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						):
					if hor == 128:
										__a		 =       ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
										__a		 =       (32, 128, 256)
										__a		 =       ('UpResnetBlock1D', 'UpResnetBlock1D')

					elif hor == 32:
										__a		 =       ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
										__a		 =       (32, 64, 128, 256)
										__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_5536,
					    'mid_block_type': 'MidResTemporalBlock1D',
					    'act_fn': 'mish',
					}
					__a		 =       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()						)}'''						)
					__a		 =       dict(zip(model.state_dict().keys()				,    hf_value_function.state_dict().keys()						)						)
					for k, v in mapping.items():
										__a		 =       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(     ):
					__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, 128, 256),
					    'layers_per_block': 1,
					    'downsample_each_block': True,
					    'sample_size': 6_5536,
					    '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(**__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()						)}'''						)

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





from collections import defaultdict
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       first_str.lower().strip()
					__a		 =       second_str.lower().strip()

					# Remove whitespace
					__a		 =       first_str.replace(' '				,    ''						)
					__a		 =       second_str.replace(' '				,    ''						)

					# Strings of different lengths are not anagrams
					if len(__lowerCamelCase						) != len(__lowerCamelCase						):
										return False

					# Default values for count should be 0
					__a		 =       defaultdict(__lowerCamelCase						)

					# For each character in input strings,
					# increment count in the corresponding
					for i in range(len(__lowerCamelCase						)						):
										count[first_str[i]] += 1
										count[second_str[i]] -= 1

					return all(_count == 0 for _count in count.values()						)


if __name__ == "__main__":
	from doctest import testmod

	testmod()
	lowerCamelCase_    :		List[str]							=	input("""Enter the first string """).strip()
	lowerCamelCase_    :		Optional[Any]							=	input("""Enter the second string """).strip()

	lowerCamelCase_    :		str							=	check_anagrams(input_a, input_b)
	print(F'''{input_a} and {input_b} are {'' if status else 'not '}anagrams.''')




