code: 
  
  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 		__magic_name__    (							lowerCamelCase__							):
  
     '''simple docstring'''
  
  
  
  
     def __init__(       self )							->					Tuple:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       self.register_to_config()
       super().__init__()
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ )							->					int:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if dicta == dicta:
         return True
       else:
         a__,   a__						=self._remove_meta_keys(lowercase_ )
         a__,   a__						=self._remove_meta_keys(lowercase_ )
         if configa == configa:
           print(F"""Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.""" )
           return True
       return False
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_ )							->					Tuple:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=[]
       a__						=config_dict.copy()
       for key in config_dict.keys():
         if key.startswith('''_''' ):
           temp_dict.pop(lowercase_ )
           meta_keys.append(lowercase_ )
       return (temp_dict, meta_keys)
  
  
  
     @torch.no_grad()
     def 				_UpperCAmelCase				(       self,					lowercase_,					**lowercase_ )							->					Optional[Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=kwargs.pop('''cache_dir''',					lowercase_ )
       a__						=kwargs.pop('''resume_download''',					lowercase_ )
       a__						=kwargs.pop('''force_download''',					lowercase_ )
       a__						=kwargs.pop('''proxies''',					lowercase_ )
       a__						=kwargs.pop('''local_files_only''',					lowercase_ )
       a__						=kwargs.pop('''use_auth_token''',					lowercase_ )
       a__						=kwargs.pop('''revision''',					lowercase_ )
       a__						=kwargs.pop('''torch_dtype''',					lowercase_ )
       a__						=kwargs.pop('''device_map''',					lowercase_ )
  
       a__						=kwargs.pop('''alpha''',					0.5 )
       a__						=kwargs.pop('''interp''',					lowercase_ )
  
       print('''Received list''',					lowercase_ )
       print(F"""Combining with alpha={alpha}, interpolation mode={interp}""" )
  
       a__						=len(lowercase_ )
       # Ignore result from model_index_json comparision of the two checkpoints
       a__						=kwargs.pop('''force''',					lowercase_ )
  
       # 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__						=[]
       for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
         a__						=DiffusionPipeline.load_config(
             lowercase_,					cache_dir=lowercase_,					resume_download=lowercase_,					force_download=lowercase_,					proxies=lowercase_,					local_files_only=lowercase_,					use_auth_token=lowercase_,					revision=lowercase_,					)
         config_dicts.append(lowercase_ )
  
       a__						=True
       for idx in range(1,					len(lowercase_ ) ):
         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__						=[]
       for pretrained_model_name_or_path, config_dict in zip(lowercase_,					lowercase_ ):
         a__						=[k for k in config_dict.keys() if not k.startswith('''_''' )]
         a__						=[os.path.join(lowercase_,					'''*''' ) for k in folder_names]
         allow_patterns += [
             WEIGHTS_NAME,
             SCHEDULER_CONFIG_NAME,
             CONFIG_NAME,
             ONNX_WEIGHTS_NAME,
             DiffusionPipeline.config_name,
         ]
         a__						=config_dict.get('''_class_name''' )
         a__						={'''diffusers''': __version__, '''pipeline_class''': requested_pipeline_class}
  
         a__						=(
             pretrained_model_name_or_path
             if os.path.isdir(lowercase_ )
             else snapshot_download(
                 lowercase_,					cache_dir=lowercase_,					resume_download=lowercase_,					proxies=lowercase_,					local_files_only=lowercase_,					use_auth_token=lowercase_,					revision=lowercase_,					allow_patterns=lowercase_,					user_agent=lowercase_,					)
         )
         print('''Cached Folder''',					lowercase_ )
         cached_folders.append(lowercase_ )
  
       # Step 3:-
       # Load the first checkpoint as a diffusion pipeline and modify its module state_dict in place
       a__						=DiffusionPipeline.from_pretrained(
           cached_folders[0],					torch_dtype=lowercase_,					device_map=lowercase_ )
       final_pipe.to(self.device )
  
       a__						=None
       if len(lowercase_ ) > 2:
         a__						=os.path.join(cached_folders[2] )
  
       if interp == "sigmoid":
         a__						=CheckpointMergerPipeline.sigmoid
       elif interp == "inv_sigmoid":
         a__						=CheckpointMergerPipeline.inv_sigmoid
       elif interp == "add_diff":
         a__						=CheckpointMergerPipeline.add_difference
       else:
         a__						=CheckpointMergerPipeline.weighted_sum
  
       # Find each module's state dict.
       for attr in final_pipe.config.keys():
         if not attr.startswith('''_''' ):
           a__						=os.path.join(cached_folders[1],					lowercase_ )
           if os.path.exists(lowercase_ ):
             a__						=[
                 *glob.glob(os.path.join(lowercase_,					'''*.safetensors''' ) ),
                 *glob.glob(os.path.join(lowercase_,					'''*.bin''' ) ),
             ]
             a__						=files[0] if len(lowercase_ ) > 0 else None
           if len(lowercase_ ) < 3:
             a__						=None
           else:
             a__						=os.path.join(cached_folders[2],					lowercase_ )
             if os.path.exists(lowercase_ ):
               a__						=[
                   *glob.glob(os.path.join(lowercase_,					'''*.safetensors''' ) ),
                   *glob.glob(os.path.join(lowercase_,					'''*.bin''' ) ),
               ]
               a__						=files[0] if len(lowercase_ ) > 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__						=getattr(lowercase_,					lowercase_ )
             if isinstance(lowercase_,					lowercase_ ):  # ignore requires_safety_checker boolean
               continue
             a__						=getattr(lowercase_,					'''state_dict''' )
             a__						=theta_a()
  
             a__						=getattr(lowercase_,					'''load_state_dict''' )
             a__						=(
                 safetensors.torch.load_file(lowercase_ )
                 if (is_safetensors_available() and checkpoint_path_a.endswith('''.safetensors''' ))
                 else torch.load(lowercase_,					map_location='''cpu''' )
             )
             a__						=None
             if checkpoint_path_a:
               a__						=(
                   safetensors.torch.load_file(lowercase_ )
                   if (is_safetensors_available() and checkpoint_path_a.endswith('''.safetensors''' ))
                   else torch.load(lowercase_,					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(lowercase_ )}""" )
             continue
           print(F"""MERGING {attr}""" )
  
           for key in theta_a.keys():
             if theta_a:
               a__						=theta_func(theta_a[key],					theta_a[key],					theta_a[key],					lowercase_ )
             else:
               a__						=theta_func(theta_a[key],					theta_a[key],					lowercase_,					lowercase_ )
  
           del theta_a
           del theta_a
           update_theta_a(lowercase_ )
  
           del theta_a
       return final_pipe
  
  
  
     @staticmethod
     def 				_UpperCAmelCase				(       lowercase_,					lowercase_,					lowercase_,					lowercase_ )							->					Union[str, Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       return ((1 - alpha) * thetaa) + (alpha * thetaa)
  
  
  
     @staticmethod
     def 				_UpperCAmelCase				(       lowercase_,					lowercase_,					lowercase_,					lowercase_ )							->					Tuple:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=alpha * alpha * (3 - (2 * alpha))
       return thetaa + ((thetaa - thetaa) * alpha)
  
  
  
     @staticmethod
     def 				_UpperCAmelCase				(       lowercase_,					lowercase_,					lowercase_,					lowercase_ )							->					List[Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       import math
  
       a__						=0.5 - math.sin(math.asin(1.0 - 2.0 * alpha ) / 3.0 )
       return thetaa + ((thetaa - thetaa) * alpha)
  
  
  
     @staticmethod
     def 				_UpperCAmelCase				(       lowercase_,					lowercase_,					lowercase_,					lowercase_ )							->					Tuple:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       return thetaa + (thetaa - thetaa) * (1.0 - alpha)
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  lowerCamelCase     =      {
      '''configuration_deberta_v2''': ['''DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaV2Config''', '''DebertaV2OnnxConfig'''],
      '''tokenization_deberta_v2''': ['''DebertaV2Tokenizer'''],
  }
  
  try:
         if not is_tokenizers_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         lowerCamelCase     =      ['''DebertaV2TokenizerFast''']
  
  try:
         if not is_tf_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         lowerCamelCase     =      [
             '''TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
             '''TFDebertaV2ForMaskedLM''',
             '''TFDebertaV2ForQuestionAnswering''',
             '''TFDebertaV2ForSequenceClassification''',
             '''TFDebertaV2ForTokenClassification''',
             '''TFDebertaV2Model''',
             '''TFDebertaV2PreTrainedModel''',
         ]
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         lowerCamelCase     =      [
             '''DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
             '''DebertaV2ForMaskedLM''',
             '''DebertaV2ForMultipleChoice''',
             '''DebertaV2ForQuestionAnswering''',
             '''DebertaV2ForSequenceClassification''',
             '''DebertaV2ForTokenClassification''',
             '''DebertaV2Model''',
             '''DebertaV2PreTrainedModel''',
         ]
  
  
  if TYPE_CHECKING:
         from .configuration_deberta_va import (
             DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
             DebertaVaConfig,
             DebertaVaOnnxConfig,
         )
         from .tokenization_deberta_va import DebertaVaTokenizer
  
         try:
                if not is_tokenizers_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .tokenization_deberta_va_fast import DebertaVaTokenizerFast
  
         try:
                if not is_tf_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_tf_deberta_va import (
                    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
                    TFDebertaVaForMaskedLM,
                    TFDebertaVaForQuestionAnswering,
                    TFDebertaVaForSequenceClassification,
                    TFDebertaVaForTokenClassification,
                    TFDebertaVaModel,
                    TFDebertaVaPreTrainedModel,
                )
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_deberta_va import (
                    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
                    DebertaVaForMaskedLM,
                    DebertaVaForMultipleChoice,
                    DebertaVaForQuestionAnswering,
                    DebertaVaForSequenceClassification,
                    DebertaVaForTokenClassification,
                    DebertaVaModel,
                    DebertaVaPreTrainedModel,
                )
  
  else:
         import sys
  
         lowerCamelCase     =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  