"""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 							__snake_case(					_lowerCamelCase	):


    '''simple docstring'''





    def __init__(  self	):




         '''simple docstring'''



         self.register_to_config()
         super().__init__()
    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase	):




         '''simple docstring'''



         if dicta == dicta:
              return True
         else:
              __A					:					Any								=		self._remove_meta_keys(a_	)
              __A					:					Union[str, Any]								=		self._remove_meta_keys(a_	)
              if configa == configa:
                   print(F'Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.'	)
                   return True
         return False
    def 						_a							(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					Dict								=		[]
         __A					:					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 						_a							(  self					,       __lowerCamelCase					,       **__lowerCamelCase	):




         '''simple docstring'''



         __A					:					str								=		kwargs.pop('cache_dir'					,       a_	)
         __A					:					List[Any]								=		kwargs.pop('resume_download'					,       a_	)
         __A					:					Tuple								=		kwargs.pop('force_download'					,       a_	)
         __A					:					str								=		kwargs.pop('proxies'					,       a_	)
         __A					:					Optional[Any]								=		kwargs.pop('local_files_only'					,       a_	)
         __A					:					Optional[int]								=		kwargs.pop('use_auth_token'					,       a_	)
         __A					:					Optional[Any]								=		kwargs.pop('revision'					,       a_	)
         __A					:					Any								=		kwargs.pop('torch_dtype'					,       a_	)
         __A					:					Dict								=		kwargs.pop('device_map'					,       a_	)

         __A					:					Union[str, Any]								=		kwargs.pop('alpha'					,       0.5	)
         __A					:					Tuple								=		kwargs.pop('interp'					,       a_	)

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

         __A					:					str								=		len(a_	)
         # Ignore result from model_index_json comparision of the two checkpoints
         __A					:					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 '_'
         __A					:					List[Any]								=		[]
         for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
              __A					:					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_	)

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

              __A					:					List[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
         __A					:					int								=		DiffusionPipeline.from_pretrained(
             cached_folders[0]					,       torch_dtype=a_					,       device_map=a_	)
         final_pipe.to(self.device	)

         __A					:					List[Any]								=		None
         if len(a_	) > 2:
              __A					:					Tuple								=		os.path.join(cached_folders[2]	)

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

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

                        __A					:					List[str]								=		getattr(a_					,       'load_state_dict'	)
                        __A					:					List[Any]								=		(
                            safetensors.torch.load_file(a_	)
                            if (is_safetensors_available() and checkpoint_path_a.endswith('.safetensors'	))
                            else torch.load(a_					,       map_location='cpu'	)
                        )
                        __A					:					Tuple								=		None
                        if checkpoint_path_a:
                             __A					:					Dict								=		(
                                 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:
                             __A					:					Any								=		theta_func(theta_a[key]					,       theta_a[key]					,       theta_a[key]					,       a_	)
                        else:
                             __A					:					Optional[int]								=		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 						_a							(  __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase	):




         '''simple docstring'''



         return ((1 - alpha) * thetaa) + (alpha * thetaa)
    @staticmethod
    def 						_a							(  __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					Dict								=		alpha * alpha * (3 - (2 * alpha))
         return thetaa + ((thetaa - thetaa) * alpha)
    @staticmethod
    def 						_a							(  __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase	):




         '''simple docstring'''



         import math

         __A					:					Union[str, Any]								=		0.5 - math.sin(math.asin(1.0 - 2.0 * alpha	) / 3.0	)
         return thetaa + ((thetaa - thetaa) * alpha)

    @staticmethod
    def 						_a							(  __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase	):




         '''simple docstring'''



         return thetaa + (thetaa - thetaa) * (1.0 - alpha)








'''simple docstring'''






def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_						)->						Union[str, Any]:





       '''simple docstring'''


       if n < 0:
              return 0
       # table[i] represents the number of ways to get to amount i
       _UpperCAmelCase    :		Union[str, Any]     	=					[0] * (n + 1)

       # There is exactly 1 way to get to zero(You pick no coins).
       _UpperCAmelCase    :		Optional[int]     	=					1

       # Pick all coins one by one and update table[] values
       # after the index greater than or equal to the value of the
       # picked coin
       for coin_val in s:
              for j in range(lowerCAmelCase_			,  n + 1						):
                     table[j] += table[j - coin_val]

       return table[n]


if __name__ == "__main__":
   import doctest

   doctest.testmod()



