
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__   (  lowerCamelCase  )					->		Optional[int]:
				if hor == 1_28:
								UpperCamelCase_:							Tuple		=  ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
								UpperCamelCase_:							Dict		=  (32, 1_28, 2_56)
								UpperCamelCase_:							Any		=  ("""UpResnetBlock1D""", """UpResnetBlock1D""")

				elif hor == 32:
								UpperCamelCase_:							Union[str, Any]		=  ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
								UpperCamelCase_:							str		=  (32, 64, 1_28, 2_56)
								UpperCamelCase_:							Optional[int]		=  ("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
				UpperCamelCase_:							Union[str, Any]		=  torch.load(F'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'''  )
				UpperCamelCase_:							List[Any]		=  model.state_dict()
				UpperCamelCase_:							List[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""",
				}
				UpperCamelCase_:							Optional[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()  )}'''  )
				UpperCamelCase_:							str		=  dict(zip(model.state_dict().keys()       ,      hf_value_function.state_dict().keys()  )  )
				for k, v in mapping.items():
								UpperCamelCase_:							int		=  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     A__   (  )					->		Optional[int]:
				UpperCamelCase_:							Union[str, Any]		=  {
				    """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""",
				}

				UpperCamelCase_:							Tuple		=  torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"""  )
				UpperCamelCase_:							Optional[Any]		=  model
				UpperCamelCase_:							Dict		=  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()  )}'''  )

				UpperCamelCase_:							int		=  dict(zip(state_dict.keys()       ,      hf_value_function.state_dict().keys()  )  )
				for k, v in mapping.items():
								UpperCamelCase_:							Union[str, Any]		=  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 ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_  :	Tuple 				=					logging.get_logger(__name__)

lowerCamelCase_  :	str 				=					{
    """unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json""",
}

class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  Any    =	"""lxmert"""
			__UpperCamelCase :  Optional[Any]    =	{}

			def __init__(    self	:    Dict  ,		snake_case_	:    List[str]=3_0522  ,		snake_case_	:    int=768  ,		snake_case_	:    str=12  ,		snake_case_	:    Tuple=9500  ,		snake_case_	:    Optional[Any]=1600  ,		snake_case_	:    str=400  ,		snake_case_	:    List[str]=3072  ,		snake_case_	:    Dict="gelu"  ,		snake_case_	:    Optional[Any]=0.1  ,		snake_case_	:    int=0.1  ,		snake_case_	:    Optional[int]=512  ,		snake_case_	:    Tuple=2  ,		snake_case_	:    Optional[int]=0.02  ,		snake_case_	:    Union[str, Any]=1e-12  ,		snake_case_	:    Tuple=9  ,		snake_case_	:    Tuple=5  ,		snake_case_	:    Union[str, Any]=5  ,		snake_case_	:    List[str]=2048  ,		snake_case_	:    Any=4  ,		snake_case_	:    int=6.67  ,		snake_case_	:    Dict=True  ,		snake_case_	:    Tuple=True  ,		snake_case_	:    int=True  ,		snake_case_	:    str=True  ,		snake_case_	:    List[Any]=True  ,		snake_case_	:    List[Any]=True  ,		snake_case_	:    List[str]=True  ,		**snake_case_	:    Optional[int]  ,		):
							UpperCamelCase_:							int		=  vocab_size
							UpperCamelCase_:							Tuple		=  hidden_size
							UpperCamelCase_:							Optional[Any]		=  num_attention_heads
							UpperCamelCase_:							List[str]		=  hidden_act
							UpperCamelCase_:							int		=  intermediate_size
							UpperCamelCase_:							Any		=  hidden_dropout_prob
							UpperCamelCase_:							List[Any]		=  attention_probs_dropout_prob
							UpperCamelCase_:							List[str]		=  max_position_embeddings
							UpperCamelCase_:							Optional[Any]		=  type_vocab_size
							UpperCamelCase_:							Optional[Any]		=  initializer_range
							UpperCamelCase_:							str		=  layer_norm_eps
							UpperCamelCase_:							List[str]		=  num_qa_labels
							UpperCamelCase_:							List[str]		=  num_object_labels
							UpperCamelCase_:							Tuple		=  num_attr_labels
							UpperCamelCase_:							List[Any]		=  l_layers
							UpperCamelCase_:							List[Any]		=  x_layers
							UpperCamelCase_:							Any		=  r_layers
							UpperCamelCase_:							str		=  visual_feat_dim
							UpperCamelCase_:							Union[str, Any]		=  visual_pos_dim
							UpperCamelCase_:							str		=  visual_loss_normalizer
							UpperCamelCase_:							Dict		=  task_matched
							UpperCamelCase_:							Tuple		=  task_mask_lm
							UpperCamelCase_:							List[str]		=  task_obj_predict
							UpperCamelCase_:							Any		=  task_qa
							UpperCamelCase_:							Dict		=  visual_obj_loss
							UpperCamelCase_:							Optional[Any]		=  visual_attr_loss
							UpperCamelCase_:							List[Any]		=  visual_feat_loss
							UpperCamelCase_:							Tuple		=  {"""vision""": r_layers, """cross_encoder""": x_layers, """language""": l_layers}
							super().__init__(**snake_case_ )






