



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  snake_case__						(	lowerCAmelCase_					):






     """simple docstring"""


     def __init__(	self  :  Optional[Any] )       ->Tuple:
            self.register_to_config()
            super().__init__()






     def        lowercase_       (	self  :  Optional[Any],			_snake_case  :  Dict,			_snake_case  :  Union[str, Any] )       ->Optional[int]:
            if dicta == dicta:
                   return True
            else:
                   snake_case__     ,    snake_case__							:     Union[str, Any]            =     self._remove_meta_keys(_snake_case )
                   snake_case__     ,    snake_case__							:     int            =     self._remove_meta_keys(_snake_case )
                   if configa == configa:
                          print(F'''Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.''' )
                          return True
            return False






     def        lowercase_       (	self  :  str,			_snake_case  :  Dict )       ->Dict:
            snake_case__							:     int            =     []
            snake_case__							:     Dict            =     config_dict.copy()
            for key in config_dict.keys():
                   if key.startswith('_' ):
                          temp_dict.pop(_snake_case )
                          meta_keys.append(_snake_case )
            return (temp_dict, meta_keys)






     @torch.no_grad()
     def        lowercase_       (	self  :  Dict,			_snake_case  :  List[Union[str, os.PathLike]],			**_snake_case  :  List[str] )       ->Optional[Any]:
            snake_case__							:     Optional[int]            =     kwargs.pop('cache_dir',			_snake_case )
            snake_case__							:     List[Any]            =     kwargs.pop('resume_download',			_snake_case )
            snake_case__							:     Dict            =     kwargs.pop('force_download',			_snake_case )
            snake_case__							:     List[Any]            =     kwargs.pop('proxies',			_snake_case )
            snake_case__							:     Optional[int]            =     kwargs.pop('local_files_only',			_snake_case )
            snake_case__							:     Union[str, Any]            =     kwargs.pop('use_auth_token',			_snake_case )
            snake_case__							:     Optional[Any]            =     kwargs.pop('revision',			_snake_case )
            snake_case__							:     int            =     kwargs.pop('torch_dtype',			_snake_case )
            snake_case__							:     Tuple            =     kwargs.pop('device_map',			_snake_case )

            snake_case__							:     List[str]            =     kwargs.pop('alpha',			0.5 )
            snake_case__							:     Tuple            =     kwargs.pop('interp',			_snake_case )

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

            snake_case__							:     List[Any]            =     len(_snake_case )
            # Ignore result from model_index_json comparision of the two checkpoints
            snake_case__							:     int            =     kwargs.pop('force',			_snake_case )

            # 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 '_'
            snake_case__							:     Optional[Any]            =     []
            for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
                   snake_case__							:     Optional[int]            =     DiffusionPipeline.load_config(
                       _snake_case,			cache_dir=_snake_case,			resume_download=_snake_case,			force_download=_snake_case,			proxies=_snake_case,			local_files_only=_snake_case,			use_auth_token=_snake_case,			revision=_snake_case,			)
                   config_dicts.append(_snake_case )

            snake_case__							:     Dict            =     True
            for idx in range(1,			len(_snake_case ) ):
                   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.
            snake_case__							:     Union[str, Any]            =     []
            for pretrained_model_name_or_path, config_dict in zip(_snake_case,			_snake_case ):
                   snake_case__							:     List[Any]            =     [k for k in config_dict.keys() if not k.startswith('_' )]
                   snake_case__							:     Optional[int]            =     [os.path.join(_snake_case,			'*' ) for k in folder_names]
                   allow_patterns += [
                       WEIGHTS_NAME,
                       SCHEDULER_CONFIG_NAME,
                       CONFIG_NAME,
                       ONNX_WEIGHTS_NAME,
                       DiffusionPipeline.config_name,
                   ]
                   snake_case__							:     List[str]            =     config_dict.get('_class_name' )
                   snake_case__							:     Optional[int]            =     {'diffusers': __version__, 'pipeline_class': requested_pipeline_class}

                   snake_case__							:     Dict            =     (
                       pretrained_model_name_or_path
                       if os.path.isdir(_snake_case )
                       else snapshot_download(
                           _snake_case,			cache_dir=_snake_case,			resume_download=_snake_case,			proxies=_snake_case,			local_files_only=_snake_case,			use_auth_token=_snake_case,			revision=_snake_case,			allow_patterns=_snake_case,			user_agent=_snake_case,			)
                   )
                   print('Cached Folder',			_snake_case )
                   cached_folders.append(_snake_case )

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

            snake_case__							:     Any            =     None
            if len(_snake_case ) > 2:
                   snake_case__							:     Union[str, Any]            =     os.path.join(cached_folders[2] )

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

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

                                 snake_case__							:     int            =     getattr(_snake_case,			'load_state_dict' )
                                 snake_case__							:     str            =     (
                                     safetensors.torch.load_file(_snake_case )
                                     if (is_safetensors_available() and checkpoint_path_a.endswith('.safetensors' ))
                                     else torch.load(_snake_case,			map_location='cpu' )
                                 )
                                 snake_case__							:     Optional[Any]            =     None
                                 if checkpoint_path_a:
                                        snake_case__							:     Any            =     (
                                            safetensors.torch.load_file(_snake_case )
                                            if (is_safetensors_available() and checkpoint_path_a.endswith('.safetensors' ))
                                            else torch.load(_snake_case,			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(_snake_case )}''' )
                                 continue
                          print(F'''MERGING {attr}''' )

                          for key in theta_a.keys():
                                 if theta_a:
                                        snake_case__							:     int            =     theta_func(theta_a[key],			theta_a[key],			theta_a[key],			_snake_case )
                                 else:
                                        snake_case__							:     str            =     theta_func(theta_a[key],			theta_a[key],			_snake_case,			_snake_case )

                          del theta_a
                          del theta_a
                          update_theta_a(_snake_case )

                          del theta_a
            return final_pipe






     @staticmethod
     def        lowercase_       (	_snake_case  :  int,			_snake_case  :  Dict,			_snake_case  :  Union[str, Any],			_snake_case  :  str )       ->Tuple:
            return ((1 - alpha) * thetaa) + (alpha * thetaa)






     @staticmethod
     def        lowercase_       (	_snake_case  :  List[str],			_snake_case  :  Union[str, Any],			_snake_case  :  Any,			_snake_case  :  List[Any] )       ->Union[str, Any]:
            snake_case__							:     Optional[int]            =     alpha * alpha * (3 - (2 * alpha))
            return thetaa + ((thetaa - thetaa) * alpha)






     @staticmethod
     def        lowercase_       (	_snake_case  :  List[Any],			_snake_case  :  str,			_snake_case  :  int,			_snake_case  :  Union[str, Any] )       ->List[str]:
            import math

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






     @staticmethod
     def        lowercase_       (	_snake_case  :  Optional[int],			_snake_case  :  str,			_snake_case  :  List[str],			_snake_case  :  Tuple )       ->Optional[int]:
            return thetaa + (thetaa - thetaa) * (1.0 - alpha)











def 	lowercase_							(A			:			list				):
       for i, _ in enumerate(A				):
              if (i % 2 == 1) == (nums[i - 1] > nums[i]):
                     snake_case__     ,    snake_case__							:     List[str]            =     nums[i], nums[i - 1]

       return nums


if __name__ == "__main__":
 print("Enter the array elements:")
 a_       :Tuple				  =				list(map(int, input().split()))
 print("The unsorted array is:")
 print(array)
 print("Array after Wiggle sort:")
 print(wiggle_sort(array))






