=1_000  ,					beta_schedule='squaredcos_cap_v2'  ,					beta_start=0.00_01  ,					beta_end=0.02  ,					thresholding=UpperCAmelCase_  ,					dynamic_thresholding_ratio=0.95  ,					sample_max_value=1.0  ,					prediction_type='epsilon'  ,					variance_type='learned_range'  ,					)

			torch.manual_seed(0)
			UpperCamelCase__				:					List[Any]					=					IFWatermarker()

			return {
			    "text_encoder": text_encoder,
			    "tokenizer": tokenizer,
			    "unet": unet,
			    "scheduler": scheduler,
			    "watermarker": watermarker,
			    "safety_checker": None,
			    "feature_extractor": None,
			}

	def      __UpperCamelCase							(      self   :						Dict):
			torch.manual_seed(0)
			UpperCamelCase__				:					List[Any]					=					TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5')

			torch.manual_seed(0)
			UpperCamelCase__				:					Any					=					AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5')

			torch.manual_seed(0)
			UpperCamelCase__				:					Any					=					UNetaDConditionModel(
			    sample_size=32  ,					layers_per_block=[1, 2]  ,					block_out_channels=[32, 64]  ,					down_block_types=[
			        'ResnetDownsampleBlock2D',
			        'SimpleCrossAttnDownBlock2D',
			    ]  ,					mid_block_type='UNetMidBlock2DSimpleCrossAttn'  ,					up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']  ,					in_channels=6  ,					out_channels=6  ,					cross_attention_dim=32  ,					encoder_hid_dim=32  ,					attention_head_dim=8  ,					addition_embed_type='text'  ,					addition_embed_type_num_heads=2  ,					cross_attention_norm='group_norm'  ,					resnet_time_scale_shift='scale_shift'  ,					act_fn='gelu'  ,					class_embed_type='timestep'  ,					mid_block_scale_factor=1.4_14  ,					time_embedding_act_fn='gelu'  ,					time_embedding_dim=32  ,					)
			unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

			torch.manual_seed(0)
			UpperCamelCase__				:					str					=					DDPMScheduler(
			    num_train_timesteps=1_000  ,					beta_schedule='squaredcos_cap_v2'  ,					beta_start=0.00_01  ,					beta_end=0.02  ,					thresholding=UpperCAmelCase_  ,					dynamic_thresholding_ratio=0.95  ,					sample_max_value=1.0  ,					prediction_type='epsilon'  ,					variance_type='learned_range'  ,					)

			torch.manual_seed(0)
			UpperCamelCase__				:					List[str]					=					DDPMScheduler(
			    num_train_timesteps=1_000  ,					beta_schedule='squaredcos_cap_v2'  ,					beta_start=0.00_01  ,					beta_end=0.02  ,					)

			torch.manual_seed(0)
			UpperCamelCase__				:					Optional[Any]					=					IFWatermarker()

			return {
			    "text_encoder": text_encoder,
			    "tokenizer": tokenizer,
			    "unet": unet,
			    "scheduler": scheduler,
			    "image_noising_scheduler": image_noising_scheduler,
			    "watermarker": watermarker,
			    "safety_checker": None,
			    "feature_extractor": None,
			}

	def      __UpperCamelCase							(      self   :						Any):
			UpperCamelCase__				:					Dict					=					self.get_dummy_components()
			UpperCamelCase__				:					List[Any]					=					self.pipeline_class(**UpperCAmelCase_)
			pipe.to(UpperCAmelCase_)
			pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					Tuple					=					self.get_dummy_inputs(UpperCAmelCase_)

			UpperCamelCase__				:					Optional[Any]					=					inputs['prompt']
			UpperCamelCase__				:					List[Any]					=					inputs['generator']
			UpperCamelCase__				:					Tuple					=					inputs['num_inference_steps']
			UpperCamelCase__				:					List[Any]					=					inputs['output_type']

			if "image" in inputs:
					UpperCamelCase__				:					Tuple					=					inputs['image']
			else:
					UpperCamelCase__				:					Union[str, Any]					=					None

			if "mask_image" in inputs:
					UpperCamelCase__				:					Optional[int]					=					inputs['mask_image']
			else:
					UpperCamelCase__				:					int					=					None

			if "original_image" in inputs:
					UpperCamelCase__				:					List[Any]					=					inputs['original_image']
			else:
					UpperCamelCase__				:					Optional[Any]					=					None

			UpperCamelCase__,	UpperCamelCase__				:					Any					=					pipe.encode_prompt(UpperCAmelCase_)

			# inputs with prompt converted to embeddings
			UpperCamelCase__				:					List[Any]					=					{
			    'prompt_embeds': prompt_embeds,
			    'negative_prompt_embeds': negative_prompt_embeds,
			    'generator': generator,
			    'num_inference_steps': num_inference_steps,
			    'output_type': output_type,
			}

			if image is not None:
					UpperCamelCase__				:					Dict					=					image

			if mask_image is not None:
					UpperCamelCase__				:					Optional[int]					=					mask_image

			if original_image is not None:
					UpperCamelCase__				:					Union[str, Any]					=					original_image

			# set all optional components to None
			for optional_component in pipe._optional_components:
					setattr(UpperCAmelCase_  ,					UpperCAmelCase_  ,					UpperCAmelCase_)

			UpperCamelCase__				:					int					=					pipe(**UpperCAmelCase_)[0]

			with tempfile.TemporaryDirectory() as tmpdir:
					pipe.save_pretrained(UpperCAmelCase_)
					UpperCamelCase__				:					Optional[Any]					=					self.pipeline_class.from_pretrained(UpperCAmelCase_)
					pipe_loaded.to(UpperCAmelCase_)
					pipe_loaded.set_progress_bar_config(disable=UpperCAmelCase_)

			pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

			for optional_component in pipe._optional_components:
					self.assertTrue(
					    getattr(UpperCAmelCase_  ,					UpperCAmelCase_) is None  ,					F'`{optional_component}` did not stay set to None after loading.'  ,					)

			UpperCamelCase__				:					Optional[int]					=					self.get_dummy_inputs(UpperCAmelCase_)

			UpperCamelCase__				:					Union[str, Any]					=					inputs['generator']
			UpperCamelCase__				:					List[Any]					=					inputs['num_inference_steps']
			UpperCamelCase__				:					Optional[int]					=					inputs['output_type']

			# inputs with prompt converted to embeddings
			UpperCamelCase__				:					Any					=					{
			    'prompt_embeds': prompt_embeds,
			    'negative_prompt_embeds': negative_prompt_embeds,
			    'generator': generator,
			    'num_inference_steps': num_inference_steps,
			    'output_type': output_type,
			}

			if image is not None:
					UpperCamelCase__				:					Tuple					=					image

			if mask_image is not None:
					UpperCamelCase__				:					Union[str, Any]					=					mask_image

			if original_image is not None:
					UpperCamelCase__				:					str					=					original_image

			UpperCamelCase__				:					Union[str, Any]					=					pipe_loaded(**UpperCAmelCase_)[0]

			UpperCamelCase__				:					Dict					=					np.abs(to_np(UpperCAmelCase_) - to_np(UpperCAmelCase_)).max()
			self.assertLess(UpperCAmelCase_  ,					1e-4)





	def      __UpperCamelCase							(      self   :						Optional[int]):
			UpperCamelCase__				:					Any					=					self.get_dummy_components()
			UpperCamelCase__				:					List[str]					=					self.pipeline_class(**UpperCAmelCase_)
			pipe.to(UpperCAmelCase_)
			pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					Union[str, Any]					=					self.get_dummy_inputs(UpperCAmelCase_)
			UpperCamelCase__				:					Any					=					pipe(**UpperCAmelCase_)[0]

			with tempfile.TemporaryDirectory() as tmpdir:
					pipe.save_pretrained(UpperCAmelCase_)
					UpperCamelCase__				:					Optional[Any]					=					self.pipeline_class.from_pretrained(UpperCAmelCase_)
					pipe_loaded.to(UpperCAmelCase_)
					pipe_loaded.set_progress_bar_config(disable=UpperCAmelCase_)

			pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

			UpperCamelCase__				:					Any					=					self.get_dummy_inputs(UpperCAmelCase_)
			UpperCamelCase__				:					Tuple					=					pipe_loaded(**UpperCAmelCase_)[0]

			UpperCamelCase__				:					Optional[int]					=					np.abs(to_np(UpperCAmelCase_) - to_np(UpperCAmelCase_)).max()
			self.assertLess(UpperCAmelCase_  ,					1e-4)




