
'''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 	__lowerCAmelCase    (  __a				):



     def __init__(self	):
          self.register_to_config()
          super().__init__()



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__	):
          if dicta == dicta:
               return True
          else:
               _UpperCAmelCase  ,     _UpperCAmelCase					:  str         =    self._remove_meta_keys(lowerCAmelCase__	)
               _UpperCAmelCase  ,     _UpperCAmelCase					:  Optional[int]         =    self._remove_meta_keys(lowerCAmelCase__	)
               if configa == configa:
                    print(F"Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}."	)
                    return True
          return False



     def     snake_case_ (self						,	lowerCAmelCase__	):
          _UpperCAmelCase					:  Tuple         =    []
          _UpperCAmelCase					:  Any         =    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     snake_case_ (self						,	lowerCAmelCase__						,	**lowerCAmelCase__	):
          _UpperCAmelCase					:  Dict         =    kwargs.pop("""cache_dir"""						,	lowerCAmelCase__	)
          _UpperCAmelCase					:  List[str]         =    kwargs.pop("""resume_download"""						,	lowerCAmelCase__	)
          _UpperCAmelCase					:  str         =    kwargs.pop("""force_download"""						,	lowerCAmelCase__	)
          _UpperCAmelCase					:  Optional[int]         =    kwargs.pop("""proxies"""						,	lowerCAmelCase__	)
          _UpperCAmelCase					:  int         =    kwargs.pop("""local_files_only"""						,	lowerCAmelCase__	)
          _UpperCAmelCase					:  Union[str, Any]         =    kwargs.pop("""use_auth_token"""						,	lowerCAmelCase__	)
          _UpperCAmelCase					:  List[Any]         =    kwargs.pop("""revision"""						,	lowerCAmelCase__	)
          _UpperCAmelCase					:  Any         =    kwargs.pop("""torch_dtype"""						,	lowerCAmelCase__	)
          _UpperCAmelCase					:  str         =    kwargs.pop("""device_map"""						,	lowerCAmelCase__	)

          _UpperCAmelCase					:  List[Any]         =    kwargs.pop("""alpha"""						,	0.5	)
          _UpperCAmelCase					:  int         =    kwargs.pop("""interp"""						,	lowerCAmelCase__	)

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

          _UpperCAmelCase					:  Optional[Any]         =    len(lowerCAmelCase__	)
          # Ignore result from model_index_json comparision of the two checkpoints
          _UpperCAmelCase					:  Optional[int]         =    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 '_'
          _UpperCAmelCase					:  Tuple         =    []
          for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
               _UpperCAmelCase					:  Union[str, Any]         =    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__	)

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

               _UpperCAmelCase					:  Dict         =    (
                   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
          _UpperCAmelCase					:  List[str]         =    DiffusionPipeline.from_pretrained(
              cached_folders[0]						,	torch_dtype=lowerCAmelCase__						,	device_map=lowerCAmelCase__	)
          final_pipe.to(self.device	)

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

          if interp == "sigmoid":
               _UpperCAmelCase					:  Tuple         =    CheckpointMergerPipeline.sigmoid
          elif interp == "inv_sigmoid":
               _UpperCAmelCase					:  int         =    CheckpointMergerPipeline.inv_sigmoid
          elif interp == "add_diff":
               _UpperCAmelCase					:  Optional[Any]         =    CheckpointMergerPipeline.add_difference
          else:
               _UpperCAmelCase					:  int         =    CheckpointMergerPipeline.weighted_sum

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

                         _UpperCAmelCase					:  str         =    getattr(lowerCAmelCase__						,	"""load_state_dict"""	)
                         _UpperCAmelCase					:  List[str]         =    (
                             safetensors.torch.load_file(lowerCAmelCase__	)
                             if (is_safetensors_available() and checkpoint_path_a.endswith(""".safetensors"""	))
                             else torch.load(lowerCAmelCase__						,	map_location="""cpu"""	)
                         )
                         _UpperCAmelCase					:  Any         =    None
                         if checkpoint_path_a:
                              _UpperCAmelCase					:  Tuple         =    (
                                  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:
                              _UpperCAmelCase					:  Tuple         =    theta_func(theta_a[key]						,	theta_a[key]						,	theta_a[key]						,	lowerCAmelCase__	)
                         else:
                              _UpperCAmelCase					:  List[str]         =    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     snake_case_ (lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__	):
          return ((1 - alpha) * thetaa) + (alpha * thetaa)



     @staticmethod
     def     snake_case_ (lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__	):
          _UpperCAmelCase					:  List[Any]         =    alpha * alpha * (3 - (2 * alpha))
          return thetaa + ((thetaa - thetaa) * alpha)



     @staticmethod
     def     snake_case_ (lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__	):
          import math

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



     @staticmethod
     def     snake_case_ (lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__	):
          return thetaa + (thetaa - thetaa) * (1.0 - alpha)




'''simple docstring'''

from __future__ import annotations







def __A  (    lowerCAmelCase_       ):
     _UpperCAmelCase					:  Optional[int]         =    {1}
     _UpperCAmelCase					:  Union[str, Any]         =    [1]
     for _ in range(2 ,       depth + 1       ):
          _UpperCAmelCase					:  Dict         =    [*previous_coefficients, 0]
          _UpperCAmelCase					:  int         =    [0, *previous_coefficients]
          _UpperCAmelCase					:  List[Any]         =    []
          for x, y in zip(lowerCAmelCase_ ,       lowerCAmelCase_       ):
               coefficients.add(x + y       )
               previous_coefficients.append(x + y       )
     return coefficients







def __A  (    lowerCAmelCase_       ):
     _UpperCAmelCase					:  Dict         =    set()
     for number in unique_coefficients:
          _UpperCAmelCase					:  Tuple         =    2
          _UpperCAmelCase					:  List[str]         =    number
          while divisor**2 <= copy_number:
               _UpperCAmelCase					:  Tuple         =    0
               while copy_number % divisor == 0:
                    copy_number //= divisor
                    multiplicity += 1
               if multiplicity >= 2:
                    non_squarefrees.add(lowerCAmelCase_       )
                    break
               divisor += 1

     return unique_coefficients.difference(lowerCAmelCase_       )







def __A  (    lowerCAmelCase_ = 51       ):
     _UpperCAmelCase					:  List[str]         =    get_pascal_triangle_unique_coefficients(lowerCAmelCase_       )
     _UpperCAmelCase					:  Optional[Any]         =    get_squarefrees(lowerCAmelCase_       )
     return sum(lowerCAmelCase_       )


if __name__ == "__main__":
   print(F"{solution() = }")


