




'''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        _a    (						__lowerCAmelCase , unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:   Dict       =       PriorTransformer
     SCREAMING_SNAKE_CASE_							:   Tuple       =       """hidden_states"""






     @property
     def     _lowercase     (			self   )							->      List[str]:
          _snake_case							=				4
          _snake_case							=				8
          _snake_case							=				7

          _snake_case							=				floats_tensor((batch_size, embedding_dim)   ).to(_SCREAMING_SNAKE_CASE   )

          _snake_case							=				floats_tensor((batch_size, embedding_dim)   ).to(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				floats_tensor((batch_size, num_embeddings, embedding_dim)   ).to(_SCREAMING_SNAKE_CASE   )

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






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE=0   )							->      Any:
          torch.manual_seed(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				4
          _snake_case							=				8
          _snake_case							=				7

          _snake_case							=				torch.randn((batch_size, embedding_dim)   ).to(_SCREAMING_SNAKE_CASE   )

          _snake_case							=				torch.randn((batch_size, embedding_dim)   ).to(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				torch.randn((batch_size, num_embeddings, embedding_dim)   ).to(_SCREAMING_SNAKE_CASE   )

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






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






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






     def     _lowercase     (			self   )							->      Any:
          _snake_case							=				{
              "num_attention_heads": 2,
              "attention_head_dim": 4,
              "num_layers": 2,
              "embedding_dim": 8,
              "num_embeddings": 7,
              "additional_embeddings": 4,
          }
          _snake_case							=				self.dummy_input
          return init_dict, inputs_dict






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

          model.to(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				model(**self.dummy_input   )[0]

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






     def     _lowercase     (			self   )							->      Optional[Any]:
          _snake_case							, _snake_case							=				self.prepare_init_args_and_inputs_for_common()

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

          _snake_case							=				["hidden_states", "timestep"]
          self.assertListEqual(arg_names[:2] ,_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      str:
          _snake_case							=				PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy"   )
          _snake_case							=				model.to(_SCREAMING_SNAKE_CASE   )

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

          _snake_case							=				self.get_dummy_seed_input()

          with torch.no_grad():
               _snake_case							=				model(**_SCREAMING_SNAKE_CASE   )[0]

          _snake_case							=				output[0, :5].flatten().cpu()
          print(_SCREAMING_SNAKE_CASE   )

          # 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.
          _snake_case							=				torch.tensor([-1.3_4_3_6, -0.2_8_7_0, 0.7_5_3_8, 0.4_3_6_8, -0.0_2_3_9]   )
          self.assertTrue(torch_all_close(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,rtol=1e-2   )   )







@slow
class        _a    (						unittest.TestCase							):






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE=1 ,_SCREAMING_SNAKE_CASE=768 ,_SCREAMING_SNAKE_CASE=77 ,_SCREAMING_SNAKE_CASE=0   )							->      Any:
          torch.manual_seed(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				batch_size
          _snake_case							=				embedding_dim
          _snake_case							=				num_embeddings

          _snake_case							=				torch.randn((batch_size, embedding_dim)   ).to(_SCREAMING_SNAKE_CASE   )

          _snake_case							=				torch.randn((batch_size, embedding_dim)   ).to(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				torch.randn((batch_size, num_embeddings, embedding_dim)   ).to(_SCREAMING_SNAKE_CASE   )

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






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






     @parameterized.expand(
         [
             # fmt: off
             [13, [-0.5_8_6_1,  0.1_2_8_3, -0.0_9_3_1,  0.0_8_8_2,  0.4_4_7_6,  0.1_3_2_9, -0.0_4_9_8,  0.0_6_4_0]],
             [37, [-0.4_9_1_3,  0.0_1_1_0, -0.0_4_8_3,  0.0_5_4_1,  0.4_9_5_4, -0.0_1_7_0,  0.0_3_5_4,  0.1_6_5_1]],
             # fmt: on
         ]   )
     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      Tuple:
          _snake_case							=				PriorTransformer.from_pretrained("kandinsky-community/kandinsky-2-1-prior" ,subfolder="prior"   )
          model.to(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				self.get_dummy_seed_input(seed=_SCREAMING_SNAKE_CASE   )

          with torch.no_grad():
               _snake_case							=				model(**_SCREAMING_SNAKE_CASE   )[0]

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

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

          assert torch_all_close(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,atol=1e-3   )








'''simple docstring'''





def      __a     (       _UpperCamelCase:   int   )						->			str:

     """simple docstring"""




     if number > 0:
          raise ValueError("input must be a negative integer"   )
     _snake_case							=				len(bin(_UpperCamelCase   )[3:]   )
     _snake_case							=				bin(abs(_UpperCamelCase   ) - (1 << binary_number_length)   )[3:]
     _snake_case							=				(
         (
             "1"
             + "0" * (binary_number_length - len(_UpperCamelCase   ))
             + twos_complement_number
         )
         if number < 0
         else "0"
     )
     return "0b" + twos_complement_number


if __name__ == "__main__":
   import doctest

   doctest.testmod()


