




'''simple docstring'''





import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


_lowerCAmelCase										=     False
_lowerCAmelCase										=     True
_lowerCAmelCase										=     False


if __name__ == "__main__":
 _lowerCAmelCase										=     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										=     parser.parse_args()

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

 _lowerCAmelCase										=     "" 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										=     reader.read()
  _lowerCAmelCase										=     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										=     UNetaDModel(**config)
 else:
  _lowerCAmelCase										=     UNetaDConditionModel if "ldm-text2im-large-256" in args.repo_path else UNetaDModel
  _lowerCAmelCase										=     class_name(**config)

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

 _lowerCAmelCase										=     dict(model.config)

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

  _lowerCAmelCase										=     [k.replace('''UNetRes''', '''''') for k in config["down_block_types"]]
  _lowerCAmelCase										=     [k.replace('''UNetRes''', '''''') for k in config["up_block_types"]]

 if do_only_weights:
  _lowerCAmelCase										=     torch.load(os.path.join(args.repo_path, subfolder, '''diffusion_pytorch_model.bin'''))

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

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







'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_lowerCAmelCase										=     logging.get_logger(__name__)

_lowerCAmelCase										=     {
    '''facebook/convnextv2-tiny-1k-224''': '''https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json''',
}






class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				Tuple           =				'''convnextv2'''




  def __init__(   self  ,__UpperCAmelCase=3  ,__UpperCAmelCase=4  ,__UpperCAmelCase=4  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase="gelu"  ,__UpperCAmelCase=0.0_2  ,__UpperCAmelCase=1E-12  ,__UpperCAmelCase=0.0  ,__UpperCAmelCase=224  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,**__UpperCAmelCase  ,)   ->		Union[str, Any]:
      super().__init__(**__UpperCAmelCase				)

      lowerCAmelCase__			:  int											= num_channels
      lowerCAmelCase__			:  List[Any]											= patch_size
      lowerCAmelCase__			:  Union[str, Any]											= num_stages
      lowerCAmelCase__			:  Tuple											= [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
      lowerCAmelCase__			:  str											= [3, 3, 9, 3] if depths is None else depths
      lowerCAmelCase__			:  Optional[Any]											= hidden_act
      lowerCAmelCase__			:  str											= initializer_range
      lowerCAmelCase__			:  List[str]											= layer_norm_eps
      lowerCAmelCase__			:  Dict											= drop_path_rate
      lowerCAmelCase__			:  int											= image_size
      lowerCAmelCase__			:  int											= ["""stem"""] + [F"""stage{idx}""" for idx in range(1  ,len(self.depths				) + 1				)]
      lowerCAmelCase__				,       lowerCAmelCase__			:  Tuple											= get_aligned_output_features_output_indices(
          out_features=__UpperCAmelCase  ,out_indices=__UpperCAmelCase  ,stage_names=self.stage_names				)


