

import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


_SCREAMING_SNAKE_CASE  :						Optional[int]         =    False
_SCREAMING_SNAKE_CASE  :						List[Any]         =    True
_SCREAMING_SNAKE_CASE  :						List[Any]         =    False


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE  :						str         =    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.''')

       _SCREAMING_SNAKE_CASE  :						List[str]         =    parser.parse_args()

       _SCREAMING_SNAKE_CASE  :						Optional[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''',
       }

       _SCREAMING_SNAKE_CASE  :						Optional[int]         =    {
           '''time_steps''': '''time_proj''',
           '''mid''': '''mid_block''',
           '''downsample_blocks''': '''down_blocks''',
           '''upsample_blocks''': '''up_blocks''',
       }

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

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

       _SCREAMING_SNAKE_CASE  :						List[str]         =    dict(model.config)

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

              _SCREAMING_SNAKE_CASE  :						Any         =    [k.replace('''UNetRes''', '''''') for k in config['''down_block_types''']]
              _SCREAMING_SNAKE_CASE  :						Any         =    [k.replace('''UNetRes''', '''''') for k in config['''up_block_types''']]

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

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

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





import math






class  UpperCAmelCase__	:






  """simple docstring"""


  def    lowercase_	(							self		:	int			,     __lowerCamelCase		:	list[list[float]]			,     __lowerCamelCase		:	list[int]      )	->							int:
       SCREAMING_SNAKE_CASE__					=					0.0
       SCREAMING_SNAKE_CASE__					=					0.0
       for i in range(len(__lowerCamelCase      )      ):
            da += math.pow((sample[i] - weights[0][i])			,     2      )
            da += math.pow((sample[i] - weights[1][i])			,     2      )
            return 0 if da > da else 1
       return 0







  def    lowercase_	(							self		:	Optional[int]			,     __lowerCamelCase		:	list[list[int | float]]			,     __lowerCamelCase		:	list[int]			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	float      )	->							list[list[int | float]]:
       for i in range(len(__lowerCamelCase      )      ):
            weights[j][i] += alpha * (sample[i] - weights[j][i])
       return weights



def        UpperCAmelCase_     (		):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

     # weight initialization ( n, C )
     SCREAMING_SNAKE_CASE__					=					[[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

     # training
     SCREAMING_SNAKE_CASE__					=					SelfOrganizingMap()
     SCREAMING_SNAKE_CASE__					=					3
     SCREAMING_SNAKE_CASE__					=					0.5

     for _ in range(_A       ):
          for j in range(len(_A       )       ):
               # training sample
               SCREAMING_SNAKE_CASE__					=					training_samples[j]

               # Compute the winning vector
               SCREAMING_SNAKE_CASE__					=					self_organizing_map.get_winner(_A   ,	_A       )

               # Update the winning vector
               SCREAMING_SNAKE_CASE__					=					self_organizing_map.update(_A   ,	_A   ,	_A   ,	_A       )

    # classify test sample
     SCREAMING_SNAKE_CASE__					=					[0, 0, 0, 1]
     SCREAMING_SNAKE_CASE__					=					self_organizing_map.get_winner(_A   ,	_A       )

     # results
     print(F'''Clusters that the test sample belongs to : {winner}'''       )
     print(F'''Weights that have been trained : {weights}'''       )


# running the main() function
if __name__ == "__main__":
       main()


