


import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
def        __lowerCamelCase		(				UpperCAmelCase_		:	Optional[int]				,						UpperCAmelCase_		:	int		):




						"""simple docstring"""





						a		:Tuple											= UNetaDModel(**_UpperCAmelCase		)
						a		:List[Any]											= checkpoint['''all_modules.0.W'''].data
						a		:str											= checkpoint['''all_modules.0.W'''].data
						a		:List[Any]											= checkpoint['''all_modules.1.weight'''].data
						a		:Optional[int]											= checkpoint['''all_modules.1.bias'''].data

						a		:str											= checkpoint['''all_modules.2.weight'''].data
						a		:str											= checkpoint['''all_modules.2.bias'''].data

						a		:Optional[int]											= checkpoint['''all_modules.3.weight'''].data
						a		:List[str]											= checkpoint['''all_modules.3.bias'''].data

						a		:List[Any]											= checkpoint[list(checkpoint.keys()		)[-4]].data
						a		:str											= checkpoint[list(checkpoint.keys()		)[-3]].data
						a		:Union[str, Any]											= checkpoint[list(checkpoint.keys()		)[-2]].data
						a		:Any											= checkpoint[list(checkpoint.keys()		)[-1]].data

						a		:int											= 4

						def set_attention_weights(UpperCAmelCase_		:	Union[str, Any]				,						UpperCAmelCase_		:	Optional[Any]				,						UpperCAmelCase_		:	List[Any]		):
												a		:Tuple											= old_checkpoint[F'''all_modules.{index}.NIN_0.W'''].data.T
												a		:List[Any]											= old_checkpoint[F'''all_modules.{index}.NIN_1.W'''].data.T
												a		:Union[str, Any]											= old_checkpoint[F'''all_modules.{index}.NIN_2.W'''].data.T

												a		:Union[str, Any]											= old_checkpoint[F'''all_modules.{index}.NIN_0.b'''].data
												a		:Optional[int]											= old_checkpoint[F'''all_modules.{index}.NIN_1.b'''].data
												a		:Any											= old_checkpoint[F'''all_modules.{index}.NIN_2.b'''].data

												a		:Optional[int]											= old_checkpoint[F'''all_modules.{index}.NIN_3.W'''].data.T
												a		:str											= old_checkpoint[F'''all_modules.{index}.NIN_3.b'''].data

												a		:Any											= old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
												a		:List[Any]											= old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

						def set_resnet_weights(UpperCAmelCase_		:	Optional[Any]				,						UpperCAmelCase_		:	List[str]				,						UpperCAmelCase_		:	str		):
												a		:Tuple											= old_checkpoint[F'''all_modules.{index}.Conv_0.weight'''].data
												a		:int											= old_checkpoint[F'''all_modules.{index}.Conv_0.bias'''].data
												a		:Tuple											= old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
												a		:Optional[int]											= old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

												a		:Tuple											= old_checkpoint[F'''all_modules.{index}.Conv_1.weight'''].data
												a		:str											= old_checkpoint[F'''all_modules.{index}.Conv_1.bias'''].data
												a		:Optional[int]											= old_checkpoint[F'''all_modules.{index}.GroupNorm_1.weight'''].data
												a		:Optional[Any]											= old_checkpoint[F'''all_modules.{index}.GroupNorm_1.bias'''].data

												a		:Optional[int]											= old_checkpoint[F'''all_modules.{index}.Dense_0.weight'''].data
												a		:List[Any]											= old_checkpoint[F'''all_modules.{index}.Dense_0.bias'''].data

												if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
																		a		:Optional[int]											= old_checkpoint[F'''all_modules.{index}.Conv_2.weight'''].data
																		a		:List[str]											= old_checkpoint[F'''all_modules.{index}.Conv_2.bias'''].data

						for i, block in enumerate(new_model_architecture.downsample_blocks		):
												a		:List[Any]											= hasattr(_UpperCAmelCase				,						'''attentions'''		)
												for j in range(len(block.resnets		)		):
																		set_resnet_weights(block.resnets[j]				,						_UpperCAmelCase				,						_UpperCAmelCase		)
																		module_index += 1
																		if has_attentions:
																								set_attention_weights(block.attentions[j]				,						_UpperCAmelCase				,						_UpperCAmelCase		)
																								module_index += 1

												if hasattr(_UpperCAmelCase				,						'''downsamplers'''		) and block.downsamplers is not None:
																		set_resnet_weights(block.resnet_down				,						_UpperCAmelCase				,						_UpperCAmelCase		)
																		module_index += 1
																		a		:Tuple											= checkpoint[F'''all_modules.{module_index}.Conv_0.weight'''].data
																		a		:List[str]											= checkpoint[F'''all_modules.{module_index}.Conv_0.bias'''].data
																		module_index += 1

						set_resnet_weights(new_model_architecture.mid_block.resnets[0]				,						_UpperCAmelCase				,						_UpperCAmelCase		)
						module_index += 1
						set_attention_weights(new_model_architecture.mid_block.attentions[0]				,						_UpperCAmelCase				,						_UpperCAmelCase		)
						module_index += 1
						set_resnet_weights(new_model_architecture.mid_block.resnets[1]				,						_UpperCAmelCase				,						_UpperCAmelCase		)
						module_index += 1

						for i, block in enumerate(new_model_architecture.up_blocks		):
												a		:List[Any]											= hasattr(_UpperCAmelCase				,						'''attentions'''		)
												for j in range(len(block.resnets		)		):
																		set_resnet_weights(block.resnets[j]				,						_UpperCAmelCase				,						_UpperCAmelCase		)
																		module_index += 1
												if has_attentions:
																		set_attention_weights(
																		    block.attentions[0]				,						_UpperCAmelCase				,						_UpperCAmelCase		)  # why can there only be a single attention layer for up?
																		module_index += 1

												if hasattr(_UpperCAmelCase				,						'''resnet_up'''		) and block.resnet_up is not None:
																		a		:Dict											= checkpoint[F'''all_modules.{module_index}.weight'''].data
																		a		:Optional[Any]											= checkpoint[F'''all_modules.{module_index}.bias'''].data
																		module_index += 1
																		a		:Dict											= checkpoint[F'''all_modules.{module_index}.weight'''].data
																		a		:Union[str, Any]											= checkpoint[F'''all_modules.{module_index}.bias'''].data
																		module_index += 1
																		set_resnet_weights(block.resnet_up				,						_UpperCAmelCase				,						_UpperCAmelCase		)
																		module_index += 1

						a		:int											= checkpoint[F'''all_modules.{module_index}.weight'''].data
						a		:List[str]											= checkpoint[F'''all_modules.{module_index}.bias'''].data
						module_index += 1
						a		:Tuple											= checkpoint[F'''all_modules.{module_index}.weight'''].data
						a		:int											= checkpoint[F'''all_modules.{module_index}.bias'''].data

						return new_model_architecture.state_dict()


if __name__ == "__main__":
					snake_case       :					Optional[int]        =  argparse.ArgumentParser()

					parser.add_argument(
					    '''--checkpoint_path''',
					    default='''/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin''',
					    type=str,
					    required=False,
					    help='''Path to the checkpoint to convert.''',
					)

					parser.add_argument(
					    '''--config_file''',
					    default='''/Users/arthurzucker/Work/diffusers/ArthurZ/config.json''',
					    type=str,
					    required=False,
					    help='''The config json file corresponding to the architecture.''',
					)

					parser.add_argument(
					    '''--dump_path''',
					    default='''/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt''',
					    type=str,
					    required=False,
					    help='''Path to the output model.''',
					)

					snake_case       :					Optional[int]        =  parser.parse_args()

					snake_case       :					int        =  torch.load(args.checkpoint_path, map_location='''cpu''')

					with open(args.config_file) as f:
										snake_case       :					Dict        =  json.loads(f.read())

					snake_case       :					Optional[int]        =  convert_ncsnpp_checkpoint(
					    checkpoint,
					    config,
					)

					if "sde" in config:
										del config["sde"]

					snake_case       :					Dict        =  UNetaDModel(**config)
					model.load_state_dict(converted_checkpoint)

					try:
										snake_case       :					Any        =  ScoreSdeVeScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

										snake_case       :					Optional[Any]        =  ScoreSdeVePipeline(unet=model, scheduler=scheduler)
										pipe.save_pretrained(args.dump_path)
					except:  # noqa: E722
										model.save_pretrained(args.dump_path)









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





