



import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class 	lowerCamelCase__      (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_     =(DDPMParallelScheduler,)



		def 		lowerCAmelCase__				(self		,**__lowerCamelCase			)					->      List[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					{
						    '''num_train_timesteps''': 10_00,
						    '''beta_start''': 0.0001,
						    '''beta_end''': 0.02,
						    '''beta_schedule''': '''linear''',
						    '''variance_type''': '''fixed_small''',
						    '''clip_sample''': True,
						}

						config.update(**__lowerCamelCase			)
						return config



		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						for timesteps in [1, 5, 1_00, 10_00]:
										self.check_over_configs(num_train_timesteps=__lowerCamelCase			)



		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]		,[0.002, 0.02, 0.2, 2]			):
										self.check_over_configs(beta_start=__lowerCamelCase		,beta_end=__lowerCamelCase			)



		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""simple docstring"""






						for schedule in ["linear", "squaredcos_cap_v2"]:
										self.check_over_configs(beta_schedule=__lowerCamelCase			)



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

						"""simple docstring"""






						for variance in ["fixed_small", "fixed_large", "other"]:
										self.check_over_configs(variance_type=__lowerCamelCase			)



		def 		lowerCAmelCase__				(self			)					->      Optional[int]:

						"""simple docstring"""






						for clip_sample in [True, False]:
										self.check_over_configs(clip_sample=__lowerCamelCase			)



		def 		lowerCAmelCase__				(self			)					->      Optional[int]:

						"""simple docstring"""






						self.check_over_configs(thresholding=__lowerCamelCase			)
						for threshold in [0.5, 1.0, 2.0]:
										for prediction_type in ["epsilon", "sample", "v_prediction"]:
														self.check_over_configs(
														    thresholding=__lowerCamelCase		,prediction_type=__lowerCamelCase		,sample_max_value=__lowerCamelCase		,)



		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						for prediction_type in ["epsilon", "sample", "v_prediction"]:
										self.check_over_configs(prediction_type=__lowerCamelCase			)



		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						for t in [0, 5_00, 9_99]:
										self.check_over_forward(time_step=__lowerCamelCase			)



		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					self.scheduler_classes[0]
						lowerCAmelCase__					:    Union[str, Any]												=					self.get_scheduler_config()
						lowerCAmelCase__					:    List[str]												=					scheduler_class(**__lowerCamelCase			)

						assert torch.sum(torch.abs(scheduler._get_variance(0			) - 0.0			)			) < 1e-5
						assert torch.sum(torch.abs(scheduler._get_variance(4_87			) - 0.0_0979			)			) < 1e-5
						assert torch.sum(torch.abs(scheduler._get_variance(9_99			) - 0.02			)			) < 1e-5



		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					self.scheduler_classes[0]
						lowerCAmelCase__					:    Optional[Any]												=					self.get_scheduler_config()
						lowerCAmelCase__					:    List[str]												=					scheduler_class(**__lowerCamelCase			)

						lowerCAmelCase__					:    Tuple												=					len(__lowerCamelCase			)

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

						lowerCAmelCase__					:    Tuple												=					samplea.shape[0]
						lowerCAmelCase__					:    int												=					torch.stack([samplea, samplea, samplea]		,dim=0			)
						lowerCAmelCase__					:    Dict												=					torch.arange(__lowerCamelCase			)[0:3, None].repeat(1		,__lowerCamelCase			)

						lowerCAmelCase__					:    Tuple												=					model(samples.flatten(0		,1			)		,timesteps.flatten(0		,1			)			)
						lowerCAmelCase__					:    Any												=					scheduler.batch_step_no_noise(__lowerCamelCase		,timesteps.flatten(0		,1			)		,samples.flatten(0		,1			)			)

						lowerCAmelCase__					:    List[str]												=					torch.sum(torch.abs(__lowerCamelCase			)			)
						lowerCAmelCase__					:    Dict												=					torch.mean(torch.abs(__lowerCamelCase			)			)

						assert abs(result_sum.item() - 1153.1833			) < 1e-2
						assert abs(result_mean.item() - 0.5005			) < 1e-3



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

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[Any]												=					self.scheduler_classes[0]
						lowerCAmelCase__					:    str												=					self.get_scheduler_config()
						lowerCAmelCase__					:    str												=					scheduler_class(**__lowerCamelCase			)

						lowerCAmelCase__					:    List[str]												=					len(__lowerCamelCase			)

						lowerCAmelCase__					:    Dict												=					self.dummy_model()
						lowerCAmelCase__					:    Any												=					self.dummy_sample_deter
						lowerCAmelCase__					:    str												=					torch.manual_seed(0			)

						for t in reversed(range(__lowerCamelCase			)			):
										# 1. predict noise residual
										lowerCAmelCase__					:    Dict												=					model(__lowerCamelCase		,__lowerCamelCase			)

										# 2. predict previous mean of sample x_t-1
										lowerCAmelCase__					:    int												=					scheduler.step(__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,generator=__lowerCamelCase			).prev_sample

										lowerCAmelCase__					:    Dict												=					pred_prev_sample

						lowerCAmelCase__					:    Dict												=					torch.sum(torch.abs(__lowerCamelCase			)			)
						lowerCAmelCase__					:    Dict												=					torch.mean(torch.abs(__lowerCamelCase			)			)

						assert abs(result_sum.item() - 258.9606			) < 1e-2
						assert abs(result_mean.item() - 0.3372			) < 1e-3



		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						lowerCAmelCase__					:    List[str]												=					self.scheduler_classes[0]
						lowerCAmelCase__					:    Optional[Any]												=					self.get_scheduler_config(prediction_type='''v_prediction'''			)
						lowerCAmelCase__					:    List[str]												=					scheduler_class(**__lowerCamelCase			)

						lowerCAmelCase__					:    Dict												=					len(__lowerCamelCase			)

						lowerCAmelCase__					:    Optional[int]												=					self.dummy_model()
						lowerCAmelCase__					:    Tuple												=					self.dummy_sample_deter
						lowerCAmelCase__					:    str												=					torch.manual_seed(0			)

						for t in reversed(range(__lowerCamelCase			)			):
										# 1. predict noise residual
										lowerCAmelCase__					:    Optional[int]												=					model(__lowerCamelCase		,__lowerCamelCase			)

										# 2. predict previous mean of sample x_t-1
										lowerCAmelCase__					:    Optional[Any]												=					scheduler.step(__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,generator=__lowerCamelCase			).prev_sample

										lowerCAmelCase__					:    str												=					pred_prev_sample

						lowerCAmelCase__					:    Optional[Any]												=					torch.sum(torch.abs(__lowerCamelCase			)			)
						lowerCAmelCase__					:    Optional[Any]												=					torch.mean(torch.abs(__lowerCamelCase			)			)

						assert abs(result_sum.item() - 202.0296			) < 1e-2
						assert abs(result_mean.item() - 0.2631			) < 1e-3



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

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					self.scheduler_classes[0]
						lowerCAmelCase__					:    Dict												=					self.get_scheduler_config()
						lowerCAmelCase__					:    int												=					scheduler_class(**__lowerCamelCase			)

						lowerCAmelCase__					:    Optional[int]												=					[1_00, 87, 50, 1, 0]

						scheduler.set_timesteps(timesteps=__lowerCamelCase			)

						lowerCAmelCase__					:    Tuple												=					scheduler.timesteps

						for i, timestep in enumerate(__lowerCamelCase			):
										if i == len(__lowerCamelCase			) - 1:
														lowerCAmelCase__					:    List[str]												=					-1
										else:
														lowerCAmelCase__					:    int												=					timesteps[i + 1]

										lowerCAmelCase__					:    Tuple												=					scheduler.previous_timestep(__lowerCamelCase			)
										lowerCAmelCase__					:    int												=					prev_t.item()

										self.assertEqual(__lowerCamelCase		,__lowerCamelCase			)



		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					self.scheduler_classes[0]
						lowerCAmelCase__					:    Union[str, Any]												=					self.get_scheduler_config()
						lowerCAmelCase__					:    Optional[Any]												=					scheduler_class(**__lowerCamelCase			)

						lowerCAmelCase__					:    str												=					[1_00, 87, 50, 51, 0]

						with self.assertRaises(__lowerCamelCase		,msg='''`custom_timesteps` must be in descending order.'''			):
										scheduler.set_timesteps(timesteps=__lowerCamelCase			)



		def 		lowerCAmelCase__				(self			)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    int												=					self.scheduler_classes[0]
						lowerCAmelCase__					:    int												=					self.get_scheduler_config()
						lowerCAmelCase__					:    Optional[Any]												=					scheduler_class(**__lowerCamelCase			)

						lowerCAmelCase__					:    str												=					[1_00, 87, 50, 1, 0]
						lowerCAmelCase__					:    Tuple												=					len(__lowerCamelCase			)

						with self.assertRaises(__lowerCamelCase		,msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''			):
										scheduler.set_timesteps(num_inference_steps=__lowerCamelCase		,timesteps=__lowerCamelCase			)



		def 		lowerCAmelCase__				(self			)					->      Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    int												=					self.scheduler_classes[0]
						lowerCAmelCase__					:    Dict												=					self.get_scheduler_config()
						lowerCAmelCase__					:    List[Any]												=					scheduler_class(**__lowerCamelCase			)

						lowerCAmelCase__					:    Dict												=					[scheduler.config.num_train_timesteps]

						with self.assertRaises(
						    __lowerCamelCase		,msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''		,):
										scheduler.set_timesteps(timesteps=__lowerCamelCase			)






import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class 	lowerCamelCase__      (							lowerCamelCase__				,    unittest.TestCase):




		'''simple docstring'''



		snake_case_     =KandinskyVaaPriorPipeline
		snake_case_     =["""prompt"""]
		snake_case_     =["""prompt""", """negative_prompt"""]
		snake_case_     =[
		    """num_images_per_prompt""",
		    """generator""",
		    """num_inference_steps""",
		    """latents""",
		    """negative_prompt""",
		    """guidance_scale""",
		    """output_type""",
		    """return_dict""",
		]
		snake_case_     =False



		@property
		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						return 32



		@property
		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						return 32



		@property
		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						return self.time_input_dim



		@property
		def 		lowerCAmelCase__				(self			)					->      Optional[int]:

						"""simple docstring"""






						return self.time_input_dim * 4



		@property
		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""simple docstring"""






						return 1_00



		@property
		def 		lowerCAmelCase__				(self			)					->      Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)
						return tokenizer



		@property
		def 		lowerCAmelCase__				(self			)					->      str:

						"""simple docstring"""






						torch.manual_seed(0			)
						lowerCAmelCase__					:    str												=					CLIPTextConfig(
						    bos_token_id=0		,eos_token_id=2		,hidden_size=self.text_embedder_hidden_size		,projection_dim=self.text_embedder_hidden_size		,intermediate_size=37		,layer_norm_eps=1e-05		,num_attention_heads=4		,num_hidden_layers=5		,pad_token_id=1		,vocab_size=10_00		,)
						return CLIPTextModelWithProjection(__lowerCamelCase			)



		@property
		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						torch.manual_seed(0			)

						lowerCAmelCase__					:    List[str]												=					{
						    '''num_attention_heads''': 2,
						    '''attention_head_dim''': 12,
						    '''embedding_dim''': self.text_embedder_hidden_size,
						    '''num_layers''': 1,
						}

						lowerCAmelCase__					:    List[str]												=					PriorTransformer(**__lowerCamelCase			)
						# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
						lowerCAmelCase__					:    int												=					nn.Parameter(torch.ones(model.clip_std.shape			)			)
						return model



		@property
		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						torch.manual_seed(0			)
						lowerCAmelCase__					:    Any												=					CLIPVisionConfig(
						    hidden_size=self.text_embedder_hidden_size		,image_size=2_24		,projection_dim=self.text_embedder_hidden_size		,intermediate_size=37		,num_attention_heads=4		,num_channels=3		,num_hidden_layers=5		,patch_size=14		,)

						lowerCAmelCase__					:    int												=					CLIPVisionModelWithProjection(__lowerCamelCase			)
						return model



		@property
		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[Any]												=					CLIPImageProcessor(
						    crop_size=2_24		,do_center_crop=__lowerCamelCase		,do_normalize=__lowerCamelCase		,do_resize=__lowerCamelCase		,image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]		,image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]		,resample=3		,size=2_24		,)

						return image_processor



		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					self.dummy_prior
						lowerCAmelCase__					:    List[Any]												=					self.dummy_image_encoder
						lowerCAmelCase__					:    Optional[Any]												=					self.dummy_text_encoder
						lowerCAmelCase__					:    Optional[int]												=					self.dummy_tokenizer
						lowerCAmelCase__					:    str												=					self.dummy_image_processor

						lowerCAmelCase__					:    Union[str, Any]												=					UnCLIPScheduler(
						    variance_type='''fixed_small_log'''		,prediction_type='''sample'''		,num_train_timesteps=10_00		,clip_sample=__lowerCamelCase		,clip_sample_range=10.0		,)

						lowerCAmelCase__					:    int												=					{
						    '''prior''': prior,
						    '''image_encoder''': image_encoder,
						    '''text_encoder''': text_encoder,
						    '''tokenizer''': tokenizer,
						    '''scheduler''': scheduler,
						    '''image_processor''': image_processor,
						}

						return components



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase=0			)					->      int:

						"""simple docstring"""






						if str(__lowerCamelCase			).startswith('''mps'''			):
										lowerCAmelCase__					:    Dict												=					torch.manual_seed(__lowerCamelCase			)
						else:
										lowerCAmelCase__					:    Any												=					torch.Generator(device=__lowerCamelCase			).manual_seed(__lowerCamelCase			)
						lowerCAmelCase__					:    Optional[Any]												=					{
						    '''prompt''': '''horse''',
						    '''generator''': generator,
						    '''guidance_scale''': 4.0,
						    '''num_inference_steps''': 2,
						    '''output_type''': '''np''',
						}
						return inputs



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

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					'''cpu'''

						lowerCAmelCase__					:    Optional[int]												=					self.get_dummy_components()

						lowerCAmelCase__					:    int												=					self.pipeline_class(**__lowerCamelCase			)
						lowerCAmelCase__					:    int												=					pipe.to(__lowerCamelCase			)

						pipe.set_progress_bar_config(disable=__lowerCamelCase			)

						lowerCAmelCase__					:    List[Any]												=					pipe(**self.get_dummy_inputs(__lowerCamelCase			)			)
						lowerCAmelCase__					:    Union[str, Any]												=					output.image_embeds

						lowerCAmelCase__					:    Tuple												=					pipe(
						    **self.get_dummy_inputs(__lowerCamelCase			)		,return_dict=__lowerCamelCase		,)[0]

						lowerCAmelCase__					:    Union[str, Any]												=					image[0, -10:]
						lowerCAmelCase__					:    str												=					image_from_tuple[0, -10:]

						assert image.shape == (1, 32)

						lowerCAmelCase__					:    int												=					np.array(
						    [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]			)

						assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2



		@skip_mps
		def 		lowerCAmelCase__				(self			)					->      List[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					torch_device == '''cpu'''
						lowerCAmelCase__					:    List[Any]												=					True
						lowerCAmelCase__					:    Optional[int]												=					False

						self._test_inference_batch_single_identical(
						    test_max_difference=__lowerCamelCase		,relax_max_difference=__lowerCamelCase		,test_mean_pixel_difference=__lowerCamelCase		,)



		@skip_mps
		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    int												=					torch_device == '''cpu'''
						lowerCAmelCase__					:    int												=					False

						self._test_attention_slicing_forward_pass(
						    test_max_difference=__lowerCamelCase		,test_mean_pixel_difference=__lowerCamelCase		,)

