




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      (						SCREAMING_SNAKE_CASE_							:				List[Any]     ):
					if hor == 1_28:
										__lowerCAmelCase				   =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
										__lowerCAmelCase				   =       (32, 1_28, 2_56)
										__lowerCAmelCase				   =       ("UpResnetBlock1D", "UpResnetBlock1D")

					elif hor == 32:
										__lowerCAmelCase				   =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
										__lowerCAmelCase				   =       (32, 64, 1_28, 2_56)
										__lowerCAmelCase				   =       ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
					__lowerCAmelCase				   =       torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""     )
					__lowerCAmelCase				   =       model.state_dict()
					__lowerCAmelCase				   =       {
					    "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",
					}
					__lowerCAmelCase				   =       UNetaDModel(**SCREAMING_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()     )}"""     )
					__lowerCAmelCase				   =       dict(zip(model.state_dict().keys()      ,      hf_value_function.state_dict().keys()     )     )
					for k, v in mapping.items():
										__lowerCAmelCase				   =       state_dict.pop(SCREAMING_SNAKE_CASE_     )
					hf_value_function.load_state_dict(SCREAMING_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(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )







def 	_a      (						):
					__lowerCAmelCase				   =       {
					    "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",
					}

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

					__lowerCAmelCase				   =       dict(zip(state_dict.keys()      ,      hf_value_function.state_dict().keys()     )     )
					for k, v in mapping.items():
										__lowerCAmelCase				   =       state_dict.pop(SCREAMING_SNAKE_CASE_     )

					hf_value_function.load_state_dict(SCREAMING_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(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )


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











import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
__UpperCAmelCase       :   Any				       =							"src/diffusers"

# Matches is_xxx_available()
__UpperCAmelCase       :   List[str]				       =							re.compile(R"is\_([a-z_]*)_available\(\)")
# Matches from xxx import bla
__UpperCAmelCase       :   Dict				       =							re.compile(R"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")


__UpperCAmelCase       :   int				       =							"\n{0} = None\n"

__UpperCAmelCase       :   List[str]				       =							"\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n"


__UpperCAmelCase       :   Tuple				       =							"\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    Any:
  __snake_case:						int   =						_re_backend.findall(SCREAMING_SNAKE_CASE__)
  if len(SCREAMING_SNAKE_CASE__) == 0:
    return None

  return "_and_".join(SCREAMING_SNAKE_CASE__)







def 			A__							(    )   ->    Optional[int]:
  with open(os.path.join(SCREAMING_SNAKE_CASE__					,					"""__init__.py""")					,					"""r"""					,					encoding="""utf-8"""					,					newline="""\n""") as f:
    __snake_case:						Optional[Any]   =						f.readlines()

  # Get to the point we do the actual imports for type checking
  __snake_case:						Tuple   =						0
  __snake_case:						Any   =						{}
  # Go through the end of the file
  while line_index < len(SCREAMING_SNAKE_CASE__):
    # If the line contains is_backend_available, we grab all objects associated with the `else` block
    __snake_case:						List[Any]   =						find_backend(lines[line_index])
    if backend is not None:
      while not lines[line_index].startswith("""else:"""):
        line_index += 1
      line_index += 1
      __snake_case:						Any   =						[]
      # Until we unindent, add backend objects to the list
      while line_index < len(SCREAMING_SNAKE_CASE__) and len(lines[line_index]) > 1:
        __snake_case:						List[Any]   =						lines[line_index]
        __snake_case:						str   =						_re_single_line_import.search(SCREAMING_SNAKE_CASE__)
        if single_line_import_search is not None:
          objects.extend(single_line_import_search.groups()[0].split(""", """))
        elif line.startswith(""" """ * 8):
          objects.append(line[8:-2])
        line_index += 1

      if len(SCREAMING_SNAKE_CASE__) > 0:
        __snake_case:						Optional[Any]   =						objects
    else:
      line_index += 1

  return backend_specific_objects







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    List[Any]:
  if name.isupper():
    return DUMMY_CONSTANT.format(SCREAMING_SNAKE_CASE__)
  elif name.islower():
    return DUMMY_FUNCTION.format(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)
  else:
    return DUMMY_CLASS.format(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)







def 			A__							(    SCREAMING_SNAKE_CASE__=None)   ->    Optional[int]:
  if backend_specific_objects is None:
    __snake_case:						Union[str, Any]   =						read_init()
  # For special correspondence backend to module name as used in the function requires_modulename
  __snake_case:						Union[str, Any]   =						{}

  for backend, objects in backend_specific_objects.items():
    __snake_case:						List[Any]   =						"""[""" + """, """.join(F'''"{b}"''' for b in backend.split("""_and_""")) + """]"""
    __snake_case:						Dict   =						"""# This file is autogenerated by the command `make fix-copies`, do not edit.\n"""
    dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
    dummy_file += "\n".join([create_dummy_object(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__) for o in objects])
    __snake_case:						List[str]   =						dummy_file

  return dummy_files







def 			A__							(    SCREAMING_SNAKE_CASE__=False)   ->    int:
  __snake_case:						List[str]   =						create_dummy_files()
  # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
  __snake_case:						Dict   =						{"""torch""": """pt"""}

  # Locate actual dummy modules and read their content.
  __snake_case:						Optional[Any]   =						os.path.join(SCREAMING_SNAKE_CASE__					,					"""utils""")
  __snake_case:						List[Any]   =						{
      backend: os.path.join(SCREAMING_SNAKE_CASE__					,					F'''dummy_{short_names.get(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)}_objects.py''')
      for backend in dummy_files.keys()
  }

  __snake_case:						int   =						{}
  for backend, file_path in dummy_file_paths.items():
    if os.path.isfile(SCREAMING_SNAKE_CASE__):
      with open(SCREAMING_SNAKE_CASE__					,					"""r"""					,					encoding="""utf-8"""					,					newline="""\n""") as f:
        __snake_case:						Any   =						f.read()
    else:
      __snake_case:						Tuple   =						""""""

  for backend in dummy_files.keys():
    if dummy_files[backend] != actual_dummies[backend]:
      if overwrite:
        print(
            F'''Updating diffusers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)}_objects.py as the main '''
            """__init__ has new objects.""")
        with open(dummy_file_paths[backend]					,					"""w"""					,					encoding="""utf-8"""					,					newline="""\n""") as f:
          f.write(dummy_files[backend])
      else:
        raise ValueError(
            """The main __init__ has objects that are not present in """
            F'''diffusers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)}_objects.py. Run `make fix-copies` '''
            """to fix this.""")


if __name__ == "__main__":
      __UpperCAmelCase       :   Optional[Any]				       =							argparse.ArgumentParser()
      parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
      __UpperCAmelCase       :   List[Any]				       =							parser.parse_args()

      check_dummies(args.fix_and_overwrite)






