



"""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__	(  snake_case__   ):


	'''simple docstring'''







	def __init__(			self							:					Any						)	->  Tuple:





					"""simple docstring"""


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





	def  _lowerCAmelCase    (			self							:					int    ,					_SCREAMING_SNAKE_CASE							:					Optional[int]    ,					_SCREAMING_SNAKE_CASE							:					Any						)	->  Any:





					"""simple docstring"""


					if dicta == dicta:
									return True
					else:
									SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE	:       Dict 							=    self._remove_meta_keys(__snake_case						)
									SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE	:       int 							=    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							:					Tuple    ,					_SCREAMING_SNAKE_CASE							:					Union[str, Any]						)	->  Optional[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       str 							=    []
					SCREAMING_SNAKE_CASE	:       List[str] 							=    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							:					Dict    ,					_SCREAMING_SNAKE_CASE							:					Optional[Any]    ,					**_SCREAMING_SNAKE_CASE							:					str						)	->  List[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Any 							=    kwargs.pop('cache_dir'    ,					__snake_case						)
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    kwargs.pop('resume_download'    ,					__snake_case						)
					SCREAMING_SNAKE_CASE	:       Any 							=    kwargs.pop('force_download'    ,					__snake_case						)
					SCREAMING_SNAKE_CASE	:       str 							=    kwargs.pop('proxies'    ,					__snake_case						)
					SCREAMING_SNAKE_CASE	:       str 							=    kwargs.pop('local_files_only'    ,					__snake_case						)
					SCREAMING_SNAKE_CASE	:       List[str] 							=    kwargs.pop('use_auth_token'    ,					__snake_case						)
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    kwargs.pop('revision'    ,					__snake_case						)
					SCREAMING_SNAKE_CASE	:       int 							=    kwargs.pop('torch_dtype'    ,					__snake_case						)
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    kwargs.pop('device_map'    ,					__snake_case						)

					SCREAMING_SNAKE_CASE	:       str 							=    kwargs.pop('alpha'    ,					0.5						)
					SCREAMING_SNAKE_CASE	:       int 							=    kwargs.pop('interp'    ,					__snake_case						)

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

					SCREAMING_SNAKE_CASE	:       List[Any] 							=    len(__snake_case						)
					# Ignore result from model_index_json comparision of the two checkpoints
					SCREAMING_SNAKE_CASE	:       Union[str, 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 '_'
					SCREAMING_SNAKE_CASE	:       str 							=    []
					for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
									SCREAMING_SNAKE_CASE	:       Tuple 							=    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						)

					SCREAMING_SNAKE_CASE	:       Dict 							=    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.
					SCREAMING_SNAKE_CASE	:       Any 							=    []
					for pretrained_model_name_or_path, config_dict in zip(__snake_case    ,					__snake_case						):
									SCREAMING_SNAKE_CASE	:       Any 							=    [k for k in config_dict.keys() if not k.startswith('_'						)]
									SCREAMING_SNAKE_CASE	:       Union[str, 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,
									]
									SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    config_dict.get('_class_name'						)
									SCREAMING_SNAKE_CASE	:       int 							=    {'diffusers': __version__, 'pipeline_class': requested_pipeline_class}

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

					SCREAMING_SNAKE_CASE	:       Any 							=    None
					if len(__snake_case						) > 2:
									SCREAMING_SNAKE_CASE	:       Tuple 							=    os.path.join(cached_folders[2]						)

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

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

																	SCREAMING_SNAKE_CASE	:       Dict 							=    getattr(__snake_case    ,					'load_state_dict'						)
																	SCREAMING_SNAKE_CASE	:       List[Any] 							=    (
																	    safetensors.torch.load_file(__snake_case						)
																	    if (is_safetensors_available() and checkpoint_path_a.endswith('.safetensors'						))
																	    else torch.load(__snake_case    ,					map_location='cpu'						)
																	)
																	SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    None
																	if checkpoint_path_a:
																					SCREAMING_SNAKE_CASE	:       Any 							=    (
																					    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:
																					SCREAMING_SNAKE_CASE	:       Any 							=    theta_func(theta_a[key]    ,					theta_a[key]    ,					theta_a[key]    ,					__snake_case						)
																	else:
																					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    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    (			_SCREAMING_SNAKE_CASE							:					Dict    ,					_SCREAMING_SNAKE_CASE							:					List[Any]    ,					_SCREAMING_SNAKE_CASE							:					Union[str, Any]    ,					_SCREAMING_SNAKE_CASE							:					List[Any]						)	->  List[str]:





					"""simple docstring"""


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





	@staticmethod
	def  _lowerCAmelCase    (			_SCREAMING_SNAKE_CASE							:					Tuple    ,					_SCREAMING_SNAKE_CASE							:					Optional[int]    ,					_SCREAMING_SNAKE_CASE							:					int    ,					_SCREAMING_SNAKE_CASE							:					str						)	->  List[str]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       str 							=    alpha * alpha * (3 - (2 * alpha))
					return thetaa + ((thetaa - thetaa) * alpha)





	@staticmethod
	def  _lowerCAmelCase    (			_SCREAMING_SNAKE_CASE							:					Optional[int]    ,					_SCREAMING_SNAKE_CASE							:					Optional[int]    ,					_SCREAMING_SNAKE_CASE							:					Any    ,					_SCREAMING_SNAKE_CASE							:					Union[str, Any]						)	->  List[str]:





					"""simple docstring"""


					import math

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







	@staticmethod
	def  _lowerCAmelCase    (			_SCREAMING_SNAKE_CASE							:					Union[str, Any]    ,					_SCREAMING_SNAKE_CASE							:					Optional[int]    ,					_SCREAMING_SNAKE_CASE							:					Optional[int]    ,					_SCREAMING_SNAKE_CASE							:					Union[str, Any]						)	->  Optional[int]:





					"""simple docstring"""


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










"""simple docstring"""

import platform
from argparse import ArgumentParser

import huggingface_hub
import pandas
import pyarrow

from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand
def    __snake_case       (  __A			:	Union[str, Any]    )    ->					Tuple:






				'''simple docstring'''
				return EnvironmentCommand()







class 						lowerCAmelCase__	(  _lowerCamelCase   ):


	'''simple docstring'''







	@staticmethod
	def  _lowerCAmelCase    (			_SCREAMING_SNAKE_CASE							:					ArgumentParser						)	->  str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       int 							=    parser.add_parser('env'    ,					help='Print relevant system environment info.'						)
					download_parser.set_defaults(func=_SCREAMING_SNAKE_CASE						)





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       int 							=    {
					    '`datasets` version': version,
					    'Platform': platform.platform(),
					    'Python version': platform.python_version(),
					    'Huggingface_hub version': huggingface_hub.__version__,
					    'PyArrow version': pyarrow.__version__,
					    'Pandas version': pandas.__version__,
					}

					print('\nCopy-and-paste the text below in your GitHub issue.\n'						)
					print(self.format_dict(_SCREAMING_SNAKE_CASE						)						)

					return info







	@staticmethod
	def  _lowerCAmelCase    (			_SCREAMING_SNAKE_CASE							:					Optional[Any]						)	->  List[Any]:





					"""simple docstring"""


					return "\n".join([f"""- {prop}: {val}""" for prop, val in d.items()]						) + "\n"





