






'''simple docstring'''





import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


__A							:							str  							=		False
__A							:							Optional[int]  							=		True
__A							:							List[Any]  							=		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							:							List[str]  							=		parser.parse_args()

 __A							:							Tuple  							=		{
     """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							:							Optional[Any]  							=		{
     """time_steps""": """time_proj""",
     """mid""": """mid_block""",
     """downsample_blocks""": """down_blocks""",
     """upsample_blocks""": """up_blocks""",
 }

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

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

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

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

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

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

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

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









'''simple docstring'''





from __future__ import annotations
class 				lowercase       :


    '''simple docstring'''

    def __init__(					self     :     Optional[int]				,     __lowerCamelCase     :     int     )							->							None:

     '''simple docstring'''




     lowerCamelCase__  =     order

     # a_{0} ... a_{k}
     lowerCamelCase__  =     [1.0] + [0.0] * order
     # b_{0} ... b_{k}
     lowerCamelCase__  =     [1.0] + [0.0] * order

     # x[n-1] ... x[n-k]
     lowerCamelCase__  =     [0.0] * self.order
     # y[n-1] ... y[n-k]
     lowerCamelCase__  =     [0.0] * self.order

    def    a__				(					self     :     Dict				,     __lowerCamelCase     :     list[float]				,     __lowerCamelCase     :     list[float]     )							->							None:

     '''simple docstring'''




     if len(__lowerCamelCase     ) < self.order:
      lowerCamelCase__  =     [1.0, *a_coeffs]

     if len(__lowerCamelCase     ) != self.order + 1:
      lowerCamelCase__  =     (
          f'''Expected a_coeffs to have {self.order + 1} elements '''
          f'''for {self.order}-order filter, got {len(__lowerCamelCase     )}'''
      )
      raise ValueError(__lowerCamelCase     )

     if len(__lowerCamelCase     ) != self.order + 1:
      lowerCamelCase__  =     (
          f'''Expected b_coeffs to have {self.order + 1} elements '''
          f'''for {self.order}-order filter, got {len(__lowerCamelCase     )}'''
      )
      raise ValueError(__lowerCamelCase     )

     lowerCamelCase__  =     a_coeffs
     lowerCamelCase__  =     b_coeffs




    def    a__				(					self     :     Dict				,     __lowerCamelCase     :     float     )							->							float:

     '''simple docstring'''




     lowerCamelCase__  =     0.0

     # Start at index 1 and do index 0 at the end.
     for i in range(1				,     self.order + 1     ):
      result += (
          self.b_coeffs[i] * self.input_history[i - 1]
          - self.a_coeffs[i] * self.output_history[i - 1]
      )

     lowerCamelCase__  =     (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

     lowerCamelCase__  =     self.input_history[:-1]
     lowerCamelCase__  =     self.output_history[:-1]

     lowerCamelCase__  =     sample
     lowerCamelCase__  =     result

     return result

