


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


lowerCAmelCase__   	=				logging.get_logger(__name__)

lowerCAmelCase__   	=				{
    'facebook/musicgen-small': 'https://huggingface.co/facebook/musicgen-small/resolve/main/config.json',
    # See all Musicgen models at https://huggingface.co/models?filter=musicgen
}







class   lowerCAmelCase__ (       a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE						    =				"musicgen_decoder"
     __SCREAMING_SNAKE_CASE						    =				["past_key_values"]


     def __init__(		self				,		__lowerCamelCase=2_0_4_8				,		__lowerCamelCase=2_0_4_8				,		__lowerCamelCase=2_4				,		__lowerCamelCase=4_0_9_6				,		__lowerCamelCase=1_6				,		__lowerCamelCase=0.0				,		__lowerCamelCase=True				,		__lowerCamelCase="gelu"				,		__lowerCamelCase=1_0_2_4				,		__lowerCamelCase=0.1				,		__lowerCamelCase=0.0				,		__lowerCamelCase=0.0				,		__lowerCamelCase=0.0_2				,		__lowerCamelCase=False				,		__lowerCamelCase=4				,		__lowerCamelCase=2_0_4_8				,		__lowerCamelCase=2_0_4_8				,		__lowerCamelCase=None				,		__lowerCamelCase=False				,		**__lowerCamelCase				,		)    ->       Optional[Any]:
            _A   : Union[str, Any]       					=				vocab_size
            _A   : Dict       					=				max_position_embeddings
            _A   : int       					=				hidden_size
            _A   : Any       					=				ffn_dim
            _A   : Any       					=				num_hidden_layers
            _A   : List[Any]       					=				num_attention_heads
            _A   : Tuple       					=				dropout
            _A   : Union[str, Any]       					=				attention_dropout
            _A   : Optional[Any]       					=				activation_dropout
            _A   : Union[str, Any]       					=				activation_function
            _A   : List[Any]       					=				initializer_factor
            _A   : Tuple       					=				layerdrop
            _A   : Dict       					=				use_cache
            _A   : Any       					=				scale_embedding  # scale factor will be sqrt(d_model) if True
            _A   : str       					=				num_codebooks
            super().__init__(
                pad_token_id=__lowerCamelCase				,		bos_token_id=__lowerCamelCase				,		eos_token_id=__lowerCamelCase				,		tie_word_embeddings=__lowerCamelCase				,		**__lowerCamelCase				,		)







class   lowerCAmelCase__ (       a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE						    =				"musicgen"
     __SCREAMING_SNAKE_CASE						    =				True


     def __init__(		self				,		**__lowerCamelCase)    ->       List[str]:
            super().__init__(**__lowerCamelCase)
            if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
                   raise ValueError("Config has to be initialized with text_encoder, audio_encoder and decoder config")

            _A   : int       					=				kwargs.pop("text_encoder")
            _A   : Optional[Any]       					=				text_encoder_config.pop("model_type")

            _A   : Dict       					=				kwargs.pop("audio_encoder")
            _A   : Tuple       					=				audio_encoder_config.pop("model_type")

            _A   : List[str]       					=				kwargs.pop("decoder")

            _A   : List[Any]       					=				AutoConfig.for_model(__lowerCamelCase				,		**__lowerCamelCase)
            _A   : Union[str, Any]       					=				AutoConfig.for_model(__lowerCamelCase				,		**__lowerCamelCase)
            _A   : Optional[int]       					=				MusicgenDecoderConfig(**__lowerCamelCase)
            _A   : List[Any]       					=				True


     @classmethod
     def 				_lowerCamelCase				(		cls				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		**__lowerCamelCase				,		)    ->       str:
            return cls(
                text_encoder=text_encoder_config.to_dict()				,		audio_encoder=audio_encoder_config.to_dict()				,		decoder=decoder_config.to_dict()				,		**__lowerCamelCase				,		)


     def 				_lowerCamelCase				(		self)    ->       Union[str, Any]:
            _A   : Optional[Any]       					=				copy.deepcopy(self.__dict__)
            _A   : Optional[Any]       					=				self.text_encoder.to_dict()
            _A   : Optional[int]       					=				self.audio_encoder.to_dict()
            _A   : Tuple       					=				self.decoder.to_dict()
            _A   : Tuple       					=				self.__class__.model_type
            return output




import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
def 							_UpperCAmelCase       (UpperCamelCase__  :						Union[str, Any]       ,							UpperCamelCase__  :						Dict		):
       _A   : Dict       					=				UNetaDModel(**UpperCamelCase__		)
       _A   : Any       					=				checkpoint["all_modules.0.W"].data
       _A   : Any       					=				checkpoint["all_modules.0.W"].data
       _A   : Union[str, Any]       					=				checkpoint["all_modules.1.weight"].data
       _A   : Optional[Any]       					=				checkpoint["all_modules.1.bias"].data

       _A   : Dict       					=				checkpoint["all_modules.2.weight"].data
       _A   : List[str]       					=				checkpoint["all_modules.2.bias"].data

       _A   : Dict       					=				checkpoint["all_modules.3.weight"].data
       _A   : Union[str, Any]       					=				checkpoint["all_modules.3.bias"].data

       _A   : List[str]       					=				checkpoint[list(checkpoint.keys()		)[-4]].data
       _A   : Any       					=				checkpoint[list(checkpoint.keys()		)[-3]].data
       _A   : List[str]       					=				checkpoint[list(checkpoint.keys()		)[-2]].data
       _A   : Optional[int]       					=				checkpoint[list(checkpoint.keys()		)[-1]].data

       _A   : str       					=				4

       def set_attention_weights(UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Tuple		):
              _A   : Any       					=				old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
              _A   : int       					=				old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
              _A   : int       					=				old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T

              _A   : Any       					=				old_checkpoint[f"all_modules.{index}.NIN_0.b"].data
              _A   : Any       					=				old_checkpoint[f"all_modules.{index}.NIN_1.b"].data
              _A   : List[str]       					=				old_checkpoint[f"all_modules.{index}.NIN_2.b"].data

              _A   : Optional[Any]       					=				old_checkpoint[f"all_modules.{index}.NIN_3.W"].data.T
              _A   : Dict       					=				old_checkpoint[f"all_modules.{index}.NIN_3.b"].data

              _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
              _A   : int       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

       def set_resnet_weights(UpperCamelCase__  :						str       ,							UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						Dict		):
              _A   : Optional[int]       					=				old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data
              _A   : Optional[int]       					=				old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data
              _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
              _A   : int       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

              _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.Conv_1.weight"].data
              _A   : Optional[int]       					=				old_checkpoint[f"all_modules.{index}.Conv_1.bias"].data
              _A   : Tuple       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_1.weight"].data
              _A   : str       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_1.bias"].data

              _A   : Tuple       					=				old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data
              _A   : Union[str, 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   : Tuple       					=				old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data
                     _A   : Optional[Any]       					=				old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data

       for i, block in enumerate(new_model_architecture.downsample_blocks		):
              _A   : 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   : List[str]       					=				checkpoint[f"all_modules.{module_index}.Conv_0.weight"].data
                     _A   : Optional[int]       					=				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   : Dict       					=				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   : List[Any]       					=				checkpoint[f"all_modules.{module_index}.bias"].data
                     module_index += 1
                     _A   : int       					=				checkpoint[f"all_modules.{module_index}.weight"].data
                     _A   : List[Any]       					=				checkpoint[f"all_modules.{module_index}.bias"].data
                     module_index += 1
                     set_resnet_weights(block.resnet_up       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                     module_index += 1

       _A   : str       					=				checkpoint[f"all_modules.{module_index}.weight"].data
       _A   : str       					=				checkpoint[f"all_modules.{module_index}.bias"].data
       module_index += 1
       _A   : Tuple       					=				checkpoint[f"all_modules.{module_index}.weight"].data
       _A   : str       					=				checkpoint[f"all_modules.{module_index}.bias"].data

       return new_model_architecture.state_dict()


if __name__ == "__main__":
    lowerCAmelCase__   	=				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.',
    )

    lowerCAmelCase__   	=				parser.parse_args()

    lowerCAmelCase__   	=				torch.load(args.checkpoint_path, map_location='cpu')

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

    lowerCAmelCase__   	=				convert_ncsnpp_checkpoint(
        checkpoint,
        config,
    )

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

    lowerCAmelCase__   	=				UNetaDModel(**config)
    model.load_state_dict(converted_checkpoint)

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

        lowerCAmelCase__   	=				ScoreSdeVePipeline(unet=model, scheduler=scheduler)
        pipe.save_pretrained(args.dump_path)
    except:  # noqa: E722
        model.save_pretrained(args.dump_path)
