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      ):




					def __init__(  self						)	->    Optional[int]:



									'''simple docstring'''







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




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    List[str]:



									'''simple docstring'''







									if dicta == dicta:
													return True
									else:
													snake_case__   :      Any   				= self._remove_meta_keys(__UpperCamelCase						)
													snake_case__   :      List[str]   				= self._remove_meta_keys(__UpperCamelCase						)
													if configa == configa:
																	print(F"""Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}."""						)
																	return True
									return False




					def      __a     (  self       ,			__UpperCamelCase						)	->    Any:



									'''simple docstring'''







									snake_case__   :      Optional[Any]   				= []
									snake_case__   :      Optional[Any]   				= config_dict.copy()
									for key in config_dict.keys():
													if key.startswith('_'						):
																	temp_dict.pop(__UpperCamelCase						)
																	meta_keys.append(__UpperCamelCase						)
									return (temp_dict, meta_keys)




					@torch.no_grad()
					def      __a     (  self       ,			__UpperCamelCase       ,			**__UpperCamelCase						)	->    Any:



									'''simple docstring'''







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

									snake_case__   :      Optional[int]   				= kwargs.pop('alpha'       ,			0.5						)
									snake_case__   :      Any   				= kwargs.pop('interp'       ,			__UpperCamelCase						)

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

									snake_case__   :      Union[str, Any]   				= len(__UpperCamelCase						)
									# Ignore result from model_index_json comparision of the two checkpoints
									snake_case__   :      List[Any]   				= kwargs.pop('force'       ,			__UpperCamelCase						)

									# 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 '_'
									snake_case__   :      Union[str, Any]   				= []
									for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
													snake_case__   :      Union[str, Any]   				= DiffusionPipeline.load_config(
													    __UpperCamelCase       ,			cache_dir=__UpperCamelCase       ,			resume_download=__UpperCamelCase       ,			force_download=__UpperCamelCase       ,			proxies=__UpperCamelCase       ,			local_files_only=__UpperCamelCase       ,			use_auth_token=__UpperCamelCase       ,			revision=__UpperCamelCase       ,			)
													config_dicts.append(__UpperCamelCase						)

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

													snake_case__   :      List[Any]   				= (
													    pretrained_model_name_or_path
													    if os.path.isdir(__UpperCamelCase						)
													    else snapshot_download(
													        __UpperCamelCase       ,			cache_dir=__UpperCamelCase       ,			resume_download=__UpperCamelCase       ,			proxies=__UpperCamelCase       ,			local_files_only=__UpperCamelCase       ,			use_auth_token=__UpperCamelCase       ,			revision=__UpperCamelCase       ,			allow_patterns=__UpperCamelCase       ,			user_agent=__UpperCamelCase       ,			)
													)
													print('Cached Folder'       ,			__UpperCamelCase						)
													cached_folders.append(__UpperCamelCase						)

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

									snake_case__   :      str   				= None
									if len(__UpperCamelCase						) > 2:
													snake_case__   :      Any   				= os.path.join(cached_folders[2]						)

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

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

																					snake_case__   :      str   				= getattr(__UpperCamelCase       ,			'load_state_dict'						)
																					snake_case__   :      List[Any]   				= (
																					    safetensors.torch.load_file(__UpperCamelCase						)
																					    if (is_safetensors_available() and checkpoint_path_a.endswith('.safetensors'						))
																					    else torch.load(__UpperCamelCase       ,			map_location='cpu'						)
																					)
																					snake_case__   :      str   				= None
																					if checkpoint_path_a:
																									snake_case__   :      Tuple   				= (
																									    safetensors.torch.load_file(__UpperCamelCase						)
																									    if (is_safetensors_available() and checkpoint_path_a.endswith('.safetensors'						))
																									    else torch.load(__UpperCamelCase       ,			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(__UpperCamelCase						)}"""						)
																					continue
																	print(F"""MERGING {attr}"""						)

																	for key in theta_a.keys():
																					if theta_a:
																									snake_case__   :      Any   				= theta_func(theta_a[key]       ,			theta_a[key]       ,			theta_a[key]       ,			__UpperCamelCase						)
																					else:
																									snake_case__   :      Optional[int]   				= theta_func(theta_a[key]       ,			theta_a[key]       ,			__UpperCamelCase       ,			__UpperCamelCase						)

																	del theta_a
																	del theta_a
																	update_theta_a(__UpperCamelCase						)

																	del theta_a
									return final_pipe




					@staticmethod
					def      __a     (  __UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    Tuple:



									'''simple docstring'''







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




					@staticmethod
					def      __a     (  __UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    Optional[Any]:



									'''simple docstring'''







									snake_case__   :      Union[str, Any]   				= alpha * alpha * (3 - (2 * alpha))
									return thetaa + ((thetaa - thetaa) * alpha)




					@staticmethod
					def      __a     (  __UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    Optional[int]:



									'''simple docstring'''







									import math

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




					@staticmethod
					def      __a     (  __UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    str:



									'''simple docstring'''







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



'''simple docstring'''
import numpy as np
from PIL import Image







def 				__UpperCamelCase (					__UpperCAmelCase  :					np.array			)							->	np.array:
  lowercase				,      lowercase				,      lowercase				:							Dict									=   rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
  return 0.2989 * r + 0.5870 * g + 0.1140 * b







def 				__UpperCamelCase (					__UpperCAmelCase  :					np.array			)							->	np.array:
  return (gray > 127) & (gray <= 255)







def 				__UpperCamelCase (					__UpperCAmelCase  :					np.array ,   __UpperCAmelCase  :					np.array			)							->	np.array:
  lowercase				:							str									=   np.zeros_like(__UpperCAmelCase			)
  lowercase				:							Tuple									=   np.zeros(
      (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)			)

  # Copy image to padded image
  lowercase				:							int									=   image

  # Iterate over image & apply kernel
  for x in range(image.shape[1]			):
    for y in range(image.shape[0]			):
      lowercase				:							str									=   (
          kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
      ).sum()
      lowercase				:							Tuple									=   int(summation == 5			)
  return output


# kernel to be applied
_A		:      Union[str, Any]						   =np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
 # read original image
 _A		:      Optional[int]						   =np.array(Image.open(r'''..\image_data\lena.jpg'''))
 # Apply erosion operation to a binary image
 _A		:      Optional[int]						   =erosion(grayabinary(rgbagray(image)), structuring_element)
 # Save the output image
 _A		:      int						   =Image.fromarray(output).convert('''RGB''')
 pil_img.save('''result_erosion.png''')