



import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class 	lowerCamelCase__      (							lowerCamelCase__				,    unittest.TestCase):




		'''simple docstring'''



		snake_case_     =DiTPipeline
		snake_case_     =CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
		snake_case_     =PipelineTesterMixin.required_optional_params - {
		    """latents""",
		    """num_images_per_prompt""",
		    """callback""",
		    """callback_steps""",
		}
		snake_case_     =CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
		snake_case_     =False



		def 		lowerCAmelCase__				(self			)					->      Union[str, Any]:

						"""simple docstring"""






						torch.manual_seed(0			)
						lowerCAmelCase__					:    Union[str, Any]												=					TransformeraDModel(
						    sample_size=16		,num_layers=2		,patch_size=4		,attention_head_dim=8		,num_attention_heads=2		,in_channels=4		,out_channels=8		,attention_bias=__lowerCamelCase		,activation_fn='''gelu-approximate'''		,num_embeds_ada_norm=10_00		,norm_type='''ada_norm_zero'''		,norm_elementwise_affine=__lowerCamelCase		,)
						lowerCAmelCase__					:    Any												=					AutoencoderKL()
						lowerCAmelCase__					:    Optional[Any]												=					DDIMScheduler()
						lowerCAmelCase__					:    Optional[Any]												=					{'''transformer''': transformer.eval(), '''vae''': vae.eval(), '''scheduler''': scheduler}
						return components



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase=0			)					->      Dict:

						"""simple docstring"""






						if str(__lowerCamelCase			).startswith('''mps'''			):
										lowerCAmelCase__					:    str												=					torch.manual_seed(__lowerCamelCase			)
						else:
										lowerCAmelCase__					:    Dict												=					torch.Generator(device=__lowerCamelCase			).manual_seed(__lowerCamelCase			)
						lowerCAmelCase__					:    Any												=					{
						    '''class_labels''': [1],
						    '''generator''': generator,
						    '''num_inference_steps''': 2,
						    '''output_type''': '''numpy''',
						}
						return inputs



		def 		lowerCAmelCase__				(self			)					->      Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					'''cpu'''

						lowerCAmelCase__					:    List[Any]												=					self.get_dummy_components()
						lowerCAmelCase__					:    Tuple												=					self.pipeline_class(**__lowerCamelCase			)
						pipe.to(__lowerCamelCase			)
						pipe.set_progress_bar_config(disable=__lowerCamelCase			)

						lowerCAmelCase__					:    List[str]												=					self.get_dummy_inputs(__lowerCamelCase			)
						lowerCAmelCase__					:    Union[str, Any]												=					pipe(**__lowerCamelCase			).images
						lowerCAmelCase__					:    Union[str, Any]												=					image[0, -3:, -3:, -1]

						self.assertEqual(image.shape		,(1, 16, 16, 3)			)
						lowerCAmelCase__					:    Dict												=					np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]			)
						lowerCAmelCase__					:    List[str]												=					np.abs(image_slice.flatten() - expected_slice			).max()
						self.assertLessEqual(__lowerCamelCase		,1e-3			)



		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""simple docstring"""






						self._test_inference_batch_single_identical(relax_max_difference=__lowerCamelCase		,expected_max_diff=1e-3			)



		@unittest.skipIf(
		    torch_device != '''cuda''' or not is_xformers_available()		,reason='''XFormers attention is only available with CUDA and `xformers` installed'''		,)
		def 		lowerCAmelCase__				(self			)					->      str:

						"""simple docstring"""






						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3			)



@require_torch_gpu
@slow
class 	lowerCamelCase__      (							unittest.TestCase):




		'''simple docstring'''



		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






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



		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					torch.manual_seed(0			)

						lowerCAmelCase__					:    Optional[Any]												=					DiTPipeline.from_pretrained('''facebook/DiT-XL-2-256'''			)
						pipe.to('''cuda'''			)

						lowerCAmelCase__					:    List[str]												=					['''vase''', '''umbrella''', '''white shark''', '''white wolf''']
						lowerCAmelCase__					:    str												=					pipe.get_label_ids(__lowerCamelCase			)

						lowerCAmelCase__					:    List[str]												=					pipe(__lowerCamelCase		,generator=__lowerCamelCase		,num_inference_steps=40		,output_type='''np'''			).images

						for word, image in zip(__lowerCamelCase		,__lowerCamelCase			):
										lowerCAmelCase__					:    Optional[Any]												=					load_numpy(
										    f"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy"""			)
										assert np.abs((expected_image - image).max()			) < 1e-2



		def 		lowerCAmelCase__				(self			)					->      Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					DiTPipeline.from_pretrained('''facebook/DiT-XL-2-512'''			)
						lowerCAmelCase__					:    Dict												=					DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
						pipe.to('''cuda'''			)

						lowerCAmelCase__					:    Dict												=					['''vase''', '''umbrella''']
						lowerCAmelCase__					:    Optional[Any]												=					pipe.get_label_ids(__lowerCamelCase			)

						lowerCAmelCase__					:    Tuple												=					torch.manual_seed(0			)
						lowerCAmelCase__					:    Tuple												=					pipe(__lowerCamelCase		,generator=__lowerCamelCase		,num_inference_steps=25		,output_type='''np'''			).images

						for word, image in zip(__lowerCamelCase		,__lowerCamelCase			):
										lowerCAmelCase__					:    Optional[Any]												=					load_numpy(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    f"""/dit/{word}_512.npy"""			)

										assert np.abs((expected_image - image).max()			) < 1e-1






import string

import numpy






def      lowerCAmelCase__		(   lowerCamelCase_		:		int	,lowerCamelCase_		:		int):





				'''simple docstring'''

				return b if a == 0 else greatest_common_divisor(b % a	,lowerCamelCase_)



class 	lowerCamelCase__      :




		'''simple docstring'''



		snake_case_     =string.ascii_uppercase + string.digits
		# This cipher takes alphanumerics into account
		# i.e. a total of 36 characters

		# take x and return x % len(key_string)
		snake_case_     =numpy.vectorize(lambda lowerCamelCase__: x % 36)

		snake_case_     =numpy.vectorize(lowerCamelCase__)



		def __init__(self		,__lowerCamelCase			)					->      None:

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					self.modulus(__lowerCamelCase			)  # mod36 calc's on the encrypt key
						self.check_determinant()  # validate the determinant of the encryption key
						lowerCAmelCase__					:    Optional[int]												=					encrypt_key.shape[0]



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      int:

						"""simple docstring"""






						return self.key_string.index(__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      str:

						"""simple docstring"""






						return self.key_string[round(__lowerCamelCase			)]



		def 		lowerCAmelCase__				(self			)					->      None:

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					round(numpy.linalg.det(self.encrypt_key			)			)

						if det < 0:
										lowerCAmelCase__					:    str												=					det % len(self.key_string			)

						lowerCAmelCase__					:    Optional[Any]												=					len(self.key_string			)
						if greatest_common_divisor(__lowerCamelCase		,len(self.key_string			)			) != 1:
										lowerCAmelCase__					:    List[str]												=					(
										    f"""determinant modular {req_l} of encryption key({det}) """
										    f"""is not co prime w.r.t {req_l}.\nTry another key."""
										)
										raise ValueError(__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					[char for char in text.upper() if char in self.key_string]

						lowerCAmelCase__					:    int												=					chars[-1]
						while len(__lowerCamelCase			) % self.break_key != 0:
										chars.append(__lowerCamelCase			)

						return "".join(__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    List[str]												=					self.process_text(text.upper()			)
						lowerCAmelCase__					:    str												=					''''''

						for i in range(0		,len(__lowerCamelCase			) - self.break_key + 1		,self.break_key			):
										lowerCAmelCase__					:    Any												=					text[i : i + self.break_key]
										lowerCAmelCase__					:    Dict												=					[self.replace_letters(__lowerCamelCase			) for char in batch]
										lowerCAmelCase__					:    int												=					numpy.array([vec]			).T
										lowerCAmelCase__					:    Union[str, Any]												=					self.modulus(self.encrypt_key.dot(__lowerCamelCase			)			).T.tolist()[
										    0
										]
										lowerCAmelCase__					:    Union[str, Any]												=					''''''.join(
										    self.replace_digits(__lowerCamelCase			) for num in batch_encrypted			)
										encrypted += encrypted_batch

						return encrypted



		def 		lowerCAmelCase__				(self			)					->      numpy.ndarray:

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					round(numpy.linalg.det(self.encrypt_key			)			)

						if det < 0:
										lowerCAmelCase__					:    int												=					det % len(self.key_string			)
						lowerCAmelCase__					:    Dict												=					None
						for i in range(len(self.key_string			)			):
										if (det * i) % len(self.key_string			) == 1:
														lowerCAmelCase__					:    Optional[Any]												=					i
														break

						lowerCAmelCase__					:    Optional[Any]												=					(
						    det_inv
						    * numpy.linalg.det(self.encrypt_key			)
						    * numpy.linalg.inv(self.encrypt_key			)
						)

						return self.to_int(self.modulus(__lowerCamelCase			)			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					self.make_decrypt_key()
						lowerCAmelCase__					:    List[str]												=					self.process_text(text.upper()			)
						lowerCAmelCase__					:    Optional[Any]												=					''''''

						for i in range(0		,len(__lowerCamelCase			) - self.break_key + 1		,self.break_key			):
										lowerCAmelCase__					:    List[Any]												=					text[i : i + self.break_key]
										lowerCAmelCase__					:    Tuple												=					[self.replace_letters(__lowerCamelCase			) for char in batch]
										lowerCAmelCase__					:    Optional[Any]												=					numpy.array([vec]			).T
										lowerCAmelCase__					:    Tuple												=					self.modulus(decrypt_key.dot(__lowerCamelCase			)			).T.tolist()[0]
										lowerCAmelCase__					:    Optional[int]												=					''''''.join(
										    self.replace_digits(__lowerCamelCase			) for num in batch_decrypted			)
										decrypted += decrypted_batch

						return decrypted






def      lowerCAmelCase__		(   ):





				'''simple docstring'''

				lowerCAmelCase__					:    Any												=					int(input('''Enter the order of the encryption key: '''))
				lowerCAmelCase__					:    Union[str, Any]												=					[]

				print('''Enter each row of the encryption key with space separated integers''')
				for _ in range(lowerCamelCase_):
								lowerCAmelCase__					:    int												=					[int(lowerCamelCase_) for x in input().split()]
								hill_matrix.append(lowerCamelCase_)

				lowerCAmelCase__					:    List[str]												=					HillCipher(numpy.array(lowerCamelCase_))

				print('''Would you like to encrypt or decrypt some text? (1 or 2)''')
				lowerCAmelCase__					:    List[Any]												=					input('''\n1. Encrypt\n2. Decrypt\n''')
				if option == "1":
								lowerCAmelCase__					:    Optional[int]												=					input('''What text would you like to encrypt?: ''')
								print('''Your encrypted text is:''')
								print(hc.encrypt(lowerCamelCase_))
				elif option == "2":
								lowerCAmelCase__					:    Dict												=					input('''What text would you like to decrypt?: ''')
								print('''Your decrypted text is:''')
								print(hc.decrypt(lowerCamelCase_))


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							main()

