

'''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			: str      ):







            """simple docstring"""







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







       def 			snake_case   (							self			: Dict						,      __lowercase			: str						,      __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			: Union[str, Any]						,      __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			: Dict						,      __lowercase			: List[Union[str, os.PathLike]]						,      **__lowercase			: Dict      ):







            """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			: Dict						,      __lowercase			: str						,      __lowercase			: Optional[int]						,      __lowercase			: Any      ):







            """simple docstring"""







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







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







            """simple docstring"""







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







       @staticmethod
       def 			snake_case   (							__lowercase			: str						,      __lowercase			: Dict						,      __lowercase			: Dict						,      __lowercase			: Optional[Any]      ):







            """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			: Any						,      __lowercase			: Union[str, Any]						,      __lowercase			: int						,      __lowercase			: Any      ):







            """simple docstring"""







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





'''simple docstring'''

import os

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

UpperCAmelCase				   =  {'''UserAgent''': UserAgent().random}
UpperCAmelCase				   =  '''https://www.mywaifulist.moe/random'''

def 				__UpperCamelCase							(	lowercase__						:  str,					lowercase__						:  str ):






     '''simple docstring'''





     __lowercase					=requests.get(lowercase__,					headers=lowercase__ )
     with open(lowercase__,					'wb' ) as file:
          file.write(image.content )

def 				__UpperCamelCase							(	):






     '''simple docstring'''





     __lowercase					=BeautifulSoup(requests.get(lowercase__,					headers=lowercase__ ).text,					'html.parser' )
     __lowercase					=soup.find('meta',					attrs={'property': 'og:title'} ).attrs['content']
     __lowercase					=soup.find('meta',					attrs={'property': 'og:image'} ).attrs['content']
     __lowercase					=soup.find('p',					id='description' ).get_text()
     __lowercase		,     __lowercase					=os.path.splitext(os.path.basename(lowercase__ ) )
     __lowercase					=title.strip().replace(' ',					'_' )
     __lowercase					=F'''{image_title}{image_extension}'''
     save_image(lowercase__,					lowercase__ )
     return (title, description, image_title)


if __name__ == "__main__":
       UpperCAmelCase   ,			UpperCAmelCase   ,			UpperCAmelCase				   =  random_anime_character()
       print(F'''{title}\n\n{desc}\n\nImage saved : {image_title}''')


