




import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 		lowercase							(     unittest.TestCase	):

     @property
     def       a__			(						self       )		->							int:
        torch.manual_seed(0       )
        _A							:  int    =		UNetaDModel(
            block_out_channels=(32, 64)	,  layers_per_block=2	,  sample_size=32	,  in_channels=3	,  out_channels=3	,  down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")	,  up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")	,  )
        return model



     def       a__			(						self       )		->							Dict:
        _A							:  List[str]    =		self.dummy_uncond_unet
        _A							:  Tuple    =		KarrasVeScheduler()

        _A							:  Any    =		KarrasVePipeline(unet=_a	,  scheduler=_a       )
        pipe.to(_a       )
        pipe.set_progress_bar_config(disable=_a       )

        _A							:  Dict    =		torch.manual_seed(0       )
        _A							:  Optional[Any]    =		pipe(num_inference_steps=2	,  generator=_a	,  output_type="""numpy"""       ).images

        _A							:  Optional[Any]    =		torch.manual_seed(0       )
        _A							:  Tuple    =		pipe(num_inference_steps=2	,  generator=_a	,  output_type="""numpy"""	,  return_dict=_a       )[0]

        _A							:  Tuple    =		image[0, -3:, -3:, -1]
        _A							:  Union[str, Any]    =		image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        _A							:  Dict    =		np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

        assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2







@slow
@require_torch
class 		lowercase							(     unittest.TestCase	):

     def       a__			(						self       )		->							Tuple:
        _A							:  int    =		"""google/ncsnpp-celebahq-256"""
        _A							:  str    =		UNetaDModel.from_pretrained(_a       )
        _A							:  int    =		KarrasVeScheduler()

        _A							:  str    =		KarrasVePipeline(unet=_a	,  scheduler=_a       )
        pipe.to(_a       )
        pipe.set_progress_bar_config(disable=_a       )

        _A							:  int    =		torch.manual_seed(0       )
        _A							:  Tuple    =		pipe(num_inference_steps=20	,  generator=_a	,  output_type="""numpy"""       ).images

        _A							:  List[Any]    =		image[0, -3:, -3:, -1]
        assert image.shape == (1, 256, 256, 3)
        _A							:  Optional[Any]    =		np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]       )

        assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2







from __future__ import annotations







class 		lowercase							:

     def __init__(						self	,  _a = 0       )		->							str:
           _A							:  Any    =		key

     def       a__			(						self	,  _a	,  _a       )		->							list[str]:
           assert isinstance(_a	,  _a       ) and isinstance(_a	,  _a       )

           _A							:  Any    =		key or self.__key or 1

           # make sure key is an appropriate size
           key %= 255

           return [chr(ord(_a       ) ^ key       ) for ch in content]

     def       a__			(						self	,  _a	,  _a       )		->							list[str]:
           assert isinstance(_a	,  _a       ) and isinstance(_a	,  _a       )

           _A							:  List[Any]    =		key or self.__key or 1

           # make sure key is an appropriate size
           key %= 255

           return [chr(ord(_a       ) ^ key       ) for ch in content]

     def       a__			(						self	,  _a	,  _a = 0       )		->							str:
           assert isinstance(_a	,  _a       ) and isinstance(_a	,  _a       )

           _A							:  List[Any]    =		key or self.__key or 1

           # make sure key can be any size
           while key > 255:
                 key -= 255

           # This will be returned
           _A							:  List[str]    =		""""""

           for ch in content:
                 ans += chr(ord(_a       ) ^ key       )

           return ans

     def       a__			(						self	,  _a	,  _a = 0       )		->							str:
           assert isinstance(_a	,  _a       ) and isinstance(_a	,  _a       )

           _A							:  List[str]    =		key or self.__key or 1

           # make sure key can be any size
           while key > 255:
                 key -= 255

           # This will be returned
           _A							:  List[str]    =		""""""

           for ch in content:
                 ans += chr(ord(_a       ) ^ key       )

           return ans

     def       a__			(						self	,  _a	,  _a = 0       )		->							bool:
           assert isinstance(_a	,  _a       ) and isinstance(_a	,  _a       )

           try:
                 with open(_a       ) as fin, open("""encrypt.out"""	,  """w+"""       ) as fout:
                       # actual encrypt-process
                       for line in fin:
                             fout.write(self.encrypt_string(_a	,  _a       )       )

           except OSError:
                 return False

           return True



     def       a__			(						self	,  _a	,  _a       )		->							bool:
           assert isinstance(_a	,  _a       ) and isinstance(_a	,  _a       )

           try:
                 with open(_a       ) as fin, open("""decrypt.out"""	,  """w+"""       ) as fout:
                       # actual encrypt-process
                       for line in fin:
                             fout.write(self.decrypt_string(_a	,  _a       )       )

           except OSError:
                 return False

           return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")

