


import glob
import os
from typing import Dict, List, Union

import torch

from diffusers.utils import is_safetensors_available


if is_safetensors_available():
      import safetensors.torch

from huggingface_hub import snapshot_download

from diffusers import DiffusionPipeline, __version__
from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
from diffusers.utils import CONFIG_NAME, DIFFUSERS_CACHE, ONNX_WEIGHTS_NAME, WEIGHTS_NAME




class    A_   ( a_     ):







 """simple docstring"""





 def __init__(			self					: Any				)    ->			Tuple:
      self.register_to_config()
      super().__init__()
 def   _UpperCamelCase    (			self					: Optional[int]       ,    _a					: Union[str, Any]       ,    _a					: List[Any]				)    ->			Any:
      if dicta == dicta:
           return True
      else:
           UpperCAmelCase  , UpperCAmelCase						:				str		  =			self._remove_meta_keys(_a				)
           UpperCAmelCase  , UpperCAmelCase						:				str		  =			self._remove_meta_keys(_a				)
           if configa == configa:
                print(f'Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.'				)
                return True
      return False
 def   _UpperCamelCase    (			self					: Optional[Any]       ,    _a					: Dict				)    ->			Dict:
      UpperCAmelCase						:				Any		  =			[]
      UpperCAmelCase						:				int		  =			config_dict.copy()
      for key in config_dict.keys():
           if key.startswith("_"				):
                temp_dict.pop(_a				)
                meta_keys.append(_a				)
      return (temp_dict, meta_keys)
 @torch.no_grad()
 def   _UpperCamelCase    (			self					: Union[str, Any]       ,    _a					: List[Union[str, os.PathLike]]       ,    **_a					: Optional[int]				)    ->			Union[str, Any]:
      UpperCAmelCase						:				List[str]		  =			kwargs.pop("cache_dir"       ,    _a				)
      UpperCAmelCase						:				List[str]		  =			kwargs.pop("resume_download"       ,    _a				)
      UpperCAmelCase						:				int		  =			kwargs.pop("force_download"       ,    _a				)
      UpperCAmelCase						:				List[Any]		  =			kwargs.pop("proxies"       ,    _a				)
      UpperCAmelCase						:				str		  =			kwargs.pop("local_files_only"       ,    _a				)
      UpperCAmelCase						:				Union[str, Any]		  =			kwargs.pop("use_auth_token"       ,    _a				)
      UpperCAmelCase						:				Tuple		  =			kwargs.pop("revision"       ,    _a				)
      UpperCAmelCase						:				Optional[Any]		  =			kwargs.pop("torch_dtype"       ,    _a				)
      UpperCAmelCase						:				Optional[int]		  =			kwargs.pop("device_map"       ,    _a				)

      UpperCAmelCase						:				str		  =			kwargs.pop("alpha"       ,    0.5				)
      UpperCAmelCase						:				List[Any]		  =			kwargs.pop("interp"       ,    _a				)

      print("Received list"       ,    _a				)
      print(f'Combining with alpha={alpha}, interpolation mode={interp}'				)

      UpperCAmelCase						:				Any		  =			len(_a				)
      # Ignore result from model_index_json comparision of the two checkpoints
      UpperCAmelCase						:				List[str]		  =			kwargs.pop("force"       ,    _a				)

      # If less than 2 checkpoints, nothing to merge. If more than 3, not supported for now.
      if checkpoint_count > 3 or checkpoint_count < 2:
           raise ValueError(
               "Received incorrect number of checkpoints to merge. Ensure that either 2 or 3 checkpoints are being"
               " passed."				)

      print("Received the right number of checkpoints"				)
      # chkpt0, chkpt1 = pretrained_model_name_or_path_list[0:2]
      # chkpt2 = pretrained_model_name_or_path_list[2] if checkpoint_count == 3 else None

      # Validate that the checkpoints can be merged
      # Step 1: Load the model config and compare the checkpoints. We'll compare the model_index.json first while ignoring the keys starting with '_'
      UpperCAmelCase						:				Tuple		  =			[]
      for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
           UpperCAmelCase						:				Union[str, Any]		  =			DiffusionPipeline.load_config(
               _a       ,    cache_dir=_a       ,    resume_download=_a       ,    force_download=_a       ,    proxies=_a       ,    local_files_only=_a       ,    use_auth_token=_a       ,    revision=_a       ,    )
           config_dicts.append(_a				)

      UpperCAmelCase						:				Any		  =			True
      for idx in range(1       ,    len(_a				)				):
           comparison_result &= self._compare_model_configs(config_dicts[idx - 1]       ,    config_dicts[idx]				)
           if not force and comparison_result is False:
                raise ValueError("Incompatible checkpoints. Please check model_index.json for the models."				)
                print(config_dicts[0]       ,    config_dicts[1]				)
      print("Compatible model_index.json files found"				)
      # Step 2: Basic Validation has succeeded. Let's download the models and save them into our local files.
      UpperCAmelCase						:				Any		  =			[]
      for pretrained_model_name_or_path, config_dict in zip(_a       ,    _a				):
           UpperCAmelCase						:				Optional[int]		  =			[k for k in config_dict.keys() if not k.startswith("_"				)]
           UpperCAmelCase						:				List[Any]		  =			[os.path.join(_a       ,    "*"				) for k in folder_names]
           allow_patterns += [
               WEIGHTS_NAME,
               SCHEDULER_CONFIG_NAME,
               CONFIG_NAME,
               ONNX_WEIGHTS_NAME,
               DiffusionPipeline.config_name,
           ]
           UpperCAmelCase						:				Any		  =			config_dict.get("_class_name"				)
           UpperCAmelCase						:				Dict		  =			{"diffusers": __version__, "pipeline_class": requested_pipeline_class}

           UpperCAmelCase						:				str		  =			(
               pretrained_model_name_or_path
               if os.path.isdir(_a				)
               else snapshot_download(
                   _a       ,    cache_dir=_a       ,    resume_download=_a       ,    proxies=_a       ,    local_files_only=_a       ,    use_auth_token=_a       ,    revision=_a       ,    allow_patterns=_a       ,    user_agent=_a       ,    )
           )
           print("Cached Folder"       ,    _a				)
           cached_folders.append(_a				)

      # Step 3:-
      # Load the first checkpoint as a diffusion pipeline and modify its module state_dict in place
      UpperCAmelCase						:				Union[str, Any]		  =			DiffusionPipeline.from_pretrained(
          cached_folders[0]       ,    torch_dtype=_a       ,    device_map=_a				)
      final_pipe.to(self.device				)

      UpperCAmelCase						:				List[Any]		  =			None
      if len(_a				) > 2:
           UpperCAmelCase						:				Any		  =			os.path.join(cached_folders[2]				)

      if interp == "sigmoid":
           UpperCAmelCase						:				Union[str, Any]		  =			CheckpointMergerPipeline.sigmoid
      elif interp == "inv_sigmoid":
           UpperCAmelCase						:				Optional[Any]		  =			CheckpointMergerPipeline.inv_sigmoid
      elif interp == "add_diff":
           UpperCAmelCase						:				int		  =			CheckpointMergerPipeline.add_difference
      else:
           UpperCAmelCase						:				str		  =			CheckpointMergerPipeline.weighted_sum

      # Find each module's state dict.
      for attr in final_pipe.config.keys():
           if not attr.startswith("_"				):
                UpperCAmelCase						:				int		  =			os.path.join(cached_folders[1]       ,    _a				)
                if os.path.exists(_a				):
                     UpperCAmelCase						:				List[Any]		  =			[
                         *glob.glob(os.path.join(_a       ,    "*.safetensors"				)				),
                         *glob.glob(os.path.join(_a       ,    "*.bin"				)				),
                     ]
                     UpperCAmelCase						:				Optional[Any]		  =			files[0] if len(_a				) > 0 else None
                if len(_a				) < 3:
                     UpperCAmelCase						:				Optional[int]		  =			None
                else:
                     UpperCAmelCase						:				List[Any]		  =			os.path.join(cached_folders[2]       ,    _a				)
                     if os.path.exists(_a				):
                          UpperCAmelCase						:				Dict		  =			[
                              *glob.glob(os.path.join(_a       ,    "*.safetensors"				)				),
                              *glob.glob(os.path.join(_a       ,    "*.bin"				)				),
                          ]
                          UpperCAmelCase						:				Any		  =			files[0] if len(_a				) > 0 else None
                # For an attr if both checkpoint_path_1 and 2 are None, ignore.
                # If atleast one is present, deal with it according to interp method, of course only if the state_dict keys match.
                if checkpoint_path_a is None and checkpoint_path_a is None:
                     print(f'Skipping {attr}: not present in 2nd or 3d model'				)
                     continue
                try:
                     UpperCAmelCase						:				List[Any]		  =			getattr(_a       ,    _a				)
                     if isinstance(_a       ,    _a				):  # ignore requires_safety_checker boolean
                          continue
                     UpperCAmelCase						:				Optional[Any]		  =			getattr(_a       ,    "state_dict"				)
                     UpperCAmelCase						:				Tuple		  =			theta_a()

                     UpperCAmelCase						:				Union[str, Any]		  =			getattr(_a       ,    "load_state_dict"				)
                     UpperCAmelCase						:				Any		  =			(
                         safetensors.torch.load_file(_a				)
                         if (is_safetensors_available() and checkpoint_path_a.endswith(".safetensors"				))
                         else torch.load(_a       ,    map_location="cpu"				)
                     )
                     UpperCAmelCase						:				Optional[Any]		  =			None
                     if checkpoint_path_a:
                          UpperCAmelCase						:				Any		  =			(
                              safetensors.torch.load_file(_a				)
                              if (is_safetensors_available() and checkpoint_path_a.endswith(".safetensors"				))
                              else torch.load(_a       ,    map_location="cpu"				)
                          )

                     if not theta_a.keys() == theta_a.keys():
                          print(f'Skipping {attr}: key mismatch'				)
                          continue
                     if theta_a and not theta_a.keys() == theta_a.keys():
                          print(f'Skipping {attr}:y mismatch'				)
                except Exception as e:
                     print(f'Skipping {attr} do to an unexpected error: {str(_a				)}'				)
                     continue
                print(f'MERGING {attr}'				)

                for key in theta_a.keys():
                     if theta_a:
                          UpperCAmelCase						:				Dict		  =			theta_func(theta_a[key]       ,    theta_a[key]       ,    theta_a[key]       ,    _a				)
                     else:
                          UpperCAmelCase						:				str		  =			theta_func(theta_a[key]       ,    theta_a[key]       ,    _a       ,    _a				)

                del theta_a
                del theta_a
                update_theta_a(_a				)

                del theta_a
      return final_pipe
 @staticmethod
 def   _UpperCamelCase    (			_a					: Optional[Any]       ,    _a					: List[Any]       ,    _a					: Union[str, Any]       ,    _a					: Optional[Any]				)    ->			Optional[int]:
      return ((1 - alpha) * thetaa) + (alpha * thetaa)
 @staticmethod
 def   _UpperCamelCase    (			_a					: str       ,    _a					: int       ,    _a					: List[Any]       ,    _a					: Optional[int]				)    ->			str:
      UpperCAmelCase						:				int		  =			alpha * alpha * (3 - (2 * alpha))
      return thetaa + ((thetaa - thetaa) * alpha)
 @staticmethod
 def   _UpperCamelCase    (			_a					: Tuple       ,    _a					: Optional[Any]       ,    _a					: List[Any]       ,    _a					: int				)    ->			Dict:
      import math

      UpperCAmelCase						:				Optional[Any]		  =			0.5 - math.sin(math.asin(1.0 - 2.0 * alpha				) / 3.0				)
      return thetaa + ((thetaa - thetaa) * alpha)

 @staticmethod
 def   _UpperCamelCase    (			_a					: Union[str, Any]       ,    _a					: int       ,    _a					: List[Any]       ,    _a					: List[Any]				)    ->			Tuple:
      return thetaa + (thetaa - thetaa) * (1.0 - alpha)







from collections.abc import Callable

_snake_case   = Callable[[float], float]  # type alias for a real -> real function







def   A   (		A__		,		A__		,		A__		,		):
     UpperCAmelCase						:				Any		  =			float(A__ )
     while True:
          try:
               UpperCAmelCase						:				Optional[Any]		  =			prev_guess - function(A__ ) / derivative(A__ )
          except ZeroDivisionError:
               raise ZeroDivisionError("Could not find root" ) from None
          if abs(prev_guess - next_guess ) < 1_0**-5:
               return next_guess
          UpperCAmelCase						:				List[str]		  =			next_guess







def   A   (		A__ ):
     return (x**3) - (2 * x) - 5







def   A   (		A__ ):
     return 3 * (x**2) - 2


if __name__ == "__main__":
      print(newton(f, fa, 3))



