


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 				SCREAMING_SNAKE_CASE__						(       lowercase__		,					unittest.TestCase    ):
      snake_case__       :  Optional[Any]       =		PriorTransformer
      snake_case__       :  Dict       =		'''hidden_states'''
      @property
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    str:
             a_      :	Dict							=						4
             a_      :	Dict							=						8
             a_      :	List[str]							=						7

             a_      :	Union[str, Any]							=						floats_tensor((batch_size, embedding_dim)  ).to(SCREAMING_SNAKE_CASE__  )

             a_      :	Any							=						floats_tensor((batch_size, embedding_dim)  ).to(SCREAMING_SNAKE_CASE__  )
             a_      :	Any							=						floats_tensor((batch_size, num_embeddings, embedding_dim)  ).to(SCREAMING_SNAKE_CASE__  )

             return {
                 "hidden_states": hidden_states,
                 "timestep": 2,
                 "proj_embedding": proj_embedding,
                 "encoder_hidden_states": encoder_hidden_states,
             }
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : List[Any]=0  )	->    List[Any]:
             torch.manual_seed(SCREAMING_SNAKE_CASE__  )
             a_      :	List[str]							=						4
             a_      :	str							=						8
             a_      :	Any							=						7

             a_      :	Any							=						torch.randn((batch_size, embedding_dim)  ).to(SCREAMING_SNAKE_CASE__  )

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

             return {
                 "hidden_states": hidden_states,
                 "timestep": 2,
                 "proj_embedding": proj_embedding,
                 "encoder_hidden_states": encoder_hidden_states,
             }
      @property
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    Any:
             return (4, 8)
      @property
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Tuple:
             return (4, 8)
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    Dict:
             a_      :	List[str]							=						{
                 'num_attention_heads': 2,
                 'attention_head_dim': 4,
                 'num_layers': 2,
                 'embedding_dim': 8,
                 'num_embeddings': 7,
                 'additional_embeddings': 4,
             }
             a_      :	List[str]							=						self.dummy_input
             return init_dict, inputs_dict
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    List[str]:
             a_							,	a_      :	Union[str, Any]							=						PriorTransformer.from_pretrained(
                 'hf-internal-testing/prior-dummy'  ,							output_loading_info=SCREAMING_SNAKE_CASE__  )
             self.assertIsNotNone(SCREAMING_SNAKE_CASE__  )
             self.assertEqual(len(loading_info['missing_keys']  )  ,							0  )

             model.to(SCREAMING_SNAKE_CASE__  )
             a_      :	int							=						model(**self.dummy_input  )[0]

             assert hidden_states is not None, "Make sure output is not None"
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    Dict:
             a_							,	a_      :	Optional[Any]							=						self.prepare_init_args_and_inputs_for_common()

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

             a_      :	Tuple							=						['hidden_states', 'timestep']
             self.assertListEqual(arg_names[:2]  ,							SCREAMING_SNAKE_CASE__  )

      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    Dict:
             a_      :	Dict							=						PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'  )
             a_      :	Any							=						model.to(SCREAMING_SNAKE_CASE__  )

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

             a_      :	Optional[Any]							=						self.get_dummy_seed_input()

             with torch.no_grad():
                    a_      :	Tuple							=						model(**SCREAMING_SNAKE_CASE__  )[0]

             a_      :	Tuple							=						output[0, :5].flatten().cpu()
             print(SCREAMING_SNAKE_CASE__  )

             # 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_      :	Any							=						torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239]  )
             self.assertTrue(torch_all_close(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							rtol=1E-2  )  )
@slow
class 				SCREAMING_SNAKE_CASE__						(       unittest.TestCase    ):
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : str=1  ,							SCREAMING_SNAKE_CASE__       : str=7_6_8  ,							SCREAMING_SNAKE_CASE__       : str=7_7  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=0  )	->    Optional[int]:
             torch.manual_seed(SCREAMING_SNAKE_CASE__  )
             a_      :	Any							=						batch_size
             a_      :	Union[str, Any]							=						embedding_dim
             a_      :	List[Any]							=						num_embeddings

             a_      :	Tuple							=						torch.randn((batch_size, embedding_dim)  ).to(SCREAMING_SNAKE_CASE__  )

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

             return {
                 "hidden_states": hidden_states,
                 "timestep": 2,
                 "proj_embedding": proj_embedding,
                 "encoder_hidden_states": encoder_hidden_states,
             }
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    Optional[Any]:
             # clean up the VRAM after each test
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()

      @parameterized.expand(
          [
              # fmt: off
              [1_3, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
              [3_7, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
              # fmt: on
          ]  )
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  ,							SCREAMING_SNAKE_CASE__       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : Tuple  )	->    str:
             a_      :	Union[str, Any]							=						PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'  ,							subfolder='prior'  )
             model.to(SCREAMING_SNAKE_CASE__  )
             a_      :	Optional[Any]							=						self.get_dummy_seed_input(seed=SCREAMING_SNAKE_CASE__  )

             with torch.no_grad():
                    a_      :	Tuple							=						model(**SCREAMING_SNAKE_CASE__  )[0]

             assert list(sample.shape  ) == [1, 7_6_8]

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

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






import random
class 				SCREAMING_SNAKE_CASE__						:
      @staticmethod
      def  SCREAMING_SNAKE_CASE							( SCREAMING_SNAKE_CASE__       : str  )	->    tuple[list[int], list[int]]:
             a_      :	int							=						[ord(SCREAMING_SNAKE_CASE__  ) for i in text]
             a_      :	Any							=						[]
             a_      :	Optional[int]							=						[]
             for i in plain:
                    a_      :	Tuple							=						random.randint(1  ,							3_0_0  )
                    a_      :	Optional[int]							=						(i + k) * k
                    cipher.append(SCREAMING_SNAKE_CASE__  )
                    key.append(SCREAMING_SNAKE_CASE__  )
             return cipher, key

      @staticmethod
      def  SCREAMING_SNAKE_CASE							( SCREAMING_SNAKE_CASE__       : list[int]  ,							SCREAMING_SNAKE_CASE__       : list[int]  )	->    str:
             a_      :	List[Any]							=						[]
             for i in range(len(SCREAMING_SNAKE_CASE__  )  ):
                    a_      :	str							=						int((cipher[i] - (key[i]) ** 2) / key[i]  )
                    plain.append(chr(SCREAMING_SNAKE_CASE__  )  )
             return "".join(SCREAMING_SNAKE_CASE__  )


if __name__ == "__main__":
   UpperCAmelCase_      ,     UpperCAmelCase_   :				Any						      = Onepad().encrypt('Hello')
   print(c, k)
   print(Onepad().decrypt(c, k))


