
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 						__lowercase(     _lowerCamelCase   ):


       """simple docstring"""







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



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]  ,      _lowerCAmelCase					:			Tuple  ,      _lowerCAmelCase					:			Optional[Any]       )					->   List[Any]:
         if dicta == dicta:
           return True
         else:
           _lowerCAmelCase	,       _lowerCAmelCase						=		self._remove_meta_keys(_UpperCamelCase       )
           _lowerCAmelCase	,       _lowerCAmelCase						=		self._remove_meta_keys(_UpperCamelCase       )
           if configa == configa:
             print(F'''Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.'''       )
             return True
         return False



       def SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]  ,      _lowerCAmelCase					:			Dict       )					->   str:
         _lowerCAmelCase						=		[]
         _lowerCAmelCase						=		config_dict.copy()
         for key in config_dict.keys():
           if key.startswith('_'       ):
             temp_dict.pop(_UpperCamelCase       )
             meta_keys.append(_UpperCamelCase       )
         return (temp_dict, meta_keys)



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

         _lowerCAmelCase						=		kwargs.pop('alpha'  ,      0.5       )
         _lowerCAmelCase						=		kwargs.pop('interp'  ,      _UpperCamelCase       )

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

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

         # 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(
               _UpperCamelCase  ,      cache_dir=_UpperCamelCase  ,      resume_download=_UpperCamelCase  ,      force_download=_UpperCamelCase  ,      proxies=_UpperCamelCase  ,      local_files_only=_UpperCamelCase  ,      use_auth_token=_UpperCamelCase  ,      revision=_UpperCamelCase  ,      )
           config_dicts.append(_UpperCamelCase       )

         _lowerCAmelCase						=		True
         for idx in range(1  ,      len(_UpperCamelCase       )       ):
           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(_UpperCamelCase  ,      _UpperCamelCase       ):
           _lowerCAmelCase						=		[k for k in config_dict.keys() if not k.startswith('_'       )]
           _lowerCAmelCase						=		[os.path.join(_UpperCamelCase  ,      '*'       ) 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(_UpperCamelCase       )
               else snapshot_download(
                   _UpperCamelCase  ,      cache_dir=_UpperCamelCase  ,      resume_download=_UpperCamelCase  ,      proxies=_UpperCamelCase  ,      local_files_only=_UpperCamelCase  ,      use_auth_token=_UpperCamelCase  ,      revision=_UpperCamelCase  ,      allow_patterns=_UpperCamelCase  ,      user_agent=_UpperCamelCase  ,      )
           )
           print('Cached Folder'  ,      _UpperCamelCase       )
           cached_folders.append(_UpperCamelCase       )

         # 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=_UpperCamelCase  ,      device_map=_UpperCamelCase       )
         final_pipe.to(self.device       )

         _lowerCAmelCase						=		None
         if len(_UpperCamelCase       ) > 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]  ,      _UpperCamelCase       )
             if os.path.exists(_UpperCamelCase       ):
               _lowerCAmelCase						=		[
                   *glob.glob(os.path.join(_UpperCamelCase  ,      '*.safetensors'       )       ),
                   *glob.glob(os.path.join(_UpperCamelCase  ,      '*.bin'       )       ),
               ]
               _lowerCAmelCase						=		files[0] if len(_UpperCamelCase       ) > 0 else None
             if len(_UpperCamelCase       ) < 3:
               _lowerCAmelCase						=		None
             else:
               _lowerCAmelCase						=		os.path.join(cached_folders[2]  ,      _UpperCamelCase       )
               if os.path.exists(_UpperCamelCase       ):
                 _lowerCAmelCase						=		[
                     *glob.glob(os.path.join(_UpperCamelCase  ,      '*.safetensors'       )       ),
                     *glob.glob(os.path.join(_UpperCamelCase  ,      '*.bin'       )       ),
                 ]
                 _lowerCAmelCase						=		files[0] if len(_UpperCamelCase       ) > 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(_UpperCamelCase  ,      _UpperCamelCase       )
               if isinstance(_UpperCamelCase  ,      _UpperCamelCase       ):  # ignore requires_safety_checker boolean
                 continue
               _lowerCAmelCase						=		getattr(_UpperCamelCase  ,      'state_dict'       )
               _lowerCAmelCase						=		theta_a()

               _lowerCAmelCase						=		getattr(_UpperCamelCase  ,      'load_state_dict'       )
               _lowerCAmelCase						=		(
                   safetensors.torch.load_file(_UpperCamelCase       )
                   if (is_safetensors_available() and checkpoint_path_a.endswith('.safetensors'       ))
                   else torch.load(_UpperCamelCase  ,      map_location='cpu'       )
               )
               _lowerCAmelCase						=		None
               if checkpoint_path_a:
                 _lowerCAmelCase						=		(
                     safetensors.torch.load_file(_UpperCamelCase       )
                     if (is_safetensors_available() and checkpoint_path_a.endswith('.safetensors'       ))
                     else torch.load(_UpperCamelCase  ,      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(_UpperCamelCase       )}'''       )
               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]  ,      _UpperCamelCase       )
               else:
                 _lowerCAmelCase						=		theta_func(theta_a[key]  ,      theta_a[key]  ,      _UpperCamelCase  ,      _UpperCamelCase       )

             del theta_a
             del theta_a
             update_theta_a(_UpperCamelCase       )

             del theta_a
         return final_pipe



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



       @staticmethod
       def SCREAMING_SNAKE_CASE_				(     _lowerCAmelCase					:			Any  ,      _lowerCAmelCase					:			List[Any]  ,      _lowerCAmelCase					:			Tuple  ,      _lowerCAmelCase					:			Any       )					->   int:
         _lowerCAmelCase						=		alpha * alpha * (3 - (2 * alpha))
         return thetaa + ((thetaa - thetaa) * alpha)



       @staticmethod
       def SCREAMING_SNAKE_CASE_				(     _lowerCAmelCase					:			int  ,      _lowerCAmelCase					:			Optional[int]  ,      _lowerCAmelCase					:			Tuple  ,      _lowerCAmelCase					:			Tuple       )					->   Union[str, Any]:
         import math

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






       @staticmethod
       def SCREAMING_SNAKE_CASE_				(     _lowerCAmelCase					:			Tuple  ,      _lowerCAmelCase					:			Optional[Any]  ,      _lowerCAmelCase					:			List[Any]  ,      _lowerCAmelCase					:			Union[str, Any]       )					->   List[str]:
         return thetaa + (thetaa - thetaa) * (1.0 - alpha)







import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class A_   (     _lowerCamelCase  ):
   lowerCAmelCase__  	=					(LMSDiscreteScheduler,)
   lowerCAmelCase__  	=					10




   def _lowerCAmelCase (self      :List[str]       ,  **_UpperCamelCase      :Optional[int]					)->   List[str]:
         __A			  =							{
             '''num_train_timesteps''': 1100,
             '''beta_start''': 0.0_0_0_1,
             '''beta_end''': 0.0_2,
             '''beta_schedule''': '''linear''',
         }

         config.update(**_UpperCamelCase					)
         return config




   def _lowerCAmelCase (self      :List[str]					)->   Tuple:
         for timesteps in [10, 50, 100, 1000]:
               self.check_over_configs(num_train_timesteps=_UpperCamelCase					)




   def _lowerCAmelCase (self      :Dict					)->   Optional[int]:
         for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]       ,  [0.0_0_0_2, 0.0_0_2, 0.0_2]					):
               self.check_over_configs(beta_start=_UpperCamelCase       ,  beta_end=_UpperCamelCase					)




   def _lowerCAmelCase (self      :Optional[int]					)->   Any:
         for schedule in ["linear", "scaled_linear"]:
               self.check_over_configs(beta_schedule=_UpperCamelCase					)




   def _lowerCAmelCase (self      :List[str]					)->   str:
         for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=_UpperCamelCase					)




   def _lowerCAmelCase (self      :str					)->   List[str]:
         for t in [0, 500, 800]:
               self.check_over_forward(time_step=_UpperCamelCase					)




   def _lowerCAmelCase (self      :Optional[Any]					)->   Tuple:
         __A			  =							self.scheduler_classes[0]
         __A			  =							self.get_scheduler_config()
         __A			  =							scheduler_class(**_UpperCamelCase					)

         scheduler.set_timesteps(self.num_inference_steps					)

         __A			  =							self.dummy_model()
         __A			  =							self.dummy_sample_deter * scheduler.init_noise_sigma

         for i, t in enumerate(scheduler.timesteps					):
               __A			  =							scheduler.scale_model_input(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							model(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							scheduler.step(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase					)
               __A			  =							output.prev_sample

         __A			  =							torch.sum(torch.abs(_UpperCamelCase					)					)
         __A			  =							torch.mean(torch.abs(_UpperCamelCase					)					)

         assert abs(result_sum.item() - 1_0_0_6.3_8_8					) < 1e-2
         assert abs(result_mean.item() - 1.3_1					) < 1e-3




   def _lowerCAmelCase (self      :Optional[Any]					)->   Optional[int]:
         __A			  =							self.scheduler_classes[0]
         __A			  =							self.get_scheduler_config(prediction_type='''v_prediction'''					)
         __A			  =							scheduler_class(**_UpperCamelCase					)

         scheduler.set_timesteps(self.num_inference_steps					)

         __A			  =							self.dummy_model()
         __A			  =							self.dummy_sample_deter * scheduler.init_noise_sigma

         for i, t in enumerate(scheduler.timesteps					):
               __A			  =							scheduler.scale_model_input(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							model(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							scheduler.step(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase					)
               __A			  =							output.prev_sample

         __A			  =							torch.sum(torch.abs(_UpperCamelCase					)					)
         __A			  =							torch.mean(torch.abs(_UpperCamelCase					)					)

         assert abs(result_sum.item() - 0.0_0_1_7					) < 1e-2
         assert abs(result_mean.item() - 2.2_676e-06					) < 1e-3




   def _lowerCAmelCase (self      :List[Any]					)->   List[Any]:
         __A			  =							self.scheduler_classes[0]
         __A			  =							self.get_scheduler_config()
         __A			  =							scheduler_class(**_UpperCamelCase					)

         scheduler.set_timesteps(self.num_inference_steps       ,  device=_UpperCamelCase					)

         __A			  =							self.dummy_model()
         __A			  =							self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         __A			  =							sample.to(_UpperCamelCase					)

         for i, t in enumerate(scheduler.timesteps					):
               __A			  =							scheduler.scale_model_input(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							model(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							scheduler.step(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase					)
               __A			  =							output.prev_sample

         __A			  =							torch.sum(torch.abs(_UpperCamelCase					)					)
         __A			  =							torch.mean(torch.abs(_UpperCamelCase					)					)

         assert abs(result_sum.item() - 1_0_0_6.3_8_8					) < 1e-2
         assert abs(result_mean.item() - 1.3_1					) < 1e-3






   def _lowerCAmelCase (self      :List[str]					)->   int:
         __A			  =							self.scheduler_classes[0]
         __A			  =							self.get_scheduler_config()
         __A			  =							scheduler_class(**_UpperCamelCase       ,  use_karras_sigmas=_UpperCamelCase					)

         scheduler.set_timesteps(self.num_inference_steps       ,  device=_UpperCamelCase					)

         __A			  =							self.dummy_model()
         __A			  =							self.dummy_sample_deter.to(_UpperCamelCase					) * scheduler.init_noise_sigma
         __A			  =							sample.to(_UpperCamelCase					)

         for t in scheduler.timesteps:
               __A			  =							scheduler.scale_model_input(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							model(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							scheduler.step(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase					)
               __A			  =							output.prev_sample

         __A			  =							torch.sum(torch.abs(_UpperCamelCase					)					)
         __A			  =							torch.mean(torch.abs(_UpperCamelCase					)					)

         assert abs(result_sum.item() - 3_8_1_2.9_9_2_7					) < 2e-2
         assert abs(result_mean.item() - 4.9_6_4_8					) < 1e-3





