
'''simple docstring'''

def 	_snake_case						(      A_       :    Union[str, Any]     ,		A_       :    List[str]     ,		A_       :    Union[str, Any]     ,		A_       :    List[str]     ,		A_       :    Union[str, Any]     ,		A_       :    Union[str, Any]   ):
  """simple docstring"""







  if index == r:
    for j in range(_lowercase   ):
      print(data[j]     ,		end=""" """   )
    print(""" """   )
    return
  #  When no more elements are there to put in data[]
  if i >= n:
    return
  # current is included, put next at next location
  a_      :	Any      =							arr[i]
  combination_util(_lowercase     ,		_lowercase     ,		_lowercase     ,		index + 1     ,		_lowercase     ,		i + 1   )
  # current is excluded, replace it with
  # next (Note that i+1 is passed, but
  # index is not changed)
  combination_util(_lowercase     ,		_lowercase     ,		_lowercase     ,		_lowercase     ,		_lowercase     ,		i + 1   )
  # The main function that prints all combinations
  # of size r in arr[] of size n. This function
  # mainly uses combinationUtil()






def 	_snake_case						(      A_       :    int     ,		A_       :    Tuple     ,		A_       :    List[Any]   ):
  """simple docstring"""







  a_      :	Tuple      =							[0] * r
  # Print all combination using temporary array 'data[]'
  combination_util(_lowercase     ,		_lowercase     ,		_lowercase     ,		0     ,		_lowercase     ,		0   )


if __name__ == "__main__":
    # Driver code to check the function above
    __snake_case:  Union[str, Any]								=			[10, 20, 30, 40, 50]
    print_combination(arr, len(arr), 3)
    # This code is contributed by Ambuj sahu

'''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  _UpperCAmelCase ( lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					PriorTransformer
     a_    	=					"hidden_states"


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							4
       a_      :	List[str]      =							8
       a_      :	List[str]      =							7

       a_      :	Tuple      =							floats_tensor((batch_size, embedding_dim) ).to(lowerCAmelCase_ )

       a_      :	List[Any]      =							floats_tensor((batch_size, embedding_dim) ).to(lowerCAmelCase_ )
       a_      :	List[Any]      =							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 							_lowerCAmelCase  (      self							,		lowerCAmelCase_=0 ):

       '''simple docstring'''

       torch.manual_seed(lowerCAmelCase_ )
       a_      :	List[Any]      =							4
       a_      :	Union[str, Any]      =							8
       a_      :	Optional[int]      =							7

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

       a_      :	Optional[Any]      =							torch.randn((batch_size, embedding_dim) ).to(lowerCAmelCase_ )
       a_      :	Optional[Any]      =							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 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return (4, 8)


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return (4, 8)


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

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


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      , a_      :	Optional[Any]      =							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_ )
       a_      :	List[Any]      =							model(**self.dummy_input )[0]

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


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      , a_      :	Optional[int]      =							self.prepare_init_args_and_inputs_for_common()

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

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






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

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

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

       a_      :	Tuple      =							self.get_dummy_seed_input()

       with torch.no_grad():
         a_      :	List[str]      =							model(**lowerCAmelCase_ )[0]

       a_      :	Optional[Any]      =							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.
       a_      :	List[str]      =							torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239] )
       self.assertTrue(torch_all_close(lowerCAmelCase_							,		lowerCAmelCase_							,		rtol=1E-2 ) )







@slow
class  _UpperCAmelCase ( unittest.TestCase    ):

     """simple docstring"""

     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_=1							,		lowerCAmelCase_=7_68							,		lowerCAmelCase_=77							,		lowerCAmelCase_=0 ):

       '''simple docstring'''

       torch.manual_seed(lowerCAmelCase_ )
       a_      :	str      =							batch_size
       a_      :	Dict      =							embedding_dim
       a_      :	Tuple      =							num_embeddings

       a_      :	Optional[Any]      =							torch.randn((batch_size, embedding_dim) ).to(lowerCAmelCase_ )

       a_      :	List[str]      =							torch.randn((batch_size, embedding_dim) ).to(lowerCAmelCase_ )
       a_      :	List[Any]      =							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 							_lowerCAmelCase  (      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 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

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

       with torch.no_grad():
         a_      :	str      =							model(**lowerCAmelCase_ )[0]

       assert list(sample.shape ) == [1, 7_68]

       a_      :	int      =							sample[0, :8].flatten().cpu()
       print(lowerCAmelCase_ )
       a_      :	str      =							torch.tensor(lowerCAmelCase_ )

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


