






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      __A					(     _lowercase ):

					'''simple docstring'''







					if hor == 1_28:
										_A								= ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
										_A								= (32, 1_28, 2_56)
										_A								= ('''UpResnetBlock1D''', '''UpResnetBlock1D''')

					elif hor == 32:
										_A								= ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
										_A								= (32, 64, 1_28, 2_56)
										_A								= ('''UpResnetBlock1D''', '''UpResnetBlock1D''', '''UpResnetBlock1D''')
					_A								= torch.load(f"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch""" )
					_A								= model.state_dict()
					_A								= {
					    '''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''',
					}
					_A								= UNetaDModel(**_lowercase )
					print(f"""length of state dict: {len(state_dict.keys() )}""" )
					print(f"""length of value function dict: {len(hf_value_function.state_dict().keys() )}""" )
					_A								= dict(zip(model.state_dict().keys()					,			hf_value_function.state_dict().keys() ) )
					for k, v in mapping.items():
										_A								= state_dict.pop(_lowercase )
					hf_value_function.load_state_dict(_lowercase )

					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(_lowercase					,			_lowercase )




def      __A					(     ):

					'''simple docstring'''







					_A								= {
					    '''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''',
					}

					_A								= torch.load('''/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch''' )
					_A								= model
					_A								= UNetaDModel(**_lowercase )
					print(f"""length of state dict: {len(state_dict.keys() )}""" )
					print(f"""length of value function dict: {len(hf_value_function.state_dict().keys() )}""" )

					_A								= dict(zip(state_dict.keys()					,			hf_value_function.state_dict().keys() ) )
					for k, v in mapping.items():
										_A								= state_dict.pop(_lowercase )

					hf_value_function.load_state_dict(_lowercase )

					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(_lowercase					,			_lowercase )


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











import itertools
import string
from collections.abc import Generator, Iterable




def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= iter(_lowercase )
					while True:
										_A								= tuple(itertools.islice(_lowercase					,			_lowercase ) )
										if not chunk:
															return
										yield chunk




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= ''''''.join([c.upper() for c in dirty if c in string.ascii_letters] )
					_A								= ''''''

					if len(_lowercase ) < 2:
										return dirty

					for i in range(len(_lowercase ) - 1 ):
										clean += dirty[i]

										if dirty[i] == dirty[i + 1]:
															clean += "X"

					clean += dirty[-1]

					if len(_lowercase ) & 1:
										clean += "X"

					return clean




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= '''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
					# we're using a list instead of a '2d' array because it makes the math
					# for setting up the table and doing the actual encoding/decoding simpler
					_A								= []

					# copy key chars into the table if they are in `alphabet` ignoring duplicates
					for char in key.upper():
										if char not in table and char in alphabet:
															table.append(_lowercase )

    # fill the rest of the table in with the remaining alphabet chars
					for char in alphabet:
										if char not in table:
															table.append(_lowercase )

					return table




def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= generate_table(_lowercase )
					_A								= prepare_input(_lowercase )
					_A								= ''''''

					# https://en.wikipedia.org/wiki/Playfair_cipher#Description
					for chara, chara in chunker(_lowercase					,			2 ):
										_A							,_A								= divmod(table.index(_lowercase )					,			5 )
										_A							,_A								= divmod(table.index(_lowercase )					,			5 )

										if rowa == rowa:
															ciphertext += table[rowa * 5 + (cola + 1) % 5]
															ciphertext += table[rowa * 5 + (cola + 1) % 5]
										elif cola == cola:
															ciphertext += table[((rowa + 1) % 5) * 5 + cola]
															ciphertext += table[((rowa + 1) % 5) * 5 + cola]
										else:  # rectangle
															ciphertext += table[rowa * 5 + cola]
															ciphertext += table[rowa * 5 + cola]

					return ciphertext




def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= generate_table(_lowercase )
					_A								= ''''''

					# https://en.wikipedia.org/wiki/Playfair_cipher#Description
					for chara, chara in chunker(_lowercase					,			2 ):
										_A							,_A								= divmod(table.index(_lowercase )					,			5 )
										_A							,_A								= divmod(table.index(_lowercase )					,			5 )

										if rowa == rowa:
															plaintext += table[rowa * 5 + (cola - 1) % 5]
															plaintext += table[rowa * 5 + (cola - 1) % 5]
										elif cola == cola:
															plaintext += table[((rowa - 1) % 5) * 5 + cola]
															plaintext += table[((rowa - 1) % 5) * 5 + cola]
										else:  # rectangle
															plaintext += table[rowa * 5 + cola]
															plaintext += table[rowa * 5 + cola]

					return plaintext



