



'''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     :    List[str]      						=					logging.get_logger(__name__)

set_seed(7_7_0)


a     :    List[Any]      						=					{
    """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     :    Dict      						=					{
    """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     :    str      						=					os.path.dirname(os.path.abspath(__file__))
a     :    Tuple      						=					os.path.join(os.path.expanduser("""~"""), """.cache""")
a     :    int      						=					os.path.join(os.getenv("""XDG_CACHE_HOME""", default_cache_dir), """suno""", """bark_v0""")



def    __lowerCamelCase						(      _lowercase			,       _lowercase=False	)   ->    List[Any]:
							UpperCAmelCase  :	int							  =	model_type
							if use_small:
														key += "_small"
							return os.path.join(_lowercase			,       REMOTE_MODEL_PATHS[key]["""file_name"""]	)



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    Optional[int]:
							os.makedirs(_lowercase			,       exist_ok=_lowercase	)
							hf_hub_download(repo_id=_lowercase			,       filename=_lowercase			,       local_dir=_lowercase	)



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase=False			,       _lowercase="text"	)   ->    Optional[Any]:
							if model_type == "text":
														UpperCAmelCase  :	int							  =	BarkSemanticModel
														UpperCAmelCase  :	Optional[Any]							  =	BarkSemanticConfig
														UpperCAmelCase  :	Union[str, Any]							  =	BarkSemanticGenerationConfig
							elif model_type == "coarse":
														UpperCAmelCase  :	List[str]							  =	BarkCoarseModel
														UpperCAmelCase  :	int							  =	BarkCoarseConfig
														UpperCAmelCase  :	Union[str, Any]							  =	BarkCoarseGenerationConfig
							elif model_type == "fine":
														UpperCAmelCase  :	str							  =	BarkFineModel
														UpperCAmelCase  :	List[str]							  =	BarkFineConfig
														UpperCAmelCase  :	str							  =	BarkFineGenerationConfig
							else:
														raise NotImplementedError()
							UpperCAmelCase  :	Union[str, Any]							  =	F'''{model_type}_small''' if use_small else model_type
							UpperCAmelCase  :	int							  =	REMOTE_MODEL_PATHS[model_key]
							if not os.path.exists(_lowercase	):
														logger.info(F'''{model_type} model not found, downloading into `{CACHE_DIR}`.'''	)
														_download(model_info["""repo_id"""]			,       model_info["""file_name"""]	)
							UpperCAmelCase  :	Dict							  =	torch.load(_lowercase			,       map_location=_lowercase	)
							# this is a hack
							UpperCAmelCase  :	int							  =	checkpoint["""model_args"""]
							if "input_vocab_size" not in model_args:
														UpperCAmelCase  :	Optional[int]							  =	model_args["""vocab_size"""]
														UpperCAmelCase  :	Optional[Any]							  =	model_args["""vocab_size"""]
														del model_args["vocab_size"]

							# convert Bark model arguments to HF Bark model arguments
							UpperCAmelCase  :	str							  =	model_args.pop("""n_head"""	)
							UpperCAmelCase  :	Optional[Any]							  =	model_args.pop("""n_embd"""	)
							UpperCAmelCase  :	Optional[int]							  =	model_args.pop("""n_layer"""	)

							UpperCAmelCase  :	str							  =	ConfigClass(**checkpoint["""model_args"""]	)
							UpperCAmelCase  :	List[str]							  =	ModelClass(config=_lowercase	)
							UpperCAmelCase  :	Optional[Any]							  =	GenerationConfigClass()

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

																					UpperCAmelCase  :	Optional[Any]							  =	state_dict.pop(_lowercase	)

							UpperCAmelCase  :	List[str]							  =	set(state_dict.keys()	) - set(model.state_dict().keys()	)
							UpperCAmelCase  :	Any							  =	{k for k in extra_keys if not k.endswith(""".attn.bias"""	)}
							UpperCAmelCase  :	Dict							  =	set(model.state_dict().keys()	) - set(state_dict.keys()	)
							UpperCAmelCase  :	Optional[int]							  =	{k for k in missing_keys if not k.endswith(""".attn.bias"""	)}
							if len(_lowercase	) != 0:
														raise ValueError(F'''extra keys found: {extra_keys}'''	)
							if len(_lowercase	) != 0:
														raise ValueError(F'''missing keys: {missing_keys}'''	)
							model.load_state_dict(_lowercase			,       strict=_lowercase	)
							UpperCAmelCase  :	str							  =	model.num_parameters(exclude_embeddings=_lowercase	)
							UpperCAmelCase  :	Union[str, Any]							  =	checkpoint["""best_val_loss"""].item()
							logger.info(F'''model loaded: {round(n_params/1e6			,       1	)}M params, {round(_lowercase			,       3	)} loss'''	)
							model.eval()
							model.to(_lowercase	)
							del checkpoint, state_dict

							return model



def    __lowerCamelCase						(      _lowercase			,       _lowercase=False			,       _lowercase="text"	)   ->    Optional[int]:
							if model_type not in ("text", "coarse", "fine"):
														raise NotImplementedError()

							UpperCAmelCase  :	List[str]							  =	"""cpu"""  # do conversion on cpu

							UpperCAmelCase  :	int							  =	_get_ckpt_path(_lowercase			,       use_small=_lowercase	)
							UpperCAmelCase  :	List[str]							  =	_load_model(_lowercase			,       _lowercase			,       model_type=_lowercase			,       use_small=_lowercase	)

							# load bark initial model
							UpperCAmelCase  :	Union[str, Any]							  =	_bark_load_model(_lowercase			,       """cpu"""			,       model_type=_lowercase			,       use_small=_lowercase	)

							if model_type == "text":
														UpperCAmelCase  :	Tuple							  =	bark_model["""model"""]

							if model.num_parameters(exclude_embeddings=_lowercase	) != 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  :	Dict							  =	5
							UpperCAmelCase  :	Tuple							  =	1_0

							if model_type in ["text", "coarse"]:
														UpperCAmelCase  :	Dict							  =	torch.randint(2_5_6			,       (batch_size, sequence_length)			,       dtype=torch.int	)
														UpperCAmelCase  :	List[Any]							  =	bark_model(_lowercase	)[0]

														UpperCAmelCase  :	List[Any]							  =	model(_lowercase	)

														# take last logits
														UpperCAmelCase  :	List[Any]							  =	output_new_model_total.logits[:, [-1], :]

							else:
														UpperCAmelCase  :	Optional[int]							  =	3
														UpperCAmelCase  :	Optional[Any]							  =	8
														UpperCAmelCase  :	List[str]							  =	torch.randint(2_5_6			,       (batch_size, sequence_length, n_codes_total)			,       dtype=torch.int	)

														UpperCAmelCase  :	str							  =	model(_lowercase			,       _lowercase	)
														UpperCAmelCase  :	int							  =	bark_model(_lowercase			,       _lowercase	)

														UpperCAmelCase  :	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(_lowercase	).mkdir(exist_ok=_lowercase	)
							model.save_pretrained(_lowercase	)



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       )   ->    str:
							UpperCAmelCase  :	Optional[Any]							  =	os.path.join(_lowercase			,       _lowercase	)

							UpperCAmelCase  :	List[Any]							  =	BarkSemanticConfig.from_pretrained(os.path.join(_lowercase			,       """config.json"""	)	)
							UpperCAmelCase  :	Tuple							  =	BarkCoarseConfig.from_pretrained(os.path.join(_lowercase			,       """config.json"""	)	)
							UpperCAmelCase  :	Optional[Any]							  =	BarkFineConfig.from_pretrained(os.path.join(_lowercase			,       """config.json"""	)	)
							UpperCAmelCase  :	List[str]							  =	EncodecConfig.from_pretrained("""facebook/encodec_24khz"""	)

							UpperCAmelCase  :	Tuple							  =	BarkSemanticModel.from_pretrained(_lowercase	)
							UpperCAmelCase  :	Dict							  =	BarkCoarseModel.from_pretrained(_lowercase	)
							UpperCAmelCase  :	Union[str, Any]							  =	BarkFineModel.from_pretrained(_lowercase	)
							UpperCAmelCase  :	Optional[int]							  =	EncodecModel.from_pretrained("""facebook/encodec_24khz"""	)

							UpperCAmelCase  :	Any							  =	BarkConfig.from_sub_model_configs(
							    _lowercase			,       _lowercase			,       _lowercase			,       _lowercase	)

							UpperCAmelCase  :	Union[str, Any]							  =	BarkGenerationConfig.from_sub_model_configs(
							    semantic.generation_config			,       coarseAcoustic.generation_config			,       fineAcoustic.generation_config	)

							UpperCAmelCase  :	Optional[int]							  =	BarkModel(_lowercase	)

							UpperCAmelCase  :	Dict							  =	semantic
							UpperCAmelCase  :	Union[str, Any]							  =	coarseAcoustic
							UpperCAmelCase  :	Optional[Any]							  =	fineAcoustic
							UpperCAmelCase  :	Dict							  =	codec

							UpperCAmelCase  :	Dict							  =	bark_generation_config

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


if __name__ == "__main__":
							a     :    List[str]      						=					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     :    Dict      						=					parser.parse_args()

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






'''simple docstring'''




from datetime import datetime as dt
import os

from github import Github


a     :    int      						=					[
    """good first issue""",
    """good second issue""",
    """good difficult issue""",
    """feature request""",
    """new model""",
    """wip""",
]



def    __lowerCamelCase						(      )   ->    Dict:
							UpperCAmelCase  :	str							  =	Github(os.environ["""GITHUB_TOKEN"""]	)
							UpperCAmelCase  :	Dict							  =	g.get_repo("""huggingface/transformers"""	)
							UpperCAmelCase  :	int							  =	repo.get_issues(state="""open"""	)

							for issue in open_issues:
														UpperCAmelCase  :	Optional[int]							  =	sorted([comment for comment in issue.get_comments()]			,       key=lambda _lowercase	: i.created_at			,       reverse=_lowercase	)
														UpperCAmelCase  :	Any							  =	comments[0] if len(_lowercase	) > 0 else None
														if (
														    last_comment is not None
														    and last_comment.user.login == "github-actions[bot]"
														    and (dt.utcnow() - issue.updated_at).days > 7
														    and (dt.utcnow() - issue.created_at).days >= 3_0
														    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()	)
														):
																					# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
																					issue.edit(state="""closed"""	)
														elif (
														    (dt.utcnow() - issue.updated_at).days > 2_3
														    and (dt.utcnow() - issue.created_at).days >= 3_0
														    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()	)
														):
																					# print(f"Would add stale comment to {issue.number}")
																					issue.create_comment(
																					    """This issue has been automatically marked as stale because it has not had """
																					    """recent activity. If you think this still needs to be addressed """
																					    """please comment on this thread.\n\nPlease note that issues that do not follow the """
																					    """[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) """
																					    """are likely to be ignored."""	)


if __name__ == "__main__":
							main()

