
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 						__magic_name__			(   __a      :	Optional[int]						):



					'''simple docstring'''




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

					elif hor == 32:
										UpperCamelCase__				=    ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
										UpperCamelCase__				=    (32, 64, 128, 256)
										UpperCamelCase__				=    ("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
					UpperCamelCase__				=    torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"						)
					UpperCamelCase__				=    model.state_dict()
					UpperCamelCase__				=    {
					    """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""": 65_536,
					    """mid_block_type""": """MidResTemporalBlock1D""",
					    """act_fn""": """mish""",
					}
					UpperCamelCase__				=    UNetaDModel(**__a						)
					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__				=    dict(zip(model.state_dict().keys()	,		hf_value_function.state_dict().keys()						)						)
					for k, v in mapping.items():
										UpperCamelCase__				=    state_dict.pop(__a						)
					hf_value_function.load_state_dict(__a						)

					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(__a	,		__a						)






def 						__magic_name__			(   ):



					'''simple docstring'''




					UpperCamelCase__				=    {
					    """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""": 65_536,
					    """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__				=    torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"""						)
					UpperCamelCase__				=    model
					UpperCamelCase__				=    UNetaDModel(**__a						)
					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__				=    dict(zip(state_dict.keys()	,		hf_value_function.state_dict().keys()						)						)
					for k, v in mapping.items():
										UpperCamelCase__				=    state_dict.pop(__a						)

					hf_value_function.load_state_dict(__a						)

					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(__a	,		__a						)


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







import argparse
from collections import defaultdict

import yaml


lowerCamelCase_						     =					'''docs/source/en/_toctree.yml'''






def 						__magic_name__			(   __a      :	Union[str, Any]						):



     '''simple docstring'''




     UpperCamelCase__				=    defaultdict(__a						)
     for doc in model_doc:
          counts[doc["local"]] += 1
     UpperCamelCase__				=    [key for key, value in counts.items() if value > 1]

     UpperCamelCase__				=    []
     for duplicate_key in duplicates:
          UpperCamelCase__				=    list({doc["""title"""] for doc in model_doc if doc["""local"""] == duplicate_key}						)
          if len(__a						) > 1:
               raise ValueError(
                   f"{duplicate_key} is present several times in the documentation table of content at "
                   """`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the """
                   """others."""						)
          # Only add this once
          new_doc.append({"""local""": duplicate_key, """title""": titles[0]}						)

     # Add none duplicate-keys
     new_doc.extend([doc for doc in model_doc if counts[doc["""local"""]] == 1]						)

     # Sort
     return sorted(__a	,		key=lambda __a						: s["title"].lower()						)






def 						__magic_name__			(   __a      :	int=False						):



     '''simple docstring'''




     with open(__a	,		encoding="""utf-8"""						) as f:
          UpperCamelCase__				=    yaml.safe_load(f.read()						)

     # Get to the API doc
     UpperCamelCase__				=    0
     while content[api_idx]["title"] != "API":
          api_idx += 1
     UpperCamelCase__				=    content[api_idx]["""sections"""]

     # Then to the model doc
     UpperCamelCase__				=    0
     while api_doc[model_idx]["title"] != "Models":
          model_idx += 1

     UpperCamelCase__				=    api_doc[model_idx]["""sections"""]

     UpperCamelCase__				=    [(idx, section) for idx, section in enumerate(__a						) if """sections""" in section]
     UpperCamelCase__				=    False
     for idx, modality_doc in modalities_docs:
          UpperCamelCase__				=    modality_doc["""sections"""]
          UpperCamelCase__				=    clean_model_doc_toc(__a						)

          if old_modality_doc != new_modality_doc:
               UpperCamelCase__				=    True
               if overwrite:
                    UpperCamelCase__				=    new_modality_doc

     if diff:
          if overwrite:
               UpperCamelCase__				=    model_doc
               UpperCamelCase__				=    api_doc
               with open(__a	,		"""w"""	,		encoding="""utf-8"""						) as f:
                    f.write(yaml.dump(__a	,		allow_unicode=__a						)						)
          else:
               raise ValueError(
                   """The model doc part of the table of content is not properly sorted, run `make style` to fix this."""						)


if __name__ == "__main__":
    lowerCamelCase_						     =					argparse.ArgumentParser()
    parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
    lowerCamelCase_						     =					parser.parse_args()

    check_model_doc(args.fix_and_overwrite)





