





'''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_     :					str			):
			if hor == 1_2_8:
						__lowercase			  =							('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
						__lowercase			  =							(3_2, 1_2_8, 2_5_6)
						__lowercase			  =							('''UpResnetBlock1D''', '''UpResnetBlock1D''')

			elif hor == 3_2:
						__lowercase			  =							('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
						__lowercase			  =							(3_2, 6_4, 1_2_8, 2_5_6)
						__lowercase			  =							('''UpResnetBlock1D''', '''UpResnetBlock1D''', '''UpResnetBlock1D''')
			__lowercase			  =							torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"			)
			__lowercase			  =							model.state_dict()
			__lowercase			  =							{
			    '''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''': 1_4,
			    '''out_channels''': 1_4,
			    '''extra_in_channels''': 0,
			    '''time_embedding_type''': '''positional''',
			    '''flip_sin_to_cos''': False,
			    '''freq_shift''': 1,
			    '''sample_size''': 6_5_5_3_6,
			    '''mid_block_type''': '''MidResTemporalBlock1D''',
			    '''act_fn''': '''mish''',
			}
			__lowercase			  =							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()			)}"			)
			__lowercase			  =							dict(zip(model.state_dict().keys()						,    hf_value_function.state_dict().keys()			)			)
			for k, v in mapping.items():
						__lowercase			  =							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  (			):
			__lowercase			  =							{
			    '''in_channels''': 1_4,
			    '''down_block_types''': ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D'''),
			    '''up_block_types''': (),
			    '''out_block_type''': '''ValueFunction''',
			    '''mid_block_type''': '''ValueFunctionMidBlock1D''',
			    '''block_out_channels''': (3_2, 6_4, 1_2_8, 2_5_6),
			    '''layers_per_block''': 1,
			    '''downsample_each_block''': True,
			    '''sample_size''': 6_5_5_3_6,
			    '''out_channels''': 1_4,
			    '''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''',
			}

			__lowercase			  =							torch.load('''/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'''			)
			__lowercase			  =							model
			__lowercase			  =							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()			)}"			)

			__lowercase			  =							dict(zip(state_dict.keys()						,    hf_value_function.state_dict().keys()			)			)
			for k, v in mapping.items():
						__lowercase			  =							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(3_2)
	# unet(128)
	value_function()












'''simple docstring'''



def 				_lowerCAmelCase  (			lowerCamelCase_     :					str						,    lowerCamelCase_     :					str			):
			__lowercase			  =							len(lowerCamelCase_			) + 1
			__lowercase			  =							len(lowerCamelCase_			) + 1

			# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
			# length i of input_string matches with prefix string of length j of
			# given pattern.
			# "dp" stands for dynamic programming.
			__lowercase			  =							[[0 for i in range(lowerCamelCase_			)] for j in range(lowerCamelCase_			)]

			# since string of zero length match pattern of zero length
			__lowercase			  =							1

			# since pattern of zero length will never match with string of non-zero length
			for i in range(1						,    lowerCamelCase_			):
						__lowercase			  =							0

			# since string of zero length will match with pattern where there
			# is at least one * alternatively
			for j in range(1						,    lowerCamelCase_			):
						__lowercase			  =							dp[0][j - 2] if pattern[j - 1] == '''*''' else 0

			# now using bottom-up approach to find for all remaining lengths
			for i in range(1						,    lowerCamelCase_			):
						for j in range(1						,    lowerCamelCase_			):
									if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
												__lowercase			  =							dp[i - 1][j - 1]

									elif pattern[j - 1] == "*":
												if dp[i][j - 2] == 1:
															__lowercase			  =							1
												elif pattern[j - 2] in (input_string[i - 1], "."):
															__lowercase			  =							dp[i - 1][j]
												else:
															__lowercase			  =							0
									else:
												__lowercase			  =							0

			return bool(dp[-1][-1]			)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	# inputing the strings
	# input_string = input("input a string :")
	# pattern = input("input a pattern :")

	_SCREAMING_SNAKE_CASE   =      '''aab'''
	_SCREAMING_SNAKE_CASE   =      '''c*a*b'''

	# using function to check whether given string matches the given pattern
	if match_pattern(input_string, pattern):
		print(f'''{input_string} matches the given pattern {pattern}''')
	else:
		print(f'''{input_string} does not match with the given pattern {pattern}''')





