




'''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 	SCREAMING_SNAKE_CASE_		(							lowerCamelCase_					):



 def __init__(	self			):






        '''simple docstring'''




        self.register_to_config()
        super().__init__()



 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A			):






        '''simple docstring'''




        if dicta == dicta:
               return True
        else:
               lowercase,  lowercase	:		Union[str, Any]    =   self._remove_meta_keys(A			)
               lowercase,  lowercase	:		str    =   self._remove_meta_keys(A			)
               if configa == configa:
                      print(f"""Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}."""			)
                      return True
        return False



 def  SCREAMING_SNAKE_CASE(	self ,		A			):






        '''simple docstring'''




        lowercase	:		Dict    =   []
        lowercase	:		Optional[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  SCREAMING_SNAKE_CASE(	self ,		A ,		**A			):






        '''simple docstring'''




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

        lowercase	:		Optional[Any]    =   kwargs.pop('''alpha''' ,		0.5			)
        lowercase	:		List[str]    =   kwargs.pop('''interp''' ,		A			)

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

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

        lowercase	:		List[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.
        lowercase	:		Tuple    =   []
        for pretrained_model_name_or_path, config_dict in zip(A ,		A			):
               lowercase	:		List[Any]    =   [k for k in config_dict.keys() if not k.startswith('''_'''			)]
               lowercase	:		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,
               ]
               lowercase	:		Optional[Any]    =   config_dict.get('''_class_name'''			)
               lowercase	:		Optional[int]    =   {'''diffusers''': __version__, '''pipeline_class''': requested_pipeline_class}

               lowercase	:		Optional[Any]    =   (
                   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
        lowercase	:		Any    =   DiffusionPipeline.from_pretrained(
            cached_folders[0] ,		torch_dtype=A ,		device_map=A			)
        final_pipe.to(self.device			)

        lowercase	:		Optional[int]    =   None
        if len(A			) > 2:
               lowercase	:		Optional[Any]    =   os.path.join(cached_folders[2]			)

        if interp == "sigmoid":
               lowercase	:		List[Any]    =   CheckpointMergerPipeline.sigmoid
        elif interp == "inv_sigmoid":
               lowercase	:		Union[str, Any]    =   CheckpointMergerPipeline.inv_sigmoid
        elif interp == "add_diff":
               lowercase	:		Tuple    =   CheckpointMergerPipeline.add_difference
        else:
               lowercase	:		Optional[Any]    =   CheckpointMergerPipeline.weighted_sum

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

                             lowercase	:		str    =   getattr(A ,		'''load_state_dict'''			)
                             lowercase	:		int    =   (
                                 safetensors.torch.load_file(A			)
                                 if (is_safetensors_available() and checkpoint_path_a.endswith('''.safetensors'''			))
                                 else torch.load(A ,		map_location='''cpu'''			)
                             )
                             lowercase	:		Any    =   None
                             if checkpoint_path_a:
                                    lowercase	:		int    =   (
                                        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:
                                    lowercase	:		Optional[int]    =   theta_func(theta_a[key] ,		theta_a[key] ,		theta_a[key] ,		A			)
                             else:
                                    lowercase	:		Optional[Any]    =   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  SCREAMING_SNAKE_CASE(	A ,		A ,		A ,		A			):






        '''simple docstring'''




        return ((1 - alpha) * thetaa) + (alpha * thetaa)



 @staticmethod
 def  SCREAMING_SNAKE_CASE(	A ,		A ,		A ,		A			):






        '''simple docstring'''




        lowercase	:		List[Any]    =   alpha * alpha * (3 - (2 * alpha))
        return thetaa + ((thetaa - thetaa) * alpha)



 @staticmethod
 def  SCREAMING_SNAKE_CASE(	A ,		A ,		A ,		A			):






        '''simple docstring'''




        import math

        lowercase	:		Dict    =   0.5 - math.sin(math.asin(1.0 - 2.0 * alpha			) / 3.0			)
        return thetaa + ((thetaa - thetaa) * alpha)





 @staticmethod
 def  SCREAMING_SNAKE_CASE(	A ,		A ,		A ,		A			):






        '''simple docstring'''




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










'''simple docstring'''






from __future__ import annotations

from collections.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

lowercase						:   Union[str, Any]      						=							TypeVar('''T''', bound=bool)

class 	SCREAMING_SNAKE_CASE_		(							Generic[T]					):



 def __init__(	self ,		A			):






        '''simple docstring'''




        lowercase	:		T    =   value
        lowercase	:		SkewNode[T] | None    =   None
        lowercase	:		SkewNode[T] | None    =   None



 @property
 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        return self._value





 @staticmethod
 def  SCREAMING_SNAKE_CASE(	A ,		A			):






        '''simple docstring'''




        if not roota:
               return roota

        if not roota:
               return roota

        if roota.value > roota.value:
               lowercase,  lowercase	:		Union[str, Any]    =   roota, roota

        lowercase	:		Optional[int]    =   roota
        lowercase	:		Tuple    =   roota.right
        lowercase	:		Union[str, Any]    =   roota.left
        lowercase	:		Any    =   SkewNode.merge(A ,		A			)

        return result






class 	SCREAMING_SNAKE_CASE_		(							Generic[T]					):



 def __init__(	self ,		A = ()			):






        '''simple docstring'''




        lowercase	:		SkewNode[T] | None    =   None
        if data:
               for item in data:
                      self.insert(A			)



 def __bool__(	self			):






        '''simple docstring'''




        return self._root is not None



 def __iter__(	self			):






        '''simple docstring'''




        lowercase	:		list[Any]    =   []
        while self:
               result.append(self.pop()			)

        # Pushing items back to the heap not to clear it.
        for item in result:
               self.insert(A			)

        return iter(A			)



 def  SCREAMING_SNAKE_CASE(	self ,		A			):






        '''simple docstring'''




        lowercase	:		List[str]    =   SkewNode.merge(self._root ,		SkewNode(A			)			)



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		List[Any]    =   self.top()
        lowercase	:		int    =   (
            SkewNode.merge(self._root.left ,		self._root.right			) if self._root else None
        )

        return result



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        if not self._root:
               raise IndexError('''Can\'t get top element for the empty heap.'''			)
        return self._root.value





 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		Optional[Any]    =   None


if __name__ == "__main__":
      import doctest

      doctest.testmod()




