




"""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  lowercase(					__a	,      unittest.TestCase			):







 '''simple docstring'''





 lowercase__			 = PriorTransformer
 lowercase__			 = "hidden_states"


 @property
 def 			UpperCamelCase_       (    self:		Optional[int]		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  4
   _snake_case							:		List[Any]								=  8
   _snake_case							:		int								=  7

   _snake_case							:		str								=  floats_tensor((batch_size, embedding_dim)		).to(a_		)

   _snake_case							:		Union[str, Any]								=  floats_tensor((batch_size, embedding_dim)		).to(a_		)
   _snake_case							:		Dict								=  floats_tensor((batch_size, num_embeddings, embedding_dim)		).to(a_		)

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


 def 			UpperCamelCase_       (    self:		List[Any],				a_:		Tuple=0		):




   '''simple docstring'''



   torch.manual_seed(a_		)
   _snake_case							:		List[Any]								=  4
   _snake_case							:		Dict								=  8
   _snake_case							:		Union[str, Any]								=  7

   _snake_case							:		List[Any]								=  torch.randn((batch_size, embedding_dim)		).to(a_		)

   _snake_case							:		Optional[int]								=  torch.randn((batch_size, embedding_dim)		).to(a_		)
   _snake_case							:		Union[str, Any]								=  torch.randn((batch_size, num_embeddings, embedding_dim)		).to(a_		)

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


 @property
 def 			UpperCamelCase_       (    self:		int		):




   '''simple docstring'''



   return (4, 8)


 @property
 def 			UpperCamelCase_       (    self:		List[str]		):




   '''simple docstring'''



   return (4, 8)


 def 			UpperCamelCase_       (    self:		List[str]		):




   '''simple docstring'''



   _snake_case							:		Any								=  {
       """num_attention_heads""": 2,
       """attention_head_dim""": 4,
       """num_layers""": 2,
       """embedding_dim""": 8,
       """num_embeddings""": 7,
       """additional_embeddings""": 4,
   }
   _snake_case							:		Optional[Any]								=  self.dummy_input
   return init_dict, inputs_dict


 def 			UpperCamelCase_       (    self:		List[str]		):




   '''simple docstring'''



   _snake_case		, _snake_case							:		Union[str, Any]								=  PriorTransformer.from_pretrained(
       """hf-internal-testing/prior-dummy""",				output_loading_info=a_		)
   self.assertIsNotNone(a_		)
   self.assertEqual(len(loading_info["""missing_keys"""]		),				0		)

   model.to(a_		)
   _snake_case							:		Any								=  model(**self.dummy_input		)[0]

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


 def 			UpperCamelCase_       (    self:		Dict		):




   '''simple docstring'''



   _snake_case		, _snake_case							:		str								=  self.prepare_init_args_and_inputs_for_common()

   _snake_case							:		Optional[int]								=  self.model_class(**a_		)
   _snake_case							:		List[str]								=  inspect.signature(model.forward		)
   # signature.parameters is an OrderedDict => so arg_names order is deterministic
   _snake_case							:		str								=  [*signature.parameters.keys()]

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


 def 			UpperCamelCase_       (    self:		int		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy"""		)
   _snake_case							:		Union[str, Any]								=  model.to(a_		)

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

   _snake_case							:		int								=  self.get_dummy_seed_input()

   with torch.no_grad():
     _snake_case							:		Optional[Any]								=  model(**a_		)[0]

   _snake_case							:		Optional[int]								=  output[0, :5].flatten().cpu()
   print(a_		)

   # 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							:		Optional[int]								=  torch.tensor([-1.3_436, -0.2_870, 0.7_538, 0.4_368, -0.0_239]		)
   self.assertTrue(torch_all_close(a_,				a_,				rtol=1E-2		)		)







@slow
class  lowercase(					unittest.TestCase			):







 '''simple docstring'''





 def 			UpperCamelCase_       (    self:		List[Any],				a_:		Union[str, Any]=1,				a_:		List[str]=768,				a_:		str=77,				a_:		List[Any]=0		):




   '''simple docstring'''



   torch.manual_seed(a_		)
   _snake_case							:		Dict								=  batch_size
   _snake_case							:		Optional[int]								=  embedding_dim
   _snake_case							:		Optional[int]								=  num_embeddings

   _snake_case							:		List[str]								=  torch.randn((batch_size, embedding_dim)		).to(a_		)

   _snake_case							:		Optional[int]								=  torch.randn((batch_size, embedding_dim)		).to(a_		)
   _snake_case							:		int								=  torch.randn((batch_size, num_embeddings, embedding_dim)		).to(a_		)

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


 def 			UpperCamelCase_       (    self:		Any		):




   '''simple docstring'''



   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()


 @parameterized.expand(
     [
         # fmt: off
         [13, [-0.5_861,  0.1_283, -0.0_931,  0.0_882,  0.4_476,  0.1_329, -0.0_498,  0.0_640]],
         [37, [-0.4_913,  0.0_110, -0.0_483,  0.0_541,  0.4_954, -0.0_170,  0.0_354,  0.1_651]],
         # fmt: on
     ]		)
 def 			UpperCamelCase_       (    self:		Dict,				a_:		Optional[Any],				a_:		List[Any]		):




   '''simple docstring'''



   _snake_case							:		Dict								=  PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior""",				subfolder="""prior"""		)
   model.to(a_		)
   _snake_case							:		List[Any]								=  self.get_dummy_seed_input(seed=a_		)

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

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

   _snake_case							:		Dict								=  sample[0, :8].flatten().cpu()
   print(a_		)
   _snake_case							:		List[str]								=  torch.tensor(a_		)

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








"""simple docstring"""


def  UpperCAmelCase__	(snake_case__							: int = 10_00   ):
  """simple docstring"""

  _snake_case		, _snake_case							:		List[Any]								=  1, 1
  _snake_case							:		str								=  []
  for i in range(1		,       n + 1   ):
    _snake_case							:		Any								=  prev_numerator + 2 * prev_denominator
    _snake_case							:		Optional[Any]								=  prev_numerator + prev_denominator
    if len(str(snake_case__   )   ) > len(str(snake_case__   )   ):
      result.append(snake_case__   )
    _snake_case							:		int								=  numerator
    _snake_case							:		Any								=  denominator

  return len(snake_case__   )


if __name__ == "__main__":
     print(F'''{solution() = }''')


