


'''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		(lowercase_    ,	unittest.TestCase	):





      '''simple docstring'''






      lowerCAmelCase__   :	int       =				PriorTransformer
      lowerCAmelCase__   :	List[Any]       =				"""hidden_states"""



      @property
      def  UpperCamelCase__						(self       :       List[Any]					):

             '''simple docstring'''







             lowercase__      =					4
             lowercase__      =					8
             lowercase__      =					7

             lowercase__      =					floats_tensor((batch_size, embedding_dim)					).to(UpperCamelCase					)

             lowercase__      =					floats_tensor((batch_size, embedding_dim)					).to(UpperCamelCase					)
             lowercase__      =					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  UpperCamelCase__						(self       :       Optional[int]  ,		UpperCamelCase       :       Optional[int]=0					):

             '''simple docstring'''







             torch.manual_seed(UpperCamelCase					)
             lowercase__      =					4
             lowercase__      =					8
             lowercase__      =					7

             lowercase__      =					torch.randn((batch_size, embedding_dim)					).to(UpperCamelCase					)

             lowercase__      =					torch.randn((batch_size, embedding_dim)					).to(UpperCamelCase					)
             lowercase__      =					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  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             return (4, 8)



      @property
      def  UpperCamelCase__						(self       :       Any					):

             '''simple docstring'''







             return (4, 8)



      def  UpperCamelCase__						(self       :       Dict					):

             '''simple docstring'''







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



      def  UpperCamelCase__						(self       :       Tuple					):

             '''simple docstring'''







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

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



      def  UpperCamelCase__						(self       :       Optional[Any]					):

             '''simple docstring'''







             lowercase__     ,lowercase__      =					self.prepare_init_args_and_inputs_for_common()

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

             lowercase__      =					['''hidden_states''', '''timestep''']
             self.assertListEqual(arg_names[:2]  ,		UpperCamelCase					)






      def  UpperCamelCase__						(self       :       Dict					):

             '''simple docstring'''







             lowercase__      =					PriorTransformer.from_pretrained('''hf-internal-testing/prior-dummy'''					)
             lowercase__      =					model.to(UpperCamelCase					)

             if hasattr(UpperCamelCase  ,		'''set_default_attn_processor'''					):
                    model.set_default_attn_processor()

             lowercase__      =					self.get_dummy_seed_input()

             with torch.no_grad():
                    lowercase__      =					model(**UpperCamelCase					)[0]

             lowercase__      =					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.
             lowercase__      =					torch.tensor([-1.34_36, -0.28_70, 0.75_38, 0.43_68, -0.02_39]					)
             self.assertTrue(torch_all_close(UpperCamelCase  ,		UpperCamelCase  ,		rtol=1E-2					)					)






@slow
class     __lowerCAmelCase		(unittest.TestCase	):





      '''simple docstring'''






      def  UpperCamelCase__						(self       :       List[str]  ,		UpperCamelCase       :       List[str]=1  ,		UpperCamelCase       :       int=768  ,		UpperCamelCase       :       Union[str, Any]=77  ,		UpperCamelCase       :       Tuple=0					):

             '''simple docstring'''







             torch.manual_seed(UpperCamelCase					)
             lowercase__      =					batch_size
             lowercase__      =					embedding_dim
             lowercase__      =					num_embeddings

             lowercase__      =					torch.randn((batch_size, embedding_dim)					).to(UpperCamelCase					)

             lowercase__      =					torch.randn((batch_size, embedding_dim)					).to(UpperCamelCase					)
             lowercase__      =					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  UpperCamelCase__						(self       :       Tuple					):

             '''simple docstring'''







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






      @parameterized.expand(
          [
              # fmt: off
              [13, [-0.58_61,  0.12_83, -0.09_31,  0.08_82,  0.44_76,  0.13_29, -0.04_98,  0.06_40]],
              [37, [-0.49_13,  0.01_10, -0.04_83,  0.05_41,  0.49_54, -0.01_70,  0.03_54,  0.16_51]],
              # fmt: on
          ]					)
      def  UpperCamelCase__						(self       :       Dict  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       Dict					):

             '''simple docstring'''







             lowercase__      =					PriorTransformer.from_pretrained('''kandinsky-community/kandinsky-2-1-prior'''  ,		subfolder='''prior'''					)
             model.to(UpperCamelCase					)
             lowercase__      =					self.get_dummy_seed_input(seed=UpperCamelCase					)

             with torch.no_grad():
                    lowercase__      =					model(**UpperCamelCase					)[0]

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

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

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








'''simple docstring'''

def _SCREAMING_SNAKE_CASE (A			)			->			bool:






       """simple docstring"""







       if not isinstance(A    ,					A			):
              raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''			)
       if len(A			) == 0:
              raise ValueError('''Input list must be a non empty list'''			)
       if len(A			) == 1:
              return True
       lowercase__      =					series[1] - series[0]
       for index in range(len(A			) - 1			):
              if series[index + 1] - series[index] != common_diff:
                     return False
       return True

def _SCREAMING_SNAKE_CASE (A			)			->			float:






       """simple docstring"""







       if not isinstance(A    ,					A			):
              raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''			)
       if len(A			) == 0:
              raise ValueError('''Input list must be a non empty list'''			)
       lowercase__      =					0
       for val in series:
              answer += val
       return answer / len(A			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()




