






"""simple docstring"""



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__	:    Tuple			 = logging.get_logger(__name__)

set_seed(770)


A__	:    Optional[int]			 = {
    '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__	:    Optional[Any]			 = {
    '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__	:    Dict			 = os.path.dirname(os.path.abspath(__file__))
A__	:    Optional[Any]			 = os.path.join(os.path.expanduser('~'), '.cache')
A__	:    Optional[int]			 = os.path.join(os.getenv('XDG_CACHE_HOME', default_cache_dir), 'suno', 'bark_v0')

def 			_snake_case    (					lowerCamelCase__     :   Dict    ,						lowerCamelCase__     :   Tuple=False  )    ->     Optional[int]:
							lowerCamelCase_	:							int									=model_type
							if use_small:
														key += "_small"
							return os.path.join(lowerCAmelCase_    ,						REMOTE_MODEL_PATHS[key]["file_name"]  )

def 			_snake_case    (					lowerCamelCase__     :   Tuple    ,						lowerCamelCase__     :   int  )    ->     int:
							os.makedirs(lowerCAmelCase_    ,						exist_ok=lowerCAmelCase_  )
							hf_hub_download(repo_id=lowerCAmelCase_    ,						filename=lowerCAmelCase_    ,						local_dir=lowerCAmelCase_  )

def 			_snake_case    (					lowerCamelCase__     :   Optional[Any]    ,						lowerCamelCase__     :   Any    ,						lowerCamelCase__     :   Any=False    ,						lowerCamelCase__     :   Dict="text"  )    ->     List[str]:
							if model_type == "text":
														lowerCamelCase_	:							List[str]									=BarkSemanticModel
														lowerCamelCase_	:							Optional[Any]									=BarkSemanticConfig
														lowerCamelCase_	:							Any									=BarkSemanticGenerationConfig
							elif model_type == "coarse":
														lowerCamelCase_	:							Tuple									=BarkCoarseModel
														lowerCamelCase_	:							str									=BarkCoarseConfig
														lowerCamelCase_	:							str									=BarkCoarseGenerationConfig
							elif model_type == "fine":
														lowerCamelCase_	:							List[str]									=BarkFineModel
														lowerCamelCase_	:							Optional[Any]									=BarkFineConfig
														lowerCamelCase_	:							str									=BarkFineGenerationConfig
							else:
														raise NotImplementedError()
							lowerCamelCase_	:							Dict									=F"""{model_type}_small""" if use_small else model_type
							lowerCamelCase_	:							Union[str, Any]									=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"]  )
							lowerCamelCase_	:							int									=torch.load(lowerCAmelCase_    ,						map_location=lowerCAmelCase_  )
							# this is a hack
							lowerCamelCase_	:							List[Any]									=checkpoint['model_args']
							if "input_vocab_size" not in model_args:
														lowerCamelCase_	:							Dict									=model_args['vocab_size']
														lowerCamelCase_	:							Dict									=model_args['vocab_size']
														del model_args["vocab_size"]

							# convert Bark model arguments to HF Bark model arguments
							lowerCamelCase_	:							List[Any]									=model_args.pop("n_head"  )
							lowerCamelCase_	:							Any									=model_args.pop("n_embd"  )
							lowerCamelCase_	:							List[Any]									=model_args.pop("n_layer"  )

							lowerCamelCase_	:							Optional[int]									=ConfigClass(**checkpoint["model_args"]  )
							lowerCamelCase_	:							List[str]									=ModelClass(config=lowerCAmelCase_  )
							lowerCamelCase_	:							Tuple									=GenerationConfigClass()

							lowerCamelCase_	:							Optional[Any]									=model_generation_config
							lowerCamelCase_	:							Optional[Any]									=checkpoint['model']
							# fixup checkpoint
							lowerCamelCase_	:							int									='_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
																					lowerCamelCase_	:							str									=k[len(lowerCAmelCase_  ) :]
																					for old_layer_name in new_layer_name_dict:
																												lowerCamelCase_	:							List[Any]									=new_k.replace(lowerCAmelCase_    ,						new_layer_name_dict[old_layer_name]  )

																					lowerCamelCase_	:							List[Any]									=state_dict.pop(lowerCAmelCase_  )

							lowerCamelCase_	:							List[Any]									=set(state_dict.keys()  ) - set(model.state_dict().keys()  )
							lowerCamelCase_	:							Tuple									={k for k in extra_keys if not k.endswith(".attn.bias"  )}
							lowerCamelCase_	:							Tuple									=set(model.state_dict().keys()  ) - set(state_dict.keys()  )
							lowerCamelCase_	:							Optional[Any]									={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_  )
							lowerCamelCase_	:							Dict									=model.num_parameters(exclude_embeddings=lowerCAmelCase_  )
							lowerCamelCase_	:							Tuple									=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 			_snake_case    (					lowerCamelCase__     :   List[str]    ,						lowerCamelCase__     :   Any=False    ,						lowerCamelCase__     :   Optional[Any]="text"  )    ->     List[Any]:
							if model_type not in ("text", "coarse", "fine"):
														raise NotImplementedError()

							lowerCamelCase_	:							Optional[int]									='cpu'  # do conversion on cpu

							lowerCamelCase_	:							Tuple									=_get_ckpt_path(lowerCAmelCase_    ,						use_small=lowerCAmelCase_  )
							lowerCamelCase_	:							List[Any]									=_load_model(lowerCAmelCase_    ,						lowerCAmelCase_    ,						model_type=lowerCAmelCase_    ,						use_small=lowerCAmelCase_  )

							# load bark initial model
							lowerCamelCase_	:							Any									=_bark_load_model(lowerCAmelCase_    ,						"cpu"    ,						model_type=lowerCAmelCase_    ,						use_small=lowerCAmelCase_  )

							if model_type == "text":
														lowerCamelCase_	:							int									=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
							lowerCamelCase_	:							Any									=5
							lowerCamelCase_	:							List[str]									=10

							if model_type in ["text", "coarse"]:
														lowerCamelCase_	:							Dict									=torch.randint(256    ,						(batch_size, sequence_length)    ,						dtype=torch.int  )
														lowerCamelCase_	:							Dict									=bark_model(lowerCAmelCase_  )[0]

														lowerCamelCase_	:							Tuple									=model(lowerCAmelCase_  )

														# take last logits
														lowerCamelCase_	:							Optional[int]									=output_new_model_total.logits[:, [-1], :]

							else:
														lowerCamelCase_	:							List[str]									=3
														lowerCamelCase_	:							List[Any]									=8
														lowerCamelCase_	:							Tuple									=torch.randint(256    ,						(batch_size, sequence_length, n_codes_total)    ,						dtype=torch.int  )

														lowerCamelCase_	:							Union[str, Any]									=model(lowerCAmelCase_    ,						lowerCAmelCase_  )
														lowerCamelCase_	:							int									=bark_model(lowerCAmelCase_    ,						lowerCAmelCase_  )

														lowerCamelCase_	:							List[str]									=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 			_snake_case    (					lowerCamelCase__     :   List[str]    ,						lowerCamelCase__     :   List[str]    ,						lowerCamelCase__     :   Optional[Any]    ,						lowerCamelCase__     :   Union[str, Any]    ,						lowerCamelCase__     :   int    ,						lowerCamelCase__     :   str    ,						)    ->     Any:
							lowerCamelCase_	:							Any									=os.path.join(lowerCAmelCase_    ,						lowerCAmelCase_  )

							lowerCamelCase_	:							int									=BarkSemanticConfig.from_pretrained(os.path.join(lowerCAmelCase_    ,						"config.json"  )  )
							lowerCamelCase_	:							Any									=BarkCoarseConfig.from_pretrained(os.path.join(lowerCAmelCase_    ,						"config.json"  )  )
							lowerCamelCase_	:							List[Any]									=BarkFineConfig.from_pretrained(os.path.join(lowerCAmelCase_    ,						"config.json"  )  )
							lowerCamelCase_	:							List[str]									=EncodecConfig.from_pretrained("facebook/encodec_24khz"  )

							lowerCamelCase_	:							str									=BarkSemanticModel.from_pretrained(lowerCAmelCase_  )
							lowerCamelCase_	:							Dict									=BarkCoarseModel.from_pretrained(lowerCAmelCase_  )
							lowerCamelCase_	:							int									=BarkFineModel.from_pretrained(lowerCAmelCase_  )
							lowerCamelCase_	:							List[Any]									=EncodecModel.from_pretrained("facebook/encodec_24khz"  )

							lowerCamelCase_	:							Any									=BarkConfig.from_sub_model_configs(
							    lowerCAmelCase_    ,						lowerCAmelCase_    ,						lowerCAmelCase_    ,						lowerCAmelCase_  )

							lowerCamelCase_	:							List[str]									=BarkGenerationConfig.from_sub_model_configs(
							    semantic.generation_config    ,						coarseAcoustic.generation_config    ,						fineAcoustic.generation_config  )

							lowerCamelCase_	:							Optional[Any]									=BarkModel(lowerCAmelCase_  )

							lowerCamelCase_	:							List[str]									=semantic
							lowerCamelCase_	:							Union[str, Any]									=coarseAcoustic
							lowerCamelCase_	:							Optional[int]									=fineAcoustic
							lowerCamelCase_	:							Optional[Any]									=codec

							lowerCamelCase_	:							List[Any]									=bark_generation_config

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


if __name__ == "__main__":
	A__	:    Optional[Any]			 = 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__	:    Optional[Any]			 = parser.parse_args()

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











"""simple docstring"""



import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


A__	:    int			 = {
    'n_samples': 64,
    'horizon': 32,
    'num_inference_steps': 20,
    'n_guide_steps': 2,  # can set to 0 for faster sampling, does not use value network
    'scale_grad_by_std': True,
    'scale': 0.1,
    'eta': 0.0,
    't_grad_cutoff': 2,
    'device': 'cpu',
}


if __name__ == "__main__":
						A__	:    str			 = 'hopper-medium-v2'
						A__	:    Dict			 = gym.make(env_name)

						A__	:    List[Any]			 = ValueGuidedRLPipeline.from_pretrained(
						    'bglick13/hopper-medium-v2-value-function-hor32',
						    env=env,
						)

						env.seed(0)
						A__	:    Dict			 = env.reset()
						A__	:    Optional[int]			 = 0
						A__	:    str			 = 0
						A__	:    List[Any]			 = 1_000
						A__	:    Tuple			 = [obs.copy()]
						try:
												for t in tqdm.tqdm(range(T)):
																		# call the policy
																		A__	:    Union[str, Any]			 = pipeline(obs, planning_horizon=32)

																		# execute action in environment
																		A__ ,						A__ ,						A__ ,						A__	:    Any			 = env.step(denorm_actions)
																		A__	:    List[Any]			 = env.get_normalized_score(total_reward)

																		# update return
																		total_reward += reward
																		total_score += score
																		print(
																		    f'Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'
																		    f' {total_score}'
																		)

																		# save observations for rendering
																		rollout.append(next_observation.copy())

																		A__	:    Optional[Any]			 = next_observation
						except KeyboardInterrupt:
												pass

						print(f'Total reward: {total_reward}')



