

'''simple docstring'''

import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowercase__         =      logging.getLogger(__name__)




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 return (preds == labels).mean()




@dataclass
class 	A_			:






     '''simple docstring'''



     UpperCAmelCase_						:     str  		=  field(
         metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}							)
     UpperCAmelCase_						:     Optional[str]  		=  field(
         default=_snake_case	,       metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}							)
     UpperCAmelCase_						:     Optional[str]  		=  field(
         default=_snake_case	,       metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}							)
     UpperCAmelCase_						:     Optional[str]  		=  field(
         default=_snake_case	,       metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}	,       )




@dataclass
class 	A_			:






     '''simple docstring'''



     UpperCAmelCase_						:     str  		=  field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys()							)}							)
     UpperCAmelCase_						:     str  		=  field(metadata={"""help""": """Should contain the data files for the task."""}							)
     UpperCAmelCase_						:     int  		=  field(
         default=128	,       metadata={
             """help""": (
                 """The maximum total input sequence length after tokenization. Sequences longer """
                 """than this will be truncated, sequences shorter will be padded."""
             )
         }	,       )
     UpperCAmelCase_						:     bool  		=  field(
         default=_snake_case	,       metadata={"""help""": """Overwrite the cached training and evaluation sets"""}							)




def 				UpperCamelCase(					):
 # See all possible arguments in src/transformers/training_args.py
 # or by passing the --help flag to this script.
 # We now keep distinct sets of args, for a cleaner separation of concerns.

 UpperCAmelCase	:     Dict						 =			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )
 UpperCAmelCase ,			UpperCAmelCase ,			UpperCAmelCase	:     int						 =			parser.parse_args_into_dataclasses()

 if (
     os.path.exists(training_args.output_dir     )
     and os.listdir(training_args.output_dir     )
     and training_args.do_train
     and not training_args.overwrite_output_dir
 ):
  raise ValueError(
      F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
      ' --overwrite_output_dir to overcome.'     )

 # Setup logging
 logging.basicConfig(
     format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'  ,	datefmt='%m/%d/%Y %H:%M:%S'  ,	level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN  ,	)
 logger.warning(
     'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'  ,	training_args.local_rank  ,	training_args.device  ,	training_args.n_gpu  ,	bool(training_args.local_rank != -1     )  ,	training_args.fpaa  ,	)
 # Set the verbosity to info of the Transformers logger (on main process only):
 if is_main_process(training_args.local_rank     ):
  transformers.utils.logging.set_verbosity_info()
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()
 logger.info('Training/evaluation parameters %s'  ,	UpperCAmelCase_     )

 # Set seed
 set_seed(training_args.seed     )

 try:
  UpperCAmelCase	:     List[str]						 =			processors[data_args.task_name]()
  UpperCAmelCase	:     int						 =			processor.get_labels()
  UpperCAmelCase	:     Union[str, Any]						 =			len(UpperCAmelCase_     )
 except KeyError:
  raise ValueError('Task not found: %s' % (data_args.task_name)     )

 # Load pretrained model and tokenizer
 #
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.

 UpperCAmelCase	:     Union[str, Any]						 =			AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path  ,	num_labels=UpperCAmelCase_  ,	finetuning_task=data_args.task_name  ,	cache_dir=model_args.cache_dir  ,	)
 UpperCAmelCase	:     Any						 =			AutoTokenizer.from_pretrained(
     model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path  ,	cache_dir=model_args.cache_dir  ,	)
 UpperCAmelCase	:     List[Any]						 =			AutoModelForMultipleChoice.from_pretrained(
     model_args.model_name_or_path  ,	from_tf=bool('.ckpt' in model_args.model_name_or_path     )  ,	config=UpperCAmelCase_  ,	cache_dir=model_args.cache_dir  ,	)

 # Get datasets
 UpperCAmelCase	:     Optional[Any]						 =			(
     MultipleChoiceDataset(
         data_dir=data_args.data_dir  ,	tokenizer=UpperCAmelCase_  ,	task=data_args.task_name  ,	max_seq_length=data_args.max_seq_length  ,	overwrite_cache=data_args.overwrite_cache  ,	mode=Split.train  ,	)
     if training_args.do_train
     else None
 )
 UpperCAmelCase	:     List[str]						 =			(
     MultipleChoiceDataset(
         data_dir=data_args.data_dir  ,	tokenizer=UpperCAmelCase_  ,	task=data_args.task_name  ,	max_seq_length=data_args.max_seq_length  ,	overwrite_cache=data_args.overwrite_cache  ,	mode=Split.dev  ,	)
     if training_args.do_eval
     else None
 )

 def compute_metrics(UpperCAmelCase_     ) -> Dict:
  UpperCAmelCase	:     List[str]						 =			np.argmax(p.predictions  ,	axis=1     )
  return {"acc": simple_accuracy(UpperCAmelCase_  ,	p.label_ids     )}

 # Data collator
 UpperCAmelCase	:     Optional[int]						 =			DataCollatorWithPadding(UpperCAmelCase_  ,	pad_to_multiple_of=8     ) if training_args.fpaa else None

 # Initialize our Trainer
 UpperCAmelCase	:     List[str]						 =			Trainer(
     model=UpperCAmelCase_  ,	args=UpperCAmelCase_  ,	train_dataset=UpperCAmelCase_  ,	eval_dataset=UpperCAmelCase_  ,	compute_metrics=UpperCAmelCase_  ,	data_collator=UpperCAmelCase_  ,	)

 # Training
 if training_args.do_train:
  trainer.train(
      model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path     ) else None     )
  trainer.save_model()
  # For convenience, we also re-save the tokenizer to the same directory,
  # so that you can share your model easily on huggingface.co/models =)
  if trainer.is_world_master():
   tokenizer.save_pretrained(training_args.output_dir     )

    # Evaluation
 UpperCAmelCase	:     Union[str, Any]						 =			{}
 if training_args.do_eval:
  logger.info('*** Evaluate ***'     )

  UpperCAmelCase	:     Any						 =			trainer.evaluate()

  UpperCAmelCase	:     Optional[Any]						 =			os.path.join(training_args.output_dir  ,	'eval_results.txt'     )
  if trainer.is_world_master():
   with open(UpperCAmelCase_  ,	'w'     ) as writer:
    logger.info('***** Eval results *****'     )
    for key, value in result.items():
     logger.info('  %s = %s'  ,	UpperCAmelCase_  ,	UpperCAmelCase_     )
     writer.write('%s = %s\n' % (key, value)     )

    results.update(UpperCAmelCase_     )

 return results




def 				UpperCamelCase(					UpperCAmelCase_     ):
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
 main()







'''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()
lowercase__         =      logging.get_logger(__name__)

set_seed(770)


lowercase__         =      {
    "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",
}


lowercase__         =      {
    "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",
    },
}

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




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_=False     ):
 UpperCAmelCase	:     List[str]						 =			model_type
 if use_small:
  key += "_small"
 return os.path.join(UpperCAmelCase_  ,	REMOTE_MODEL_PATHS[key]['file_name']     )




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 os.makedirs(UpperCAmelCase_  ,	exist_ok=UpperCAmelCase_     )
 hf_hub_download(repo_id=UpperCAmelCase_  ,	filename=UpperCAmelCase_  ,	local_dir=UpperCAmelCase_     )




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

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

 UpperCAmelCase	:     List[Any]						 =			ConfigClass(**checkpoint['model_args']     )
 UpperCAmelCase	:     List[str]						 =			ModelClass(config=UpperCAmelCase_     )
 UpperCAmelCase	:     List[str]						 =			GenerationConfigClass()

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

   UpperCAmelCase	:     List[Any]						 =			state_dict.pop(UpperCAmelCase_     )

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

 return model




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_=False  ,	UpperCAmelCase_="text"     ):
 if model_type not in ("text", "coarse", "fine"):
  raise NotImplementedError()

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

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

 # load bark initial model
 UpperCAmelCase	:     List[str]						 =			_bark_load_model(UpperCAmelCase_  ,	'cpu'  ,	model_type=UpperCAmelCase_  ,	use_small=UpperCAmelCase_     )

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

 if model.num_parameters(exclude_embeddings=UpperCAmelCase_     ) != 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[int]						 =			5
 UpperCAmelCase	:     Optional[int]						 =			10

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

  UpperCAmelCase	:     List[str]						 =			model(UpperCAmelCase_     )

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

 else:
  UpperCAmelCase	:     Optional[int]						 =			3
  UpperCAmelCase	:     List[Any]						 =			8
  UpperCAmelCase	:     Dict						 =			torch.randint(2_56  ,	(batch_size, sequence_length, n_codes_total)  ,	dtype=torch.int     )

  UpperCAmelCase	:     str						 =			model(UpperCAmelCase_  ,	UpperCAmelCase_     )
  UpperCAmelCase	:     Dict						 =			bark_model(UpperCAmelCase_  ,	UpperCAmelCase_     )

  UpperCAmelCase	:     Any						 =			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(UpperCAmelCase_     ).mkdir(exist_ok=UpperCAmelCase_     )
 model.save_pretrained(UpperCAmelCase_     )




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	):
 UpperCAmelCase	:     int						 =			os.path.join(UpperCAmelCase_  ,	UpperCAmelCase_     )

 UpperCAmelCase	:     Dict						 =			BarkSemanticConfig.from_pretrained(os.path.join(UpperCAmelCase_  ,	'config.json'     )     )
 UpperCAmelCase	:     Any						 =			BarkCoarseConfig.from_pretrained(os.path.join(UpperCAmelCase_  ,	'config.json'     )     )
 UpperCAmelCase	:     Union[str, Any]						 =			BarkFineConfig.from_pretrained(os.path.join(UpperCAmelCase_  ,	'config.json'     )     )
 UpperCAmelCase	:     Any						 =			EncodecConfig.from_pretrained('facebook/encodec_24khz'     )

 UpperCAmelCase	:     Dict						 =			BarkSemanticModel.from_pretrained(UpperCAmelCase_     )
 UpperCAmelCase	:     Tuple						 =			BarkCoarseModel.from_pretrained(UpperCAmelCase_     )
 UpperCAmelCase	:     Optional[int]						 =			BarkFineModel.from_pretrained(UpperCAmelCase_     )
 UpperCAmelCase	:     str						 =			EncodecModel.from_pretrained('facebook/encodec_24khz'     )

 UpperCAmelCase	:     Optional[Any]						 =			BarkConfig.from_sub_model_configs(
     UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     )

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

 UpperCAmelCase	:     str						 =			BarkModel(UpperCAmelCase_     )

 UpperCAmelCase	:     int						 =			semantic
 UpperCAmelCase	:     Tuple						 =			coarseAcoustic
 UpperCAmelCase	:     Union[str, Any]						 =			fineAcoustic
 UpperCAmelCase	:     Union[str, Any]						 =			codec

 UpperCAmelCase	:     Optional[int]						 =			bark_generation_config

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


if __name__ == "__main__":
 lowercase__         =      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.")

 lowercase__         =      parser.parse_args()

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




