


'''simple docstring'''







from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


lowerCAmelCase    :     Union[str, Any]					=						"""Create a default config file for Accelerate with only a few flags set."""
def 			lowercase				(_A="no"   ,						_A = default_json_config_file   ,						_A = False					):







       """simple docstring"""







       _lowerCAmelCase    :							List[Any]   			=       Path(_A					)
       path.parent.mkdir(parents=_A   ,						exist_ok=_A					)
       if path.exists():
              print(
                  f'Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'					)
              return False
       _lowerCAmelCase    :							Any   			=       mixed_precision.lower()
       if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
              raise ValueError(
                  f'`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'					)
       _lowerCAmelCase    :							Any   			=       {
           'compute_environment': 'LOCAL_MACHINE',
           'mixed_precision': mixed_precision,
       }
       if torch.cuda.is_available():
              _lowerCAmelCase    :							Dict   			=       torch.cuda.device_count()
              _lowerCAmelCase    :							Optional[int]   			=       num_gpus
              _lowerCAmelCase    :							str   			=       False
              if num_gpus > 1:
                     _lowerCAmelCase    :							Optional[int]   			=       'MULTI_GPU'
              else:
                     _lowerCAmelCase    :							int   			=       'NO'
       elif is_xpu_available() and use_xpu:
              _lowerCAmelCase    :							Dict   			=       torch.xpu.device_count()
              _lowerCAmelCase    :							Union[str, Any]   			=       num_xpus
              _lowerCAmelCase    :							Union[str, Any]   			=       False
              if num_xpus > 1:
                     _lowerCAmelCase    :							Union[str, Any]   			=       'MULTI_XPU'
              else:
                     _lowerCAmelCase    :							Optional[Any]   			=       'NO'
       elif is_npu_available():
              _lowerCAmelCase    :							str   			=       torch.npu.device_count()
              _lowerCAmelCase    :							int   			=       num_npus
              _lowerCAmelCase    :							Optional[Any]   			=       False
              if num_npus > 1:
                     _lowerCAmelCase    :							Any   			=       'MULTI_NPU'
              else:
                     _lowerCAmelCase    :							Dict   			=       'NO'
       else:
              _lowerCAmelCase    :							Optional[Any]   			=       0
              _lowerCAmelCase    :							Optional[Any]   			=       True
              _lowerCAmelCase    :							Tuple   			=       1
              _lowerCAmelCase    :							Union[str, Any]   			=       'NO'
       _lowerCAmelCase    :							Dict   			=       ClusterConfig(**_A					)
       config.to_json_file(_A					)
       return path
def 			lowercase				(_A   ,						_A					):







       """simple docstring"""







       _lowerCAmelCase    :							List[str]   			=       parser.add_parser('default'   ,						parents=_A   ,						help=_A   ,						formatter_class=_A					)
       parser.add_argument(
           '--config_file'   ,						default=_A   ,						help=(
               'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
               'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
               'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
               'with \'huggingface\'.'
           )   ,						dest='save_location'   ,						)

       parser.add_argument(
           '--mixed_precision'   ,						choices=['no', 'fp16', 'bf16']   ,						type=_A   ,						help='Whether or not to use mixed precision training. '
           'Choose between FP16 and BF16 (bfloat16) training. '
           'BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.'   ,						default='no'   ,						)
       parser.set_defaults(func=_A					)
       return parser
def 			lowercase				(_A					):







       """simple docstring"""







       _lowerCAmelCase    :							Union[str, Any]   			=       write_basic_config(args.mixed_precision   ,						args.save_location					)
       if config_file:
              print(f'accelerate configuration saved at {config_file}'					)





'''simple docstring'''







import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowerCAmelCase    :     Tuple					=						False
lowerCAmelCase    :     str					=						True
lowerCAmelCase    :     List[Any]					=						False


if __name__ == "__main__":
       lowerCAmelCase    :     Any					=						argparse.ArgumentParser()

       parser.add_argument(
           """--repo_path""",
           default=None,
           type=str,
           required=True,
           help="""The config json file corresponding to the architecture.""",
       )

       parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

       lowerCAmelCase    :     Optional[int]					=						parser.parse_args()

       lowerCAmelCase    :     int					=						{
           """image_size""": """sample_size""",
           """num_res_blocks""": """layers_per_block""",
           """block_channels""": """block_out_channels""",
           """down_blocks""": """down_block_types""",
           """up_blocks""": """up_block_types""",
           """downscale_freq_shift""": """freq_shift""",
           """resnet_num_groups""": """norm_num_groups""",
           """resnet_act_fn""": """act_fn""",
           """resnet_eps""": """norm_eps""",
           """num_head_channels""": """attention_head_dim""",
       }

       lowerCAmelCase    :     int					=						{
           """time_steps""": """time_proj""",
           """mid""": """mid_block""",
           """downsample_blocks""": """down_blocks""",
           """upsample_blocks""": """up_blocks""",
       }

       lowerCAmelCase    :     Optional[Any]					=						"""""" if has_file(args.repo_path, """config.json""") else """unet"""

       with open(os.path.join(args.repo_path, subfolder, """config.json"""), """r""", encoding="""utf-8""") as reader:
              lowerCAmelCase    :     int					=						reader.read()
              lowerCAmelCase    :     List[str]					=						json.loads(text)

       if do_only_config:
              for key in config_parameters_to_change.keys():
                     config.pop(key, None)

       if has_file(args.repo_path, """config.json"""):
              lowerCAmelCase    :     str					=						UNetaDModel(**config)
       else:
              lowerCAmelCase    :     Union[str, Any]					=						UNetaDConditionModel if """ldm-text2im-large-256""" in args.repo_path else UNetaDModel
              lowerCAmelCase    :     Dict					=						class_name(**config)

       if do_only_config:
              model.save_config(os.path.join(args.repo_path, subfolder))

       lowerCAmelCase    :     Union[str, Any]					=						dict(model.config)

       if do_only_renaming:
              for key, value in config_parameters_to_change.items():
                     if key in config:
                            lowerCAmelCase    :     str					=						config[key]
                            del config[key]

              lowerCAmelCase    :     Optional[int]					=						[k.replace("""UNetRes""", """""") for k in config["""down_block_types"""]]
              lowerCAmelCase    :     Dict					=						[k.replace("""UNetRes""", """""") for k in config["""up_block_types"""]]

       if do_only_weights:
              lowerCAmelCase    :     Tuple					=						torch.load(os.path.join(args.repo_path, subfolder, """diffusion_pytorch_model.bin"""))

              lowerCAmelCase    :     str					=						{}
              for param_key, param_value in state_dict.items():
                     if param_key.endswith(""".op.bias""") or param_key.endswith(""".op.weight"""):
                            continue
                     lowerCAmelCase    :     str					=						False
                     for key, new_key in key_parameters_to_change.items():
                            if not has_changed and param_key.split(""".""")[0] == key:
                                   lowerCAmelCase    :     Dict					=						param_value
                                   lowerCAmelCase    :     Tuple					=						True
                     if not has_changed:
                            lowerCAmelCase    :     Tuple					=						param_value

              model.load_state_dict(new_state_dict)
              model.save_pretrained(os.path.join(args.repo_path, subfolder))

