

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__       (   UpperCAmelCase	,			unittest.TestCase       ):







  """simple docstring"""




  UpperCAmelCase__      : List[str]			       =PriorTransformer
  UpperCAmelCase__      : str			       ="""hidden_states"""


  @property
  def  _lowercase      (			self    : Optional[Any]		)					->List[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[Any]       =   4
        SCREAMING_SNAKE_CASE :      List[str]       =   8
        SCREAMING_SNAKE_CASE :      Any       =   7

        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   floats_tensor((batch_size, embedding_dim)		).to(UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      Any       =   floats_tensor((batch_size, embedding_dim)		).to(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Dict       =   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  _lowercase      (			self    : str				,				UpperCAmelCase__    : Tuple=0		)					->Optional[Any]:
        """simple docstring"""







        torch.manual_seed(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      List[str]       =   4
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   8
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   7

        SCREAMING_SNAKE_CASE :      List[str]       =   torch.randn((batch_size, embedding_dim)		).to(UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      Any       =   torch.randn((batch_size, embedding_dim)		).to(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Dict       =   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  _lowercase      (			self    : str		)					->Dict:
        """simple docstring"""







        return (4, 8)


  @property
  def  _lowercase      (			self    : str		)					->Union[str, Any]:
        """simple docstring"""







        return (4, 8)


  def  _lowercase      (			self    : Union[str, Any]		)					->Tuple:
        """simple docstring"""







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


  def  _lowercase      (			self    : int		)					->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      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__		)
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   model(**self.dummy_input		)[0]

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


  def  _lowercase      (			self    : int		)					->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      Union[str, Any]       =   self.prepare_init_args_and_inputs_for_common()

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

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




  def  _lowercase      (			self    : Optional[Any]		)					->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Tuple       =   PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy"""		)
        SCREAMING_SNAKE_CASE :      Dict       =   model.to(UpperCAmelCase__		)

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

        SCREAMING_SNAKE_CASE :      Tuple       =   self.get_dummy_seed_input()

        with torch.no_grad():
              SCREAMING_SNAKE_CASE :      Any       =   model(**UpperCAmelCase__		)[0]

        SCREAMING_SNAKE_CASE :      Dict       =   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.
        SCREAMING_SNAKE_CASE :      str       =   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        a__       (   unittest.TestCase       ):







  """simple docstring"""




  def  _lowercase      (			self    : List[str]				,				UpperCAmelCase__    : List[str]=1				,				UpperCAmelCase__    : Union[str, Any]=7_6_8				,				UpperCAmelCase__    : Tuple=7_7				,				UpperCAmelCase__    : int=0		)					->Tuple:
        """simple docstring"""







        torch.manual_seed(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      List[str]       =   batch_size
        SCREAMING_SNAKE_CASE :      List[str]       =   embedding_dim
        SCREAMING_SNAKE_CASE :      Tuple       =   num_embeddings

        SCREAMING_SNAKE_CASE :      str       =   torch.randn((batch_size, embedding_dim)		).to(UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   torch.randn((batch_size, embedding_dim)		).to(UpperCAmelCase__		)
        SCREAMING_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,
        }


  def  _lowercase      (			self    : Any		)					->Dict:
        """simple docstring"""







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




  @parameterized.expand(
      [
          # fmt: off
          [1_3, [-0.58_61,  0.12_83, -0.09_31,  0.08_82,  0.44_76,  0.13_29, -0.04_98,  0.06_40]],
          [3_7, [-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  _lowercase      (			self    : List[Any]				,				UpperCAmelCase__    : Tuple				,				UpperCAmelCase__    : Any		)					->Optional[int]:
        """simple docstring"""







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

        with torch.no_grad():
              SCREAMING_SNAKE_CASE :      str       =   model(**UpperCAmelCase__		)[0]

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

        SCREAMING_SNAKE_CASE :      Dict       =   sample[0, :8].flatten().cpu()
        print(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      List[str]       =   torch.tensor(UpperCAmelCase__		)

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







def 					__lowercase      (     _A			)     ->						str:
      SCREAMING_SNAKE_CASE :      Optional[Any]       =   """"""
      for ch in key:
            if ch == " " or ch not in key_no_dups and ch.isalpha():
                  key_no_dups += ch
      return key_no_dups






def 					__lowercase      (     _A			)     ->						dict[str, str]:
      SCREAMING_SNAKE_CASE :      int       =   [chr(i + 65			) for i in range(26			)]
      # Remove duplicate characters from key
      SCREAMING_SNAKE_CASE :      List[str]       =   remove_duplicates(key.upper()			)
      SCREAMING_SNAKE_CASE :      Dict       =   len(_A			)
      # First fill cipher with key characters
      SCREAMING_SNAKE_CASE :      int       =   {alphabet[i]: char for i, char in enumerate(_A			)}
      # Then map remaining characters in alphabet to
      # the alphabet from the beginning
      for i in range(len(_A			)						,    26			):
            SCREAMING_SNAKE_CASE :      List[Any]       =   alphabet[i - offset]
            # Ensure we are not mapping letters to letters previously mapped
            while char in key:
                  offset -= 1
                  SCREAMING_SNAKE_CASE :      str       =   alphabet[i - offset]
            SCREAMING_SNAKE_CASE :      List[Any]       =   char
      return cipher_alphabet






def 					__lowercase      (     _A						,    _A			)     ->						str:
      return "".join(cipher_map.get(_A						,    _A			) for ch in message.upper()			)






def 					__lowercase      (     _A						,    _A			)     ->						str:
      SCREAMING_SNAKE_CASE :      Optional[int]       =   {v: k for k, v in cipher_map.items()}
      return "".join(rev_cipher_map.get(_A						,    _A			) for ch in message.upper()			)






def 					__lowercase      (     )     ->						None:
      SCREAMING_SNAKE_CASE :      List[Any]       =   input("""Enter message to encode or decode: """			).strip()
      SCREAMING_SNAKE_CASE :      str       =   input("""Enter keyword: """			).strip()
      SCREAMING_SNAKE_CASE :      Dict       =   input("""Encipher or decipher? E/D:"""			).strip()[0].lower()
      try:
            SCREAMING_SNAKE_CASE :      int       =   {"""e""": encipher, """d""": decipher}[option]
      except KeyError:
            raise KeyError("""invalid input option"""			)
      SCREAMING_SNAKE_CASE :      Optional[int]       =   create_cipher_map(_A			)
      print(func(_A						,    _A			)			)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()




