




import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest
class 			a__    (					_UpperCAmelCase      ):
						_a							:    Optional[Any]					=    (DDPMScheduler,)






						def 				__SCREAMING_SNAKE_CASE(   self		,			**_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       {
											    """num_train_timesteps""": 1_0_0_0,
											    """beta_start""": 0.00_01,
											    """beta_end""": 0.02,
											    """beta_schedule""": """linear""",
											    """variance_type""": """fixed_small""",
											    """clip_sample""": True,
											}

											config.update(**lowercase_			)
											return config






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
																self.check_over_configs(num_train_timesteps=lowercase_			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											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=lowercase_		,			beta_end=lowercase_			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											for t in [0, 5_0_0, 9_9_9]:
																self.check_over_forward(time_step=lowercase_			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.scheduler_classes[0]
											__lowerCAmelCase				   =       self.get_scheduler_config()
											__lowerCAmelCase				   =       scheduler_class(**lowercase_			)

											assert torch.sum(torch.abs(scheduler._get_variance(0			) - 0.0			)			) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(4_8_7			) - 0.0_09_79			)			) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(9_9_9			) - 0.02			)			) < 1E-5






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.scheduler_classes[0]
											__lowerCAmelCase				   =       self.get_scheduler_config()
											__lowerCAmelCase				   =       scheduler_class(**lowercase_			)

											__lowerCAmelCase				   =       len(lowercase_			)

											__lowerCAmelCase				   =       self.dummy_model()
											__lowerCAmelCase				   =       self.dummy_sample_deter
											__lowerCAmelCase				   =       torch.manual_seed(0			)

											for t in reversed(range(lowercase_			)			):
																# 1. predict noise residual
																__lowerCAmelCase				   =       model(lowercase_		,			lowercase_			)

																# 2. predict previous mean of sample x_t-1
																__lowerCAmelCase				   =       scheduler.step(lowercase_		,			lowercase_		,			lowercase_		,			generator=lowercase_			).prev_sample

																# if t > 0:
																#     noise = self.dummy_sample_deter
																#     variance = scheduler.get_variance(t) ** (0.5) * noise
																#
																# sample = pred_prev_sample + variance
																__lowerCAmelCase				   =       pred_prev_sample

											__lowerCAmelCase				   =       torch.sum(torch.abs(lowercase_			)			)
											__lowerCAmelCase				   =       torch.mean(torch.abs(lowercase_			)			)

											assert abs(result_sum.item() - 2_5_8.9_6_0_6			) < 1E-2
											assert abs(result_mean.item() - 0.33_72			) < 1E-3






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.scheduler_classes[0]
											__lowerCAmelCase				   =       self.get_scheduler_config(prediction_type="v_prediction"			)
											__lowerCAmelCase				   =       scheduler_class(**lowercase_			)

											__lowerCAmelCase				   =       len(lowercase_			)

											__lowerCAmelCase				   =       self.dummy_model()
											__lowerCAmelCase				   =       self.dummy_sample_deter
											__lowerCAmelCase				   =       torch.manual_seed(0			)

											for t in reversed(range(lowercase_			)			):
																# 1. predict noise residual
																__lowerCAmelCase				   =       model(lowercase_		,			lowercase_			)

																# 2. predict previous mean of sample x_t-1
																__lowerCAmelCase				   =       scheduler.step(lowercase_		,			lowercase_		,			lowercase_		,			generator=lowercase_			).prev_sample

																# if t > 0:
																#     noise = self.dummy_sample_deter
																#     variance = scheduler.get_variance(t) ** (0.5) * noise
																#
																# sample = pred_prev_sample + variance
																__lowerCAmelCase				   =       pred_prev_sample

											__lowerCAmelCase				   =       torch.sum(torch.abs(lowercase_			)			)
											__lowerCAmelCase				   =       torch.mean(torch.abs(lowercase_			)			)

											assert abs(result_sum.item() - 2_0_2.0_2_9_6			) < 1E-2
											assert abs(result_mean.item() - 0.26_31			) < 1E-3






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.scheduler_classes[0]
											__lowerCAmelCase				   =       self.get_scheduler_config()
											__lowerCAmelCase				   =       scheduler_class(**lowercase_			)

											__lowerCAmelCase				   =       [1_0_0, 8_7, 5_0, 1, 0]

											scheduler.set_timesteps(timesteps=lowercase_			)

											__lowerCAmelCase				   =       scheduler.timesteps

											for i, timestep in enumerate(lowercase_			):
																if i == len(lowercase_			) - 1:
																					__lowerCAmelCase				   =       -1
																else:
																					__lowerCAmelCase				   =       timesteps[i + 1]

																__lowerCAmelCase				   =       scheduler.previous_timestep(lowercase_			)
																__lowerCAmelCase				   =       prev_t.item()

																self.assertEqual(lowercase_		,			lowercase_			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.scheduler_classes[0]
											__lowerCAmelCase				   =       self.get_scheduler_config()
											__lowerCAmelCase				   =       scheduler_class(**lowercase_			)

											__lowerCAmelCase				   =       [1_0_0, 8_7, 5_0, 5_1, 0]

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






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.scheduler_classes[0]
											__lowerCAmelCase				   =       self.get_scheduler_config()
											__lowerCAmelCase				   =       scheduler_class(**lowercase_			)

											__lowerCAmelCase				   =       [1_0_0, 8_7, 5_0, 1, 0]
											__lowerCAmelCase				   =       len(lowercase_			)

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






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.scheduler_classes[0]
											__lowerCAmelCase				   =       self.get_scheduler_config()
											__lowerCAmelCase				   =       scheduler_class(**lowercase_			)

											__lowerCAmelCase				   =       [scheduler.config.num_train_timesteps]

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





'''simple docstring'''




import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()





class     __magic_name__						(    _UpperCAmelCase,     unittest.TestCase):
			UpperCamelCase__				=       PriorTransformer
			UpperCamelCase__				=       '''hidden_states'''







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  int    ):
										lowercase_					:			Optional[Any]										=       4
										lowercase_					:			int										=       8
										lowercase_					:			Union[str, Any]										=       7

										lowercase_					:			List[str]										=       floats_tensor((batch_size, embedding_dim)    ).to(lowercase_    )

										lowercase_					:			Tuple										=       floats_tensor((batch_size, embedding_dim)    ).to(lowercase_    )
										lowercase_					:			List[str]										=       floats_tensor((batch_size, num_embeddings, embedding_dim)    ).to(lowercase_    )

										return {
										    "hidden_states": hidden_states,
										    "timestep": 2,
										    "proj_embedding": proj_embedding,
										    "encoder_hidden_states": encoder_hidden_states,
										}







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]				,			lowercase_				:  Optional[Any]=0    ):
										torch.manual_seed(lowercase_    )
										lowercase_					:			int										=       4
										lowercase_					:			Any										=       8
										lowercase_					:			Tuple										=       7

										lowercase_					:			Union[str, Any]										=       torch.randn((batch_size, embedding_dim)    ).to(lowercase_    )

										lowercase_					:			Tuple										=       torch.randn((batch_size, embedding_dim)    ).to(lowercase_    )
										lowercase_					:			Union[str, Any]										=       torch.randn((batch_size, num_embeddings, embedding_dim)    ).to(lowercase_    )

										return {
										    "hidden_states": hidden_states,
										    "timestep": 2,
										    "proj_embedding": proj_embedding,
										    "encoder_hidden_states": encoder_hidden_states,
										}







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  str    ):
										return (4, 8)







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										return (4, 8)







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										lowercase_					:			Optional[Any]										=       {
										    """num_attention_heads""": 2,
										    """attention_head_dim""": 4,
										    """num_layers""": 2,
										    """embedding_dim""": 8,
										    """num_embeddings""": 7,
										    """additional_embeddings""": 4,
										}
										lowercase_					:			List[Any]										=       self.dummy_input
										return init_dict, inputs_dict







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]    ):
										lowercase_						,						lowercase_					:			Tuple										=       PriorTransformer.from_pretrained(
										    """hf-internal-testing/prior-dummy"""				,			output_loading_info=lowercase_    )
										self.assertIsNotNone(lowercase_    )
										self.assertEqual(len(loading_info["""missing_keys"""]    )				,			0    )

										model.to(lowercase_    )
										lowercase_					:			Optional[int]										=       model(**self.dummy_input    )[0]

										assert hidden_states is not None, "Make sure output is not None"







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any    ):
										lowercase_						,						lowercase_					:			str										=       self.prepare_init_args_and_inputs_for_common()

										lowercase_					:			List[Any]										=       self.model_class(**lowercase_    )
										lowercase_					:			Any										=       inspect.signature(model.forward    )
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										lowercase_					:			Any										=       [*signature.parameters.keys()]

										lowercase_					:			Optional[Any]										=       ["""hidden_states""", """timestep"""]
										self.assertListEqual(arg_names[:2]				,			lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]    ):
										lowercase_					:			Optional[Any]										=       PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy"""    )
										lowercase_					:			Any										=       model.to(lowercase_    )

										if hasattr(lowercase_				,			"""set_default_attn_processor"""    ):
																	model.set_default_attn_processor()

										lowercase_					:			Any										=       self.get_dummy_seed_input()

										with torch.no_grad():
																	lowercase_					:			List[str]										=       model(**lowercase_    )[0]

										lowercase_					:			Tuple										=       output[0, :5].flatten().cpu()
										print(lowercase_    )

										# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
										# the expected output slices are not the same for CPU and GPU.
										lowercase_					:			Dict										=       torch.tensor([-1.34_36, -0.28_70, 0.75_38, 0.43_68, -0.02_39]    )
										self.assertTrue(torch_all_close(lowercase_				,			lowercase_				,			rtol=1E-2    )    )





@slow
class     __magic_name__						(    unittest.TestCase):







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]				,			lowercase_				:  Union[str, Any]=1				,			lowercase_				:  Optional[Any]=768				,			lowercase_				:  Tuple=77				,			lowercase_				:  Optional[int]=0    ):
										torch.manual_seed(lowercase_    )
										lowercase_					:			Optional[Any]										=       batch_size
										lowercase_					:			int										=       embedding_dim
										lowercase_					:			int										=       num_embeddings

										lowercase_					:			Dict										=       torch.randn((batch_size, embedding_dim)    ).to(lowercase_    )

										lowercase_					:			Any										=       torch.randn((batch_size, embedding_dim)    ).to(lowercase_    )
										lowercase_					:			List[Any]										=       torch.randn((batch_size, num_embeddings, embedding_dim)    ).to(lowercase_    )

										return {
										    "hidden_states": hidden_states,
										    "timestep": 2,
										    "proj_embedding": proj_embedding,
										    "encoder_hidden_states": encoder_hidden_states,
										}







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







			@parameterized.expand(
			    [
			        # fmt: off
			        [13, [-0.58_61,  0.12_83, -0.09_31,  0.08_82,  0.44_76,  0.13_29, -0.04_98,  0.06_40]],
			        [37, [-0.49_13,  0.01_10, -0.04_83,  0.05_41,  0.49_54, -0.01_70,  0.03_54,  0.16_51]],
			        # fmt: on
			    ]    )
			def 	SCREAMING_SNAKE_CASE_     (	self				:  int				,			lowercase_				:  Any				,			lowercase_				:  List[str]    ):
										lowercase_					:			List[Any]										=       PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior"""				,			subfolder="""prior"""    )
										model.to(lowercase_    )
										lowercase_					:			Optional[Any]										=       self.get_dummy_seed_input(seed=lowercase_    )

										with torch.no_grad():
																	lowercase_					:			Tuple										=       model(**lowercase_    )[0]

										assert list(sample.shape    ) == [1, 768]

										lowercase_					:			Union[str, Any]										=       sample[0, :8].flatten().cpu()
										print(lowercase_    )
										lowercase_					:			Optional[Any]										=       torch.tensor(lowercase_    )

										assert torch_all_close(lowercase_				,			lowercase_				,			atol=1E-3    )





