




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 		__UpperCAmelCase(		A_ ):





     """simple docstring"""



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






         """simple docstring"""
         self.register_to_config()
         super().__init__()

     def 				__SCREAMING_SNAKE_CASE		(      self :       Union[str, Any]						,				A :       str						,				A :       int    )			->			Optional[Any]:






         """simple docstring"""
         if dicta == dicta:
             return True
         else:
             a__ ,		a__      =       self._remove_meta_keys(A    )
             a__ ,		a__      =       self._remove_meta_keys(A    )
             if configa == configa:
                 print(f"""Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}."""    )
                 return True
         return False

     def 				__SCREAMING_SNAKE_CASE		(      self :       str						,				A :       str    )			->			Union[str, Any]:






         """simple docstring"""
         a__      =       []
         a__      =       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 				__SCREAMING_SNAKE_CASE		(      self :       Optional[int]						,				A :       int						,				**A :       Optional[Any]    )			->			Any:






         """simple docstring"""
         a__      =       kwargs.pop("""cache_dir"""						,				A    )
         a__      =       kwargs.pop("""resume_download"""						,				A    )
         a__      =       kwargs.pop("""force_download"""						,				A    )
         a__      =       kwargs.pop("""proxies"""						,				A    )
         a__      =       kwargs.pop("""local_files_only"""						,				A    )
         a__      =       kwargs.pop("""use_auth_token"""						,				A    )
         a__      =       kwargs.pop("""revision"""						,				A    )
         a__      =       kwargs.pop("""torch_dtype"""						,				A    )
         a__      =       kwargs.pop("""device_map"""						,				A    )

         a__      =       kwargs.pop("""alpha"""						,				0.5    )
         a__      =       kwargs.pop("""interp"""						,				A    )

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

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

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

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

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

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

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

                     a__      =       getattr(A						,				"""load_state_dict"""    )
                     a__      =       (
                         safetensors.torch.load_file(A    )
                         if (is_safetensors_available() and checkpoint_path_a.endswith(""".safetensors"""    ))
                         else torch.load(A						,				map_location="""cpu"""    )
                     )
                     a__      =       None
                     if checkpoint_path_a:
                         a__      =       (
                             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:
                         a__      =       theta_func(theta_a[key]						,				theta_a[key]						,				theta_a[key]						,				A    )
                     else:
                         a__      =       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 				__SCREAMING_SNAKE_CASE		(      A :       Optional[int]						,				A :       Optional[int]						,				A :       int						,				A :       Union[str, Any]    )			->			str:






         """simple docstring"""
         return ((1 - alpha) * thetaa) + (alpha * thetaa)

     @staticmethod
     def 				__SCREAMING_SNAKE_CASE		(      A :       Optional[int]						,				A :       Optional[int]						,				A :       Optional[int]						,				A :       Optional[Any]    )			->			Tuple:






         """simple docstring"""
         a__      =       alpha * alpha * (3 - (2 * alpha))
         return thetaa + ((thetaa - thetaa) * alpha)

     @staticmethod
     def 				__SCREAMING_SNAKE_CASE		(      A :       Tuple						,				A :       str						,				A :       Union[str, Any]						,				A :       List[Any]    )			->			Optional[int]:






         """simple docstring"""
         import math

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






     @staticmethod
     def 				__SCREAMING_SNAKE_CASE		(      A :       str						,				A :       str						,				A :       str						,				A :       int    )			->			List[str]:






         """simple docstring"""
         return thetaa + (thetaa - thetaa) * (1.0 - alpha)












from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


SCREAMING_SNAKE_CASE_						:					Any			   =       logging.get_logger(__name__)







class 		__UpperCAmelCase(		lowercase__ ):





     """simple docstring"""



     __UpperCAmelCase	: Optional[Any]						=    ["""pixel_values"""]

     def __init__(      self :       int						,				A :       bool = True						,				A :       Dict[str, int] = None						,				A :       PILImageResampling = PIL.Image.NEAREST						,				A :       bool = False						,				A :       Dict[str, int] = None						,				A :       Union[int, float] = 1 / 2_55						,				A :       bool = False						,				A :       bool = True						,				A :       bool = True						,				A :       Optional[Union[float, List[float]]] = None						,				A :       Optional[Union[float, List[float]]] = None						,				A :       bool = True						,				**A :       Optional[int]						,				)			->			None:






         """simple docstring"""
         super().__init__(**A    )
         a__      =       size if size is not None else {"""height""": 3_46, """width""": 3_46}
         a__      =       get_size_dict(A    )
         a__      =       crop_size if crop_size is not None else {"""height""": 2_89, """width""": 2_89}
         a__      =       get_size_dict(A						,				param_name="""crop_size"""    )

         a__      =       do_resize
         a__      =       size
         a__      =       resample
         a__      =       do_center_crop
         a__      =       crop_size
         a__      =       do_rescale
         a__      =       rescale_factor
         a__      =       rescale_offset
         a__      =       do_normalize
         a__      =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         a__      =       image_std if image_std is not None else IMAGENET_STANDARD_STD
         a__      =       include_top

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[Any]						,				A :       np.ndarray						,				A :       Dict[str, int]						,				A :       PILImageResampling = PIL.Image.NEAREST						,				A :       Optional[Union[str, ChannelDimension]] = None						,				**A :       Dict						,				)			->			np.ndarray:






         """simple docstring"""
         a__      =       get_size_dict(A    )
         if "height" not in size or "width" not in size:
             raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""    )
         return resize(
             A						,				size=(size["""height"""], size["""width"""])						,				resample=A						,				data_format=A						,				**A    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       Tuple						,				A :       np.ndarray						,				A :       Dict[str, int]						,				A :       Optional[Union[str, ChannelDimension]] = None						,				**A :       Optional[Any]						,				)			->			np.ndarray:






         """simple docstring"""
         a__      =       get_size_dict(A    )
         if "height" not in size or "width" not in size:
             raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""    )
         return center_crop(A						,				size=(size["""height"""], size["""width"""])						,				data_format=A						,				**A    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict						,				A :       np.ndarray						,				A :       Union[int, float]						,				A :       bool = True						,				A :       Optional[Union[str, ChannelDimension]] = None						,				**A :       List[str]						,				)			->			Any:






         """simple docstring"""
         if offset:
             a__      =       (image - 1_2_7.5) * scale
             if data_format is not None:
                 a__      =       to_channel_dimension_format(A						,				A    )
             a__      =       rescaled_image.astype(np.floataa    )
         else:
             a__      =       rescale(A						,				scale=A						,				data_format=A						,				**A    )
         return rescaled_image

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[int]						,				A :       np.ndarray						,				A :       Union[float, List[float]]						,				A :       Union[float, List[float]]						,				A :       Optional[Union[str, ChannelDimension]] = None						,				**A :       str						,				)			->			np.ndarray:






         """simple docstring"""
         return normalize(A						,				mean=A						,				std=A						,				data_format=A						,				**A    )






     def 				__SCREAMING_SNAKE_CASE		(      self :       Union[str, Any]						,				A :       ImageInput						,				A :       bool = None						,				A :       Dict[str, int] = None						,				A :       Any=None						,				A :       bool = None						,				A :       Dict[str, int] = None						,				A :       bool = None						,				A :       float = None						,				A :       bool = None						,				A :       bool = None						,				A :       Optional[Union[float, List[float]]] = None						,				A :       Optional[Union[float, List[float]]] = None						,				A :       bool = None						,				A :       Optional[Union[str, TensorType]] = None						,				A :       ChannelDimension = ChannelDimension.FIRST						,				**A :       Tuple						,				)			->			PIL.Image.Image:






         """simple docstring"""
         a__      =       do_resize if do_resize is not None else self.do_resize
         a__      =       resample if resample is not None else self.resample
         a__      =       do_center_crop if do_center_crop is not None else self.do_center_crop
         a__      =       do_rescale if do_rescale is not None else self.do_rescale
         a__      =       rescale_factor if rescale_factor is not None else self.rescale_factor
         a__      =       rescale_offset if rescale_offset is not None else self.rescale_offset
         a__      =       do_normalize if do_normalize is not None else self.do_normalize
         a__      =       image_mean if image_mean is not None else self.image_mean
         a__      =       image_std if image_std is not None else self.image_std
         a__      =       include_top if include_top is not None else self.include_top

         a__      =       size if size is not None else self.size
         a__      =       get_size_dict(A    )
         a__      =       crop_size if crop_size is not None else self.crop_size
         a__      =       get_size_dict(A						,				param_name="""crop_size"""    )

         a__      =       make_list_of_images(A    )

         if not valid_images(A    ):
             raise ValueError(
                 """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                 """torch.Tensor, tf.Tensor or jax.ndarray."""    )

         if do_resize and size is None or resample is None:
             raise ValueError("""Size and resample must be specified if do_resize is True."""    )

         if do_center_crop and crop_size is None:
             raise ValueError("""Crop size must be specified if do_center_crop is True."""    )

         if do_rescale and rescale_factor is None:
             raise ValueError("""Rescale factor must be specified if do_rescale is True."""    )

         if do_normalize and (image_mean is None or image_std is None):
             raise ValueError("""Image mean and std must be specified if do_normalize is True."""    )

         # All transformations expect numpy arrays.
         a__      =       [to_numpy_array(A    ) for image in images]

         if do_resize:
             a__      =       [self.resize(image=A						,				size=A						,				resample=A    ) for image in images]

         if do_center_crop:
             a__      =       [self.center_crop(image=A						,				size=A    ) for image in images]

         if do_rescale:
             a__      =       [self.rescale(image=A						,				scale=A						,				offset=A    ) for image in images]

         if do_normalize:
             a__      =       [self.normalize(image=A						,				mean=A						,				std=A    ) for image in images]

         if include_top:
             a__      =       [self.normalize(image=A						,				mean=[0, 0, 0]						,				std=A    ) for image in images]

         a__      =       [to_channel_dimension_format(A						,				A    ) for image in images]

         a__      =       {"""pixel_values""": images}
         return BatchFeature(data=A						,				tensor_type=A    )






