





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  (       __magic_name__			):


   if hor == 128:
      lowercase__					  =    ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
      lowercase__					  =    (32, 128, 256)
      lowercase__					  =    ("UpResnetBlock1D", "UpResnetBlock1D")

   elif hor == 32:
      lowercase__					  =    ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
      lowercase__					  =    (32, 64, 128, 256)
      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": 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",
   }
   lowercase__					  =    UNetaDModel(**snake_case__			)
   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(snake_case__			)
   hf_value_function.load_state_dict(snake_case__			)

   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(snake_case__    ,   snake_case__			)


def _A  (       ):
   lowercase__					  =    {
       "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",
   }

   lowercase__					  =    torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"			)
   lowercase__					  =    model
   lowercase__					  =    UNetaDModel(**snake_case__			)
   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(snake_case__			)

   hf_value_function.load_state_dict(snake_case__			)

   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(snake_case__    ,   snake_case__			)


if __name__ == "__main__":
  unet(32)
  # unet(128)
  value_function()









"""simple docstring"""




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


A_				:      List[Any]           =			logging.get_logger(__name__)

A_				:      Optional[int]           =			{
    "facebook/dpr-ctx_encoder-single-nq-base": (
        "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-question_encoder-single-nq-base": (
        "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-reader-single-nq-base": (
        "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-ctx_encoder-multiset-base": (
        "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"
    ),
    "facebook/dpr-question_encoder-multiset-base": (
        "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"
    ),
    "facebook/dpr-reader-multiset-base": (
        "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"
    ),
}





class  lowerCamelCase    (A__      ):
			lowerCamelCase__ :		Optional[int]       		=				'dpr'

			def __init__(       self					:    Optional[int]     ,					__UpperCAmelCase					:    List[str]=3_0_5_2_2     ,					__UpperCAmelCase					:    int=7_6_8     ,					__UpperCAmelCase					:    Optional[Any]=1_2     ,					__UpperCAmelCase					:    Dict=1_2     ,					__UpperCAmelCase					:    Any=3_0_7_2     ,					__UpperCAmelCase					:    int="gelu"     ,					__UpperCAmelCase					:    int=0.1     ,					__UpperCAmelCase					:    Optional[Any]=0.1     ,					__UpperCAmelCase					:    Any=5_1_2     ,					__UpperCAmelCase					:    Union[str, Any]=2     ,					__UpperCAmelCase					:    Any=0.02     ,					__UpperCAmelCase					:    Dict=1e-12     ,					__UpperCAmelCase					:    Optional[Any]=0     ,					__UpperCAmelCase					:    Union[str, Any]="absolute"     ,					__UpperCAmelCase					:    int = 0     ,					**__UpperCAmelCase					:    Dict     ,					) ->     Tuple:
						super().__init__(pad_token_id=__UpperCAmelCase     ,					**__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   vocab_size
						SCREAMING_SNAKE_CASE__										=   hidden_size
						SCREAMING_SNAKE_CASE__										=   num_hidden_layers
						SCREAMING_SNAKE_CASE__										=   num_attention_heads
						SCREAMING_SNAKE_CASE__										=   hidden_act
						SCREAMING_SNAKE_CASE__										=   intermediate_size
						SCREAMING_SNAKE_CASE__										=   hidden_dropout_prob
						SCREAMING_SNAKE_CASE__										=   attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__										=   max_position_embeddings
						SCREAMING_SNAKE_CASE__										=   type_vocab_size
						SCREAMING_SNAKE_CASE__										=   initializer_range
						SCREAMING_SNAKE_CASE__										=   layer_norm_eps
						SCREAMING_SNAKE_CASE__										=   projection_dim
						SCREAMING_SNAKE_CASE__										=   position_embedding_type


