



'''simple docstring'''





import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


A					:       Optional[int]									=     False
A					:       List[Any]									=     True
A					:       Tuple									=     False


if __name__ == "__main__":
      A					:       Optional[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.""")

      A					:       Any									=     parser.parse_args()

      A					:       Any									=     {
          """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""",
      }

      A					:       Tuple									=     {
          """time_steps""": """time_proj""",
          """mid""": """mid_block""",
          """downsample_blocks""": """down_blocks""",
          """upsample_blocks""": """up_blocks""",
      }

      A					:       Optional[int]									=     """""" 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:
            A					:       Tuple									=     reader.read()
            A					:       List[Any]									=     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"""):
            A					:       Tuple									=     UNetaDModel(**config)
      else:
            A					:       Dict									=     UNetaDConditionModel if """ldm-text2im-large-256""" in args.repo_path else UNetaDModel
            A					:       Any									=     class_name(**config)

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

      A					:       str									=     dict(model.config)

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

            A					:       Optional[int]									=     [k.replace("""UNetRes""", """""") for k in config["""down_block_types"""]]
            A					:       List[Any]									=     [k.replace("""UNetRes""", """""") for k in config["""up_block_types"""]]

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

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

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









'''simple docstring'''






import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text





def lowerCamelCase__     (		__lowerCamelCase			:				List[str]				):







   '''simple docstring'''




   _UpperCAmelCase  :  int				       =[]
   for line in lines:
      _UpperCAmelCase  :  Dict				       =re.sub(R'#.*'			,       ''			,       __lowerCamelCase				)  # remove comments
      if line:
         filtered_lines.append(__lowerCamelCase				)
   _UpperCAmelCase  :  str				       ='\n'.join(__lowerCamelCase				)

   # Make a hash from all this code
   _UpperCAmelCase  :  str				       =full_str.encode('utf-8'				)
   return shaaaa(__lowerCamelCase				).hexdigest()


# get importable module names and hash for caching
lowercase				={
    'csv': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    'json': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    'pandas': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    'parquet': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    'arrow': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    'text': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    'imagefolder': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    'audiofolder': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
lowercase				={
    '.csv': ('csv', {}),
    '.tsv': ('csv', {'sep': '\t'}),
    '.json': ('json', {}),
    '.jsonl': ('json', {}),
    '.parquet': ('parquet', {}),
    '.arrow': ('arrow', {}),
    '.txt': ('text', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
lowercase				={'imagefolder', 'audiofolder'}

# Used to filter data files based on extensions given a module name
lowercase				={}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
 _MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append('.zip')




_MODULE_TO_EXTENSIONS["audiofolder"].append('.zip')

