"""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    a__    (   _a					,					unittest.TestCase	):
   snake_case_							=      PriorTransformer
   snake_case_							=      "hidden_states"


   @property
   def 	snake_case__	(     self  ):
        '''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 	snake_case__	(     self,				_UpperCAmelCase=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 	snake_case__	(     self  ):
        '''simple docstring'''



        return (4, 8)


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return (4, 8)


   def 	snake_case__	(     self  ):
        '''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 	snake_case__	(     self  ):
        '''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 	snake_case__	(     self  ):
        '''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 	snake_case__	(     self  ):
        '''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([-1.3_436, -0.2_870, 0.7_538, 0.4_368, -0.0_239]  )
        self.assertTrue(torch_all_close(_UpperCAmelCase,				_UpperCAmelCase,				rtol=1E-2  )  )






@slow
class    a__    (   unittest.TestCase	):


   def 	snake_case__	(     self,				_UpperCAmelCase=1,				_UpperCAmelCase=768,				_UpperCAmelCase=77,				_UpperCAmelCase=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 	snake_case__	(     self  ):
        '''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 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''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"""


lowerCAmelCase_:					Any     =					"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"







def 					__a   (		A      ):






     '''simple docstring'''



     if not isinstance(A    ,				A      ):
          lowercase__				 =							f'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
          raise TypeError(A      )

     lowercase__				 =							"".join(bin(A      )[2:].zfill(8      ) for byte in data      )

     lowercase__				 =							len(A      ) % 6 != 0

     if padding_needed:
          # The padding that will be added later
          lowercase__				 =							b"=" * ((6 - len(A      ) % 6) // 2)

          # Append binary_stream with arbitrary binary digits (0's by default) to make its
          # length a multiple of 6.
          binary_stream += "0" * (6 - len(A      ) % 6)
     else:
          lowercase__				 =							b""

     # Encode every 6 binary digits to their corresponding Base64 character
     return (
         "".join(
             B64_CHARSET[int(binary_stream[index : index + 6]    ,				2      )]
             for index in range(0    ,				len(A      )    ,				6      )      ).encode()
         + padding
     )







def 					__a   (		A      ):






     '''simple docstring'''



     if not isinstance(A    ,				A      ) and not isinstance(A    ,				A      ):
          lowercase__				 =							(
              "argument should be a bytes-like object or ASCII string, "
              f'''not \'{encoded_data.__class__.__name__}\''''
          )
          raise TypeError(A      )

     # In case encoded_data is a bytes-like object, make sure it contains only
     # ASCII characters so we convert it to a string object
     if isinstance(A    ,				A      ):
          try:
               lowercase__				 =							encoded_data.decode("utf-8"      )
          except UnicodeDecodeError:
               raise ValueError("base64 encoded data should only contain ASCII characters"      )

     lowercase__				 =							encoded_data.count("="      )

     # Check if the encoded string contains non base64 characters
     if padding:
          assert all(
              char in B64_CHARSET for char in encoded_data[:-padding]      ), "Invalid base64 character(s) found."
     else:
          assert all(
              char in B64_CHARSET for char in encoded_data      ), "Invalid base64 character(s) found."

     # Check the padding
     assert len(A      ) % 4 == 0 and padding < 3, "Incorrect padding"

     if padding:
          # Remove padding if there is one
          lowercase__				 =							encoded_data[:-padding]

          lowercase__				 =							"".join(
              bin(B64_CHARSET.index(A      )      )[2:].zfill(6      ) for char in encoded_data      )[: -padding * 2]
     else:
          lowercase__				 =							"".join(
              bin(B64_CHARSET.index(A      )      )[2:].zfill(6      ) for char in encoded_data      )

     lowercase__				 =							[
         int(binary_stream[index : index + 8]    ,				2      )
         for index in range(0    ,				len(A      )    ,				8      )
     ]

     return bytes(A      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()




