"""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 	__UpperCAmelCase						(__A    ):







			'''simple docstring'''





			def __init__( self       ):







										'''simple docstring'''


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





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_       ):







										'''simple docstring'''


										if dicta == dicta:
																	return True
										else:
																	A__							,       A__					:    Optional[int]														= self._remove_meta_keys(snake_case_       )
																	A__							,       A__					:    Optional[Any]														= self._remove_meta_keys(snake_case_       )
																	if configa == configa:
																								print(F'''Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.'''       )
																								return True
										return False





			def 		lowerCamelCase				( self      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    int														= []
										A__					:    Tuple														= config_dict.copy()
										for key in config_dict.keys():
																	if key.startswith("""_"""       ):
																								temp_dict.pop(snake_case_       )
																								meta_keys.append(snake_case_       )
										return (temp_dict, meta_keys)





			@torch.no_grad()
			def 		lowerCamelCase				( self      ,			snake_case_      ,			**snake_case_       ):







										'''simple docstring'''


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

										A__					:    Any														= kwargs.pop("""alpha"""      ,			0.5       )
										A__					:    List[str]														= kwargs.pop("""interp"""      ,			snake_case_       )

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

										A__					:    List[str]														= len(snake_case_       )
										# Ignore result from model_index_json comparision of the two checkpoints
										A__					:    Optional[Any]														= kwargs.pop("""force"""      ,			snake_case_       )

										# 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__					:    Tuple														= []
										for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
																	A__					:    Any														= DiffusionPipeline.load_config(
																	    snake_case_      ,			cache_dir=snake_case_      ,			resume_download=snake_case_      ,			force_download=snake_case_      ,			proxies=snake_case_      ,			local_files_only=snake_case_      ,			use_auth_token=snake_case_      ,			revision=snake_case_      ,			)
																	config_dicts.append(snake_case_       )

										A__					:    Optional[Any]														= True
										for idx in range(1      ,			len(snake_case_       )       ):
																	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__					:    Union[str, Any]														= []
										for pretrained_model_name_or_path, config_dict in zip(snake_case_      ,			snake_case_       ):
																	A__					:    Dict														= [k for k in config_dict.keys() if not k.startswith("""_"""       )]
																	A__					:    Any														= [os.path.join(snake_case_      ,			"""*"""       ) for k in folder_names]
																	allow_patterns += [
																	    WEIGHTS_NAME,
																	    SCHEDULER_CONFIG_NAME,
																	    CONFIG_NAME,
																	    ONNX_WEIGHTS_NAME,
																	    DiffusionPipeline.config_name,
																	]
																	A__					:    str														= config_dict.get("""_class_name"""       )
																	A__					:    Optional[Any]														= {"""diffusers""": __version__, """pipeline_class""": requested_pipeline_class}

																	A__					:    Optional[int]														= (
																	    pretrained_model_name_or_path
																	    if os.path.isdir(snake_case_       )
																	    else snapshot_download(
																	        snake_case_      ,			cache_dir=snake_case_      ,			resume_download=snake_case_      ,			proxies=snake_case_      ,			local_files_only=snake_case_      ,			use_auth_token=snake_case_      ,			revision=snake_case_      ,			allow_patterns=snake_case_      ,			user_agent=snake_case_      ,			)
																	)
																	print("""Cached Folder"""      ,			snake_case_       )
																	cached_folders.append(snake_case_       )

										# Step 3:-
										# Load the first checkpoint as a diffusion pipeline and modify its module state_dict in place
										A__					:    Optional[Any]														= DiffusionPipeline.from_pretrained(
										    cached_folders[0]      ,			torch_dtype=snake_case_      ,			device_map=snake_case_       )
										final_pipe.to(self.device       )

										A__					:    List[str]														= None
										if len(snake_case_       ) > 2:
																	A__					:    Optional[Any]														= os.path.join(cached_folders[2]       )

										if interp == "sigmoid":
																	A__					:    List[str]														= CheckpointMergerPipeline.sigmoid
										elif interp == "inv_sigmoid":
																	A__					:    List[str]														= CheckpointMergerPipeline.inv_sigmoid
										elif interp == "add_diff":
																	A__					:    int														= CheckpointMergerPipeline.add_difference
										else:
																	A__					:    List[str]														= CheckpointMergerPipeline.weighted_sum

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

																															A__					:    int														= getattr(snake_case_      ,			"""load_state_dict"""       )
																															A__					:    Dict														= (
																															    safetensors.torch.load_file(snake_case_       )
																															    if (is_safetensors_available() and checkpoint_path_a.endswith(""".safetensors"""       ))
																															    else torch.load(snake_case_      ,			map_location="""cpu"""       )
																															)
																															A__					:    Optional[int]														= None
																															if checkpoint_path_a:
																																						A__					:    int														= (
																																						    safetensors.torch.load_file(snake_case_       )
																																						    if (is_safetensors_available() and checkpoint_path_a.endswith(""".safetensors"""       ))
																																						    else torch.load(snake_case_      ,			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(snake_case_       )}'''       )
																															continue
																								print(F'''MERGING {attr}'''       )

																								for key in theta_a.keys():
																															if theta_a:
																																						A__					:    Union[str, Any]														= theta_func(theta_a[key]      ,			theta_a[key]      ,			theta_a[key]      ,			snake_case_       )
																															else:
																																						A__					:    str														= theta_func(theta_a[key]      ,			theta_a[key]      ,			snake_case_      ,			snake_case_       )

																								del theta_a
																								del theta_a
																								update_theta_a(snake_case_       )

																								del theta_a
										return final_pipe





			@staticmethod
			def 		lowerCamelCase				( snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_       ):







										'''simple docstring'''


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





			@staticmethod
			def 		lowerCamelCase				( snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    Optional[Any]														= alpha * alpha * (3 - (2 * alpha))
										return thetaa + ((thetaa - thetaa) * alpha)





			@staticmethod
			def 		lowerCamelCase				( snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_       ):







										'''simple docstring'''


										import math

										A__					:    Optional[Any]														= 0.5 - math.sin(math.asin(1.0 - 2.0 * alpha       ) / 3.0       )
										return thetaa + ((thetaa - thetaa) * alpha)





			@staticmethod
			def 		lowerCamelCase				( snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_       ):







										'''simple docstring'''


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






"""simple docstring"""



def    _A(		lowerCAmelCase       ):
							A__					:    str														= len(lowerCAmelCase       )
							A__					:    List[Any]														= [0] * length
							A__					:    Union[str, Any]														= [[False for i in range(lowerCAmelCase       )] for j in range(lowerCAmelCase       )]
							for i, c in enumerate(lowerCAmelCase       ):
														A__					:    Dict														= i
														for j in range(i + 1       ):
																					if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
																												A__					:    List[str]														= True
																												A__					:    Optional[Any]														= min(lowerCAmelCase ,     0 if j == 0 else (cut[j - 1] + 1)       )
														A__					:    Optional[int]														= mincut
							return cut[length - 1]


if __name__ == "__main__":
				_UpperCamelCase         =		input("Enter the string: ").strip()
				_UpperCamelCase         =		find_minimum_partitions(s)
				print(F'Minimum number of partitions required for the \'{s}\' is {ans}')





