




import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, slow, torch_device

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin

class 	__lowerCAmelCase			(			SCREAMING_SNAKE_CASE__   , SCREAMING_SNAKE_CASE__   , unittest.TestCase				):
       UpperCamelCase__        =						UNetaDModel
       UpperCamelCase__        =						'sample'
       @property
       def    lowerCamelCase__							(		self		:Dict       ):







        '''simple docstring'''







        a		       =							4
        a		       =							14
        a		       =							16

        a		       =							floats_tensor((batch_size, num_features, seq_len)       ).to(a_       )
        a		       =							torch.tensor([10] * batch_size       ).to(a_       )

        return {"sample": noise, "timestep": time_step}
       @property
       def    lowerCamelCase__							(		self		:List[Any]       ):







        '''simple docstring'''







        return (4, 14, 16)
       @property
       def    lowerCamelCase__							(		self		:Dict       ):







        '''simple docstring'''







        return (4, 14, 16)
       def    lowerCamelCase__							(		self		:List[Any]       ):







        '''simple docstring'''







        pass
       def    lowerCamelCase__							(		self		:Tuple       ):







        '''simple docstring'''







        pass
       @unittest.skipIf(torch_device == """mps"""	,   """mish op not supported in MPS"""       )
       def    lowerCamelCase__							(		self		:Union[str, Any]       ):







        '''simple docstring'''







        super().test_determinism()
       @unittest.skipIf(torch_device == """mps"""	,   """mish op not supported in MPS"""       )
       def    lowerCamelCase__							(		self		:List[Any]       ):







        '''simple docstring'''







        super().test_outputs_equivalence()
       @unittest.skipIf(torch_device == """mps"""	,   """mish op not supported in MPS"""       )
       def    lowerCamelCase__							(		self		:Dict       ):







        '''simple docstring'''







        super().test_from_save_pretrained()
       @unittest.skipIf(torch_device == """mps"""	,   """mish op not supported in MPS"""       )
       def    lowerCamelCase__							(		self		:str       ):







        '''simple docstring'''







        super().test_from_save_pretrained_variant()
       @unittest.skipIf(torch_device == """mps"""	,   """mish op not supported in MPS"""       )
       def    lowerCamelCase__							(		self		:Tuple       ):







        '''simple docstring'''







        super().test_model_from_pretrained()
       @unittest.skipIf(torch_device == """mps"""	,   """mish op not supported in MPS"""       )
       def    lowerCamelCase__							(		self		:Tuple       ):







        '''simple docstring'''







        super().test_output()
       def    lowerCamelCase__							(		self		:List[str]       ):







        '''simple docstring'''







        a		       =							{
            """block_out_channels""": (32, 64, 128, 256),
            """in_channels""": 14,
            """out_channels""": 14,
            """time_embedding_type""": """positional""",
            """use_timestep_embedding""": True,
            """flip_sin_to_cos""": False,
            """freq_shift""": 1.0,
            """out_block_type""": """OutConv1DBlock""",
            """mid_block_type""": """MidResTemporalBlock1D""",
            """down_block_types""": ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D"""),
            """up_block_types""": ("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D"""),
            """act_fn""": """mish""",
        }
        a		       =							self.dummy_input
        return init_dict, inputs_dict
       @unittest.skipIf(torch_device == """mps"""	,   """mish op not supported in MPS"""       )
       def    lowerCamelCase__							(		self		:Dict       ):







        '''simple docstring'''







        a       ,				a		       =							UNetaDModel.from_pretrained(
            """bglick13/hopper-medium-v2-value-function-hor32"""	,   output_loading_info=a_	,   subfolder="""unet"""       )
        self.assertIsNotNone(a_       )
        self.assertEqual(len(loading_info["""missing_keys"""]       )	,   0       )

        model.to(a_       )
        a		       =							model(**self.dummy_input       )

        assert image is not None, "Make sure output is not None"
       @unittest.skipIf(torch_device == """mps"""	,   """mish op not supported in MPS"""       )
       def    lowerCamelCase__							(		self		:Optional[int]       ):







        '''simple docstring'''







        a		       =							UNetaDModel.from_pretrained("""bglick13/hopper-medium-v2-value-function-hor32"""	,   subfolder="""unet"""       )
        torch.manual_seed(0       )
        if torch.cuda.is_available():
         torch.cuda.manual_seed_all(0       )

        a		       =							model.config.in_channels
        a		       =							16
        a		       =							torch.randn((1, seq_len, num_features)       ).permute(
            0	,   2	,   1       )  # match original, we can update values and remove
        a		       =							torch.full((num_features,)	,   0       )

        with torch.no_grad():
         a		       =							model(a_	,   a_       ).sample.permute(0	,   2	,   1       )

        a		       =							output[0, -3:, -3:].flatten()
        # fmt: off
        a		       =							torch.tensor([-2.137172, 1.1426016, 0.3688687, -0.766922, 0.7303146, 0.11038864, -0.4760633, 0.13270172, 0.02591348]       )
        # fmt: on
        self.assertTrue(torch.allclose(a_	,   a_	,   rtol=1E-3       )       )
       def    lowerCamelCase__							(		self		:str       ):







        '''simple docstring'''







        pass





       @slow
       def    lowerCamelCase__							(		self		:Optional[int]       ):







        '''simple docstring'''







        a		       =							"""harmonai/maestro-150k"""
        a		       =							UNetaDModel.from_pretrained(a_	,   subfolder="""unet"""       )
        model.to(a_       )

        a		       =							6_5536
        a		       =							torch.sin(torch.arange(a_       )[None, None, :].repeat(1	,   2	,   1       )       ).to(a_       )
        a		       =							torch.tensor([1]       ).to(a_       )

        with torch.no_grad():
         a		       =							model(a_	,   a_       ).sample

        a		       =							output.abs().sum()
        a		       =							output.abs().max()

        assert (output_sum - 224.0896).abs() < 0.5
        assert (output_max - 0.0607).abs() < 4E-4







class 	__lowerCAmelCase			(			SCREAMING_SNAKE_CASE__   , SCREAMING_SNAKE_CASE__   , unittest.TestCase				):
       UpperCamelCase__        =						UNetaDModel
       UpperCamelCase__        =						'sample'
       @property
       def    lowerCamelCase__							(		self		:Dict       ):







        '''simple docstring'''







        a		       =							4
        a		       =							14
        a		       =							16

        a		       =							floats_tensor((batch_size, num_features, seq_len)       ).to(a_       )
        a		       =							torch.tensor([10] * batch_size       ).to(a_       )

        return {"sample": noise, "timestep": time_step}
       @property
       def    lowerCamelCase__							(		self		:Any       ):







        '''simple docstring'''







        return (4, 14, 16)
       @property
       def    lowerCamelCase__							(		self		:str       ):







        '''simple docstring'''







        return (4, 14, 1)
       @unittest.skipIf(torch_device == """mps"""	,   """mish op not supported in MPS"""       )
       def    lowerCamelCase__							(		self		:Optional[int]       ):







        '''simple docstring'''







        super().test_determinism()
       @unittest.skipIf(torch_device == """mps"""	,   """mish op not supported in MPS"""       )
       def    lowerCamelCase__							(		self		:Optional[Any]       ):







        '''simple docstring'''







        super().test_outputs_equivalence()
       @unittest.skipIf(torch_device == """mps"""	,   """mish op not supported in MPS"""       )
       def    lowerCamelCase__							(		self		:Tuple       ):







        '''simple docstring'''







        super().test_from_save_pretrained()
       @unittest.skipIf(torch_device == """mps"""	,   """mish op not supported in MPS"""       )
       def    lowerCamelCase__							(		self		:Union[str, Any]       ):







        '''simple docstring'''







        super().test_from_save_pretrained_variant()
       @unittest.skipIf(torch_device == """mps"""	,   """mish op not supported in MPS"""       )
       def    lowerCamelCase__							(		self		:List[Any]       ):







        '''simple docstring'''







        super().test_model_from_pretrained()
       @unittest.skipIf(torch_device == """mps"""	,   """mish op not supported in MPS"""       )
       def    lowerCamelCase__							(		self		:Any       ):







        '''simple docstring'''







        a       ,				a		       =							self.prepare_init_args_and_inputs_for_common()
        a		       =							self.model_class(**a_       )
        model.to(a_       )
        model.eval()

        with torch.no_grad():
         a		       =							model(**a_       )

         if isinstance(a_	,   a_       ):
          a		       =							output.sample

        self.assertIsNotNone(a_       )
        a		       =							torch.Size((inputs_dict["""sample"""].shape[0], 1)       )
        self.assertEqual(output.shape	,   a_	,   """Input and output shapes do not match"""       )
       def    lowerCamelCase__							(		self		:Dict       ):







        '''simple docstring'''







        pass
       def    lowerCamelCase__							(		self		:Optional[Any]       ):







        '''simple docstring'''







        pass
       def    lowerCamelCase__							(		self		:str       ):







        '''simple docstring'''







        a		       =							{
            """in_channels""": 14,
            """out_channels""": 14,
            """down_block_types""": ["""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D"""],
            """up_block_types""": [],
            """out_block_type""": """ValueFunction""",
            """mid_block_type""": """ValueFunctionMidBlock1D""",
            """block_out_channels""": [32, 64, 128, 256],
            """layers_per_block""": 1,
            """downsample_each_block""": True,
            """use_timestep_embedding""": True,
            """freq_shift""": 1.0,
            """flip_sin_to_cos""": False,
            """time_embedding_type""": """positional""",
            """act_fn""": """mish""",
        }
        a		       =							self.dummy_input
        return init_dict, inputs_dict
       @unittest.skipIf(torch_device == """mps"""	,   """mish op not supported in MPS"""       )
       def    lowerCamelCase__							(		self		:List[str]       ):







        '''simple docstring'''







        a       ,				a		       =							UNetaDModel.from_pretrained(
            """bglick13/hopper-medium-v2-value-function-hor32"""	,   output_loading_info=a_	,   subfolder="""value_function"""       )
        self.assertIsNotNone(a_       )
        self.assertEqual(len(vf_loading_info["""missing_keys"""]       )	,   0       )

        value_function.to(a_       )
        a		       =							value_function(**self.dummy_input       )

        assert image is not None, "Make sure output is not None"
       @unittest.skipIf(torch_device == """mps"""	,   """mish op not supported in MPS"""       )
       def    lowerCamelCase__							(		self		:Any       ):







        '''simple docstring'''







        a       ,				a		       =							UNetaDModel.from_pretrained(
            """bglick13/hopper-medium-v2-value-function-hor32"""	,   output_loading_info=a_	,   subfolder="""value_function"""       )
        torch.manual_seed(0       )
        if torch.cuda.is_available():
         torch.cuda.manual_seed_all(0       )

        a		       =							value_function.config.in_channels
        a		       =							14
        a		       =							torch.randn((1, seq_len, num_features)       ).permute(
            0	,   2	,   1       )  # match original, we can update values and remove
        a		       =							torch.full((num_features,)	,   0       )

        with torch.no_grad():
         a		       =							value_function(a_	,   a_       ).sample

        # fmt: off
        a		       =							torch.tensor([165.25] * seq_len       )
        # fmt: on
        self.assertTrue(torch.allclose(a_	,   a_	,   rtol=1E-3       )       )





       def    lowerCamelCase__							(		self		:Optional[int]       ):







        '''simple docstring'''







        pass







__UpperCamelCase      : Union[str, Any]					      =   {
    "A": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "B": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "C": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "D": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "E": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "F": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "G": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "H": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "I": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "J": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "K": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "L": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "M": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "N": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "O": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "P": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "Q": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "R": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "S": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "T": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "U": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "V": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "W": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "X": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "Y": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
    "Z": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
}





def 	__A	(			__lowerCamelCase					)   -> list[tuple[str, str]]:
	return [alphabet[char] for char in key.upper()]





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> str:
	a		       =							""""""
	a		       =							0
	a		       =							generate_table(__lowerCamelCase					)
	for char in words.upper():
		cipher += get_opponent(table[count]							,    __lowerCamelCase					)
		a		       =							(count + 1) % len(__lowerCamelCase					)
	return cipher





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> str:
	return encrypt(__lowerCamelCase							,    __lowerCamelCase					)





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> tuple[int, int]:
	a		       =							0 if char in table[0] else 1
	a		       =							table[row].index(__lowerCamelCase					)
	return row, col





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> str:
	a       ,				a		       =							get_position(__lowerCamelCase							,    char.upper()					)
	if row == 1:
		return table[0][col]
	else:
		return table[1][col] if row == 0 else char


if __name__ == "__main__":
		import doctest

		doctest.testmod()  # Fist ensure that all our tests are passing...
		__UpperCamelCase      : int					      =   input("Enter key: ").strip()
		__UpperCamelCase      : int					      =   input("Enter text to encrypt: ").strip()
		__UpperCamelCase      : Optional[Any]					      =   encrypt(key, text)

		print(F'Encrypted: {cipher_text}')
		print(F'Decrypted with key: {decrypt(key, cipher_text)}')

