


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 	_snake_case						(   _snake_case							):




 def __init__(	self					):
       self.register_to_config()
       super().__init__()




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase					):
       if dicta == dicta:
             return True
       else:
             a							,       a		:Any											= self._remove_meta_keys(_lowerCamelCase					)
             a							,       a		:Any											= self._remove_meta_keys(_lowerCamelCase					)
             if configa == configa:
                   print(F'''Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.'''					)
                   return True
       return False




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase					):
       a		:Dict											= []
       a		:Any											= config_dict.copy()
       for key in config_dict.keys():
             if key.startswith('''_'''					):
                   temp_dict.pop(_lowerCamelCase					)
                   meta_keys.append(_lowerCamelCase					)
       return (temp_dict, meta_keys)




 @torch.no_grad()
 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						**_lowerCamelCase					):
       a		:str											= kwargs.pop('''cache_dir'''      ,						_lowerCamelCase					)
       a		:Dict											= kwargs.pop('''resume_download'''      ,						_lowerCamelCase					)
       a		:List[str]											= kwargs.pop('''force_download'''      ,						_lowerCamelCase					)
       a		:str											= kwargs.pop('''proxies'''      ,						_lowerCamelCase					)
       a		:str											= kwargs.pop('''local_files_only'''      ,						_lowerCamelCase					)
       a		:Optional[Any]											= kwargs.pop('''use_auth_token'''      ,						_lowerCamelCase					)
       a		:Dict											= kwargs.pop('''revision'''      ,						_lowerCamelCase					)
       a		:Dict											= kwargs.pop('''torch_dtype'''      ,						_lowerCamelCase					)
       a		:Optional[Any]											= kwargs.pop('''device_map'''      ,						_lowerCamelCase					)

       a		:int											= kwargs.pop('''alpha'''      ,						0.5					)
       a		:Dict											= kwargs.pop('''interp'''      ,						_lowerCamelCase					)

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

       a		:List[str]											= len(_lowerCamelCase					)
       # Ignore result from model_index_json comparision of the two checkpoints
       a		:Tuple											= kwargs.pop('''force'''      ,						_lowerCamelCase					)

       # 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		:str											= []
       for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
             a		:Tuple											= DiffusionPipeline.load_config(
                 _lowerCamelCase      ,						cache_dir=_lowerCamelCase      ,						resume_download=_lowerCamelCase      ,						force_download=_lowerCamelCase      ,						proxies=_lowerCamelCase      ,						local_files_only=_lowerCamelCase      ,						use_auth_token=_lowerCamelCase      ,						revision=_lowerCamelCase      ,						)
             config_dicts.append(_lowerCamelCase					)

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

             a		:Optional[int]											= (
                 pretrained_model_name_or_path
                 if os.path.isdir(_lowerCamelCase					)
                 else snapshot_download(
                     _lowerCamelCase      ,						cache_dir=_lowerCamelCase      ,						resume_download=_lowerCamelCase      ,						proxies=_lowerCamelCase      ,						local_files_only=_lowerCamelCase      ,						use_auth_token=_lowerCamelCase      ,						revision=_lowerCamelCase      ,						allow_patterns=_lowerCamelCase      ,						user_agent=_lowerCamelCase      ,						)
             )
             print('''Cached Folder'''      ,						_lowerCamelCase					)
             cached_folders.append(_lowerCamelCase					)

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

       a		:List[Any]											= None
       if len(_lowerCamelCase					) > 2:
             a		:str											= os.path.join(cached_folders[2]					)

       if interp == "sigmoid":
             a		:Dict											= CheckpointMergerPipeline.sigmoid
       elif interp == "inv_sigmoid":
             a		:Union[str, Any]											= CheckpointMergerPipeline.inv_sigmoid
       elif interp == "add_diff":
             a		:Any											= CheckpointMergerPipeline.add_difference
       else:
             a		:str											= CheckpointMergerPipeline.weighted_sum

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

                         a		:List[Any]											= getattr(_lowerCamelCase      ,						'''load_state_dict'''					)
                         a		:Any											= (
                             safetensors.torch.load_file(_lowerCamelCase					)
                             if (is_safetensors_available() and checkpoint_path_a.endswith('''.safetensors'''					))
                             else torch.load(_lowerCamelCase      ,						map_location='''cpu'''					)
                         )
                         a		:Optional[Any]											= None
                         if checkpoint_path_a:
                               a		:int											= (
                                   safetensors.torch.load_file(_lowerCamelCase					)
                                   if (is_safetensors_available() and checkpoint_path_a.endswith('''.safetensors'''					))
                                   else torch.load(_lowerCamelCase      ,						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(_lowerCamelCase					)}'''					)
                         continue
                   print(F'''MERGING {attr}'''					)

                   for key in theta_a.keys():
                         if theta_a:
                               a		:List[Any]											= theta_func(theta_a[key]      ,						theta_a[key]      ,						theta_a[key]      ,						_lowerCamelCase					)
                         else:
                               a		:Tuple											= theta_func(theta_a[key]      ,						theta_a[key]      ,						_lowerCamelCase      ,						_lowerCamelCase					)

                   del theta_a
                   del theta_a
                   update_theta_a(_lowerCamelCase					)

                   del theta_a
       return final_pipe




 @staticmethod
 def  SCREAMING_SNAKE_CASE__       (	_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase					):
       return ((1 - alpha) * thetaa) + (alpha * thetaa)




 @staticmethod
 def  SCREAMING_SNAKE_CASE__       (	_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase					):
       a		:Union[str, Any]											= alpha * alpha * (3 - (2 * alpha))
       return thetaa + ((thetaa - thetaa) * alpha)




 @staticmethod
 def  SCREAMING_SNAKE_CASE__       (	_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase					):
       import math

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




 @staticmethod
 def  SCREAMING_SNAKE_CASE__       (	_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase					):
       return thetaa + (thetaa - thetaa) * (1.0 - alpha)









import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


snake_case       :					Dict        =  logging.get_logger(__name__)







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = 'semantic'




 def __init__(	self      ,						_lowerCamelCase=1_0000      ,						_lowerCamelCase=True      ,						_lowerCamelCase=768      ,						_lowerCamelCase=False      ,						_lowerCamelCase=False      ,						_lowerCamelCase=False      ,						_lowerCamelCase=False      ,						_lowerCamelCase=0.7      ,						_lowerCamelCase=True      ,						_lowerCamelCase=1_0048      ,						_lowerCamelCase=12_9595      ,						_lowerCamelCase=12_9599      ,						_lowerCamelCase=1_0000      ,						_lowerCamelCase=256      ,						_lowerCamelCase=49.9      ,						**_lowerCamelCase      ,						):
       super().__init__(
           temperature=_lowerCamelCase      ,						do_sample=_lowerCamelCase      ,						eos_token_id=_lowerCamelCase      ,						renormalize_logits=_lowerCamelCase      ,						max_new_tokens=_lowerCamelCase      ,						output_scores=_lowerCamelCase      ,						return_dict_in_generate=_lowerCamelCase      ,						output_hidden_states=_lowerCamelCase      ,						output_attentions=_lowerCamelCase      ,						**_lowerCamelCase      ,						)

       a		:Dict											= text_encoding_offset
       a		:Any											= text_pad_token
       a		:List[Any]											= eos_token_id
       a		:str											= semantic_infer_token
       a		:Any											= semantic_vocab_size
       a		:Any											= max_input_semantic_length
       a		:Dict											= semantic_rate_hz







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = 'coarse_acoustics'




 def __init__(	self      ,						_lowerCamelCase=True      ,						_lowerCamelCase=False      ,						_lowerCamelCase=False      ,						_lowerCamelCase=False      ,						_lowerCamelCase=False      ,						_lowerCamelCase=0.7      ,						_lowerCamelCase=True      ,						_lowerCamelCase=1_2048      ,						_lowerCamelCase=75      ,						_lowerCamelCase=2      ,						_lowerCamelCase=1_2050      ,						_lowerCamelCase=256      ,						_lowerCamelCase = 630      ,						_lowerCamelCase = 60      ,						**_lowerCamelCase      ,						):
       super().__init__(
           temperature=_lowerCamelCase      ,						do_sample=_lowerCamelCase      ,						renormalize_logits=_lowerCamelCase      ,						output_scores=_lowerCamelCase      ,						return_dict_in_generate=_lowerCamelCase      ,						output_hidden_states=_lowerCamelCase      ,						output_attentions=_lowerCamelCase      ,						**_lowerCamelCase      ,						)

       a		:Optional[Any]											= coarse_semantic_pad_token
       a		:Dict											= coarse_rate_hz
       a		:List[str]											= n_coarse_codebooks
       a		:List[Any]											= coarse_infer_token
       a		:Optional[Any]											= max_coarse_input_length
       a		:Any											= max_coarse_history
       a		:Optional[int]											= sliding_window_len







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = 'fine_acoustics'




 def __init__(	self      ,						_lowerCamelCase=0.5      ,						_lowerCamelCase=512      ,						_lowerCamelCase=1024      ,						_lowerCamelCase=8      ,						**_lowerCamelCase      ,						):
       super().__init__(temperature=_lowerCamelCase					)

       a		:Union[str, Any]											= max_fine_history_length
       a		:int											= max_fine_input_length
       a		:str											= n_fine_codebooks







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = 'bark'
 SCREAMING_SNAKE_CASE__   = True




 def __init__(	self      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase=2_4000      ,						_lowerCamelCase=1024      ,						**_lowerCamelCase      ,						):
       if semantic_config is None:
             a		:str											= {}
             logger.info('''semantic_config is None. initializing the semantic model with default values.'''					)

       if coarse_acoustics_config is None:
             a		:List[str]											= {}
             logger.info('''coarse_acoustics_config is None. initializing the coarse model with default values.'''					)

       if fine_acoustics_config is None:
             a		:Any											= {}
             logger.info('''fine_acoustics_config is None. initializing the fine model with default values.'''					)

       a		:Any											= BarkSemanticGenerationConfig(**_lowerCamelCase					)
       a		:int											= BarkCoarseGenerationConfig(**_lowerCamelCase					)
       a		:Optional[int]											= BarkFineGenerationConfig(**_lowerCamelCase					)

       a		:Tuple											= sample_rate
       a		:Dict											= codebook_size




 @classmethod
 def  SCREAMING_SNAKE_CASE__       (	cls      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						**_lowerCamelCase      ,						):
       return cls(
           semantic_config=semantic_config.to_dict()      ,						coarse_acoustics_config=coarse_acoustics_config.to_dict()      ,						fine_acoustics_config=fine_acoustics_config.to_dict()      ,						**_lowerCamelCase      ,						)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:int											= copy.deepcopy(self.__dict__					)

       a		:List[Any]											= self.semantic_config.to_dict()
       a		:Union[str, Any]											= self.coarse_acoustics_config.to_dict()
       a		:int											= self.fine_acoustics_config.to_dict()

       a		:Tuple											= self.__class__.model_type
       return output





