



'''simple docstring'''




import string

import numpy


def        lowerCAmelCase_     (			_lowerCamelCase:     int ,   _lowerCamelCase:     int      ):
		return b if a == 0 else greatest_common_divisor(b % a ,   _lowerCamelCase      )






class 		_UpperCamelCase						:


			'''simple docstring'''

			_A   :	Union[str, Any]    					=	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)
			_A   :	Union[str, Any]    					=	numpy.vectorize(lambda lowerCamelCase__					: x % 36					)

			_A   :	str    					=	numpy.vectorize(lowerCamelCase__					)

			def __init__(							self  :				Any							,  lowerCAmelCase__  :				numpy.ndarray		):





					"""simple docstring"""






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

			def UpperCamelCase__      (							self  :				Any							,  lowerCAmelCase__  :				str		):





					"""simple docstring"""






					return self.key_string.index(lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				str							,  lowerCAmelCase__  :				int		):





					"""simple docstring"""






					return self.key_string[round(lowerCAmelCase__		)]

			def UpperCamelCase__      (							self  :				int		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	round(numpy.linalg.det(self.encrypt_key		)		)

					if det < 0:
							__SCREAMING_SNAKE_CASE :	Tuple					 =	det % len(self.key_string		)

					__SCREAMING_SNAKE_CASE :	Any					 =	len(self.key_string		)
					if greatest_common_divisor(lowerCAmelCase__							,  len(self.key_string		)		) != 1:
							__SCREAMING_SNAKE_CASE :	Optional[int]					 =	(
							    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 UpperCamelCase__      (							self  :				Any							,  lowerCAmelCase__  :				str		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	[char for char in text.upper() if char in self.key_string]

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

					return "".join(lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				Any							,  lowerCAmelCase__  :				str		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Dict					 =	self.process_text(text.upper()		)
					__SCREAMING_SNAKE_CASE :	Any					 =	""""""

					for i in range(0							,  len(lowerCAmelCase__		) - self.break_key + 1							,  self.break_key		):
							__SCREAMING_SNAKE_CASE :	Optional[int]					 =	text[i : i + self.break_key]
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	[self.replace_letters(lowerCAmelCase__		) for char in batch]
							__SCREAMING_SNAKE_CASE :	str					 =	numpy.array([vec]		).T
							__SCREAMING_SNAKE_CASE :	Tuple					 =	self.modulus(self.encrypt_key.dot(lowerCAmelCase__		)		).T.tolist()[
							    0
							]
							__SCREAMING_SNAKE_CASE :	str					 =	"""""".join(
							    self.replace_digits(lowerCAmelCase__		) for num in batch_encrypted		)
							encrypted += encrypted_batch

					return encrypted

			def UpperCamelCase__      (							self  :				Optional[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	round(numpy.linalg.det(self.encrypt_key		)		)

					if det < 0:
							__SCREAMING_SNAKE_CASE :	Dict					 =	det % len(self.key_string		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	None
					for i in range(len(self.key_string		)		):
							if (det * i) % len(self.key_string		) == 1:
									__SCREAMING_SNAKE_CASE :	Dict					 =	i
									break

					__SCREAMING_SNAKE_CASE :	int					 =	(
					    det_inv
					    * numpy.linalg.det(self.encrypt_key		)
					    * numpy.linalg.inv(self.encrypt_key		)
					)

					return self.to_int(self.modulus(lowerCAmelCase__		)		)



			def UpperCamelCase__      (							self  :				Optional[int]							,  lowerCAmelCase__  :				str		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	int					 =	self.make_decrypt_key()
					__SCREAMING_SNAKE_CASE :	List[str]					 =	self.process_text(text.upper()		)
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	""""""

					for i in range(0							,  len(lowerCAmelCase__		) - self.break_key + 1							,  self.break_key		):
							__SCREAMING_SNAKE_CASE :	Optional[int]					 =	text[i : i + self.break_key]
							__SCREAMING_SNAKE_CASE :	Any					 =	[self.replace_letters(lowerCAmelCase__		) for char in batch]
							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	numpy.array([vec]		).T
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.modulus(decrypt_key.dot(lowerCAmelCase__		)		).T.tolist()[0]
							__SCREAMING_SNAKE_CASE :	int					 =	"""""".join(
							    self.replace_digits(lowerCAmelCase__		) for num in batch_decrypted		)
							decrypted += decrypted_batch

					return decrypted


def        lowerCAmelCase_     (			):
		__SCREAMING_SNAKE_CASE :	List[str]					 =	int(input("""Enter the order of the encryption key: """      )      )
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	[]

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

		__SCREAMING_SNAKE_CASE :	List[Any]					 =	HillCipher(numpy.array(_lowerCamelCase      )      )

		print("""Would you like to encrypt or decrypt some text? (1 or 2)"""      )
		__SCREAMING_SNAKE_CASE :	List[str]					 =	input("""\n1. Encrypt\n2. Decrypt\n"""      )
		if option == "1":
				__SCREAMING_SNAKE_CASE :	str					 =	input("""What text would you like to encrypt?: """      )
				print("""Your encrypted text is:"""      )
				print(hc.encrypt(_lowerCamelCase      )      )
		elif option == "2":
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	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()




'''simple docstring'''




import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs('''hub/hopper-medium-v2/unet/hor32''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/unet/hor128''', exist_ok=True)

os.makedirs('''hub/hopper-medium-v2/value_function''', exist_ok=True)


def        lowerCAmelCase_     (			_lowerCamelCase:     int      ):
		if hor == 1_28:
				__SCREAMING_SNAKE_CASE :	Any					 =	("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
				__SCREAMING_SNAKE_CASE :	List[Any]					 =	(32, 1_28, 2_56)
				__SCREAMING_SNAKE_CASE :	str					 =	("""UpResnetBlock1D""", """UpResnetBlock1D""")

		elif hor == 32:
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
				__SCREAMING_SNAKE_CASE :	str					 =	(32, 64, 1_28, 2_56)
				__SCREAMING_SNAKE_CASE :	Tuple					 =	("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	torch.load(F"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"      )
		__SCREAMING_SNAKE_CASE :	Any					 =	model.state_dict()
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	{
		    """down_block_types""": down_block_types,
		    """block_out_channels""": block_out_channels,
		    """up_block_types""": up_block_types,
		    """layers_per_block""": 1,
		    """use_timestep_embedding""": True,
		    """out_block_type""": """OutConv1DBlock""",
		    """norm_num_groups""": 8,
		    """downsample_each_block""": False,
		    """in_channels""": 14,
		    """out_channels""": 14,
		    """extra_in_channels""": 0,
		    """time_embedding_type""": """positional""",
		    """flip_sin_to_cos""": False,
		    """freq_shift""": 1,
		    """sample_size""": 6_55_36,
		    """mid_block_type""": """MidResTemporalBlock1D""",
		    """act_fn""": """mish""",
		}
		__SCREAMING_SNAKE_CASE :	int					 =	UNetaDModel(**_lowerCamelCase      )
		print(F"length of state dict: {len(state_dict.keys()      )}"      )
		print(F"length of value function dict: {len(hf_value_function.state_dict().keys()      )}"      )
		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	dict(zip(model.state_dict().keys() ,   hf_value_function.state_dict().keys()      )      )
		for k, v in mapping.items():
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	state_dict.pop(_lowerCamelCase      )
		hf_value_function.load_state_dict(_lowerCamelCase      )

		torch.save(hf_value_function.state_dict() ,   F"hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin"      )
		with open(F"hub/hopper-medium-v2/unet/hor{hor}/config.json" ,   """w"""      ) as f:
				json.dump(_lowerCamelCase ,   _lowerCamelCase      )


def        lowerCAmelCase_     (			):
		__SCREAMING_SNAKE_CASE :	Dict					 =	{
		    """in_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, 1_28, 2_56),
		    """layers_per_block""": 1,
		    """downsample_each_block""": True,
		    """sample_size""": 6_55_36,
		    """out_channels""": 14,
		    """extra_in_channels""": 0,
		    """time_embedding_type""": """positional""",
		    """use_timestep_embedding""": True,
		    """flip_sin_to_cos""": False,
		    """freq_shift""": 1,
		    """norm_num_groups""": 8,
		    """act_fn""": """mish""",
		}

		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"""      )
		__SCREAMING_SNAKE_CASE :	Dict					 =	model
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	UNetaDModel(**_lowerCamelCase      )
		print(F"length of state dict: {len(state_dict.keys()      )}"      )
		print(F"length of value function dict: {len(hf_value_function.state_dict().keys()      )}"      )

		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	dict(zip(state_dict.keys() ,   hf_value_function.state_dict().keys()      )      )
		for k, v in mapping.items():
				__SCREAMING_SNAKE_CASE :	str					 =	state_dict.pop(_lowerCamelCase      )

		hf_value_function.load_state_dict(_lowerCamelCase      )

		torch.save(hf_value_function.state_dict() ,   """hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin"""      )
		with open("""hub/hopper-medium-v2/value_function/config.json""" ,   """w"""      ) as f:
				json.dump(_lowerCamelCase ,   _lowerCamelCase      )


if __name__ == "__main__":
						unet(32)
						# unet(128)
						value_function()