






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))










import math

lowerCamelCase__							=     10
lowerCamelCase__							=     7
lowerCamelCase__							=     BALLS_PER_COLOUR * NUM_COLOURS






def   UpperCamelCase  (       snake_case__     :   int = 20  ):




     '''simple docstring'''






     __snake_case			:Dict          =						math.comb(snake_case__	,snake_case__  )
     __snake_case			:int          =						math.comb(NUM_BALLS - BALLS_PER_COLOUR	,snake_case__  )

     __snake_case			:List[str]          =						NUM_COLOURS * (1 - missing_colour / total)

     return f'''{result:.9f}'''


if __name__ == "__main__":
     print(solution(20))


