



"""simple docstring"""





import logging
import os
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 classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


SCREAMING_SNAKE_CASE				:     List[str]          =	logging.getLogger(__name__)


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





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






def 	__UpperCAmelCase					(       )							->	str:






   """simple docstring"""


   _lowerCAmelCase					     =		HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)   )
   _lowerCAmelCase     ,						_lowerCAmelCase     ,						_lowerCAmelCase					     =		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."""   )

   _lowerCAmelCase					     =		import_module("""tasks"""   )

   try:
      _lowerCAmelCase					     =		getattr(snake_case_   ,	model_args.task_type   )
      _lowerCAmelCase					     =		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   ,	)
   logger.info(
       """n_replicas: %s, distributed training: %s, 16-bits training: %s"""   ,	training_args.n_replicas   ,	bool(training_args.n_replicas > 1   )   ,	training_args.fpaa   ,	)
   logger.info("""Training/evaluation parameters %s"""   ,	snake_case_   )

   # Prepare Token Classification task
   _lowerCAmelCase					     =		token_classification_task.get_labels(data_args.labels   )
   _lowerCAmelCase					     =		dict(enumerate(snake_case_   )   )
   _lowerCAmelCase					     =		len(snake_case_   )

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

   _lowerCAmelCase					     =		AutoConfig.from_pretrained(
       model_args.config_name if model_args.config_name else model_args.model_name_or_path   ,	num_labels=snake_case_   ,	idalabel=snake_case_   ,	labelaid={label: i for i, label in enumerate(snake_case_   )}   ,	cache_dir=model_args.cache_dir   ,	)
   _lowerCAmelCase					     =		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   ,	)

   with training_args.strategy.scope():
      _lowerCAmelCase					     =		TFAutoModelForTokenClassification.from_pretrained(
          model_args.model_name_or_path   ,	from_pt=bool(""".bin""" in model_args.model_name_or_path   )   ,	config=snake_case_   ,	cache_dir=model_args.cache_dir   ,	)

   # Get datasets
   _lowerCAmelCase					     =		(
       TFTokenClassificationDataset(
           token_classification_task=snake_case_   ,	data_dir=data_args.data_dir   ,	tokenizer=snake_case_   ,	labels=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
   )
   _lowerCAmelCase					     =		(
       TFTokenClassificationDataset(
           token_classification_task=snake_case_   ,	data_dir=data_args.data_dir   ,	tokenizer=snake_case_   ,	labels=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(snake_case_  :  np.ndarray   ,	snake_case_  :  np.ndarray   ) -> Tuple[List[int], List[int]]:
      _lowerCAmelCase					     =		np.argmax(snake_case_   ,	axis=2   )
      _lowerCAmelCase     ,						_lowerCAmelCase					     =		preds.shape
      _lowerCAmelCase					     =		[[] for _ in range(snake_case_   )]
      _lowerCAmelCase					     =		[[] for _ in range(snake_case_   )]

      for i in range(snake_case_   ):
         for j in range(snake_case_   ):
            if label_ids[i, j] != -100:
               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(snake_case_  :  EvalPrediction   ) -> Dict:
      _lowerCAmelCase     ,						_lowerCAmelCase					     =		align_predictions(p.predictions   ,	p.label_ids   )

      return {
          "precision": precision_score(snake_case_   ,	snake_case_   ),
          "recall": recall_score(snake_case_   ,	snake_case_   ),
          "f1": fa_score(snake_case_   ,	snake_case_   ),
      }

   # Initialize our Trainer
   _lowerCAmelCase					     =		TFTrainer(
       model=snake_case_   ,	args=snake_case_   ,	train_dataset=train_dataset.get_dataset() if train_dataset else None   ,	eval_dataset=eval_dataset.get_dataset() if eval_dataset else None   ,	compute_metrics=snake_case_   ,	)

   # Training
   if training_args.do_train:
      trainer.train()
      trainer.save_model()
      tokenizer.save_pretrained(training_args.output_dir   )

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

      _lowerCAmelCase					     =		trainer.evaluate()
      _lowerCAmelCase					     =		os.path.join(training_args.output_dir   ,	"""eval_results.txt"""   )

      with open(snake_case_   ,	"""w"""   ) as writer:
         logger.info("""***** Eval results *****"""   )

         for key, value in result.items():
            logger.info("""  %s = %s"""   ,	snake_case_   ,	snake_case_   )
            writer.write("""%s = %s\n""" % (key, value)   )

         results.update(snake_case_   )

    # Predict
   if training_args.do_predict:
      _lowerCAmelCase					     =		TFTokenClassificationDataset(
          token_classification_task=snake_case_   ,	data_dir=data_args.data_dir   ,	tokenizer=snake_case_   ,	labels=snake_case_   ,	model_type=config.model_type   ,	max_seq_length=data_args.max_seq_length   ,	overwrite_cache=data_args.overwrite_cache   ,	mode=Split.test   ,	)

      _lowerCAmelCase     ,						_lowerCAmelCase     ,						_lowerCAmelCase					     =		trainer.predict(test_dataset.get_dataset()   )
      _lowerCAmelCase     ,						_lowerCAmelCase					     =		align_predictions(snake_case_   ,	snake_case_   )
      _lowerCAmelCase					     =		classification_report(snake_case_   ,	snake_case_   )

      logger.info("""\n%s"""   ,	snake_case_   )

      _lowerCAmelCase					     =		os.path.join(training_args.output_dir   ,	"""test_results.txt"""   )

      with open(snake_case_   ,	"""w"""   ) as writer:
         writer.write("""%s\n""" % report   )

      # Save predictions
      _lowerCAmelCase					     =		os.path.join(training_args.output_dir   ,	"""test_predictions.txt"""   )

      with open(snake_case_   ,	"""w"""   ) as writer:
         with open(os.path.join(data_args.data_dir   ,	"""test.txt"""   )   ,	"""r"""   ) as f:
            _lowerCAmelCase					     =		0

            for line in f:
               if line.startswith("""-DOCSTART-"""   ) or line == "" or line == "\n":
                  writer.write(snake_case_   )

                  if not preds_list[example_id]:
                     example_id += 1
               elif preds_list[example_id]:
                  _lowerCAmelCase					     =		line.split()[0] + """ """ + preds_list[example_id].pop(0   ) + """\n"""

                  writer.write(snake_case_   )
               else:
                  logger.warning("""Maximum sequence length exceeded: No prediction for '%s'."""   ,	line.split()[0]   )

   return results


if __name__ == "__main__":
  main()




"""simple docstring"""





import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE				:     str          =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE				:     List[str]          =	{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''mask_emb''': '''masked_spec_embed''',
}






def 	__UpperCAmelCase					(       snake_case_  :  List[str]   ,	snake_case_  :  Dict   ,	snake_case_  :  Any   ,	snake_case_  :  str   ,	snake_case_  :  Tuple   )							->	str:






   """simple docstring"""


   for attribute in key.split("""."""   ):
      _lowerCAmelCase					     =		getattr(snake_case_   ,	snake_case_   )

   if weight_type is not None:
      _lowerCAmelCase					     =		getattr(snake_case_   ,	snake_case_   ).shape
   else:
      _lowerCAmelCase					     =		hf_pointer.shape

   assert hf_shape == value.shape, (
       F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
       F""" {value.shape} for {full_name}"""
   )

   if weight_type == "weight":
      _lowerCAmelCase					     =		value
   elif weight_type == "weight_g":
      _lowerCAmelCase					     =		value
   elif weight_type == "weight_v":
      _lowerCAmelCase					     =		value
   elif weight_type == "bias":
      _lowerCAmelCase					     =		value
   else:
      _lowerCAmelCase					     =		value

   logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""   )






def 	__UpperCAmelCase					(       snake_case_  :  str   ,	snake_case_  :  Any   )							->	List[Any]:






   """simple docstring"""


   _lowerCAmelCase					     =		[]
   _lowerCAmelCase					     =		fairseq_model.state_dict()

   _lowerCAmelCase					     =		hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      _lowerCAmelCase					     =		False
      if "conv_layers" in name:
         load_conv_layer(
             snake_case_   ,	snake_case_   ,	snake_case_   ,	snake_case_   ,	hf_model.config.feat_extract_norm == """group"""   ,	)
         _lowerCAmelCase					     =		True
      else:
         for key, mapped_key in MAPPING.items():
            _lowerCAmelCase					     =		mapped_key

            if key in name:
               _lowerCAmelCase					     =		True
               if "*" in mapped_key:
                  _lowerCAmelCase					     =		name.split(snake_case_   )[0].split("""."""   )[-2]
                  _lowerCAmelCase					     =		mapped_key.replace("""*"""   ,	snake_case_   )
               if "weight_g" in name:
                  _lowerCAmelCase					     =		"""weight_g"""
               elif "weight_v" in name:
                  _lowerCAmelCase					     =		"""weight_v"""
               elif "weight" in name:
                  _lowerCAmelCase					     =		"""weight"""
               elif "bias" in name:
                  _lowerCAmelCase					     =		"""bias"""
               else:
                  _lowerCAmelCase					     =		None
               set_recursively(snake_case_   ,	snake_case_   ,	snake_case_   ,	snake_case_   ,	snake_case_   )
            continue
      if not is_used:
         unused_weights.append(snake_case_   )

   logger.warning(F"""Unused weights: {unused_weights}"""   )






def 	__UpperCAmelCase					(       snake_case_  :  Any   ,	snake_case_  :  List[Any]   ,	snake_case_  :  Dict   ,	snake_case_  :  Union[str, Any]   ,	snake_case_  :  Optional[Any]   )							->	List[Any]:






   """simple docstring"""


   _lowerCAmelCase					     =		full_name.split("""conv_layers."""   )[-1]
   _lowerCAmelCase					     =		name.split("""."""   )
   _lowerCAmelCase					     =		int(items[0]   )
   _lowerCAmelCase					     =		int(items[1]   )

   if type_id == 0:
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
             F"""{full_name} has size {value.shape}, but"""
             F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
         )
         _lowerCAmelCase					     =		value
         logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""   )
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
             F"""{full_name} has size {value.shape}, but"""
             F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
         )
         _lowerCAmelCase					     =		value
         logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""   )
   elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
             F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
             " found."
         )
         _lowerCAmelCase					     =		value
         logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
             F"""{full_name} has size {value.shape}, but"""
             F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
         )
         _lowerCAmelCase					     =		value
         logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
   else:
      unused_weights.append(snake_case_   )






def 	__UpperCAmelCase					(       snake_case_  :  Optional[int]   )							->	Any:






   """simple docstring"""


   _lowerCAmelCase					     =		HubertConfig()
   _lowerCAmelCase					     =		model.config

   _lowerCAmelCase					     =		fs_config.activation_dropout
   _lowerCAmelCase					     =		False
   _lowerCAmelCase					     =		fs_config.attention_dropout
   _lowerCAmelCase					     =		False
   _lowerCAmelCase					     =		eval(fs_config.extractor_conv_feature_layers   )
   _lowerCAmelCase					     =		[x[0] for x in conv_layers]
   _lowerCAmelCase					     =		[x[1] for x in conv_layers]
   _lowerCAmelCase					     =		[x[2] for x in conv_layers]
   _lowerCAmelCase					     =		"""gelu"""
   _lowerCAmelCase					     =		"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
   _lowerCAmelCase					     =		False
   _lowerCAmelCase					     =		0.0
   _lowerCAmelCase					     =		0.0
   _lowerCAmelCase					     =		fs_config.activation_fn
   _lowerCAmelCase					     =		fs_config.dropout
   _lowerCAmelCase					     =		fs_config.encoder_embed_dim
   _lowerCAmelCase					     =		0.0_2
   _lowerCAmelCase					     =		fs_config.encoder_ffn_embed_dim
   _lowerCAmelCase					     =		1e-5
   _lowerCAmelCase					     =		0.0
   _lowerCAmelCase					     =		fs_config.encoder_attention_heads
   _lowerCAmelCase					     =		fs_config.conv_pos_groups
   _lowerCAmelCase					     =		fs_config.conv_pos
   _lowerCAmelCase					     =		len(snake_case_   )
   _lowerCAmelCase					     =		fs_config.encoder_layers

   return config






@torch.no_grad()
def 	__UpperCAmelCase					(       snake_case_  :  Optional[Any]   ,	snake_case_  :  Union[str, Any]=None   )							->	List[str]:






   """simple docstring"""


   _lowerCAmelCase					     =		distilhubert().model.model

   if config_path is not None:
      _lowerCAmelCase					     =		HubertConfig.from_pretrained(snake_case_   )
   else:
      _lowerCAmelCase					     =		convert_config(snake_case_   )
   _lowerCAmelCase					     =		model.eval()

   _lowerCAmelCase					     =		WavaVecaFeatureExtractor(
       feature_size=1   ,	sampling_rate=16000   ,	padding_value=0   ,	do_normalize=snake_case_   ,	return_attention_mask=snake_case_   ,	)
   _lowerCAmelCase					     =		HubertModel(snake_case_   )

   recursively_load_weights(snake_case_   ,	snake_case_   )

   feature_extractor.save_pretrained(snake_case_   )
   hf_model.save_pretrained(snake_case_   )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE				:     List[str]          =	argparse.ArgumentParser()
  parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
  parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
  SCREAMING_SNAKE_CASE				:     Optional[int]          =	parser.parse_args()
  convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)