


'''simple docstring'''







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    lowercase_	(       _A  ):


    def __init__(	self       ):
          self.register_to_config()
          super().__init__()


    def   __a				(	self    ,   a    ,   a       ):
          if dicta == dicta:
                return True
          else:
                UpperCamelCase__      =							self._remove_meta_keys(__lowerCamelCase       )
                UpperCamelCase__      =							self._remove_meta_keys(__lowerCamelCase       )
                if configa == configa:
                      print(f'''Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.'''       )
                      return True
          return False


    def   __a				(	self    ,   a       ):
          UpperCamelCase__      =							[]
          UpperCamelCase__      =							config_dict.copy()
          for key in config_dict.keys():
                if key.startswith("_"       ):
                      temp_dict.pop(__lowerCamelCase       )
                      meta_keys.append(__lowerCamelCase       )
          return (temp_dict, meta_keys)


    @torch.no_grad()
    def   __a				(	self    ,   a    ,   **a       ):
          UpperCamelCase__      =							kwargs.pop("cache_dir"    ,   __lowerCamelCase       )
          UpperCamelCase__      =							kwargs.pop("resume_download"    ,   __lowerCamelCase       )
          UpperCamelCase__      =							kwargs.pop("force_download"    ,   __lowerCamelCase       )
          UpperCamelCase__      =							kwargs.pop("proxies"    ,   __lowerCamelCase       )
          UpperCamelCase__      =							kwargs.pop("local_files_only"    ,   __lowerCamelCase       )
          UpperCamelCase__      =							kwargs.pop("use_auth_token"    ,   __lowerCamelCase       )
          UpperCamelCase__      =							kwargs.pop("revision"    ,   __lowerCamelCase       )
          UpperCamelCase__      =							kwargs.pop("torch_dtype"    ,   __lowerCamelCase       )
          UpperCamelCase__      =							kwargs.pop("device_map"    ,   __lowerCamelCase       )

          UpperCamelCase__      =							kwargs.pop("alpha"    ,   0.5       )
          UpperCamelCase__      =							kwargs.pop("interp"    ,   __lowerCamelCase       )

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

          UpperCamelCase__      =							len(__lowerCamelCase       )
          # Ignore result from model_index_json comparision of the two checkpoints
          UpperCamelCase__      =							kwargs.pop("force"    ,   __lowerCamelCase       )

          # 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__      =							[]
          for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
                UpperCamelCase__      =							DiffusionPipeline.load_config(
                    __lowerCamelCase    ,   cache_dir=__lowerCamelCase    ,   resume_download=__lowerCamelCase    ,   force_download=__lowerCamelCase    ,   proxies=__lowerCamelCase    ,   local_files_only=__lowerCamelCase    ,   use_auth_token=__lowerCamelCase    ,   revision=__lowerCamelCase    ,   )
                config_dicts.append(__lowerCamelCase       )

          UpperCamelCase__      =							True
          for idx in range(1    ,   len(__lowerCamelCase       )       ):
                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__      =							[]
          for pretrained_model_name_or_path, config_dict in zip(__lowerCamelCase    ,   __lowerCamelCase       ):
                UpperCamelCase__      =							[k for k in config_dict.keys() if not k.startswith("_"       )]
                UpperCamelCase__      =							[os.path.join(__lowerCamelCase    ,   "*"       ) for k in folder_names]
                allow_patterns += [
                    WEIGHTS_NAME,
                    SCHEDULER_CONFIG_NAME,
                    CONFIG_NAME,
                    ONNX_WEIGHTS_NAME,
                    DiffusionPipeline.config_name,
                ]
                UpperCamelCase__      =							config_dict.get("_class_name"       )
                UpperCamelCase__      =							{"diffusers": __version__, "pipeline_class": requested_pipeline_class}

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

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

          UpperCamelCase__      =							None
          if len(__lowerCamelCase       ) > 2:
                UpperCamelCase__      =							os.path.join(cached_folders[2]       )

          if interp == "sigmoid":
                UpperCamelCase__      =							CheckpointMergerPipeline.sigmoid
          elif interp == "inv_sigmoid":
                UpperCamelCase__      =							CheckpointMergerPipeline.inv_sigmoid
          elif interp == "add_diff":
                UpperCamelCase__      =							CheckpointMergerPipeline.add_difference
          else:
                UpperCamelCase__      =							CheckpointMergerPipeline.weighted_sum

          # Find each module's state dict.
          for attr in final_pipe.config.keys():
                if not attr.startswith("_"       ):
                      UpperCamelCase__      =							os.path.join(cached_folders[1]    ,   __lowerCamelCase       )
                      if os.path.exists(__lowerCamelCase       ):
                            UpperCamelCase__      =							[
                                *glob.glob(os.path.join(__lowerCamelCase    ,   "*.safetensors"       )       ),
                                *glob.glob(os.path.join(__lowerCamelCase    ,   "*.bin"       )       ),
                            ]
                            UpperCamelCase__      =							files[0] if len(__lowerCamelCase       ) > 0 else None
                      if len(__lowerCamelCase       ) < 3:
                            UpperCamelCase__      =							None
                      else:
                            UpperCamelCase__      =							os.path.join(cached_folders[2]    ,   __lowerCamelCase       )
                            if os.path.exists(__lowerCamelCase       ):
                                  UpperCamelCase__      =							[
                                      *glob.glob(os.path.join(__lowerCamelCase    ,   "*.safetensors"       )       ),
                                      *glob.glob(os.path.join(__lowerCamelCase    ,   "*.bin"       )       ),
                                  ]
                                  UpperCamelCase__      =							files[0] if len(__lowerCamelCase       ) > 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__      =							getattr(__lowerCamelCase    ,   __lowerCamelCase       )
                            if isinstance(__lowerCamelCase    ,   __lowerCamelCase       ):  # ignore requires_safety_checker boolean
                                  continue
                            UpperCamelCase__      =							getattr(__lowerCamelCase    ,   "state_dict"       )
                            UpperCamelCase__      =							theta_a()

                            UpperCamelCase__      =							getattr(__lowerCamelCase    ,   "load_state_dict"       )
                            UpperCamelCase__      =							(
                                safetensors.torch.load_file(__lowerCamelCase       )
                                if (is_safetensors_available() and checkpoint_path_a.endswith(".safetensors"       ))
                                else torch.load(__lowerCamelCase    ,   map_location="cpu"       )
                            )
                            UpperCamelCase__      =							None
                            if checkpoint_path_a:
                                  UpperCamelCase__      =							(
                                      safetensors.torch.load_file(__lowerCamelCase       )
                                      if (is_safetensors_available() and checkpoint_path_a.endswith(".safetensors"       ))
                                      else torch.load(__lowerCamelCase    ,   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(__lowerCamelCase       )}'''       )
                            continue
                      print(f'''MERGING {attr}'''       )

                      for key in theta_a.keys():
                            if theta_a:
                                  UpperCamelCase__      =							theta_func(theta_a[key]    ,   theta_a[key]    ,   theta_a[key]    ,   __lowerCamelCase       )
                            else:
                                  UpperCamelCase__      =							theta_func(theta_a[key]    ,   theta_a[key]    ,   __lowerCamelCase    ,   __lowerCamelCase       )

                      del theta_a
                      del theta_a
                      update_theta_a(__lowerCamelCase       )

                      del theta_a
          return final_pipe


    @staticmethod
    def   __a				(	a    ,   a    ,   a    ,   a       ):
          return ((1 - alpha) * thetaa) + (alpha * thetaa)


    @staticmethod
    def   __a				(	a    ,   a    ,   a    ,   a       ):
          UpperCamelCase__      =							alpha * alpha * (3 - (2 * alpha))
          return thetaa + ((thetaa - thetaa) * alpha)


    @staticmethod
    def   __a				(	a    ,   a    ,   a    ,   a       ):
          import math

          UpperCamelCase__      =							0.5 - math.sin(math.asin(1.0 - 2.0 * alpha       ) / 3.0       )
          return thetaa + ((thetaa - thetaa) * alpha)


    @staticmethod
    def   __a				(	a    ,   a    ,   a    ,   a       ):
          return thetaa + (thetaa - thetaa) * (1.0 - alpha)










from collections.abc import Callable

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




def 						UpperCAmelCase (    _lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase			,     ):
			A				:       int														=    float(_lowerCamelCase     )
			while True:
						try:
									A				:       Optional[int]														=    prev_guess - function(_lowerCamelCase     ) / derivative(_lowerCamelCase     )
						except ZeroDivisionError:
									raise ZeroDivisionError("Could not find root"     ) from None
						if abs(prev_guess - next_guess     ) < 10**-5:
									return next_guess
						A				:       Any														=    next_guess




def 						UpperCAmelCase (    _lowerCamelCase     ):
			return (x**3) - (2 * x) - 5




def 						UpperCAmelCase (    _lowerCamelCase     ):
			return 3 * (x**2) - 2


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