

'''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     :	Optional[int] ):







												"""simple docstring"""







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







							def      snake_case				(							self     :	Optional[int]  , __lowercase     :	Any  , __lowercase     :	str ):







												"""simple docstring"""







												if dicta == dicta:
																	return True
												else:
																	__lowercase						,       __lowercase     						=self._remove_meta_keys(__lowercase )
																	__lowercase						,       __lowercase     						=self._remove_meta_keys(__lowercase )
																	if configa == configa:
																						print(f'''Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.''' )
																						return True
												return False







							def      snake_case				(							self     :	List[str]  , __lowercase     :	Dict ):







												"""simple docstring"""







												__lowercase     						=[]
												__lowercase     						=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      snake_case				(							self     :	int  , __lowercase     :	List[Union[str, os.PathLike]]  , **__lowercase     :	str ):







												"""simple docstring"""







												__lowercase     						=kwargs.pop('cache_dir'  , __lowercase )
												__lowercase     						=kwargs.pop('resume_download'  , __lowercase )
												__lowercase     						=kwargs.pop('force_download'  , __lowercase )
												__lowercase     						=kwargs.pop('proxies'  , __lowercase )
												__lowercase     						=kwargs.pop('local_files_only'  , __lowercase )
												__lowercase     						=kwargs.pop('use_auth_token'  , __lowercase )
												__lowercase     						=kwargs.pop('revision'  , __lowercase )
												__lowercase     						=kwargs.pop('torch_dtype'  , __lowercase )
												__lowercase     						=kwargs.pop('device_map'  , __lowercase )

												__lowercase     						=kwargs.pop('alpha'  , 0.5 )
												__lowercase     						=kwargs.pop('interp'  , __lowercase )

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

												__lowercase     						=len(__lowercase )
												# Ignore result from model_index_json comparision of the two checkpoints
												__lowercase     						=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 '_'
												__lowercase     						=[]
												for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
																	__lowercase     						=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 )

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

																	__lowercase     						=(
																	    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
												__lowercase     						=DiffusionPipeline.from_pretrained(
												    cached_folders[0]  , torch_dtype=__lowercase  , device_map=__lowercase )
												final_pipe.to(self.device )

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

												if interp == "sigmoid":
																	__lowercase     						=CheckpointMergerPipeline.sigmoid
												elif interp == "inv_sigmoid":
																	__lowercase     						=CheckpointMergerPipeline.inv_sigmoid
												elif interp == "add_diff":
																	__lowercase     						=CheckpointMergerPipeline.add_difference
												else:
																	__lowercase     						=CheckpointMergerPipeline.weighted_sum

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

																											__lowercase     						=getattr(__lowercase  , 'load_state_dict' )
																											__lowercase     						=(
																											    safetensors.torch.load_file(__lowercase )
																											    if (is_safetensors_available() and checkpoint_path_a.endswith('.safetensors' ))
																											    else torch.load(__lowercase  , map_location='cpu' )
																											)
																											__lowercase     						=None
																											if checkpoint_path_a:
																																__lowercase     						=(
																																    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:
																																__lowercase     						=theta_func(theta_a[key]  , theta_a[key]  , theta_a[key]  , __lowercase )
																											else:
																																__lowercase     						=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      snake_case				(							__lowercase     :	List[Any]  , __lowercase     :	Union[str, Any]  , __lowercase     :	Optional[Any]  , __lowercase     :	List[str] ):







												"""simple docstring"""







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







							@staticmethod
							def      snake_case				(							__lowercase     :	int  , __lowercase     :	Any  , __lowercase     :	str  , __lowercase     :	Tuple ):







												"""simple docstring"""







												__lowercase     						=alpha * alpha * (3 - (2 * alpha))
												return thetaa + ((thetaa - thetaa) * alpha)







							@staticmethod
							def      snake_case				(							__lowercase     :	List[str]  , __lowercase     :	str  , __lowercase     :	Optional[int]  , __lowercase     :	str ):







												"""simple docstring"""







												import math

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







							@staticmethod
							def      snake_case				(							__lowercase     :	Tuple  , __lowercase     :	int  , __lowercase     :	Optional[int]  , __lowercase     :	int ):







												"""simple docstring"""







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





'''simple docstring'''

from .number_theory.prime_numbers import next_prime


class 					lowerCAmelCase       :







       def __init__(							self     :	int  , __lowercase     :	int  , __lowercase     :	int | None = None  , __lowercase     :	float | None = None  , ):







            """simple docstring"""







            __lowercase     						=size_table
            __lowercase     						=[None] * self.size_table
            __lowercase     						=0.7_5 if lim_charge is None else lim_charge
            __lowercase     						=1 if charge_factor is None else charge_factor
            __lowercase     						=[]
            __lowercase     						={}







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            return self._keys







       def      snake_case				(							self     :	Optional[Any] ):







            """simple docstring"""







            return sum(1 for slot in self.values if slot is not None ) / (
                self.size_table * self.charge_factor
            )







       def      snake_case				(							self     :	int  , __lowercase     :	int ):







            """simple docstring"""







            return key % self.size_table







       def      snake_case				(							self     :	int  , __lowercase     :	Any ):







            """simple docstring"""







            print(f'''step {step_ord}''' )
            print(list(range(len(self.values ) ) ) )
            print(self.values )







       def      snake_case				(							self     :	int  , __lowercase     :	int ):







            """simple docstring"""







            __lowercase     						=1
            __lowercase     						=values
            for value in values:
                 self.insert_data(__lowercase )
                 self._step_by_step(__lowercase )
                 i += 1







       def      snake_case				(							self     :	List[str]  , __lowercase     :	Optional[int]  , __lowercase     :	List[str] ):







            """simple docstring"""







            __lowercase     						=data
            __lowercase     						=data







       def      snake_case				(							self     :	int  , __lowercase     :	Any  , __lowercase     :	Optional[Any]=None ):







            """simple docstring"""







            __lowercase     						=self.hash_function(key + 1 )

            while self.values[new_key] is not None and self.values[new_key] != key:
                 if self.values.count(__lowercase ) > 0:
                      __lowercase     						=self.hash_function(new_key + 1 )
                 else:
                      __lowercase     						=None
                      break

            return new_key







       def      snake_case				(							self     :	Any ):







            """simple docstring"""







            __lowercase     						=[value for value in self.values if value is not None]
            __lowercase     						=next_prime(self.size_table  , factor=2 )
            self._keys.clear()
            __lowercase     						=[None] * self.size_table  # hell's pointers D: don't DRY ;/
            for value in survivor_values:
                 self.insert_data(__lowercase )







       def      snake_case				(							self     :	Optional[Any]  , __lowercase     :	Any ):







            """simple docstring"""







            __lowercase     						=self.hash_function(__lowercase )

            if self.values[key] is None:
                 self._set_value(__lowercase  , __lowercase )

            elif self.values[key] == data:
                 pass

            else:
                 __lowercase     						=self._collision_resolution(__lowercase  , __lowercase )
                 if collision_resolution is not None:
                      self._set_value(__lowercase  , __lowercase )
                 else:
                      self.rehashing()
                      self.insert_data(__lowercase )


