






from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		_snake_case    :    UNetaDModel
		_snake_case    :    ScoreSdeVeScheduler



		def __init__(							self				,		_UpperCamelCase				,		_UpperCamelCase   )	->							int:
							super().__init__()
							self.register_modules(unet=_UpperCamelCase				,		scheduler=_UpperCamelCase   )



		@torch.no_grad()
		def __call__(							self				,		_UpperCamelCase = 1				,		_UpperCamelCase = 2_0_0_0				,		_UpperCamelCase = None				,		_UpperCamelCase = "pil"				,		_UpperCamelCase = True				,		**_UpperCamelCase				,		)	->							Union[ImagePipelineOutput, Tuple]:
							UpperCAmelCase_    :      List[Any]            =						self.unet.config.sample_size
							UpperCAmelCase_    :      Optional[Any]            =						(batch_size, 3, img_size, img_size)

							UpperCAmelCase_    :      Dict            =						self.unet

							UpperCAmelCase_    :      Optional[Any]            =						randn_tensor(_UpperCamelCase				,		generator=_UpperCamelCase   ) * self.scheduler.init_noise_sigma
							UpperCAmelCase_    :      List[Any]            =						sample.to(self.device   )

							self.scheduler.set_timesteps(_UpperCamelCase   )
							self.scheduler.set_sigmas(_UpperCamelCase   )

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps   )   ):
												UpperCAmelCase_    :      str            =						self.scheduler.sigmas[i] * torch.ones(shape[0]				,		device=self.device   )

												# correction step
												for _ in range(self.scheduler.config.correct_steps   ):
																	UpperCAmelCase_    :      str            =						self.unet(_UpperCamelCase				,		_UpperCamelCase   ).sample
																	UpperCAmelCase_    :      Tuple            =						self.scheduler.step_correct(_UpperCamelCase				,		_UpperCamelCase				,		generator=_UpperCamelCase   ).prev_sample

												# prediction step
												UpperCAmelCase_    :      str            =						model(_UpperCamelCase				,		_UpperCamelCase   ).sample
												UpperCAmelCase_    :      List[Any]            =						self.scheduler.step_pred(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		generator=_UpperCamelCase   )

												UpperCAmelCase_       ,  UpperCAmelCase_    :      Any            =						output.prev_sample, output.prev_sample_mean

							UpperCAmelCase_    :      Any            =						sample_mean.clamp(0				,		1   )
							UpperCAmelCase_    :      Optional[Any]            =						sample.cpu().permute(0				,		2				,		3				,		1   ).numpy()
							if output_type == "pil":
												UpperCAmelCase_    :      Tuple            =						self.numpy_to_pil(_UpperCamelCase   )

							if not return_dict:
												return (sample,)

							return ImagePipelineOutput(images=_UpperCamelCase   )










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       A    (   A_					,				unittest.TestCase ):
							UpperCamelCase_   :							Any       =PriorTransformer
							UpperCamelCase_   :							List[str]       ='''hidden_states'''


							@property
							def 			_A       (self					):
									__lowercase=			4
									__lowercase=			8
									__lowercase=			7

									__lowercase=			floats_tensor((batch_size, embedding_dim)					).to(lowerCAmelCase					)

									__lowercase=			floats_tensor((batch_size, embedding_dim)					).to(lowerCAmelCase					)
									__lowercase=			floats_tensor((batch_size, num_embeddings, embedding_dim)					).to(lowerCAmelCase					)

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


							def 			_A       (self      ,				lowerCAmelCase=0					):
									torch.manual_seed(lowerCAmelCase					)
									__lowercase=			4
									__lowercase=			8
									__lowercase=			7

									__lowercase=			torch.randn((batch_size, embedding_dim)					).to(lowerCAmelCase					)

									__lowercase=			torch.randn((batch_size, embedding_dim)					).to(lowerCAmelCase					)
									__lowercase=			torch.randn((batch_size, num_embeddings, embedding_dim)					).to(lowerCAmelCase					)

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


							@property
							def 			_A       (self					):
									return (4, 8)


							@property
							def 			_A       (self					):
									return (4, 8)


							def 			_A       (self					):
									__lowercase=			{
									    'num_attention_heads': 2,
									    'attention_head_dim': 4,
									    'num_layers': 2,
									    'embedding_dim': 8,
									    'num_embeddings': 7,
									    'additional_embeddings': 4,
									}
									__lowercase=			self.dummy_input
									return init_dict, inputs_dict


							def 			_A       (self					):
									__lowercase,							__lowercase=			PriorTransformer.from_pretrained(
									    'hf-internal-testing/prior-dummy'      ,				output_loading_info=lowerCAmelCase					)
									self.assertIsNotNone(lowerCAmelCase					)
									self.assertEqual(len(loading_info['missing_keys']					)      ,				0					)

									model.to(lowerCAmelCase					)
									__lowercase=			model(**self.dummy_input					)[0]

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


							def 			_A       (self					):
									__lowercase,							__lowercase=			self.prepare_init_args_and_inputs_for_common()

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

									__lowercase=			['hidden_states', 'timestep']
									self.assertListEqual(arg_names[:2]      ,				lowerCAmelCase					)







							def 			_A       (self					):
									__lowercase=			PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'					)
									__lowercase=			model.to(lowerCAmelCase					)

									if hasattr(lowerCAmelCase      ,				'set_default_attn_processor'					):
											model.set_default_attn_processor()

									__lowercase=			self.get_dummy_seed_input()

									with torch.no_grad():
											__lowercase=			model(**lowerCAmelCase					)[0]

									__lowercase=			output[0, :5].flatten().cpu()
									print(lowerCAmelCase					)

									# 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=			torch.tensor([-1.34_36, -0.28_70, 0.75_38, 0.43_68, -0.02_39]					)
									self.assertTrue(torch_all_close(lowerCAmelCase      ,				lowerCAmelCase      ,				rtol=1E-2					)					)





@slow
class       A    (   unittest.TestCase ):


							def 			_A       (self      ,				lowerCAmelCase=1      ,				lowerCAmelCase=7_6_8      ,				lowerCAmelCase=7_7      ,				lowerCAmelCase=0					):
									torch.manual_seed(lowerCAmelCase					)
									__lowercase=			batch_size
									__lowercase=			embedding_dim
									__lowercase=			num_embeddings

									__lowercase=			torch.randn((batch_size, embedding_dim)					).to(lowerCAmelCase					)

									__lowercase=			torch.randn((batch_size, embedding_dim)					).to(lowerCAmelCase					)
									__lowercase=			torch.randn((batch_size, num_embeddings, embedding_dim)					).to(lowerCAmelCase					)

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


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







							@parameterized.expand(
							    [
							        # fmt: off
							        [1_3, [-0.58_61,  0.12_83, -0.09_31,  0.08_82,  0.44_76,  0.13_29, -0.04_98,  0.06_40]],
							        [3_7, [-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 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase					):
									__lowercase=			PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'      ,				subfolder='prior'					)
									model.to(lowerCAmelCase					)
									__lowercase=			self.get_dummy_seed_input(seed=lowerCAmelCase					)

									with torch.no_grad():
											__lowercase=			model(**lowerCAmelCase					)[0]

									assert list(sample.shape					) == [1, 7_6_8]

									__lowercase=			sample[0, :8].flatten().cpu()
									print(lowerCAmelCase					)
									__lowercase=			torch.tensor(lowerCAmelCase					)

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






