


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    ):




              """simple docstring"""




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






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              if dicta == dicta:
                     return True
              else:
                     UpperCamelCase						,			UpperCamelCase     :						Union[str, Any] 						=		self._remove_meta_keys(__SCREAMING_SNAKE_CASE    )
                     UpperCamelCase						,			UpperCamelCase     :						Optional[int] 						=		self._remove_meta_keys(__SCREAMING_SNAKE_CASE    )
                     if configa == configa:
                            print(f"""Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}."""    )
                            return True
              return False






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						Union[str, Any] 						=		[]
              UpperCamelCase     :						str 						=		config_dict.copy()
              for key in config_dict.keys():
                     if key.startswith('''_'''    ):
                            temp_dict.pop(__SCREAMING_SNAKE_CASE    )
                            meta_keys.append(__SCREAMING_SNAKE_CASE    )
              return (temp_dict, meta_keys)






       @torch.no_grad()
       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       **__SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						Optional[Any] 						=		kwargs.pop('''cache_dir'''			,       __SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Optional[Any] 						=		kwargs.pop('''resume_download'''			,       __SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Dict 						=		kwargs.pop('''force_download'''			,       __SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						List[Any] 						=		kwargs.pop('''proxies'''			,       __SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						str 						=		kwargs.pop('''local_files_only'''			,       __SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Dict 						=		kwargs.pop('''use_auth_token'''			,       __SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						str 						=		kwargs.pop('''revision'''			,       __SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						str 						=		kwargs.pop('''torch_dtype'''			,       __SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						List[str] 						=		kwargs.pop('''device_map'''			,       __SCREAMING_SNAKE_CASE    )

              UpperCamelCase     :						Any 						=		kwargs.pop('''alpha'''			,       0.5    )
              UpperCamelCase     :						Any 						=		kwargs.pop('''interp'''			,       __SCREAMING_SNAKE_CASE    )

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

              UpperCamelCase     :						Tuple 						=		len(__SCREAMING_SNAKE_CASE    )
              # Ignore result from model_index_json comparision of the two checkpoints
              UpperCamelCase     :						Union[str, Any] 						=		kwargs.pop('''force'''			,       __SCREAMING_SNAKE_CASE    )

              # 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 '_'
              UpperCamelCase     :						Optional[Any] 						=		[]
              for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
                     UpperCamelCase     :						Dict 						=		DiffusionPipeline.load_config(
                         __SCREAMING_SNAKE_CASE			,       cache_dir=__SCREAMING_SNAKE_CASE			,       resume_download=__SCREAMING_SNAKE_CASE			,       force_download=__SCREAMING_SNAKE_CASE			,       proxies=__SCREAMING_SNAKE_CASE			,       local_files_only=__SCREAMING_SNAKE_CASE			,       use_auth_token=__SCREAMING_SNAKE_CASE			,       revision=__SCREAMING_SNAKE_CASE			,       )
                     config_dicts.append(__SCREAMING_SNAKE_CASE    )

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

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

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

              UpperCamelCase     :						Dict 						=		None
              if len(__SCREAMING_SNAKE_CASE    ) > 2:
                     UpperCamelCase     :						Any 						=		os.path.join(cached_folders[2]    )

              if interp == "sigmoid":
                     UpperCamelCase     :						int 						=		CheckpointMergerPipeline.sigmoid
              elif interp == "inv_sigmoid":
                     UpperCamelCase     :						List[Any] 						=		CheckpointMergerPipeline.inv_sigmoid
              elif interp == "add_diff":
                     UpperCamelCase     :						Union[str, Any] 						=		CheckpointMergerPipeline.add_difference
              else:
                     UpperCamelCase     :						Tuple 						=		CheckpointMergerPipeline.weighted_sum

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

                                   UpperCamelCase     :						Union[str, Any] 						=		getattr(__SCREAMING_SNAKE_CASE			,       '''load_state_dict'''    )
                                   UpperCamelCase     :						Optional[int] 						=		(
                                       safetensors.torch.load_file(__SCREAMING_SNAKE_CASE    )
                                       if (is_safetensors_available() and checkpoint_path_a.endswith('''.safetensors'''    ))
                                       else torch.load(__SCREAMING_SNAKE_CASE			,       map_location='''cpu'''    )
                                   )
                                   UpperCamelCase     :						str 						=		None
                                   if checkpoint_path_a:
                                          UpperCamelCase     :						Union[str, Any] 						=		(
                                              safetensors.torch.load_file(__SCREAMING_SNAKE_CASE    )
                                              if (is_safetensors_available() and checkpoint_path_a.endswith('''.safetensors'''    ))
                                              else torch.load(__SCREAMING_SNAKE_CASE			,       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(__SCREAMING_SNAKE_CASE    )}"""    )
                                   continue
                            print(f"""MERGING {attr}"""    )

                            for key in theta_a.keys():
                                   if theta_a:
                                          UpperCamelCase     :						Dict 						=		theta_func(theta_a[key]			,       theta_a[key]			,       theta_a[key]			,       __SCREAMING_SNAKE_CASE    )
                                   else:
                                          UpperCamelCase     :						Union[str, Any] 						=		theta_func(theta_a[key]			,       theta_a[key]			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )

                            del theta_a
                            del theta_a
                            update_theta_a(__SCREAMING_SNAKE_CASE    )

                            del theta_a
              return final_pipe






       @staticmethod
       def 			_lowercase (  __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




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






       @staticmethod
       def 			_lowercase (  __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						Tuple 						=		alpha * alpha * (3 - (2 * alpha))
              return thetaa + ((thetaa - thetaa) * alpha)






       @staticmethod
       def 			_lowercase (  __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              import math

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






       @staticmethod
       def 			_lowercase (  __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




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





import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
  import librosa  # noqa: E402

  __UpperCAmelCase			:			Union[str, Any]			      =					True
  __UpperCAmelCase			:			Optional[int]			      =					""
except Exception as e:
  __UpperCAmelCase			:			Union[str, Any]			      =					False
  __UpperCAmelCase			:			Tuple			      =					(
      f'''Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it.'''
  )


from PIL import Image  # noqa: E402



class  UpperCAmelCase_						(				_a,  _a):
       '''simple docstring'''
       __UpperCamelCase   :  Tuple				=       "mel_config.json"






       @register_to_config
       def __init__(  self			,       __SCREAMING_SNAKE_CASE = 256			,       __SCREAMING_SNAKE_CASE = 256			,       __SCREAMING_SNAKE_CASE = 22_050			,       __SCREAMING_SNAKE_CASE = 2_048			,       __SCREAMING_SNAKE_CASE = 512			,       __SCREAMING_SNAKE_CASE = 80			,       __SCREAMING_SNAKE_CASE = 32			,       ):




              """simple docstring"""




              UpperCamelCase     :						Union[str, Any] 						=		hop_length
              UpperCamelCase     :						List[str] 						=		sample_rate
              UpperCamelCase     :						List[Any] 						=		n_fft
              UpperCamelCase     :						List[str] 						=		top_db
              UpperCamelCase     :						Tuple 						=		n_iter
              self.set_resolution(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Any 						=		None

              if not _librosa_can_be_imported:
                     raise ValueError(_import_error    )






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						Any 						=		x_res
              UpperCamelCase     :						Union[str, Any] 						=		y_res
              UpperCamelCase     :						Any 						=		self.y_res
              UpperCamelCase     :						Union[str, Any] 						=		self.x_res * self.hop_length - 1






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE = None			,       __SCREAMING_SNAKE_CASE = None    ):




              """simple docstring"""




              if audio_file is not None:
                     UpperCamelCase						,			UpperCamelCase     :						Tuple 						=		librosa.load(__SCREAMING_SNAKE_CASE			,       mono=__SCREAMING_SNAKE_CASE			,       sr=self.sr    )
              else:
                     UpperCamelCase     :						Tuple 						=		raw_audio

              # Pad with silence if necessary.
              if len(self.audio    ) < self.x_res * self.hop_length:
                     UpperCamelCase     :						List[str] 						=		np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio    ),)    )]    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              return len(self.audio    ) // self.slice_size






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE = 0    ):




              """simple docstring"""




              return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]






       def 			_lowercase (  self    ):




              """simple docstring"""




              return self.sr






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						List[str] 						=		librosa.feature.melspectrogram(
                  y=self.get_audio_slice(__SCREAMING_SNAKE_CASE    )			,       sr=self.sr			,       n_fft=self.n_fft			,       hop_length=self.hop_length			,       n_mels=self.n_mels    )
              UpperCamelCase     :						Optional[int] 						=		librosa.power_to_db(__SCREAMING_SNAKE_CASE			,       ref=np.max			,       top_db=self.top_db    )
              UpperCamelCase     :						Optional[int] 						=		(((log_S + self.top_db) * 255 / self.top_db).clip(0			,       255    ) + 0.5).astype(np.uinta    )
              UpperCamelCase     :						List[Any] 						=		Image.fromarray(__SCREAMING_SNAKE_CASE    )
              return image






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						int 						=		np.frombuffer(image.tobytes()			,       dtype='''uint8'''    ).reshape((image.height, image.width)    )
              UpperCamelCase     :						Tuple 						=		bytedata.astype('''float'''    ) * self.top_db / 255 - self.top_db
              UpperCamelCase     :						List[Any] 						=		librosa.db_to_power(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Optional[int] 						=		librosa.feature.inverse.mel_to_audio(
                  __SCREAMING_SNAKE_CASE			,       sr=self.sr			,       n_fft=self.n_fft			,       hop_length=self.hop_length			,       n_iter=self.n_iter    )
              return audio

