
import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


lowerCamelCase_  :	Any 				=					logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()







def     A__   (  lowerCamelCase  )					->		Any:
				if "token" in model_name_or_path:
								return "rag_token"
				if "sequence" in model_name_or_path:
								return "rag_sequence"
				if "bart" in model_name_or_path:
								return "bart"
				return None







def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		str:
				return max(metric_fn(lowerCamelCase       ,      lowerCamelCase  ) for gt in ground_truths  )







def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		int:
				UpperCamelCase_:							str		=  [line.strip() for line in open(lowerCamelCase       ,      """r"""  ).readlines()]
				UpperCamelCase_:							Optional[int]		=  []

				if args.gold_data_mode == "qa":
								UpperCamelCase_:							Tuple		=  pd.read_csv(lowerCamelCase       ,      sep="""\t"""       ,      header=lowerCamelCase  )
								for answer_list in data[1]:
												UpperCamelCase_:							Dict		=  ast.literal_eval(lowerCamelCase  )
												answers.append(lowerCamelCase  )
				else:
								UpperCamelCase_:							str		=  [line.strip() for line in open(lowerCamelCase       ,      """r"""  ).readlines()]
								UpperCamelCase_:							List[Any]		=  [[reference] for reference in references]

				UpperCamelCase_:							Tuple		=  0
				for prediction, ground_truths in zip(lowerCamelCase       ,      lowerCamelCase  ):
								total += 1
								em += metric_max_over_ground_truths(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )
								fa += metric_max_over_ground_truths(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )

				UpperCamelCase_:							Any		=  100.0 * em / total
				UpperCamelCase_:							str		=  100.0 * fa / total

				logger.info(F'''F1: {fa:.2f}'''  )
				logger.info(F'''EM: {em:.2f}'''  )







def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		str:
				UpperCamelCase_:							int		=  args.k
				UpperCamelCase_:							List[str]		=  [line.strip() for line in open(lowerCamelCase       ,      """r"""  ).readlines()]
				UpperCamelCase_:							Tuple		=  [line.strip() for line in open(lowerCamelCase       ,      """r"""  ).readlines()]

				UpperCamelCase_:							str		=  0
				for hypo, reference in zip(lowerCamelCase       ,      lowerCamelCase  ):
								UpperCamelCase_:							Any		=  set(hypo.split("""\t"""  )[:k]  )
								UpperCamelCase_:							List[str]		=  set(reference.split("""\t"""  )  )
								total += 1
								em += len(hypo_provenance & ref_provenance  ) / k

				UpperCamelCase_:							Optional[Any]		=  100.0 * em / total
				logger.info(F'''Precision@{k}: {em: .2f}'''  )







def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Tuple:
				def strip_title(lowerCamelCase  ):
								if title.startswith("""\""""  ):
												UpperCamelCase_:							Union[str, Any]		=  title[1:]
								if title.endswith("""\""""  ):
												UpperCamelCase_:							Union[str, Any]		=  title[:-1]
								return title

				UpperCamelCase_:							Dict		=  rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
				    lowerCamelCase       ,      return_tensors="""pt"""       ,      padding=lowerCamelCase       ,      truncation=lowerCamelCase       ,      )["""input_ids"""].to(args.device  )

				UpperCamelCase_:							Any		=  rag_model.rag.question_encoder(lowerCamelCase  )
				UpperCamelCase_:							str		=  question_enc_outputs[0]

				UpperCamelCase_:							int		=  rag_model.retriever(
				    lowerCamelCase       ,      question_enc_pool_output.cpu().detach().to(torch.floataa  ).numpy()       ,      prefix=rag_model.rag.generator.config.prefix       ,      n_docs=rag_model.config.n_docs       ,      return_tensors="""pt"""       ,      )
				UpperCamelCase_:							List[str]		=  rag_model.retriever.index.get_doc_dicts(result.doc_ids  )
				UpperCamelCase_:							int		=  []
				for docs in all_docs:
								UpperCamelCase_:							Optional[Any]		=  [strip_title(lowerCamelCase  ) for title in docs["""title"""]]
								provenance_strings.append("""\t""".join(lowerCamelCase  )  )
				return provenance_strings







def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Optional[Any]:
				with torch.no_grad():
								UpperCamelCase_:							Optional[Any]		=  rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
								    lowerCamelCase       ,      return_tensors="""pt"""       ,      padding=lowerCamelCase       ,      truncation=lowerCamelCase  )

								UpperCamelCase_:							int		=  inputs_dict.input_ids.to(args.device  )
								UpperCamelCase_:							List[Any]		=  inputs_dict.attention_mask.to(args.device  )
								UpperCamelCase_:							Tuple		=  rag_model.generate(  # rag_model overwrites generate
								    lowerCamelCase       ,      attention_mask=lowerCamelCase       ,      num_beams=args.num_beams       ,      min_length=args.min_length       ,      max_length=args.max_length       ,      early_stopping=lowerCamelCase       ,      num_return_sequences=1       ,      bad_words_ids=[[0, 0]]       ,      )
								UpperCamelCase_:							Any		=  rag_model.retriever.generator_tokenizer.batch_decode(lowerCamelCase       ,      skip_special_tokens=lowerCamelCase  )

								if args.print_predictions:
												for q, a in zip(lowerCamelCase       ,      lowerCamelCase  ):
																logger.info("""Q: {} - A: {}""".format(lowerCamelCase       ,      lowerCamelCase  )  )

								return answers







def     A__   (  )					->		Any:
				UpperCamelCase_:							Any		=  argparse.ArgumentParser()
				parser.add_argument(
				    """--model_type"""       ,      choices=["""rag_sequence""", """rag_token""", """bart"""]       ,      type=lowerCamelCase       ,      help=(
				        """RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"""
				        """ model_name_or_path"""
				    )       ,      )
				parser.add_argument(
				    """--index_name"""       ,      default=lowerCamelCase       ,      choices=["""exact""", """compressed""", """legacy"""]       ,      type=lowerCamelCase       ,      help="""RAG model retriever type"""       ,      )
				parser.add_argument(
				    """--index_path"""       ,      default=lowerCamelCase       ,      type=lowerCamelCase       ,      help="""Path to the retrieval index"""       ,      )
				parser.add_argument("""--n_docs"""       ,      default=5       ,      type=lowerCamelCase       ,      help="""Number of retrieved docs"""  )
				parser.add_argument(
				    """--model_name_or_path"""       ,      default=lowerCamelCase       ,      type=lowerCamelCase       ,      required=lowerCamelCase       ,      help="""Path to pretrained checkpoints or model identifier from huggingface.co/models"""       ,      )
				parser.add_argument(
				    """--eval_mode"""       ,      choices=["""e2e""", """retrieval"""]       ,      default="""e2e"""       ,      type=lowerCamelCase       ,      help=(
				        """Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"""
				        """ precision@k."""
				    )       ,      )
				parser.add_argument("""--k"""       ,      default=1       ,      type=lowerCamelCase       ,      help="""k for the precision@k calculation"""  )
				parser.add_argument(
				    """--evaluation_set"""       ,      default=lowerCamelCase       ,      type=lowerCamelCase       ,      required=lowerCamelCase       ,      help="""Path to a file containing evaluation samples"""       ,      )
				parser.add_argument(
				    """--gold_data_path"""       ,      default=lowerCamelCase       ,      type=lowerCamelCase       ,      required=lowerCamelCase       ,      help="""Path to a tab-separated file with gold samples"""       ,      )
				parser.add_argument(
				    """--gold_data_mode"""       ,      default="""qa"""       ,      type=lowerCamelCase       ,      choices=["""qa""", """ans"""]       ,      help=(
				        """Format of the gold data file"""
				        """qa - a single line in the following format: question [tab] answer_list"""
				        """ans - a single line of the gold file contains the expected answer string"""
				    )       ,      )
				parser.add_argument(
				    """--predictions_path"""       ,      type=lowerCamelCase       ,      default="""predictions.txt"""       ,      help="""Name of the predictions file, to be stored in the checkpoints directory"""       ,      )
				parser.add_argument(
				    """--eval_all_checkpoints"""       ,      action="""store_true"""       ,      help="""Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number"""       ,      )
				parser.add_argument(
				    """--eval_batch_size"""       ,      default=8       ,      type=lowerCamelCase       ,      help="""Batch size per GPU/CPU for evaluation."""       ,      )
				parser.add_argument(
				    """--recalculate"""       ,      help="""Recalculate predictions even if the prediction file exists"""       ,      action="""store_true"""       ,      )
				parser.add_argument(
				    """--num_beams"""       ,      default=4       ,      type=lowerCamelCase       ,      help="""Number of beams to be used when generating answers"""       ,      )
				parser.add_argument("""--min_length"""       ,      default=1       ,      type=lowerCamelCase       ,      help="""Min length of the generated answers"""  )
				parser.add_argument("""--max_length"""       ,      default=50       ,      type=lowerCamelCase       ,      help="""Max length of the generated answers"""  )

				parser.add_argument(
				    """--print_predictions"""       ,      action="""store_true"""       ,      help="""If True, prints predictions while evaluating."""       ,      )
				parser.add_argument(
				    """--print_docs"""       ,      action="""store_true"""       ,      help="""If True, prints docs retried while generating."""       ,      )
				UpperCamelCase_:							str		=  parser.parse_args()
				UpperCamelCase_:							str		=  torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""  )
				return args







def     A__   (  lowerCamelCase  )					->		str:
				UpperCamelCase_:							List[Any]		=  {}
				if args.model_type is None:
								UpperCamelCase_:							str		=  infer_model_type(args.model_name_or_path  )
								assert args.model_type is not None
				if args.model_type.startswith("""rag"""  ):
								UpperCamelCase_:							Optional[Any]		=  RagTokenForGeneration if args.model_type == """rag_token""" else RagSequenceForGeneration
								UpperCamelCase_:							Tuple		=  args.n_docs
								if args.index_name is not None:
												UpperCamelCase_:							Optional[int]		=  args.index_name
								if args.index_path is not None:
												UpperCamelCase_:							List[str]		=  args.index_path
				else:
								UpperCamelCase_:							Dict		=  BartForConditionalGeneration

				UpperCamelCase_:							Optional[int]		=  (
				    [f.path for f in os.scandir(args.model_name_or_path  ) if f.is_dir()]
				    if args.eval_all_checkpoints
				    else [args.model_name_or_path]
				)

				logger.info("""Evaluate the following checkpoints: %s"""       ,      lowerCamelCase  )

				UpperCamelCase_:							Dict		=  get_scores if args.eval_mode == """e2e""" else get_precision_at_k
				UpperCamelCase_:							Optional[int]		=  evaluate_batch_eae if args.eval_mode == """e2e""" else evaluate_batch_retrieval

				for checkpoint in checkpoints:
								if os.path.exists(args.predictions_path  ) and (not args.recalculate):
												logger.info("""Calculating metrics based on an existing predictions file: {}""".format(args.predictions_path  )  )
												score_fn(lowerCamelCase       ,      args.predictions_path       ,      args.gold_data_path  )
												continue

								logger.info("""***** Running evaluation for {} *****""".format(lowerCamelCase  )  )
								logger.info("""  Batch size = %d"""       ,      args.eval_batch_size  )
								logger.info("""  Predictions will be stored under {}""".format(args.predictions_path  )  )

								if args.model_type.startswith("""rag"""  ):
												UpperCamelCase_:							Dict		=  RagRetriever.from_pretrained(lowerCamelCase       ,      **lowerCamelCase  )
												UpperCamelCase_:							Optional[int]		=  model_class.from_pretrained(lowerCamelCase       ,      retriever=lowerCamelCase       ,      **lowerCamelCase  )
												model.retriever.init_retrieval()
								else:
												UpperCamelCase_:							Optional[int]		=  model_class.from_pretrained(lowerCamelCase       ,      **lowerCamelCase  )
								model.to(args.device  )

								with open(args.evaluation_set       ,      """r"""  ) as eval_file, open(args.predictions_path       ,      """w"""  ) as preds_file:
												UpperCamelCase_:							Dict		=  []
												for line in tqdm(lowerCamelCase  ):
																questions.append(line.strip()  )
																if len(lowerCamelCase  ) == args.eval_batch_size:
																				UpperCamelCase_:							str		=  evaluate_batch_fn(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )
																				preds_file.write("""\n""".join(lowerCamelCase  ) + """\n"""  )
																				preds_file.flush()
																				UpperCamelCase_:							Dict		=  []
												if len(lowerCamelCase  ) > 0:
																UpperCamelCase_:							Optional[Any]		=  evaluate_batch_fn(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )
																preds_file.write("""\n""".join(lowerCamelCase  )  )
																preds_file.flush()

												score_fn(lowerCamelCase       ,      args.predictions_path       ,      args.gold_data_path  )


if __name__ == "__main__":
							lowerCamelCase_  :	Dict 				=					get_args()
							main(args)








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()
lowerCamelCase_  :	Union[str, Any] 				=					logging.get_logger(__name__)

set_seed(7_70)


lowerCamelCase_  :	str 				=					{
    """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""",
}


lowerCamelCase_  :	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""",
    },
}

lowerCamelCase_  :	str 				=					os.path.dirname(os.path.abspath(__file__))
lowerCamelCase_  :	Any 				=					os.path.join(os.path.expanduser("""~"""), """.cache""")
lowerCamelCase_  :	Dict 				=					os.path.join(os.getenv("""XDG_CACHE_HOME""", default_cache_dir), """suno""", """bark_v0""")







def     A__   (  lowerCamelCase       ,      lowerCamelCase=False  )					->		int:
				UpperCamelCase_:							Union[str, Any]		=  model_type
				if use_small:
								key += "_small"
				return os.path.join(lowerCamelCase       ,      REMOTE_MODEL_PATHS[key]["""file_name"""]  )







def     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		Tuple:
				os.makedirs(lowerCamelCase       ,      exist_ok=lowerCamelCase  )
				hf_hub_download(repo_id=lowerCamelCase       ,      filename=lowerCamelCase       ,      local_dir=lowerCamelCase  )







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

				# convert Bark model arguments to HF Bark model arguments
				UpperCamelCase_:							Tuple		=  model_args.pop("""n_head"""  )
				UpperCamelCase_:							Dict		=  model_args.pop("""n_embd"""  )
				UpperCamelCase_:							List[str]		=  model_args.pop("""n_layer"""  )

				UpperCamelCase_:							Optional[Any]		=  ConfigClass(**checkpoint["""model_args"""]  )
				UpperCamelCase_:							Optional[Any]		=  ModelClass(config=lowerCamelCase  )
				UpperCamelCase_:							List[Any]		=  GenerationConfigClass()

				UpperCamelCase_:							Optional[Any]		=  model_generation_config
				UpperCamelCase_:							Optional[int]		=  checkpoint["""model"""]
				# fixup checkpoint
				UpperCamelCase_:							Dict		=  """_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_:							Optional[int]		=  k[len(lowerCamelCase  ) :]
												for old_layer_name in new_layer_name_dict:
																UpperCamelCase_:							Dict		=  new_k.replace(lowerCamelCase       ,      new_layer_name_dict[old_layer_name]  )

												UpperCamelCase_:							List[str]		=  state_dict.pop(lowerCamelCase  )

				UpperCamelCase_:							Optional[int]		=  set(state_dict.keys()  ) - set(model.state_dict().keys()  )
				UpperCamelCase_:							Dict		=  {k for k in extra_keys if not k.endswith(""".attn.bias"""  )}
				UpperCamelCase_:							Optional[Any]		=  set(model.state_dict().keys()  ) - set(state_dict.keys()  )
				UpperCamelCase_:							Union[str, 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  )
				UpperCamelCase_:							str		=  model.num_parameters(exclude_embeddings=lowerCamelCase  )
				UpperCamelCase_:							int		=  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       ,      lowerCamelCase=False       ,      lowerCamelCase="text"  )					->		Any:
				if model_type not in ("text", "coarse", "fine"):
								raise NotImplementedError()

				UpperCamelCase_:							Union[str, Any]		=  """cpu"""  # do conversion on cpu

				UpperCamelCase_:							int		=  _get_ckpt_path(lowerCamelCase       ,      use_small=lowerCamelCase  )
				UpperCamelCase_:							Dict		=  _load_model(lowerCamelCase       ,      lowerCamelCase       ,      model_type=lowerCamelCase       ,      use_small=lowerCamelCase  )

				# load bark initial model
				UpperCamelCase_:							List[Any]		=  _bark_load_model(lowerCamelCase       ,      """cpu"""       ,      model_type=lowerCamelCase       ,      use_small=lowerCamelCase  )

				if model_type == "text":
								UpperCamelCase_:							Tuple		=  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_:							Optional[Any]		=  5
				UpperCamelCase_:							List[str]		=  10

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

								UpperCamelCase_:							Optional[Any]		=  model(lowerCamelCase  )

								# take last logits
								UpperCamelCase_:							Union[str, Any]		=  output_new_model_total.logits[:, [-1], :]

				else:
								UpperCamelCase_:							Tuple		=  3
								UpperCamelCase_:							List[Any]		=  8
								UpperCamelCase_:							List[str]		=  torch.randint(2_56       ,      (batch_size, sequence_length, n_codes_total)       ,      dtype=torch.int  )

								UpperCamelCase_:							int		=  model(lowerCamelCase       ,      lowerCamelCase  )
								UpperCamelCase_:							Any		=  bark_model(lowerCamelCase       ,      lowerCamelCase  )

								UpperCamelCase_:							Optional[int]		=  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       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      )					->		str:
				UpperCamelCase_:							List[str]		=  os.path.join(lowerCamelCase       ,      lowerCamelCase  )

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

				UpperCamelCase_:							Optional[Any]		=  BarkSemanticModel.from_pretrained(lowerCamelCase  )
				UpperCamelCase_:							Tuple		=  BarkCoarseModel.from_pretrained(lowerCamelCase  )
				UpperCamelCase_:							List[str]		=  BarkFineModel.from_pretrained(lowerCamelCase  )
				UpperCamelCase_:							Tuple		=  EncodecModel.from_pretrained("""facebook/encodec_24khz"""  )

				UpperCamelCase_:							int		=  BarkConfig.from_sub_model_configs(
				    lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )

				UpperCamelCase_:							Optional[int]		=  BarkGenerationConfig.from_sub_model_configs(
				    semantic.generation_config       ,      coarseAcoustic.generation_config       ,      fineAcoustic.generation_config  )

				UpperCamelCase_:							Optional[Any]		=  BarkModel(lowerCamelCase  )

				UpperCamelCase_:							int		=  semantic
				UpperCamelCase_:							Tuple		=  coarseAcoustic
				UpperCamelCase_:							Optional[int]		=  fineAcoustic
				UpperCamelCase_:							Any		=  codec

				UpperCamelCase_:							Dict		=  bark_generation_config

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


if __name__ == "__main__":
							lowerCamelCase_  :	List[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.""")

							lowerCamelCase_  :	Dict 				=					parser.parse_args()

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






