



import argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


logging.set_verbosity_info()
__A       =						logging.get_logger(__name__)

set_seed(770)


__A       =						{
    '''c_attn''': '''att_proj''',
    '''c_proj''': '''out_proj''',
    '''c_fc''': '''in_proj''',
    '''transformer.''': '''''',
    '''h.''': '''layers.''',
    '''ln_1''': '''layernorm_1''',
    '''ln_2''': '''layernorm_2''',
    '''ln_f''': '''layernorm_final''',
    '''wpe''': '''position_embeds_layer''',
    '''wte''': '''input_embeds_layer''',
}


__A       =						{
    '''text_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''text.pt''',
    },
    '''coarse_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''coarse.pt''',
    },
    '''fine_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''fine.pt''',
    },
    '''text''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''text_2.pt''',
    },
    '''coarse''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''coarse_2.pt''',
    },
    '''fine''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''fine_2.pt''',
    },
}

__A       =						os.path.dirname(os.path.abspath(__file__))
__A       =						os.path.join(os.path.expanduser('''~'''), '''.cache''')
__A       =						os.path.join(os.getenv('''XDG_CACHE_HOME''', default_cache_dir), '''suno''', '''bark_v0''')



def 	__a					(   lowerCAmelCase_       : Tuple      ,lowerCAmelCase_       : Tuple=False    )				->			Dict:






						'''simple docstring'''



						UpperCAmelCase_=       model_type
						if use_small:
												key += "_small"
						return os.path.join(lowerCAmelCase_      ,REMOTE_MODEL_PATHS[key]["""file_name"""]    )



def 	__a					(   lowerCAmelCase_       : Optional[int]      ,lowerCAmelCase_       : Optional[int]    )				->			Dict:






						'''simple docstring'''



						os.makedirs(lowerCAmelCase_      ,exist_ok=lowerCAmelCase_    )
						hf_hub_download(repo_id=lowerCAmelCase_      ,filename=lowerCAmelCase_      ,local_dir=lowerCAmelCase_    )



def 	__a					(   lowerCAmelCase_       : Union[str, Any]      ,lowerCAmelCase_       : int      ,lowerCAmelCase_       : Dict=False      ,lowerCAmelCase_       : int="text"    )				->			List[Any]:






						'''simple docstring'''



						if model_type == "text":
												UpperCAmelCase_=       BarkSemanticModel
												UpperCAmelCase_=       BarkSemanticConfig
												UpperCAmelCase_=       BarkSemanticGenerationConfig
						elif model_type == "coarse":
												UpperCAmelCase_=       BarkCoarseModel
												UpperCAmelCase_=       BarkCoarseConfig
												UpperCAmelCase_=       BarkCoarseGenerationConfig
						elif model_type == "fine":
												UpperCAmelCase_=       BarkFineModel
												UpperCAmelCase_=       BarkFineConfig
												UpperCAmelCase_=       BarkFineGenerationConfig
						else:
												raise NotImplementedError()
						UpperCAmelCase_=       F"""{model_type}_small""" if use_small else model_type
						UpperCAmelCase_=       REMOTE_MODEL_PATHS[model_key]
						if not os.path.exists(lowerCAmelCase_    ):
												logger.info(F"""{model_type} model not found, downloading into `{CACHE_DIR}`."""    )
												_download(model_info["""repo_id"""]      ,model_info["""file_name"""]    )
						UpperCAmelCase_=       torch.load(lowerCAmelCase_      ,map_location=lowerCAmelCase_    )
						# this is a hack
						UpperCAmelCase_=       checkpoint["""model_args"""]
						if "input_vocab_size" not in model_args:
												UpperCAmelCase_=       model_args["""vocab_size"""]
												UpperCAmelCase_=       model_args["""vocab_size"""]
												del model_args["vocab_size"]

						# convert Bark model arguments to HF Bark model arguments
						UpperCAmelCase_=       model_args.pop("""n_head"""    )
						UpperCAmelCase_=       model_args.pop("""n_embd"""    )
						UpperCAmelCase_=       model_args.pop("""n_layer"""    )

						UpperCAmelCase_=       ConfigClass(**checkpoint["""model_args"""]    )
						UpperCAmelCase_=       ModelClass(config=lowerCAmelCase_    )
						UpperCAmelCase_=       GenerationConfigClass()

						UpperCAmelCase_=       model_generation_config
						UpperCAmelCase_=       checkpoint["""model"""]
						# fixup checkpoint
						UpperCAmelCase_=       """_orig_mod."""
						for k, v in list(state_dict.items()    ):
												if k.startswith(lowerCAmelCase_    ):
																		# replace part of the key with corresponding layer name in HF implementation
																		UpperCAmelCase_=       k[len(lowerCAmelCase_    ) :]
																		for old_layer_name in new_layer_name_dict:
																								UpperCAmelCase_=       new_k.replace(lowerCAmelCase_      ,new_layer_name_dict[old_layer_name]    )

																		UpperCAmelCase_=       state_dict.pop(lowerCAmelCase_    )

						UpperCAmelCase_=       set(state_dict.keys()    ) - set(model.state_dict().keys()    )
						UpperCAmelCase_=       {k for k in extra_keys if not k.endswith(""".attn.bias"""    )}
						UpperCAmelCase_=       set(model.state_dict().keys()    ) - set(state_dict.keys()    )
						UpperCAmelCase_=       {k for k in missing_keys if not k.endswith(""".attn.bias"""    )}
						if len(lowerCAmelCase_    ) != 0:
												raise ValueError(F"""extra keys found: {extra_keys}"""    )
						if len(lowerCAmelCase_    ) != 0:
												raise ValueError(F"""missing keys: {missing_keys}"""    )
						model.load_state_dict(lowerCAmelCase_      ,strict=lowerCAmelCase_    )
						UpperCAmelCase_=       model.num_parameters(exclude_embeddings=lowerCAmelCase_    )
						UpperCAmelCase_=       checkpoint["""best_val_loss"""].item()
						logger.info(F"""model loaded: {round(n_params/1E6      ,1    )}M params, {round(lowerCAmelCase_      ,3    )} loss"""    )
						model.eval()
						model.to(lowerCAmelCase_    )
						del checkpoint, state_dict

						return model



def 	__a					(   lowerCAmelCase_       : Dict      ,lowerCAmelCase_       : int=False      ,lowerCAmelCase_       : Any="text"    )				->			Tuple:






						'''simple docstring'''



						if model_type not in ("text", "coarse", "fine"):
												raise NotImplementedError()

						UpperCAmelCase_=       """cpu"""  # do conversion on cpu

						UpperCAmelCase_=       _get_ckpt_path(lowerCAmelCase_      ,use_small=lowerCAmelCase_    )
						UpperCAmelCase_=       _load_model(lowerCAmelCase_      ,lowerCAmelCase_      ,model_type=lowerCAmelCase_      ,use_small=lowerCAmelCase_    )

						# load bark initial model
						UpperCAmelCase_=       _bark_load_model(lowerCAmelCase_      ,"""cpu"""      ,model_type=lowerCAmelCase_      ,use_small=lowerCAmelCase_    )

						if model_type == "text":
												UpperCAmelCase_=       bark_model["""model"""]

						if model.num_parameters(exclude_embeddings=lowerCAmelCase_    ) != bark_model.get_num_params():
												raise ValueError("""initial and new models don't have the same number of parameters"""    )

						# check if same output as the bark model
						UpperCAmelCase_=       5
						UpperCAmelCase_=       10

						if model_type in ["text", "coarse"]:
												UpperCAmelCase_=       torch.randint(2_56      ,(batch_size, sequence_length)      ,dtype=torch.int    )
												UpperCAmelCase_=       bark_model(lowerCAmelCase_    )[0]

												UpperCAmelCase_=       model(lowerCAmelCase_    )

												# take last logits
												UpperCAmelCase_=       output_new_model_total.logits[:, [-1], :]

						else:
												UpperCAmelCase_=       3
												UpperCAmelCase_=       8
												UpperCAmelCase_=       torch.randint(2_56      ,(batch_size, sequence_length, n_codes_total)      ,dtype=torch.int    )

												UpperCAmelCase_=       model(lowerCAmelCase_      ,lowerCAmelCase_    )
												UpperCAmelCase_=       bark_model(lowerCAmelCase_      ,lowerCAmelCase_    )

												UpperCAmelCase_=       output_new_model_total.logits

						# output difference should come from the difference of self-attention implementation design
						if output_new_model.shape != output_old_model.shape:
												raise ValueError("""initial and new outputs don't have the same shape"""    )
						if (output_new_model - output_old_model).abs().max().item() > 1E-3:
												raise ValueError("""initial and new outputs are not equal"""    )

						Path(lowerCAmelCase_    ).mkdir(exist_ok=lowerCAmelCase_    )
						model.save_pretrained(lowerCAmelCase_    )



def 	__a					(   lowerCAmelCase_       : Dict      ,lowerCAmelCase_       : Dict      ,lowerCAmelCase_       : List[str]      ,lowerCAmelCase_       : Any      ,lowerCAmelCase_       : str      ,lowerCAmelCase_       : str      ,)				->			Optional[Any]:






						'''simple docstring'''



						UpperCAmelCase_=       os.path.join(lowerCAmelCase_      ,lowerCAmelCase_    )

						UpperCAmelCase_=       BarkSemanticConfig.from_pretrained(os.path.join(lowerCAmelCase_      ,"""config.json"""    )    )
						UpperCAmelCase_=       BarkCoarseConfig.from_pretrained(os.path.join(lowerCAmelCase_      ,"""config.json"""    )    )
						UpperCAmelCase_=       BarkFineConfig.from_pretrained(os.path.join(lowerCAmelCase_      ,"""config.json"""    )    )
						UpperCAmelCase_=       EncodecConfig.from_pretrained("""facebook/encodec_24khz"""    )

						UpperCAmelCase_=       BarkSemanticModel.from_pretrained(lowerCAmelCase_    )
						UpperCAmelCase_=       BarkCoarseModel.from_pretrained(lowerCAmelCase_    )
						UpperCAmelCase_=       BarkFineModel.from_pretrained(lowerCAmelCase_    )
						UpperCAmelCase_=       EncodecModel.from_pretrained("""facebook/encodec_24khz"""    )

						UpperCAmelCase_=       BarkConfig.from_sub_model_configs(
						    lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )

						UpperCAmelCase_=       BarkGenerationConfig.from_sub_model_configs(
						    semantic.generation_config      ,coarseAcoustic.generation_config      ,fineAcoustic.generation_config    )

						UpperCAmelCase_=       BarkModel(lowerCAmelCase_    )

						UpperCAmelCase_=       semantic
						UpperCAmelCase_=       coarseAcoustic
						UpperCAmelCase_=       fineAcoustic
						UpperCAmelCase_=       codec

						UpperCAmelCase_=       bark_generation_config

						Path(lowerCAmelCase_    ).mkdir(exist_ok=lowerCAmelCase_    )
						bark.save_pretrained(lowerCAmelCase_      ,repo_id=lowerCAmelCase_      ,push_to_hub=lowerCAmelCase_    )


if __name__ == "__main__":
	__A       =						argparse.ArgumentParser()
	# Required parameters

	parser.add_argument('''model_type''', type=str, help='''text, coarse or fine.''')
	parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
	parser.add_argument('''--is_small''', action='''store_true''', help='''convert the small version instead of the large.''')

	__A       =						parser.parse_args()

	load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)









from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	def __init__(     self     :						Dict						,  __UpperCAmelCase     :						Tuple						,  __UpperCAmelCase     :						List[Any]					)      ->			List[str]:
							super().__init__()

							# make sure scheduler can always be converted to DDIM
							UpperCAmelCase_=       DDIMScheduler.from_config(scheduler.config					)

							self.register_modules(unet=__UpperCAmelCase						,  scheduler=__UpperCAmelCase					)




	@torch.no_grad()
	def __call__(     self     :						Union[str, Any]						,  __UpperCAmelCase     :						int = 1						,  __UpperCAmelCase     :						Optional[Union[torch.Generator, List[torch.Generator]]] = None						,  __UpperCAmelCase     :						float = 0.0						,  __UpperCAmelCase     :						int = 50						,  __UpperCAmelCase     :						Optional[bool] = None						,  __UpperCAmelCase     :						Optional[str] = "pil"						,  __UpperCAmelCase     :						bool = True						,  )      ->			Union[ImagePipelineOutput, Tuple]:

							# Sample gaussian noise to begin loop
							if isinstance(self.unet.config.sample_size						,  __UpperCAmelCase					):
													UpperCAmelCase_=       (
													    batch_size,
													    self.unet.config.in_channels,
													    self.unet.config.sample_size,
													    self.unet.config.sample_size,
													)
							else:
													UpperCAmelCase_=       (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

							if isinstance(__UpperCAmelCase						,  __UpperCAmelCase					) and len(__UpperCAmelCase					) != batch_size:
													raise ValueError(
													    F"""You have passed a list of generators of length {len(__UpperCAmelCase					)}, but requested an effective batch"""
													    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""					)

							UpperCAmelCase_=       randn_tensor(__UpperCAmelCase						,  generator=__UpperCAmelCase						,  device=self.device						,  dtype=self.unet.dtype					)

							# set step values
							self.scheduler.set_timesteps(__UpperCAmelCase					)

							for t in self.progress_bar(self.scheduler.timesteps					):
													# 1. predict noise model_output
													UpperCAmelCase_=       self.unet(__UpperCAmelCase						,  __UpperCAmelCase					).sample

													# 2. predict previous mean of image x_t-1 and add variance depending on eta
													# eta corresponds to η in paper and should be between [0, 1]
													# do x_t -> x_t-1
													UpperCAmelCase_=       self.scheduler.step(
													    __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  eta=__UpperCAmelCase						,  use_clipped_model_output=__UpperCAmelCase						,  generator=__UpperCAmelCase					).prev_sample

							UpperCAmelCase_=       (image / 2 + 0.5).clamp(0						,  1					)
							UpperCAmelCase_=       image.cpu().permute(0						,  2						,  3						,  1					).numpy()
							if output_type == "pil":
													UpperCAmelCase_=       self.numpy_to_pil(__UpperCAmelCase					)

							if not return_dict:
													return (image,)

							return ImagePipelineOutput(images=__UpperCAmelCase					)




