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							(UpperCAmelCase__     :							Union[str, Any]  ):
					if hor == 1_2_8:
										SCREAMING_SNAKE_CASE    			=       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
										SCREAMING_SNAKE_CASE    			=       (3_2, 1_2_8, 2_5_6)
										SCREAMING_SNAKE_CASE    			=       ("UpResnetBlock1D", "UpResnetBlock1D")

					elif hor == 3_2:
										SCREAMING_SNAKE_CASE    			=       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
										SCREAMING_SNAKE_CASE    			=       (3_2, 6_4, 1_2_8, 2_5_6)
										SCREAMING_SNAKE_CASE    			=       ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
					SCREAMING_SNAKE_CASE    			=       torch.load(F"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"  )
					SCREAMING_SNAKE_CASE    			=       model.state_dict()
					SCREAMING_SNAKE_CASE    			=       {
					    "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",
					}
					SCREAMING_SNAKE_CASE    			=       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    			=       dict(zip(model.state_dict().keys()      , hf_value_function.state_dict().keys()  )  )
					for k, v in mapping.items():
										SCREAMING_SNAKE_CASE    			=       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    			=       {
					    "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",
					}

					SCREAMING_SNAKE_CASE    			=       torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"  )
					SCREAMING_SNAKE_CASE    			=       model
					SCREAMING_SNAKE_CASE    			=       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    			=       dict(zip(state_dict.keys()      , hf_value_function.state_dict().keys()  )  )
					for k, v in mapping.items():
										SCREAMING_SNAKE_CASE    			=       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()





# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import platform
import sys


_lowerCamelCase					:   List[Any]							=		'''3'''

print('''Python version:''', sys.version)

print('''OS platform:''', platform.platform())
print('''OS architecture:''', platform.machine())

try:
							import torch

							print('''Torch version:''', torch.__version__)
							print('''Cuda available:''', torch.cuda.is_available())
							print('''Cuda version:''', torch.version.cuda)
							print('''CuDNN version:''', torch.backends.cudnn.version())
							print('''Number of GPUs available:''', torch.cuda.device_count())
except ImportError:
							print('''Torch version:''', None)

try:
							import transformers

							print('''transformers version:''', transformers.__version__)
except ImportError:
							print('''transformers version:''', None)




