


'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

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


UpperCAmelCase_			:  Any										=   logging.getLogger(__name__)

@dataclass
class     _lowerCamelCase       :




 '''simple docstring'''
 __lowercase					:		str     				=    field(
     metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}					)
 __lowercase					:		Optional[str]     				=    field(
     default=snake_case_     ,		metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}					)
 __lowercase					:		Optional[str]     				=    field(
     default='''NER'''     ,		metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''}					)
 __lowercase					:		Optional[str]     				=    field(
     default=snake_case_     ,		metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}					)
 __lowercase					:		bool     				=    field(default=snake_case_     ,		metadata={'''help''': '''Set this flag to use fast tokenization.'''}					)
 # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
 # or just modify its tokenizer_config.json.
 __lowercase					:		Optional[str]     				=    field(
     default=snake_case_     ,		metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}     ,		)


@dataclass
class     _lowerCamelCase       :




 '''simple docstring'''
 __lowercase					:		str     				=    field(
     metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}					)
 __lowercase					:		Optional[str]     				=    field(
     default=snake_case_     ,		metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}     ,		)
 __lowercase					:		int     				=    field(
     default=1_2_8     ,		metadata={
         '''help''': (
             '''The maximum total input sequence length after tokenization. Sequences longer '''
             '''than this will be truncated, sequences shorter will be padded.'''
         )
     }     ,		)
 __lowercase					:		bool     				=    field(
     default=snake_case_     ,		metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}					)


def       _lowercase							(					):
 # 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.

 __A	:		int	  =      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
 if len(sys.argv      ) == 2 and sys.argv[1].endswith('.json'      ):
  # If we pass only one argument to the script and it's the path to a json file,
  # let's parse it to get our arguments.
  __A  ,__A  ,__A	:		List[Any]	  =      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
 else:
  __A  ,__A  ,__A	:		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.'      )

 __A	:		int	  =      import_module('tasks'      )
 try:
  __A	:		Tuple	  =      getattr(UpperCamelCase__,					model_args.task_type      )
  __A	:		TokenClassificationTask	  =      token_classification_task_clazz()
 except AttributeError:
  raise ValueError(
      f"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
      f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""      )

 # 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      )

 # Prepare CONLL-2003 task
 __A	:		Dict	  =      token_classification_task.get_labels(data_args.labels      )
 __A	:		Dict[int, str]	  =      dict(enumerate(UpperCamelCase__      )      )
 __A	:		Any	  =      len(UpperCamelCase__      )

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

 __A	:		str	  =      AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path,					num_labels=UpperCamelCase__,					idalabel=UpperCamelCase__,					labelaid={label: i for i, label in enumerate(UpperCamelCase__      )},					cache_dir=model_args.cache_dir,					)
 __A	:		Tuple	  =      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,					use_fast=model_args.use_fast,					)
 __A	:		str	  =      AutoModelForTokenClassification.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
 __A	:		Optional[Any]	  =      (
     TokenClassificationDataset(
         token_classification_task=UpperCamelCase__,					data_dir=data_args.data_dir,					tokenizer=UpperCamelCase__,					labels=UpperCamelCase__,					model_type=config.model_type,					max_seq_length=data_args.max_seq_length,					overwrite_cache=data_args.overwrite_cache,					mode=Split.train,					)
     if training_args.do_train
     else None
 )
 __A	:		Dict	  =      (
     TokenClassificationDataset(
         token_classification_task=UpperCamelCase__,					data_dir=data_args.data_dir,					tokenizer=UpperCamelCase__,					labels=UpperCamelCase__,					model_type=config.model_type,					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 align_predictions(UpperCamelCase__							:       np.ndarray,					UpperCamelCase__							:       np.ndarray      ) -> Tuple[List[int], List[int]]:
  __A	:		Optional[Any]	  =      np.argmax(UpperCamelCase__,					axis=2      )

  __A  ,__A	:		List[str]	  =      preds.shape

  __A	:		Any	  =      [[] for _ in range(UpperCamelCase__      )]
  __A	:		Optional[Any]	  =      [[] for _ in range(UpperCamelCase__      )]

  for i in range(UpperCamelCase__      ):
   for j in range(UpperCamelCase__      ):
    if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
     out_label_list[i].append(label_map[label_ids[i][j]]      )
     preds_list[i].append(label_map[preds[i][j]]      )

  return preds_list, out_label_list

 def compute_metrics(UpperCamelCase__							:       EvalPrediction      ) -> Dict:
  __A  ,__A	:		Tuple	  =      align_predictions(p.predictions,					p.label_ids      )
  return {
      "accuracy_score": accuracy_score(UpperCamelCase__,					UpperCamelCase__      ),
      "precision": precision_score(UpperCamelCase__,					UpperCamelCase__      ),
      "recall": recall_score(UpperCamelCase__,					UpperCamelCase__      ),
      "f1": fa_score(UpperCamelCase__,					UpperCamelCase__      ),
  }

 # Data collator
 __A	:		int	  =      DataCollatorWithPadding(UpperCamelCase__,					pad_to_multiple_of=8      ) if training_args.fpaa else None

 # Initialize our Trainer
 __A	:		Any	  =      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_process_zero():
   tokenizer.save_pretrained(training_args.output_dir      )

    # Evaluation
 __A	:		Optional[Any]	  =      {}
 if training_args.do_eval:
  logger.info('*** Evaluate ***'      )

  __A	:		Optional[Any]	  =      trainer.evaluate()

  __A	:		Optional[int]	  =      os.path.join(training_args.output_dir,					'eval_results.txt'      )
  if trainer.is_world_process_zero():
   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__      )

    # Predict
 if training_args.do_predict:
  __A	:		Union[str, Any]	  =      TokenClassificationDataset(
      token_classification_task=UpperCamelCase__,					data_dir=data_args.data_dir,					tokenizer=UpperCamelCase__,					labels=UpperCamelCase__,					model_type=config.model_type,					max_seq_length=data_args.max_seq_length,					overwrite_cache=data_args.overwrite_cache,					mode=Split.test,					)

  __A  ,__A  ,__A	:		Optional[Any]	  =      trainer.predict(UpperCamelCase__      )
  __A  ,__A	:		List[Any]	  =      align_predictions(UpperCamelCase__,					UpperCamelCase__      )

  __A	:		Optional[Any]	  =      os.path.join(training_args.output_dir,					'test_results.txt'      )
  if trainer.is_world_process_zero():
   with open(UpperCamelCase__,					'w'      ) as writer:
    for key, value in metrics.items():
     logger.info('  %s = %s',					UpperCamelCase__,					UpperCamelCase__      )
     writer.write('%s = %s\n' % (key, value)      )

        # Save predictions
  __A	:		Dict	  =      os.path.join(training_args.output_dir,					'test_predictions.txt'      )
  if trainer.is_world_process_zero():
   with open(UpperCamelCase__,					'w'      ) as writer:
    with open(os.path.join(data_args.data_dir,					'test.txt'      ),					'r'      ) as f:
     token_classification_task.write_predictions_to_file(UpperCamelCase__,					UpperCamelCase__,					UpperCamelCase__      )

 return results


def       _lowercase							(					UpperCamelCase__							:       List[str]      ):
 # 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()
UpperCAmelCase_			:  Union[str, Any]										=   logging.get_logger(__name__)

set_seed(7_7_0)


UpperCAmelCase_			:  List[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',
}


UpperCAmelCase_			:  List[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',
    },
}

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


def       _lowercase							(					UpperCamelCase__							:       Dict,					UpperCamelCase__							:       Tuple=False      ):
 __A	:		List[Any]	  =      model_type
 if use_small:
  key += "_small"
 return os.path.join(UpperCamelCase__,					REMOTE_MODEL_PATHS[key]['file_name']      )


def       _lowercase							(					UpperCamelCase__							:       Any,					UpperCamelCase__							:       Any      ):
 os.makedirs(UpperCamelCase__,					exist_ok=UpperCamelCase__      )
 hf_hub_download(repo_id=UpperCamelCase__,					filename=UpperCamelCase__,					local_dir=UpperCamelCase__      )


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

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

 __A	:		Optional[Any]	  =      ConfigClass(**checkpoint['model_args']      )
 __A	:		Union[str, Any]	  =      ModelClass(config=UpperCamelCase__      )
 __A	:		Tuple	  =      GenerationConfigClass()

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

   __A	:		Optional[Any]	  =      state_dict.pop(UpperCamelCase__      )

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

 __A	:		Any	  =      'cpu'  # do conversion on cpu

 __A	:		int	  =      _get_ckpt_path(UpperCamelCase__,					use_small=UpperCamelCase__      )
 __A	:		Any	  =      _load_model(UpperCamelCase__,					UpperCamelCase__,					model_type=UpperCamelCase__,					use_small=UpperCamelCase__      )

 # load bark initial model
 __A	:		Optional[int]	  =      _bark_load_model(UpperCamelCase__,					'cpu',					model_type=UpperCamelCase__,					use_small=UpperCamelCase__      )

 if model_type == "text":
  __A	:		Optional[int]	  =      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
 __A	:		Optional[Any]	  =      5
 __A	:		str	  =      10

 if model_type in ["text", "coarse"]:
  __A	:		str	  =      torch.randint(256,					(batch_size, sequence_length),					dtype=torch.int      )
  __A	:		Any	  =      bark_model(UpperCamelCase__      )[0]

  __A	:		Union[str, Any]	  =      model(UpperCamelCase__      )

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

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

  __A	:		List[str]	  =      model(UpperCamelCase__,					UpperCamelCase__      )
  __A	:		Optional[int]	  =      bark_model(UpperCamelCase__,					UpperCamelCase__      )

  __A	:		Dict	  =      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       _lowercase							(					UpperCamelCase__							:       int,					UpperCamelCase__							:       int,					UpperCamelCase__							:       Optional[int],					UpperCamelCase__							:       Optional[Any],					UpperCamelCase__							:       Tuple,					UpperCamelCase__							:       str,					):
 __A	:		Optional[Any]	  =      os.path.join(UpperCamelCase__,					UpperCamelCase__      )

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

 __A	:		List[Any]	  =      BarkSemanticModel.from_pretrained(UpperCamelCase__      )
 __A	:		Optional[int]	  =      BarkCoarseModel.from_pretrained(UpperCamelCase__      )
 __A	:		str	  =      BarkFineModel.from_pretrained(UpperCamelCase__      )
 __A	:		Optional[int]	  =      EncodecModel.from_pretrained('facebook/encodec_24khz'      )

 __A	:		int	  =      BarkConfig.from_sub_model_configs(
     UpperCamelCase__,					UpperCamelCase__,					UpperCamelCase__,					UpperCamelCase__      )

 __A	:		Optional[Any]	  =      BarkGenerationConfig.from_sub_model_configs(
     semantic.generation_config,					coarseAcoustic.generation_config,					fineAcoustic.generation_config      )

 __A	:		int	  =      BarkModel(UpperCamelCase__      )

 __A	:		List[str]	  =      semantic
 __A	:		Any	  =      coarseAcoustic
 __A	:		Tuple	  =      fineAcoustic
 __A	:		Optional[Any]	  =      codec

 __A	:		Dict	  =      bark_generation_config

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


if __name__ == "__main__":
 UpperCAmelCase_			:  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.')

 UpperCAmelCase_			:  Any										=   parser.parse_args()

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






