


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()


lowerCamelCase		      = logging.getLogger(__name__)




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




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




def 			a_			(					):
       '''simple docstring'''





       _lowerCamelCase :	Any						=HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)  )
       _lowerCamelCase :	List[Any]						=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 :	Optional[Any]						=import_module('tasks'  )

       try:
              _lowerCamelCase :	List[Any]						=getattr(lowercase_	,    model_args.task_type  )
              _lowerCamelCase :	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	,    )
       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'	,    lowercase_  )

       # Prepare Token Classification task
       _lowerCamelCase :	Optional[int]						=token_classification_task.get_labels(data_args.labels  )
       _lowerCamelCase :	Dict[int, str]						=dict(enumerate(lowercase_  )  )
       _lowerCamelCase :	Any						=len(lowercase_  )

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

       _lowerCamelCase :	Dict						=AutoConfig.from_pretrained(
           model_args.config_name if model_args.config_name else model_args.model_name_or_path	,    num_labels=lowercase_	,    idalabel=lowercase_	,    labelaid={label: i for i, label in enumerate(lowercase_  )}	,    cache_dir=model_args.cache_dir	,    )
       _lowerCamelCase :	Optional[int]						=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 :	List[str]						=TFAutoModelForTokenClassification.from_pretrained(
                  model_args.model_name_or_path	,    from_pt=bool('.bin' in model_args.model_name_or_path  )	,    config=lowercase_	,    cache_dir=model_args.cache_dir	,    )

       # Get datasets
       _lowerCamelCase :	Dict						=(
           TFTokenClassificationDataset(
               token_classification_task=lowercase_	,    data_dir=data_args.data_dir	,    tokenizer=lowercase_	,    labels=lowercase_	,    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 :	List[Any]						=(
           TFTokenClassificationDataset(
               token_classification_task=lowercase_	,    data_dir=data_args.data_dir	,    tokenizer=lowercase_	,    labels=lowercase_	,    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(SCREAMING_SNAKE_CASE__      :   Optional[int]	,    SCREAMING_SNAKE_CASE__      :   List[str]  ) -> Tuple[List[int], List[int]]:
              _lowerCamelCase :	str						=np.argmax(lowercase_	,    axis=2  )
              _lowerCamelCase :	Optional[int]						=preds.shape
              _lowerCamelCase :	Any						=[[] for _ in range(lowercase_  )]
              _lowerCamelCase :	List[str]						=[[] for _ in range(lowercase_  )]

              for i in range(lowercase_  ):
                     for j in range(lowercase_  ):
                            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(SCREAMING_SNAKE_CASE__      :   Optional[int]  ) -> Dict:
              _lowerCamelCase :	Tuple						=align_predictions(p.predictions	,    p.label_ids  )

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

       # Initialize our Trainer
       _lowerCamelCase :	Dict						=TFTrainer(
           model=lowercase_	,    args=lowercase_	,    train_dataset=train_dataset.get_dataset() if train_dataset else None	,    eval_dataset=eval_dataset.get_dataset() if eval_dataset else None	,    compute_metrics=lowercase_	,    )

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

       # Evaluation
       _lowerCamelCase :	str						={}
       if training_args.do_eval:
              logger.info('*** Evaluate ***'  )

              _lowerCamelCase :	List[str]						=trainer.evaluate()
              _lowerCamelCase :	Optional[Any]						=os.path.join(training_args.output_dir	,    'eval_results.txt'  )

              with open(lowercase_	,    'w'  ) as writer:
                     logger.info('***** Eval results *****'  )

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

                     results.update(lowercase_  )

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

              _lowerCamelCase :	Dict						=trainer.predict(test_dataset.get_dataset()  )
              _lowerCamelCase :	List[Any]						=align_predictions(lowercase_	,    lowercase_  )
              _lowerCamelCase :	List[Any]						=classification_report(lowercase_	,    lowercase_  )

              logger.info('\n%s'	,    lowercase_  )

              _lowerCamelCase :	Any						=os.path.join(training_args.output_dir	,    'test_results.txt'  )

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

              # Save predictions
              _lowerCamelCase :	Union[str, Any]						=os.path.join(training_args.output_dir	,    'test_predictions.txt'  )

              with open(lowercase_	,    'w'  ) as writer:
                     with open(os.path.join(data_args.data_dir	,    'test.txt'  )	,    'r'  ) as f:
                            _lowerCamelCase :	List[str]						=0

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

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

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

       return results


if __name__ == "__main__":
 main()






import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.9.0'):
 raise Exception('requires fairseq >= 0.9.0')


logging.set_verbosity_info()
lowerCamelCase		      = logging.get_logger(__name__)

lowerCamelCase		      = 'Hello world! cécé herlolip'




def 			a_   (					SCREAMING_SNAKE_CASE__      :   str	,    SCREAMING_SNAKE_CASE__      :   str	,    SCREAMING_SNAKE_CASE__      :   bool  ):
       '''simple docstring'''





       _lowerCamelCase :	Dict						=FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE__  )
       roberta.eval()  # disable dropout
       _lowerCamelCase :	int						=roberta.model.encoder.sentence_encoder
       _lowerCamelCase :	Union[str, Any]						=RobertaConfig(
           vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings	,    hidden_size=roberta.args.encoder_embed_dim	,    num_hidden_layers=roberta.args.encoder_layers	,    num_attention_heads=roberta.args.encoder_attention_heads	,    intermediate_size=roberta.args.encoder_ffn_embed_dim	,    max_position_embeddings=514	,    type_vocab_size=1	,    layer_norm_eps=1e-5	,    )
       if classification_head:
              _lowerCamelCase :	int						=roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
       print('Our BERT config:'	,    SCREAMING_SNAKE_CASE__  )

       _lowerCamelCase :	Optional[int]						=RobertaForSequenceClassification(SCREAMING_SNAKE_CASE__  ) if classification_head else RobertaForMaskedLM(SCREAMING_SNAKE_CASE__  )
       model.eval()

       # Now let's copy all the weights.
       # Embeddings
       _lowerCamelCase :	List[str]						=roberta_sent_encoder.embed_tokens.weight
       _lowerCamelCase :	int						=roberta_sent_encoder.embed_positions.weight
       _lowerCamelCase :	Tuple						=torch.zeros_like(
           model.roberta.embeddings.token_type_embeddings.weight  )  # just zero them out b/c RoBERTa doesn't use them.
       _lowerCamelCase :	Optional[Any]						=roberta_sent_encoder.emb_layer_norm.weight
       _lowerCamelCase :	int						=roberta_sent_encoder.emb_layer_norm.bias

       for i in range(config.num_hidden_layers  ):
              # Encoder: start of layer
              _lowerCamelCase :	BertLayer						=model.roberta.encoder.layer[i]
              _lowerCamelCase :	TransformerSentenceEncoderLayer						=roberta_sent_encoder.layers[i]

              # self attention
              _lowerCamelCase :	BertSelfAttention						=layer.attention.self
              assert (
                  roberta_layer.self_attn.k_proj.weight.data.shape
                  == roberta_layer.self_attn.q_proj.weight.data.shape
                  == roberta_layer.self_attn.v_proj.weight.data.shape
                  == torch.Size((config.hidden_size, config.hidden_size)  )
              )

              _lowerCamelCase :	Dict						=roberta_layer.self_attn.q_proj.weight
              _lowerCamelCase :	List[Any]						=roberta_layer.self_attn.q_proj.bias
              _lowerCamelCase :	Optional[Any]						=roberta_layer.self_attn.k_proj.weight
              _lowerCamelCase :	int						=roberta_layer.self_attn.k_proj.bias
              _lowerCamelCase :	Optional[Any]						=roberta_layer.self_attn.v_proj.weight
              _lowerCamelCase :	int						=roberta_layer.self_attn.v_proj.bias

              # self-attention output
              _lowerCamelCase :	BertSelfOutput						=layer.attention.output
              assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
              _lowerCamelCase :	Union[str, Any]						=roberta_layer.self_attn.out_proj.weight
              _lowerCamelCase :	str						=roberta_layer.self_attn.out_proj.bias
              _lowerCamelCase :	List[Any]						=roberta_layer.self_attn_layer_norm.weight
              _lowerCamelCase :	List[Any]						=roberta_layer.self_attn_layer_norm.bias

              # intermediate
              _lowerCamelCase :	BertIntermediate						=layer.intermediate
              assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
              _lowerCamelCase :	str						=roberta_layer.fca.weight
              _lowerCamelCase :	Optional[int]						=roberta_layer.fca.bias

              # output
              _lowerCamelCase :	BertOutput						=layer.output
              assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
              _lowerCamelCase :	str						=roberta_layer.fca.weight
              _lowerCamelCase :	List[Any]						=roberta_layer.fca.bias
              _lowerCamelCase :	List[str]						=roberta_layer.final_layer_norm.weight
              _lowerCamelCase :	Optional[int]						=roberta_layer.final_layer_norm.bias
              # end of layer

       if classification_head:
              _lowerCamelCase :	Any						=roberta.model.classification_heads['mnli'].dense.weight
              _lowerCamelCase :	Optional[Any]						=roberta.model.classification_heads['mnli'].dense.bias
              _lowerCamelCase :	List[str]						=roberta.model.classification_heads['mnli'].out_proj.weight
              _lowerCamelCase :	Tuple						=roberta.model.classification_heads['mnli'].out_proj.bias
       else:
              # LM Head
              _lowerCamelCase :	Optional[Any]						=roberta.model.encoder.lm_head.dense.weight
              _lowerCamelCase :	Tuple						=roberta.model.encoder.lm_head.dense.bias
              _lowerCamelCase :	Tuple						=roberta.model.encoder.lm_head.layer_norm.weight
              _lowerCamelCase :	int						=roberta.model.encoder.lm_head.layer_norm.bias
              _lowerCamelCase :	Dict						=roberta.model.encoder.lm_head.weight
              _lowerCamelCase :	str						=roberta.model.encoder.lm_head.bias

       # Let's check that we get the same results.
       _lowerCamelCase :	torch.Tensor						=roberta.encode(SCREAMING_SNAKE_CASE__  ).unsqueeze(0  )  # batch of size 1

       _lowerCamelCase :	List[Any]						=model(SCREAMING_SNAKE_CASE__  )[0]
       if classification_head:
              _lowerCamelCase :	int						=roberta.model.classification_heads['mnli'](roberta.extract_features(SCREAMING_SNAKE_CASE__  )  )
       else:
              _lowerCamelCase :	int						=roberta.model(SCREAMING_SNAKE_CASE__  )[0]
       print(our_output.shape	,    their_output.shape  )
       _lowerCamelCase :	Any						=torch.max(torch.abs(our_output - their_output  )  ).item()
       print(F'''max_absolute_diff = {max_absolute_diff}'''  )  # ~ 1e-7
       _lowerCamelCase :	Optional[Any]						=torch.allclose(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__	,    atol=1e-3  )
       print('Do both models output the same tensors?'	,    '🔥' if success else '💩'  )
       if not success:
              raise Exception('Something went wRoNg'  )

       pathlib.Path(SCREAMING_SNAKE_CASE__  ).mkdir(parents=SCREAMING_SNAKE_CASE__	,    exist_ok=SCREAMING_SNAKE_CASE__  )
       print(F'''Saving model to {pytorch_dump_folder_path}'''  )
       model.save_pretrained(SCREAMING_SNAKE_CASE__  )


if __name__ == "__main__":
 lowerCamelCase		      = argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--roberta_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
 )
 parser.add_argument(
     '--classification_head', action='store_true', help='Whether to convert a final classification head.'
 )
 lowerCamelCase		      = parser.parse_args()
 convert_roberta_checkpoint_to_pytorch(
     args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )


