



"""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 			_a     (   SCREAMING_SNAKE_CASE__):



			def __init__( self       :							List[str]		)       ->				Any:
									self.register_to_config()
									super().__init__()



			def 							__lowercase				( self       :							Optional[Any]      ,  _lowercase       :							List[str]      ,  _lowercase       :							Dict		)       ->				Optional[int]:
									if dicta == dicta:
															return True
									else:
															snake_case						, snake_case      : Tuple							=   self._remove_meta_keys(_lowercase		)
															snake_case						, snake_case      : Union[str, Any]							=   self._remove_meta_keys(_lowercase		)
															if configa == configa:
																					print(F'''Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.'''		)
																					return True
									return False



			def 							__lowercase				( self       :							Optional[Any]      ,  _lowercase       :							Dict		)       ->				List[str]:
									snake_case      : List[str]							=   []
									snake_case      : Any							=   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 							__lowercase				( self       :							Dict      ,  _lowercase       :							List[Union[str, os.PathLike]]      ,  **_lowercase       :							List[str]		)       ->				Optional[Any]:
									snake_case      : List[Any]							=   kwargs.pop("cache_dir"      ,  _lowercase		)
									snake_case      : str							=   kwargs.pop("resume_download"      ,  _lowercase		)
									snake_case      : Any							=   kwargs.pop("force_download"      ,  _lowercase		)
									snake_case      : List[str]							=   kwargs.pop("proxies"      ,  _lowercase		)
									snake_case      : Union[str, Any]							=   kwargs.pop("local_files_only"      ,  _lowercase		)
									snake_case      : Any							=   kwargs.pop("use_auth_token"      ,  _lowercase		)
									snake_case      : Tuple							=   kwargs.pop("revision"      ,  _lowercase		)
									snake_case      : Dict							=   kwargs.pop("torch_dtype"      ,  _lowercase		)
									snake_case      : List[Any]							=   kwargs.pop("device_map"      ,  _lowercase		)

									snake_case      : str							=   kwargs.pop("alpha"      ,  0.5		)
									snake_case      : Optional[int]							=   kwargs.pop("interp"      ,  _lowercase		)

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

									snake_case      : List[Any]							=   len(_lowercase		)
									# Ignore result from model_index_json comparision of the two checkpoints
									snake_case      : List[Any]							=   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 '_'
									snake_case      : List[str]							=   []
									for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
															snake_case      : Optional[Any]							=   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		)

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

															snake_case      : Any							=   (
															    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
									snake_case      : Optional[int]							=   DiffusionPipeline.from_pretrained(
									    cached_folders[0]      ,  torch_dtype=_lowercase      ,  device_map=_lowercase		)
									final_pipe.to(self.device		)

									snake_case      : Union[str, Any]							=   None
									if len(_lowercase		) > 2:
															snake_case      : Optional[int]							=   os.path.join(cached_folders[2]		)

									if interp == "sigmoid":
															snake_case      : Tuple							=   CheckpointMergerPipeline.sigmoid
									elif interp == "inv_sigmoid":
															snake_case      : Optional[Any]							=   CheckpointMergerPipeline.inv_sigmoid
									elif interp == "add_diff":
															snake_case      : List[str]							=   CheckpointMergerPipeline.add_difference
									else:
															snake_case      : Any							=   CheckpointMergerPipeline.weighted_sum

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

																											snake_case      : Dict							=   getattr(_lowercase      ,  "load_state_dict"		)
																											snake_case      : List[Any]							=   (
																											    safetensors.torch.load_file(_lowercase		)
																											    if (is_safetensors_available() and checkpoint_path_a.endswith(".safetensors"		))
																											    else torch.load(_lowercase      ,  map_location="cpu"		)
																											)
																											snake_case      : Optional[int]							=   None
																											if checkpoint_path_a:
																																	snake_case      : Dict							=   (
																																	    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:
																																	snake_case      : Union[str, Any]							=   theta_func(theta_a[key]      ,  theta_a[key]      ,  theta_a[key]      ,  _lowercase		)
																											else:
																																	snake_case      : Tuple							=   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 							__lowercase				( _lowercase       :							Tuple      ,  _lowercase       :							str      ,  _lowercase       :							Optional[int]      ,  _lowercase       :							Optional[int]		)       ->				int:
									return ((1 - alpha) * thetaa) + (alpha * thetaa)



			@staticmethod
			def 							__lowercase				( _lowercase       :							Tuple      ,  _lowercase       :							Optional[int]      ,  _lowercase       :							Union[str, Any]      ,  _lowercase       :							Optional[Any]		)       ->				Optional[int]:
									snake_case      : int							=   alpha * alpha * (3 - (2 * alpha))
									return thetaa + ((thetaa - thetaa) * alpha)



			@staticmethod
			def 							__lowercase				( _lowercase       :							Tuple      ,  _lowercase       :							Any      ,  _lowercase       :							List[Any]      ,  _lowercase       :							int		)       ->				Tuple:
									import math

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



			@staticmethod
			def 							__lowercase				( _lowercase       :							Any      ,  _lowercase       :							str      ,  _lowercase       :							Optional[Any]      ,  _lowercase       :							Optional[Any]		)       ->				Tuple:
									return thetaa + (thetaa - thetaa) * (1.0 - alpha)








"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


A    =	{
    'configuration_mobilevitv2': [
        'MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileViTV2Config',
        'MobileViTV2OnnxConfig',
    ],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	[
			    'MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'MobileViTV2ForImageClassification',
			    'MobileViTV2ForSemanticSegmentation',
			    'MobileViTV2Model',
			    'MobileViTV2PreTrainedModel',
			]

if TYPE_CHECKING:
			from .configuration_mobilevitva import (
			    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    MobileViTVaConfig,
			    MobileViTVaOnnxConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mobilevitva import (
						    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    MobileViTVaForImageClassification,
						    MobileViTVaForSemanticSegmentation,
						    MobileViTVaModel,
						    MobileViTVaPreTrainedModel,
						)

else:
			import sys

			A    =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



