




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	(						_lowerCAmelCase						):
     """simple docstring"""

     def __init__(			self     :      Optional[Any]	) ->				Dict:
           self.register_to_config()
           super().__init__()




     def        __snake_case      (			self     :      str		,     A__     :      Union[str, Any]		,     A__     :      Tuple	) ->				Dict:
           if dicta == dicta:
                 return True
           else:
                 lowerCamelCase__     ,						lowerCamelCase__					     = self._remove_meta_keys(A__	)
                 lowerCamelCase__     ,						lowerCamelCase__					     = self._remove_meta_keys(A__	)
                 if configa == configa:
                       print(F"""Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}."""	)
                       return True
           return False




     def        __snake_case      (			self     :      Optional[Any]		,     A__     :      Dict	) ->				Optional[Any]:
           lowerCamelCase__					     = []
           lowerCamelCase__					     = config_dict.copy()
           for key in config_dict.keys():
                 if key.startswith('_'	):
                       temp_dict.pop(A__	)
                       meta_keys.append(A__	)
           return (temp_dict, meta_keys)




     @torch.no_grad()
     def        __snake_case      (			self     :      Union[str, Any]		,     A__     :      List[Union[str, os.PathLike]]		,     **A__     :      Union[str, Any]	) ->				Union[str, Any]:
           lowerCamelCase__					     = kwargs.pop('cache_dir'		,     A__	)
           lowerCamelCase__					     = kwargs.pop('resume_download'		,     A__	)
           lowerCamelCase__					     = kwargs.pop('force_download'		,     A__	)
           lowerCamelCase__					     = kwargs.pop('proxies'		,     A__	)
           lowerCamelCase__					     = kwargs.pop('local_files_only'		,     A__	)
           lowerCamelCase__					     = kwargs.pop('use_auth_token'		,     A__	)
           lowerCamelCase__					     = kwargs.pop('revision'		,     A__	)
           lowerCamelCase__					     = kwargs.pop('torch_dtype'		,     A__	)
           lowerCamelCase__					     = kwargs.pop('device_map'		,     A__	)

           lowerCamelCase__					     = kwargs.pop('alpha'		,     0.5	)
           lowerCamelCase__					     = kwargs.pop('interp'		,     A__	)

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

           lowerCamelCase__					     = len(A__	)
           # Ignore result from model_index_json comparision of the two checkpoints
           lowerCamelCase__					     = kwargs.pop('force'		,     A__	)

           # 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__					     = []
           for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
                 lowerCamelCase__					     = DiffusionPipeline.load_config(
                     A__		,     cache_dir=A__		,     resume_download=A__		,     force_download=A__		,     proxies=A__		,     local_files_only=A__		,     use_auth_token=A__		,     revision=A__		,     )
                 config_dicts.append(A__	)

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

                 lowerCamelCase__					     = (
                     pretrained_model_name_or_path
                     if os.path.isdir(A__	)
                     else snapshot_download(
                         A__		,     cache_dir=A__		,     resume_download=A__		,     proxies=A__		,     local_files_only=A__		,     use_auth_token=A__		,     revision=A__		,     allow_patterns=A__		,     user_agent=A__		,     )
                 )
                 print('Cached Folder'		,     A__	)
                 cached_folders.append(A__	)

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

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

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

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

                             lowerCamelCase__					     = getattr(A__		,     'load_state_dict'	)
                             lowerCamelCase__					     = (
                                 safetensors.torch.load_file(A__	)
                                 if (is_safetensors_available() and checkpoint_path_a.endswith('.safetensors'	))
                                 else torch.load(A__		,     map_location='cpu'	)
                             )
                             lowerCamelCase__					     = None
                             if checkpoint_path_a:
                                   lowerCamelCase__					     = (
                                       safetensors.torch.load_file(A__	)
                                       if (is_safetensors_available() and checkpoint_path_a.endswith('.safetensors'	))
                                       else torch.load(A__		,     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(A__	)}"""	)
                             continue
                       print(F"""MERGING {attr}"""	)

                       for key in theta_a.keys():
                             if theta_a:
                                   lowerCamelCase__					     = theta_func(theta_a[key]		,     theta_a[key]		,     theta_a[key]		,     A__	)
                             else:
                                   lowerCamelCase__					     = theta_func(theta_a[key]		,     theta_a[key]		,     A__		,     A__	)

                       del theta_a
                       del theta_a
                       update_theta_a(A__	)

                       del theta_a
           return final_pipe




     @staticmethod
     def        __snake_case      (			A__     :      Tuple		,     A__     :      List[Any]		,     A__     :      Tuple		,     A__     :      Union[str, Any]	) ->				Union[str, Any]:
           return ((1 - alpha) * thetaa) + (alpha * thetaa)




     @staticmethod
     def        __snake_case      (			A__     :      Any		,     A__     :      Optional[int]		,     A__     :      Any		,     A__     :      Dict	) ->				Any:
           lowerCamelCase__					     = alpha * alpha * (3 - (2 * alpha))
           return thetaa + ((thetaa - thetaa) * alpha)




     @staticmethod
     def        __snake_case      (			A__     :      Any		,     A__     :      Union[str, Any]		,     A__     :      Any		,     A__     :      Optional[Any]	) ->				Any:
           import math

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




     @staticmethod
     def        __snake_case      (			A__     :      Optional[Any]		,     A__     :      Optional[int]		,     A__     :      List[str]		,     A__     :      List[Any]	) ->				Dict:
           return thetaa + (thetaa - thetaa) * (1.0 - alpha)






'''simple docstring'''







from collections import namedtuple

_a					: Any      =    namedtuple("""from_to""", """from_ to""")

_a					: Dict      =    {
    """millimeter""": """mm""",
    """centimeter""": """cm""",
    """meter""": """m""",
    """kilometer""": """km""",
    """inch""": """in""",
    """inche""": """in""",  # Trailing 's' has been stripped off
    """feet""": """ft""",
    """foot""": """ft""",
    """yard""": """yd""",
    """mile""": """mi""",
}

_a					: Optional[Any]      =    {
    """mm""": from_to(0.001, 1_0_0_0),
    """cm""": from_to(0.01, 1_0_0),
    """m""": from_to(1, 1),
    """km""": from_to(1_0_0_0, 0.001),
    """in""": from_to(0.0254, 39.3701),
    """ft""": from_to(0.3048, 3.28084),
    """yd""": from_to(0.9144, 1.09361),
    """mi""": from_to(1609.34, 0.000621371),
}





def    _lowerCAmelCase						(						lowercase					,	lowercase					,	lowercase	)					->					float:
			__lowerCAmelCase											=				from_type.lower().rstrip("""s"""	)
			__lowerCAmelCase											=				TYPE_CONVERSION.get(lowercase					,	lowercase	)
			__lowerCAmelCase											=				to_type.lower().rstrip("""s"""	)
			__lowerCAmelCase											=				TYPE_CONVERSION.get(lowercase					,	lowercase	)
			if new_from not in METRIC_CONVERSION:
						__lowerCAmelCase											=				(
						    f'Invalid \'from_type\' value: {from_type!r}.\n'
						    f'Conversion abbreviations are: {", ".join(lowercase	)}'
						)
						raise ValueError(lowercase	)
			if new_to not in METRIC_CONVERSION:
						__lowerCAmelCase											=				(
						    f'Invalid \'to_type\' value: {to_type!r}.\n'
						    f'Conversion abbreviations are: {", ".join(lowercase	)}'
						)
						raise ValueError(lowercase	)
			return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
	import doctest

	doctest.testmod()

