

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_      (	_snake_case      ):


  """simple docstring"""



  def __init__(						self     :					Tuple						)       ->			int:
      self.register_to_config()
      super().__init__()



  def   _UpperCamelCase     (						self     :					str  ,	A_     :					List[Any]  ,	A_     :					Tuple						)       ->			List[Any]:
      if dicta == dicta:
          return True
      else:
          SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__         =    self._remove_meta_keys(A_						)
          SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__         =    self._remove_meta_keys(A_						)
          if configa == configa:
              print(f"Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}."						)
              return True
      return False



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

      SCREAMING_SNAKE_CASE__         =    kwargs.pop("alpha"  ,	0.5						)
      SCREAMING_SNAKE_CASE__         =    kwargs.pop("interp"  ,	A_						)

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

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

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

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

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

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

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

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



  @staticmethod
  def   _UpperCamelCase     (						A_     :					int  ,	A_     :					Optional[int]  ,	A_     :					str  ,	A_     :					Tuple						)       ->			List[Any]:
      SCREAMING_SNAKE_CASE__         =    alpha * alpha * (3 - (2 * alpha))
      return thetaa + ((thetaa - thetaa) * alpha)



  @staticmethod
  def   _UpperCamelCase     (						A_     :					int  ,	A_     :					Optional[int]  ,	A_     :					Any  ,	A_     :					List[str]						)       ->			List[str]:
      import math

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







  @staticmethod
  def   _UpperCamelCase     (						A_     :					Optional[int]  ,	A_     :					Dict  ,	A_     :					int  ,	A_     :					Tuple						)       ->			List[Any]:
      return thetaa + (thetaa - thetaa) * (1.0 - alpha)





from sklearn.metrics import accuracy_score

import datasets


A__										=      "\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n"


A__										=      "\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {'accuracy': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {'accuracy': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])\n        >>> print(results)\n        {'accuracy': 0.8778625954198473}\n"


A__										=      "\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n"







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,  _KWARGS_DESCRIPTION      )
class  UpperCamelCase_      (	datasets.Metric      ):


  """simple docstring"""



  def   _UpperCamelCase     (						self     :					int						)       ->			List[str]:
      return datasets.MetricInfo(
          description=_DESCRIPTION  ,	citation=_CITATION  ,	inputs_description=_KWARGS_DESCRIPTION  ,	features=datasets.Features(
              {
                  "predictions": datasets.Sequence(datasets.Value("int32"						)						),
                  "references": datasets.Sequence(datasets.Value("int32"						)						),
              }
              if self.config_name == "multilabel"
              else {
                  "predictions": datasets.Value("int32"						),
                  "references": datasets.Value("int32"						),
              }						)  ,	reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html"]  ,	)







  def   _UpperCamelCase     (						self     :					Dict  ,	A_     :					List[Any]  ,	A_     :					Tuple  ,	A_     :					Optional[Any]=True  ,	A_     :					Tuple=None						)       ->			Any:
      return {
          "accuracy": float(
              accuracy_score(A_  ,	A_  ,	normalize=A_  ,	sample_weight=A_						)						)
      }


