

"""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      (	A_ ):






					"""simple docstring"""



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







					def  __lowerCAmelCase				( self    :       Dict     ,lowercase_    :       Any     ,lowercase_    :       int	):
						if dicta == dicta:
							return True
						else:
							lowerCAmelCase__       ,lowerCAmelCase__			:		List[Any]						       =			self._remove_meta_keys(lowercase_	)
							lowerCAmelCase__       ,lowerCAmelCase__			:		List[str]						       =			self._remove_meta_keys(lowercase_	)
							if configa == configa:
								print(F'Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.'	)
								return True
						return False







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

						lowerCAmelCase__			:		List[Any]						       =			kwargs.pop('''alpha'''     ,0.5	)
						lowerCAmelCase__			:		List[Any]						       =			kwargs.pop('''interp'''     ,lowercase_	)

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

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

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

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

						lowerCAmelCase__			:		str						       =			None
						if len(lowercase_	) > 2:
							lowerCAmelCase__			:		str						       =			os.path.join(cached_folders[2]	)

						if interp == "sigmoid":
							lowerCAmelCase__			:		Any						       =			CheckpointMergerPipeline.sigmoid
						elif interp == "inv_sigmoid":
							lowerCAmelCase__			:		Dict						       =			CheckpointMergerPipeline.inv_sigmoid
						elif interp == "add_diff":
							lowerCAmelCase__			:		List[Any]						       =			CheckpointMergerPipeline.add_difference
						else:
							lowerCAmelCase__			:		str						       =			CheckpointMergerPipeline.weighted_sum

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

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







					@staticmethod
					def  __lowerCAmelCase				( lowercase_    :       str     ,lowercase_    :       str     ,lowercase_    :       Any     ,lowercase_    :       int	):
						lowerCAmelCase__			:		Optional[Any]						       =			alpha * alpha * (3 - (2 * alpha))
						return thetaa + ((thetaa - thetaa) * alpha)







					@staticmethod
					def  __lowerCAmelCase				( lowercase_    :       Union[str, Any]     ,lowercase_    :       str     ,lowercase_    :       str     ,lowercase_    :       List[Any]	):
						import math

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







					@staticmethod
					def  __lowerCAmelCase				( lowercase_    :       int     ,lowercase_    :       Union[str, Any]     ,lowercase_    :       Dict     ,lowercase_    :       Optional[int]	):
						return thetaa + (thetaa - thetaa) * (1.0 - alpha)


"""simple docstring"""





import random
import sys

__UpperCamelCase						:   Tuple							   =	'''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''

def 						__SCREAMING_SNAKE_CASE						(   ):
	lowerCAmelCase__			:		List[str]						       =			input('''Enter message: '''				)
	lowerCAmelCase__			:		Optional[int]						       =			'''LFWOAYUISVKMNXPBDCRJTQEGHZ'''
	lowerCAmelCase__			:		List[Any]						       =			input('''Encrypt/Decrypt [e/d]: '''				)

	check_valid_key(A_				)

	if resp.lower().startswith('''e'''				):
		lowerCAmelCase__			:		Tuple						       =			'''encrypt'''
		lowerCAmelCase__			:		Union[str, Any]						       =			encrypt_message(A_		,  A_				)
	elif resp.lower().startswith('''d'''				):
		lowerCAmelCase__			:		Dict						       =			'''decrypt'''
		lowerCAmelCase__			:		str						       =			decrypt_message(A_		,  A_				)

	print(f'\n{mode.title()}ion: \n{translated}'				)

def 						__SCREAMING_SNAKE_CASE						(   A_				):
	lowerCAmelCase__			:		str						       =			list(A_				)
	lowerCAmelCase__			:		int						       =			list(A_				)
	key_list.sort()
	letters_list.sort()

	if key_list != letters_list:
		sys.exit('''Error in the key or symbol set.'''				)

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_				):
	return translate_message(A_		,  A_		,  '''encrypt'''				)

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_				):
	return translate_message(A_		,  A_		,  '''decrypt'''				)

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_		,  A_				):
	lowerCAmelCase__			:		str						       =			''''''
	lowerCAmelCase__			:		List[str]						       =			LETTERS
	lowerCAmelCase__			:		Optional[int]						       =			key

	if mode == "decrypt":
		lowerCAmelCase__       ,lowerCAmelCase__			:		int						       =			chars_b, chars_a

	for symbol in message:
		if symbol.upper() in chars_a:
			lowerCAmelCase__			:		int						       =			chars_a.find(symbol.upper()				)
			if symbol.isupper():
				translated += chars_b[sym_index].upper()
			else:
				translated += chars_b[sym_index].lower()
		else:
			translated += symbol

	return translated

def 						__SCREAMING_SNAKE_CASE						(   ):
	lowerCAmelCase__			:		Optional[int]						       =			list(A_				)
	random.shuffle(A_				)
	return "".join(A_				)


if __name__ == "__main__":
							main()




