






"""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					:	Union[str, Any]    ):

     '''simple docstring'''
     self.register_to_config()
     super().__init__()


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int],			_lowerCamelCase					:	Tuple,			_lowerCamelCase					:	List[str]    ):

     '''simple docstring'''
     if dicta == dicta:
       return True
     else:
       __A     ,       __A		     =					self._remove_meta_keys(_lowerCamelCase    )
       __A     ,       __A		     =					self._remove_meta_keys(_lowerCamelCase    )
       if configa == configa:
         print(f'Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.'    )
         return True
     return False


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any,			_lowerCamelCase					:	Dict    ):

     '''simple docstring'''
     __A		     =					[]
     __A		     =					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 							_SCREAMING_SNAKE_CASE							(      self					:	int,			_lowerCamelCase					:	List[Union[str, os.PathLike]],			**_lowerCamelCase					:	Optional[int]    ):

     '''simple docstring'''
     __A		     =					kwargs.pop('''cache_dir''',			_lowerCamelCase    )
     __A		     =					kwargs.pop('''resume_download''',			_lowerCamelCase    )
     __A		     =					kwargs.pop('''force_download''',			_lowerCamelCase    )
     __A		     =					kwargs.pop('''proxies''',			_lowerCamelCase    )
     __A		     =					kwargs.pop('''local_files_only''',			_lowerCamelCase    )
     __A		     =					kwargs.pop('''use_auth_token''',			_lowerCamelCase    )
     __A		     =					kwargs.pop('''revision''',			_lowerCamelCase    )
     __A		     =					kwargs.pop('''torch_dtype''',			_lowerCamelCase    )
     __A		     =					kwargs.pop('''device_map''',			_lowerCamelCase    )

     __A		     =					kwargs.pop('''alpha''',			0.5    )
     __A		     =					kwargs.pop('''interp''',			_lowerCamelCase    )

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

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

     __A		     =					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.
     __A		     =					[]
     for pretrained_model_name_or_path, config_dict in zip(_lowerCamelCase,			_lowerCamelCase    ):
       __A		     =					[k for k in config_dict.keys() if not k.startswith('''_'''    )]
       __A		     =					[os.path.join(_lowerCamelCase,			'''*'''    ) 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(_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
     __A		     =					DiffusionPipeline.from_pretrained(
         cached_folders[0],			torch_dtype=_lowerCamelCase,			device_map=_lowerCamelCase    )
     final_pipe.to(self.device    )

     __A		     =					None
     if len(_lowerCamelCase    ) > 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],			_lowerCamelCase    )
         if os.path.exists(_lowerCamelCase    ):
           __A		     =					[
               *glob.glob(os.path.join(_lowerCamelCase,			'''*.safetensors'''    )    ),
               *glob.glob(os.path.join(_lowerCamelCase,			'''*.bin'''    )    ),
           ]
           __A		     =					files[0] if len(_lowerCamelCase    ) > 0 else None
         if len(_lowerCamelCase    ) < 3:
           __A		     =					None
         else:
           __A		     =					os.path.join(cached_folders[2],			_lowerCamelCase    )
           if os.path.exists(_lowerCamelCase    ):
             __A		     =					[
                 *glob.glob(os.path.join(_lowerCamelCase,			'''*.safetensors'''    )    ),
                 *glob.glob(os.path.join(_lowerCamelCase,			'''*.bin'''    )    ),
             ]
             __A		     =					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:
           __A		     =					getattr(_lowerCamelCase,			_lowerCamelCase    )
           if isinstance(_lowerCamelCase,			_lowerCamelCase    ):  # ignore requires_safety_checker boolean
             continue
           __A		     =					getattr(_lowerCamelCase,			'''state_dict'''    )
           __A		     =					theta_a()

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

     '''simple docstring'''
     return ((1 - alpha) * thetaa) + (alpha * thetaa)


   @staticmethod
   def 							_SCREAMING_SNAKE_CASE							(      _lowerCamelCase					:	Optional[Any],			_lowerCamelCase					:	List[Any],			_lowerCamelCase					:	Optional[int],			_lowerCamelCase					:	Union[str, Any]    ):

     '''simple docstring'''
     __A		     =					alpha * alpha * (3 - (2 * alpha))
     return thetaa + ((thetaa - thetaa) * alpha)


   @staticmethod
   def 							_SCREAMING_SNAKE_CASE							(      _lowerCamelCase					:	Tuple,			_lowerCamelCase					:	Tuple,			_lowerCamelCase					:	Any,			_lowerCamelCase					:	Dict    ):

     '''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 							_SCREAMING_SNAKE_CASE							(      _lowerCamelCase					:	List[str],			_lowerCamelCase					:	Tuple,			_lowerCamelCase					:	Any,			_lowerCamelCase					:	int    ):

     '''simple docstring'''
     return thetaa + (thetaa - thetaa) * (1.0 - alpha)












"""simple docstring"""


class    snake_case						:


   '''simple docstring'''

   def __init__(      self					:	Optional[Any]    ):

     '''simple docstring'''
     # numbermap keys are the number and its values are lists of indexes sorted
     # in ascending order
     __A		     =					{}
     # indexmap keys are an index and it's values are the number at that index
     __A		     =					{}


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str],			_lowerCamelCase					:	list | str | range,			_lowerCamelCase					:	int    ):

     '''simple docstring'''
     if isinstance(_lowerCamelCase,			(range, str)    ):
       __A		     =					list(_lowerCamelCase    )
     elif not isinstance(_lowerCamelCase,			_lowerCamelCase    ):
       raise TypeError(
           '''binary_search_delete() only accepts either a list, range or str'''    )

     __A		     =					0
     __A		     =					len(_lowerCamelCase    ) - 1

     while low <= high:
       __A		     =					(low + high) // 2
       if array[mid] == item:
         array.pop(_lowerCamelCase    )
         return array
       elif array[mid] < item:
         __A		     =					mid + 1
       else:
         __A		     =					mid - 1
     raise ValueError(
         '''Either the item is not in the array or the array was unsorted'''    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any,			_lowerCamelCase					:	list | str | range,			_lowerCamelCase					:	int    ):

     '''simple docstring'''
     if isinstance(_lowerCamelCase,			(range, str)    ):
       __A		     =					list(_lowerCamelCase    )
     elif not isinstance(_lowerCamelCase,			_lowerCamelCase    ):
       raise TypeError(
           '''binary_search_insert() only accepts either a list, range or str'''    )

     __A		     =					0
     __A		     =					len(_lowerCamelCase    ) - 1

     while low <= high:
       __A		     =					(low + high) // 2
       if array[mid] == index:
         # If the item already exists in the array,
         # insert it after the existing item
         array.insert(mid + 1,			_lowerCamelCase    )
         return array
       elif array[mid] < index:
         __A		     =					mid + 1
       else:
         __A		     =					mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
     array.insert(_lowerCamelCase,			_lowerCamelCase    )
     return array


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			_lowerCamelCase					:	int,			_lowerCamelCase					:	int    ):

     '''simple docstring'''
     # Remove previous index
     if index in self.indexmap:
       __A		     =					self.indexmap[index]
       if len(self.numbermap[n]    ) == 1:
         del self.numbermap[n]
       else:
         __A		     =					self.binary_search_delete(self.numbermap[n],			_lowerCamelCase    )

        # Set new index
     __A		     =					number

     # Number not seen before or empty so insert number value
     if number not in self.numbermap:
       __A		     =					[index]

     # Here we need to perform a binary search insertion in order to insert
     # The item in the correct place
     else:
       __A		     =					self.binary_search_insert(
           self.numbermap[number],			_lowerCamelCase    )






   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			_lowerCamelCase					:	int    ):

     '''simple docstring'''
     return self.numbermap.get(_lowerCamelCase,			[-1]    )[0]


if __name__ == "__main__":
       import doctest

       doctest.testmod()




