





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


__lowerCamelCase	:					Tuple			  =					logging.getLogger(__name__)



@dataclass
class  A__       :
      _UpperCAmelCase     :str						   =       field(
          metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}      )
      _UpperCAmelCase     :Optional[str]						   =       field(
          default=__SCREAMING_SNAKE_CASE	,       metadata={'help': 'Pretrained config name or path if not the same as model_name'}      )
      _UpperCAmelCase     :Optional[str]						   =       field(
          default='NER'	,       metadata={'help': 'Task type to fine tune in training (e.g. NER, POS, etc)'}      )
      _UpperCAmelCase     :Optional[str]						   =       field(
          default=__SCREAMING_SNAKE_CASE	,       metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}      )
      _UpperCAmelCase     :bool						   =       field(default=__SCREAMING_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.
      _UpperCAmelCase     :Optional[str]						   =       field(
          default=__SCREAMING_SNAKE_CASE	,       metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}	,       )



@dataclass
class  A__       :
      _UpperCAmelCase     :str						   =       field(
          metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'}      )
      _UpperCAmelCase     :Optional[str]						   =       field(
          default=__SCREAMING_SNAKE_CASE	,       metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'}	,       )
      _UpperCAmelCase     :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.'
              )
          }	,       )
      _UpperCAmelCase     :bool						   =       field(
          default=__SCREAMING_SNAKE_CASE	,       metadata={'help': 'Overwrite the cached training and evaluation sets'}      )



def 		A_    (					)						->			Optional[int]:
 UpperCamelCase     : List[str]		=       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.
  UpperCamelCase     : Dict		=       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
 else:
  UpperCamelCase     : Tuple		=       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."					)

 UpperCamelCase     : Tuple		=       import_module("tasks"					)
 try:
  UpperCamelCase     : Union[str, Any]		=       getattr(_SCREAMING_SNAKE_CASE							,       model_args.task_type					)
  UpperCamelCase     : 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"							,       _SCREAMING_SNAKE_CASE					)

 # Set seed
 set_seed(training_args.seed					)

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

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

 UpperCamelCase     : Dict		=       AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path							,       num_labels=_SCREAMING_SNAKE_CASE							,       idalabel=_SCREAMING_SNAKE_CASE							,       labelaid={label: i for i, label in enumerate(_SCREAMING_SNAKE_CASE					)}							,       cache_dir=model_args.cache_dir							,       )
 UpperCamelCase     : Optional[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							,       use_fast=model_args.use_fast							,       )
 UpperCamelCase     : Dict		=       AutoModelForTokenClassification.from_pretrained(
     model_args.model_name_or_path							,       from_tf=bool(".ckpt" in model_args.model_name_or_path					)							,       config=_SCREAMING_SNAKE_CASE							,       cache_dir=model_args.cache_dir							,       )

 # Get datasets
 UpperCamelCase     : Optional[Any]		=       (
     TokenClassificationDataset(
         token_classification_task=_SCREAMING_SNAKE_CASE							,       data_dir=data_args.data_dir							,       tokenizer=_SCREAMING_SNAKE_CASE							,       labels=_SCREAMING_SNAKE_CASE							,       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
 )
 UpperCamelCase     : Tuple		=       (
     TokenClassificationDataset(
         token_classification_task=_SCREAMING_SNAKE_CASE							,       data_dir=data_args.data_dir							,       tokenizer=_SCREAMING_SNAKE_CASE							,       labels=_SCREAMING_SNAKE_CASE							,       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(_lowerCAmelCase							,       _lowerCAmelCase					) -> Tuple[List[int], List[int]]:
  UpperCamelCase     : Optional[int]		=       np.argmax(_SCREAMING_SNAKE_CASE							,       axis=2					)

  UpperCamelCase     : Optional[Any]		=       preds.shape

  UpperCamelCase     : str		=       [[] for _ in range(_SCREAMING_SNAKE_CASE					)]
  UpperCamelCase     : Any		=       [[] for _ in range(_SCREAMING_SNAKE_CASE					)]

  for i in range(_SCREAMING_SNAKE_CASE					):
   for j in range(_SCREAMING_SNAKE_CASE					):
    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(_lowerCAmelCase					) -> Dict:
  UpperCamelCase     : Optional[Any]		=       align_predictions(p.predictions							,       p.label_ids					)
  return {
      "accuracy_score": accuracy_score(_SCREAMING_SNAKE_CASE							,       _SCREAMING_SNAKE_CASE					),
      "precision": precision_score(_SCREAMING_SNAKE_CASE							,       _SCREAMING_SNAKE_CASE					),
      "recall": recall_score(_SCREAMING_SNAKE_CASE							,       _SCREAMING_SNAKE_CASE					),
      "f1": fa_score(_SCREAMING_SNAKE_CASE							,       _SCREAMING_SNAKE_CASE					),
  }

 # Data collator
 UpperCamelCase     : List[Any]		=       DataCollatorWithPadding(_SCREAMING_SNAKE_CASE							,       pad_to_multiple_of=8					) if training_args.fpaa else None

 # Initialize our Trainer
 UpperCamelCase     : Tuple		=       Trainer(
     model=_SCREAMING_SNAKE_CASE							,       args=_SCREAMING_SNAKE_CASE							,       train_dataset=_SCREAMING_SNAKE_CASE							,       eval_dataset=_SCREAMING_SNAKE_CASE							,       compute_metrics=_SCREAMING_SNAKE_CASE							,       data_collator=_SCREAMING_SNAKE_CASE							,       )

 # 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
 UpperCamelCase     : str		=       {}
 if training_args.do_eval:
  logger.info("*** Evaluate ***"					)

  UpperCamelCase     : List[Any]		=       trainer.evaluate()

  UpperCamelCase     : Any		=       os.path.join(training_args.output_dir							,       "eval_results.txt"					)
  if trainer.is_world_process_zero():
   with open(_SCREAMING_SNAKE_CASE							,       "w"					) as writer:
    logger.info("***** Eval results *****"					)
    for key, value in result.items():
     logger.info("  %s = %s"							,       _SCREAMING_SNAKE_CASE							,       _SCREAMING_SNAKE_CASE					)
     writer.write("%s = %s\n" % (key, value)					)

   results.update(_SCREAMING_SNAKE_CASE					)

    # Predict
 if training_args.do_predict:
  UpperCamelCase     : str		=       TokenClassificationDataset(
      token_classification_task=_SCREAMING_SNAKE_CASE							,       data_dir=data_args.data_dir							,       tokenizer=_SCREAMING_SNAKE_CASE							,       labels=_SCREAMING_SNAKE_CASE							,       model_type=config.model_type							,       max_seq_length=data_args.max_seq_length							,       overwrite_cache=data_args.overwrite_cache							,       mode=Split.test							,       )

  UpperCamelCase     : List[Any]		=       trainer.predict(_SCREAMING_SNAKE_CASE					)
  UpperCamelCase     : int		=       align_predictions(_SCREAMING_SNAKE_CASE							,       _SCREAMING_SNAKE_CASE					)

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

        # Save predictions
  UpperCamelCase     : List[Any]		=       os.path.join(training_args.output_dir							,       "test_predictions.txt"					)
  if trainer.is_world_process_zero():
   with open(_SCREAMING_SNAKE_CASE							,       "w"					) as writer:
    with open(os.path.join(data_args.data_dir							,       "test.txt"					)							,       "r"					) as f:
     token_classification_task.write_predictions_to_file(_SCREAMING_SNAKE_CASE							,       _SCREAMING_SNAKE_CASE							,       _SCREAMING_SNAKE_CASE					)

 return results



def 		A_    (					_lowerCAmelCase					)						->			Optional[int]:
 main()


if __name__ == "__main__":
    main()







import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__lowerCamelCase	:					Dict			  =					logging.get_logger("""transformers.models.speecht5""")



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)						->			Tuple:
 hf_model.apply_weight_norm()

 UpperCamelCase     : int	     =       checkpoint["input_conv.weight_g"]
 UpperCamelCase     : Dict	     =       checkpoint["input_conv.weight_v"]
 UpperCamelCase     : List[Any]	     =       checkpoint["input_conv.bias"]

 for i in range(len(config.upsample_rates					)					):
  UpperCamelCase     : Any	     =       checkpoint[F"""upsamples.{i}.1.weight_g"""]
  UpperCamelCase     : List[Any]	     =       checkpoint[F"""upsamples.{i}.1.weight_v"""]
  UpperCamelCase     : Optional[Any]	     =       checkpoint[F"""upsamples.{i}.1.bias"""]

 for i in range(len(config.upsample_rates					) * len(config.resblock_kernel_sizes					)					):
  for j in range(len(config.resblock_dilation_sizes					)					):
   UpperCamelCase     : Union[str, Any]	     =       checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
   UpperCamelCase     : int	     =       checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
   UpperCamelCase     : str	     =       checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]

   UpperCamelCase     : Union[str, Any]	     =       checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
   UpperCamelCase     : int	     =       checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
   UpperCamelCase     : Optional[Any]	     =       checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

 UpperCamelCase     : Tuple	     =       checkpoint["output_conv.1.weight_g"]
 UpperCamelCase     : Tuple	     =       checkpoint["output_conv.1.weight_v"]
 UpperCamelCase     : int	     =       checkpoint["output_conv.1.bias"]

 hf_model.remove_weight_norm()



@torch.no_grad()
def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase=None							,       _lowerCAmelCase=None							,       )						->			Tuple:
 if config_path is not None:
  UpperCamelCase     : List[Any]	     =       SpeechTaHifiGanConfig.from_pretrained(_lowerCAmelCase					)
 else:
  UpperCamelCase     : Optional[int]	     =       SpeechTaHifiGanConfig()

 UpperCamelCase     : List[str]	     =       SpeechTaHifiGan(_lowerCAmelCase					)

 UpperCamelCase     : str	     =       torch.load(_lowerCAmelCase					)
 load_weights(orig_checkpoint["model"]["generator"]							,       _lowerCAmelCase							,       _lowerCAmelCase					)

 UpperCamelCase     : List[Any]	     =       np.load(_lowerCAmelCase					)
 UpperCamelCase     : List[str]	     =       stats[0].reshape(-1					)
 UpperCamelCase     : Tuple	     =       stats[1].reshape(-1					)
 UpperCamelCase     : Any	     =       torch.from_numpy(_lowerCAmelCase					).float()
 UpperCamelCase     : Any	     =       torch.from_numpy(_lowerCAmelCase					).float()

 model.save_pretrained(_lowerCAmelCase					)

 if repo_id:
  print("Pushing to the hub..."					)
  model.push_to_hub(_lowerCAmelCase					)


if __name__ == "__main__":
    __lowerCamelCase	:					Union[str, Any]			  =					argparse.ArgumentParser()
    parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
    parser.add_argument("""--stats_path""", required=True, default=None, type=str, help="""Path to stats.npy file""")
    parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
    parser.add_argument(
        """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
    )
    parser.add_argument(
        """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
    )

    __lowerCamelCase	:					Tuple			  =					parser.parse_args()
    convert_hifigan_checkpoint(
        args.checkpoint_path,
        args.stats_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.push_to_hub,
    )
