




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 	UpperCamelCase__			(UpperCamelCase__  ):

    '''simple docstring'''



    def __init__(					self  )  ->   List[str]:
           self.register_to_config()
           super().__init__()







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__  )  ->   int:




           if dicta == dicta:
                  return True
           else:
                  lowerCamelCase     :   Tuple    				=      self._remove_meta_keys(lowerCAmelCase__  )
                  lowerCamelCase     :   str    				=      self._remove_meta_keys(lowerCAmelCase__  )
                  if configa == configa:
                         print(F'''Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.'''  )
                         return True
           return False







    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   Tuple:
           lowerCamelCase     :   Dict    				=      []
           lowerCamelCase     :   List[str]    				=      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 					_lowercase  (					self  ,						UpperCamelCase__  ,						**UpperCamelCase__  )  ->   Optional[int]:
           lowerCamelCase     :   Dict    				=      kwargs.pop("cache_dir"  ,						lowerCAmelCase__  )
           lowerCamelCase     :   Dict    				=      kwargs.pop("resume_download"  ,						lowerCAmelCase__  )
           lowerCamelCase     :   Optional[Any]    				=      kwargs.pop("force_download"  ,						lowerCAmelCase__  )
           lowerCamelCase     :   Tuple    				=      kwargs.pop("proxies"  ,						lowerCAmelCase__  )
           lowerCamelCase     :   Dict    				=      kwargs.pop("local_files_only"  ,						lowerCAmelCase__  )
           lowerCamelCase     :   List[Any]    				=      kwargs.pop("use_auth_token"  ,						lowerCAmelCase__  )
           lowerCamelCase     :   int    				=      kwargs.pop("revision"  ,						lowerCAmelCase__  )
           lowerCamelCase     :   int    				=      kwargs.pop("torch_dtype"  ,						lowerCAmelCase__  )
           lowerCamelCase     :   List[str]    				=      kwargs.pop("device_map"  ,						lowerCAmelCase__  )

           lowerCamelCase     :   Union[str, Any]    				=      kwargs.pop("alpha"  ,						0.5  )
           lowerCamelCase     :   Any    				=      kwargs.pop("interp"  ,						lowerCAmelCase__  )

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

           lowerCamelCase     :   List[str]    				=      len(lowerCAmelCase__  )
           # Ignore result from model_index_json comparision of the two checkpoints
           lowerCamelCase     :   Dict    				=      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 '_'
           lowerCamelCase     :   List[Any]    				=      []
           for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
                  lowerCamelCase     :   int    				=      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__  )

           lowerCamelCase     :   Tuple    				=      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.
           lowerCamelCase     :   List[str]    				=      []
           for pretrained_model_name_or_path, config_dict in zip(lowerCAmelCase__  ,						lowerCAmelCase__  ):
                  lowerCamelCase     :   int    				=      [k for k in config_dict.keys() if not k.startswith("_"  )]
                  lowerCamelCase     :   Dict    				=      [os.path.join(lowerCAmelCase__  ,						"*"  ) for k in folder_names]
                  allow_patterns += [
                      WEIGHTS_NAME,
                      SCHEDULER_CONFIG_NAME,
                      CONFIG_NAME,
                      ONNX_WEIGHTS_NAME,
                      DiffusionPipeline.config_name,
                  ]
                  lowerCamelCase     :   Dict    				=      config_dict.get("_class_name"  )
                  lowerCamelCase     :   Optional[int]    				=      {"diffusers": __version__, "pipeline_class": requested_pipeline_class}

                  lowerCamelCase     :   List[str]    				=      (
                      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
           lowerCamelCase     :   Any    				=      DiffusionPipeline.from_pretrained(
               cached_folders[0]  ,						torch_dtype=lowerCAmelCase__  ,						device_map=lowerCAmelCase__  )
           final_pipe.to(self.device  )

           lowerCamelCase     :   List[str]    				=      None
           if len(lowerCAmelCase__  ) > 2:
                  lowerCamelCase     :   Any    				=      os.path.join(cached_folders[2]  )

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

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

                                lowerCamelCase     :   Any    				=      getattr(lowerCAmelCase__  ,						"load_state_dict"  )
                                lowerCamelCase     :   List[str]    				=      (
                                    safetensors.torch.load_file(lowerCAmelCase__  )
                                    if (is_safetensors_available() and checkpoint_path_a.endswith(".safetensors"  ))
                                    else torch.load(lowerCAmelCase__  ,						map_location="cpu"  )
                                )
                                lowerCamelCase     :   Tuple    				=      None
                                if checkpoint_path_a:
                                       lowerCamelCase     :   str    				=      (
                                           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:
                                       lowerCamelCase     :   Dict    				=      theta_func(theta_a[key]  ,						theta_a[key]  ,						theta_a[key]  ,						lowerCAmelCase__  )
                                else:
                                       lowerCamelCase     :   Dict    				=      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 					_lowercase  (					UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  )  ->   Union[str, Any]:
           return ((1 - alpha) * thetaa) + (alpha * thetaa)







    @staticmethod
    def 					_lowercase  (					UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  )  ->   List[str]:
           lowerCamelCase     :   Any    				=      alpha * alpha * (3 - (2 * alpha))
           return thetaa + ((thetaa - thetaa) * alpha)







    @staticmethod
    def 					_lowercase  (					UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  )  ->   str:
           import math

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







    @staticmethod
    def 					_lowercase  (					UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  )  ->   str:
           return thetaa + (thetaa - thetaa) * (1.0 - alpha)










SCREAMING_SNAKE_CASE__ :   str						=     {
    '+': lambda x, y: x + y,
    '-': lambda x, y: x - y,
    '*': lambda x, y: x * y,
    '/': lambda x, y: x / y,
}

def   A   (   _SCREAMING_SNAKE_CASE					)			->				List[str]:
       return c.isdigit()

def   A   (   _SCREAMING_SNAKE_CASE					)			->				Optional[Any]:
       lowerCamelCase     :   Tuple    				=      []

       # iterate over the string in reverse order
       for c in expression.split()[::-1]:
              # push operand to stack
              if is_operand(_SCREAMING_SNAKE_CASE					):
                     stack.append(int(_SCREAMING_SNAKE_CASE					)					)

              else:
                     # pop values from stack can calculate the result
                     # push the result onto the stack again
                     lowerCamelCase     :   str    				=      stack.pop()
                     lowerCamelCase     :   Optional[int]    				=      stack.pop()
                     stack.append(calc[c](_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)					)

       return stack.pop()


# Driver code
if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__ :   Optional[int]						=     '+ 9 * 2 6'
       print(evaluate(test_expression))

       SCREAMING_SNAKE_CASE__ :   Union[str, Any]						=     '/ * 10 2 + 4 1 '
       print(evaluate(test_expression))






