


"""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 _lowerCAmelCase				(      snake_case_    ,							unittest.TestCase	):
   __UpperCAmelCase :       Tuple							  =  PriorTransformer
   __UpperCAmelCase :       List[str]							  =  '''hidden_states'''







   @property
   def  lowerCamelCase		(	self						)	->		List[Any]:

       '''simple docstring'''




       snake_case    :      Dict      					=    4
       snake_case    :      Any      					=    8
       snake_case    :      Dict      					=    7

       snake_case    :      Union[str, Any]      					=    floats_tensor((batch_size, embedding_dim)						).to(UpperCamelCase__						)

       snake_case    :      List[str]      					=    floats_tensor((batch_size, embedding_dim)						).to(UpperCamelCase__						)
       snake_case    :      str      					=    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  lowerCamelCase		(	self						,	UpperCamelCase__=0						)	->		Union[str, Any]:

       '''simple docstring'''




       torch.manual_seed(UpperCamelCase__						)
       snake_case    :      Dict      					=    4
       snake_case    :      Union[str, Any]      					=    8
       snake_case    :      Dict      					=    7

       snake_case    :      Tuple      					=    torch.randn((batch_size, embedding_dim)						).to(UpperCamelCase__						)

       snake_case    :      Optional[Any]      					=    torch.randn((batch_size, embedding_dim)						).to(UpperCamelCase__						)
       snake_case    :      Any      					=    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  lowerCamelCase		(	self						)	->		Optional[int]:

       '''simple docstring'''




       return (4, 8)







   @property
   def  lowerCamelCase		(	self						)	->		Union[str, Any]:

       '''simple docstring'''




       return (4, 8)







   def  lowerCamelCase		(	self						)	->		Tuple:

       '''simple docstring'''




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







   def  lowerCamelCase		(	self						)	->		int:

       '''simple docstring'''




       snake_case					,snake_case    :      Union[str, Any]      					=    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__						)
       snake_case    :      Any      					=    model(**self.dummy_input						)[0]

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







   def  lowerCamelCase		(	self						)	->		List[Any]:

       '''simple docstring'''




       snake_case					,snake_case    :      int      					=    self.prepare_init_args_and_inputs_for_common()

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

       snake_case    :      Tuple      					=    ["hidden_states", "timestep"]
       self.assertListEqual(arg_names[:2]						,	UpperCamelCase__						)







   def  lowerCamelCase		(	self						)	->		Optional[Any]:

       '''simple docstring'''




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

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

       snake_case    :      Tuple      					=    self.get_dummy_seed_input()

       with torch.no_grad():
           snake_case    :      Union[str, Any]      					=    model(**UpperCamelCase__						)[0]

       snake_case    :      Optional[Any]      					=    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.
       snake_case    :      Dict      					=    torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239]						)
       self.assertTrue(torch_all_close(UpperCamelCase__						,	UpperCamelCase__						,	rtol=1e-2						)						)







@slow
class _lowerCAmelCase				(      unittest.TestCase	):







   def  lowerCamelCase		(	self						,	UpperCamelCase__=1						,	UpperCamelCase__=768						,	UpperCamelCase__=77						,	UpperCamelCase__=0						)	->		str:

       '''simple docstring'''




       torch.manual_seed(UpperCamelCase__						)
       snake_case    :      Optional[int]      					=    batch_size
       snake_case    :      int      					=    embedding_dim
       snake_case    :      Optional[Any]      					=    num_embeddings

       snake_case    :      str      					=    torch.randn((batch_size, embedding_dim)						).to(UpperCamelCase__						)

       snake_case    :      Optional[int]      					=    torch.randn((batch_size, embedding_dim)						).to(UpperCamelCase__						)
       snake_case    :      str      					=    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  lowerCamelCase		(	self						)	->		Union[str, Any]:

       '''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						,	UpperCamelCase__						,	UpperCamelCase__						)	->		Tuple:

       '''simple docstring'''




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

       with torch.no_grad():
           snake_case    :      Optional[Any]      					=    model(**UpperCamelCase__						)[0]

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

       snake_case    :      Dict      					=    sample[0, :8].flatten().cpu()
       print(UpperCamelCase__						)
       snake_case    :      List[Any]      					=    torch.tensor(UpperCamelCase__						)

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









"""simple docstring"""



def   __lowerCAmelCase						(						lowercase  :  List[str]						,							lowercase  :  Union[str, Any]						,							lowercase  :  List[str]						,							lowercase  :  Tuple						,							lowercase  :  List[Any]						,							lowercase  :  int )	->						List[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
    snake_case    :      Union[str, 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   __lowerCAmelCase						(						lowercase  :  Any						,							lowercase  :  Union[str, Any]						,							lowercase  :  Optional[int] )	->						List[Any]:


    """simple docstring"""





    snake_case    :      Any      					=    [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   			=    [10, 20, 30, 40, 50]
  print_combination(arr, len(arr), 3)
  # This code is contributed by Ambuj sahu





