



'''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 				snake_case (UpperCamelCase		,      unittest.TestCase   ):
       lowerCAmelCase__  :Tuple						   =					PriorTransformer
       lowerCAmelCase__  :List[Any]						   =					"hidden_states"



       @property
       def 							_a      (				self      )  ->							Optional[Any]:
           lowercase__		       =						4
           lowercase__		       =						8
           lowercase__		       =						7

           lowercase__		       =						floats_tensor((batch_size, embedding_dim)      ).to(UpperCAmelCase_      )

           lowercase__		       =						floats_tensor((batch_size, embedding_dim)      ).to(UpperCAmelCase_      )
           lowercase__		       =						floats_tensor((batch_size, num_embeddings, embedding_dim)      ).to(UpperCAmelCase_      )

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



       def 							_a      (				self     ,UpperCAmelCase_=0      )  ->							Any:
           torch.manual_seed(UpperCAmelCase_      )
           lowercase__		       =						4
           lowercase__		       =						8
           lowercase__		       =						7

           lowercase__		       =						torch.randn((batch_size, embedding_dim)      ).to(UpperCAmelCase_      )

           lowercase__		       =						torch.randn((batch_size, embedding_dim)      ).to(UpperCAmelCase_      )
           lowercase__		       =						torch.randn((batch_size, num_embeddings, embedding_dim)      ).to(UpperCAmelCase_      )

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



       @property
       def 							_a      (				self      )  ->							List[str]:
           return (4, 8)



       @property
       def 							_a      (				self      )  ->							Dict:
           return (4, 8)



       def 							_a      (				self      )  ->							Optional[int]:
           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      )  ->							int:
           lowercase__       ,       lowercase__		       =						PriorTransformer.from_pretrained(
               "hf-internal-testing/prior-dummy"     ,output_loading_info=UpperCAmelCase_      )
           self.assertIsNotNone(UpperCAmelCase_      )
           self.assertEqual(len(loading_info["missing_keys"]      )     ,0      )

           model.to(UpperCAmelCase_      )
           lowercase__		       =						model(**self.dummy_input      )[0]

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



       def 							_a      (				self      )  ->							Any:
           lowercase__       ,       lowercase__		       =						self.prepare_init_args_and_inputs_for_common()

           lowercase__		       =						self.model_class(**UpperCAmelCase_      )
           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]     ,UpperCAmelCase_      )







       def 							_a      (				self      )  ->							Optional[Any]:
           lowercase__		       =						PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy"      )
           lowercase__		       =						model.to(UpperCAmelCase_      )

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

           lowercase__		       =						self.get_dummy_seed_input()

           with torch.no_grad():
               lowercase__		       =						model(**UpperCAmelCase_      )[0]

           lowercase__		       =						output[0, :5].flatten().cpu()
           print(UpperCAmelCase_      )

           # 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(UpperCAmelCase_     ,UpperCAmelCase_     ,rtol=1E-2      )      )





@slow
class 				snake_case (unittest.TestCase   ):



       def 							_a      (				self     ,UpperCAmelCase_=1     ,UpperCAmelCase_=768     ,UpperCAmelCase_=77     ,UpperCAmelCase_=0      )  ->							Dict:
           torch.manual_seed(UpperCAmelCase_      )
           lowercase__		       =						batch_size
           lowercase__		       =						embedding_dim
           lowercase__		       =						num_embeddings

           lowercase__		       =						torch.randn((batch_size, embedding_dim)      ).to(UpperCAmelCase_      )

           lowercase__		       =						torch.randn((batch_size, embedding_dim)      ).to(UpperCAmelCase_      )
           lowercase__		       =						torch.randn((batch_size, num_embeddings, embedding_dim)      ).to(UpperCAmelCase_      )

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



       def 							_a      (				self      )  ->							Tuple:
           # 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 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Optional[int]:
           lowercase__		       =						PriorTransformer.from_pretrained("kandinsky-community/kandinsky-2-1-prior"     ,subfolder="prior"      )
           model.to(UpperCAmelCase_      )
           lowercase__		       =						self.get_dummy_seed_input(seed=UpperCAmelCase_      )

           with torch.no_grad():
               lowercase__		       =						model(**UpperCAmelCase_      )[0]

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

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

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









'''simple docstring'''





print((lambda quine: quine % quine)("print((lambda quine: quine %% quine)(%r))"))




