

"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCamelCase		     =    {
    """configuration_table_transformer""": [
        """TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TableTransformerConfig""",
        """TableTransformerOnnxConfig""",
    ]
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		     =    [
        """TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TableTransformerForObjectDetection""",
        """TableTransformerModel""",
        """TableTransformerPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_table_transformer import (
        TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
        TableTransformerConfig,
        TableTransformerOnnxConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_table_transformer import (
            TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            TableTransformerForObjectDetection,
            TableTransformerModel,
            TableTransformerPreTrainedModel,
        )

else:
    import sys

    _UpperCamelCase		     =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)









"""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()






