



from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase		       =      logging.get_logger(__name__)

lowerCamelCase		       =      {
    """s-JoL/Open-Llama-V1""": """https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json""",
}


class 				_a						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							A				:Any =       'open-llama'



							def __init__(							self ,					__UpperCAmelCase=10_0000 ,					__UpperCAmelCase=4096 ,					__UpperCAmelCase=1_1008 ,					__UpperCAmelCase=32 ,					__UpperCAmelCase=32 ,					__UpperCAmelCase="silu" ,					__UpperCAmelCase=2048 ,					__UpperCAmelCase=0.0_2 ,					__UpperCAmelCase=1E-6 ,					__UpperCAmelCase=True ,					__UpperCAmelCase=0 ,					__UpperCAmelCase=1 ,					__UpperCAmelCase=2 ,					__UpperCAmelCase=False ,					__UpperCAmelCase=True ,					__UpperCAmelCase=0.1 ,					__UpperCAmelCase=0.1 ,					__UpperCAmelCase=True ,					__UpperCAmelCase=True ,					__UpperCAmelCase=None ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											a__	:							int      						= vocab_size
											a__	:							Dict      						= max_position_embeddings
											a__	:							Any      						= hidden_size
											a__	:							Optional[Any]      						= intermediate_size
											a__	:							Any      						= num_hidden_layers
											a__	:							Any      						= num_attention_heads
											a__	:							List[Any]      						= hidden_act
											a__	:							str      						= initializer_range
											a__	:							str      						= rms_norm_eps
											a__	:							int      						= use_cache
											a__	:							str      						= kwargs.pop(
											    "use_memorry_efficient_attention" ,					__UpperCAmelCase				)
											a__	:							str      						= hidden_dropout_prob
											a__	:							List[str]      						= attention_dropout_prob
											a__	:							int      						= use_stable_embedding
											a__	:							List[Any]      						= shared_input_output_embedding
											a__	:							Tuple      						= rope_scaling
											self._rope_scaling_validation()

											super().__init__(
											    pad_token_id=__UpperCAmelCase ,					bos_token_id=__UpperCAmelCase ,					eos_token_id=__UpperCAmelCase ,					tie_word_embeddings=__UpperCAmelCase ,					**__UpperCAmelCase ,					)




							def 		_A	(							self				):







											"""simple docstring"""



											if self.rope_scaling is None:
															return

											if not isinstance(self.rope_scaling ,					__UpperCAmelCase				) or len(self.rope_scaling				) != 2:
															raise ValueError(
															    "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
															    f'got {self.rope_scaling}'				)
											a__	:							str      						= self.rope_scaling.get("type" ,					__UpperCAmelCase				)
											a__	:							int      						= self.rope_scaling.get("factor" ,					__UpperCAmelCase				)
											if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
															raise ValueError(
															    f'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'				)
											if rope_scaling_factor is None or not isinstance(__UpperCAmelCase ,					__UpperCAmelCase				) or rope_scaling_factor <= 1.0:
															raise ValueError(f'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'				)





"""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       SCREAMING_SNAKE_CASE							( lowercase__   )			->						Optional[Any]:
 if hor == 1_2_8:
  lowerCAmelCase__   :				str 			=				("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
  lowerCAmelCase__   :				int 			=				(3_2, 1_2_8, 2_5_6)
  lowerCAmelCase__   :				Optional[Any] 			=				("UpResnetBlock1D", "UpResnetBlock1D")

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

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

 lowerCAmelCase__   :				str 			=				dict(zip(state_dict.keys()					,					hf_value_function.state_dict().keys()   )   )
 for k, v in mapping.items():
  lowerCAmelCase__   :				List[Any] 			=				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(3_2)
    # unet(128)
    value_function()






