



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 					__lowerCAmelCase (     lowerCamelCase__       ,						unittest.TestCase       ):
				__lowerCamelCase =				PriorTransformer
				__lowerCamelCase =				'hidden_states'




				@property
				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    4
									_lowerCAmelCase						=    8
									_lowerCAmelCase						=    7

									_lowerCAmelCase						=    floats_tensor((batch_size, embedding_dim)		).to(lowercase_		)

									_lowerCAmelCase						=    floats_tensor((batch_size, embedding_dim)		).to(lowercase_		)
									_lowerCAmelCase						=    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 							snake_case   (							self						,				_snake_case=0		):



									"""simple docstring"""





									torch.manual_seed(lowercase_		)
									_lowerCAmelCase						=    4
									_lowerCAmelCase						=    8
									_lowerCAmelCase						=    7

									_lowerCAmelCase						=    torch.randn((batch_size, embedding_dim)		).to(lowercase_		)

									_lowerCAmelCase						=    torch.randn((batch_size, embedding_dim)		).to(lowercase_		)
									_lowerCAmelCase						=    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 							snake_case   (							self		):



									"""simple docstring"""





									return (4, 8)




				@property
				def 							snake_case   (							self		):



									"""simple docstring"""





									return (4, 8)




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    {
									    """num_attention_heads""": 2,
									    """attention_head_dim""": 4,
									    """num_layers""": 2,
									    """embedding_dim""": 8,
									    """num_embeddings""": 7,
									    """additional_embeddings""": 4,
									}
									_lowerCAmelCase						=    self.dummy_input
									return init_dict, inputs_dict




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase	,	_lowerCAmelCase						=    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_		)
									_lowerCAmelCase						=    model(**self.dummy_input		)[0]

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




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase	,	_lowerCAmelCase						=    self.prepare_init_args_and_inputs_for_common()

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

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




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy"""		)
									_lowerCAmelCase						=    model.to(lowercase_		)

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

									_lowerCAmelCase						=    self.get_dummy_seed_input()

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

									_lowerCAmelCase						=    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.
									_lowerCAmelCase						=    torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239]		)
									self.assertTrue(torch_all_close(lowercase_						,				lowercase_						,				rtol=1e-2		)		)





@slow
class 					__lowerCAmelCase (     unittest.TestCase       ):




				def 							snake_case   (							self						,				_snake_case=1						,				_snake_case=768						,				_snake_case=77						,				_snake_case=0		):



									"""simple docstring"""





									torch.manual_seed(lowercase_		)
									_lowerCAmelCase						=    batch_size
									_lowerCAmelCase						=    embedding_dim
									_lowerCAmelCase						=    num_embeddings

									_lowerCAmelCase						=    torch.randn((batch_size, embedding_dim)		).to(lowercase_		)

									_lowerCAmelCase						=    torch.randn((batch_size, embedding_dim)		).to(lowercase_		)
									_lowerCAmelCase						=    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 							snake_case   (							self		):



									"""simple docstring"""





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




				@parameterized.expand(
				    [
				        # fmt: off
				        [13, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
				        [37, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
				        # fmt: on
				    ]		)
				def 							snake_case   (							self						,				_snake_case						,				_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase						=    PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior"""						,				subfolder="""prior"""		)
									model.to(lowercase_		)
									_lowerCAmelCase						=    self.get_dummy_seed_input(seed=lowercase_		)

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

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

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

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






def 			UpperCAmelCase__				(			_A      : dict							):





  '''simple docstring'''







  a__  =set()
  # To detect a back edge, keep track of vertices currently in the recursion stack
  a__  =set()
  return any(
      node not in visited and depth_first_search(_A	,      _A	,      _A	,      _A							)
      for node in graph							)




def 			UpperCAmelCase__				(			_A      : dict	,      _A      : int	,      _A      : set	,      _A      : set							):





  '''simple docstring'''







  visited.add(_A							)
  rec_stk.add(_A							)

  for node in graph[vertex]:
    if node not in visited:
      if depth_first_search(_A	,      _A	,      _A	,      _A							):
        return True
    elif node in rec_stk:
      return True

    # The node needs to be removed from recursion stack before function ends
  rec_stk.remove(_A							)
  return False


if __name__ == "__main__":
       from doctest import testmod

       testmod()
