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: 
  
  import unittest
  
  import numpy as np
  
  from transformers.testing_utils import require_torch, require_vision
  from transformers.utils import is_torch_available, is_vision_available
  
  from ...test_image_processing_common import ImageProcessingSavingTestMixin
  
  
  if is_torch_available():
         import torch
  
  if is_vision_available():
         from PIL import Image
  
         from transformers import TvltImageProcessor
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				Optional[int]			,   _A   :				List[Any]=10			,   _A   :				Optional[int]=10			,   _A   :				Dict=False			,   _A   :				Any=False							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=[]
    for i in range(image_processor_tester.num_frames							):
      video.append(np.random.randint(2_55			,   size=(image_processor_tester.num_channels, width, height)			,   dtype=np.uinta							)							)
  
    if not numpify and not torchify:
      # PIL expects the channel dimension as last dimension
      a__						=[Image.fromarray(np.moveaxis(_A			,   0			,   -1							)							) for frame in video]
  
    if torchify:
      a__						=[torch.from_numpy(_A							) for frame in video]
  
    return video
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				List[str]			,   _A   :				Optional[int]=False			,   _A   :				Dict=False			,   _A   :				Optional[Any]=False							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
  
    a__						=[]
    for i in range(image_processor_tester.batch_size							):
      if equal_resolution:
        a__						=a__						=image_processor_tester.max_resolution
      else:
        a__,   a__						=np.random.choice(
            np.arange(image_processor_tester.min_resolution			,   image_processor_tester.max_resolution							)			,   2							)
        a__						=prepare_video(
            image_processor_tester=_A			,   width=_A			,   height=_A			,   numpify=_A			,   torchify=_A			,   )
      video_inputs.append(_A							)
  
    return video_inputs
  
  
  
  
  
  
  
  class 		__magic_name__    (							unittest.TestCase							):
  
     '''simple docstring'''
  
  
  
  
     def __init__(       self,					lowercase_,					lowercase_=7,					lowercase_=3,					lowercase_=4,					lowercase_=18,					lowercase_=30,					lowercase_=400,					lowercase_=True,					lowercase_=None,					lowercase_=True,					lowercase_=[0.5, 0.5, 0.5],					lowercase_=[0.5, 0.5, 0.5],					lowercase_=True,					lowercase_=None,					)							->					Optional[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=size if size is not None else {'''shortest_edge''': 18}
       a__						=crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
  
       a__						=parent
       a__						=batch_size
       a__						=num_channels
       a__						=num_frames
       a__						=image_size
       a__						=min_resolution
       a__						=max_resolution
       a__						=do_resize
       a__						=size
       a__						=do_normalize
       a__						=image_mean
       a__						=image_std
       a__						=do_center_crop
       a__						=crop_size
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "do_center_crop": self.do_center_crop,
           "crop_size": self.crop_size,
       }
  
  
  
  
  
  
  
  @require_torch
  @require_vision
  class 		__magic_name__    (							lowerCamelCase__	, unittest.TestCase							):
  
     '''simple docstring'''
  
  
  
  
     lowerCamelCase__				:			str												=     TvltImageProcessor if is_vision_available() else None
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Optional[Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=TvltImageProcessorTester(self )
  
  
  
     @property
     def 				_UpperCAmelCase				(       self )							->					Optional[Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
     def 				_UpperCAmelCase				(       self )							->					str:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.image_processing_class(**self.image_processor_dict )
       self.assertTrue(hasattr(lowercase_,					'''image_mean''' ) )
       self.assertTrue(hasattr(lowercase_,					'''image_std''' ) )
       self.assertTrue(hasattr(lowercase_,					'''do_normalize''' ) )
       self.assertTrue(hasattr(lowercase_,					'''do_resize''' ) )
       self.assertTrue(hasattr(lowercase_,					'''do_center_crop''' ) )
       self.assertTrue(hasattr(lowercase_,					'''size''' ) )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Tuple:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.image_processing_class(**self.image_processor_dict )
       # create random PIL videos
       a__						=prepare_video_inputs(self.image_processor_tester,					equal_resolution=lowercase_ )
       for video in video_inputs:
         self.assertIsInstance(lowercase_,					lowercase_ )
         self.assertIsInstance(video[0],					Image.Image )
  
       # Test not batched input
       a__						=image_processor(video_inputs[0],					return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_videos.shape,					(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           ),					)
  
       # Test batched
       a__						=image_processor(lowercase_,					return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_videos.shape,					(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           ),					)
  
  
  
     def 				_UpperCAmelCase				(       self )							->					int:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.image_processing_class(**self.image_processor_dict )
       # create random numpy tensors
       a__						=prepare_video_inputs(self.image_processor_tester,					equal_resolution=lowercase_,					numpify=lowercase_ )
       for video in video_inputs:
         self.assertIsInstance(lowercase_,					lowercase_ )
         self.assertIsInstance(video[0],					np.ndarray )
  
       # Test not batched input
       a__						=image_processor(video_inputs[0],					return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_videos.shape,					(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           ),					)
  
       # Test batched
       a__						=image_processor(lowercase_,					return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_videos.shape,					(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           ),					)
  
  
  
     def 				_UpperCAmelCase				(       self )							->					int:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.image_processing_class(**self.image_processor_dict )
       # create random PyTorch tensors
       a__						=prepare_video_inputs(self.image_processor_tester,					equal_resolution=lowercase_,					torchify=lowercase_ )
       for video in video_inputs:
         self.assertIsInstance(lowercase_,					lowercase_ )
         self.assertIsInstance(video[0],					torch.Tensor )
  
       # Test not batched input
       a__						=image_processor(video_inputs[0],					return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_videos.shape,					(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           ),					)
  
       # Test batched
       a__						=image_processor(lowercase_,					return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_videos.shape,					(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           ),					)
  