"""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__     						=  logging.getLogger(__name__)


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



@dataclass
class 				a :
    _snake_case   :     str   							=							field(
        metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'}      )
    _snake_case   :     Optional[str]   							=							field(
        default=a__		,							metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'}		,							)
    _snake_case   :     int   							=							field(
        default=1_28		,							metadata={
            'help': (
                'The maximum total input sequence length after tokenization. Sequences longer '
                'than this will be truncated, sequences shorter will be padded.'
            )
        }		,							)
    _snake_case   :     bool   							=							field(
        default=a__		,							metadata={'help': 'Overwrite the cached training and evaluation sets'}      )






def 			__UpperCAmelCase    (	):







  """simple docstring"""
  # 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         =					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         =					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
  else:
    _UpperCAmelCase         =					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         =					import_module("""tasks"""						)
  try:
    _UpperCAmelCase         =					getattr(_snake_case      ,model_args.task_type						)
    _UpperCAmelCase         =					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"""      ,_snake_case						)

  # Set seed
  set_seed(training_args.seed						)

  # Prepare CONLL-2003 task
  _UpperCAmelCase         =					token_classification_task.get_labels(data_args.labels						)
  _UpperCAmelCase         =					dict(enumerate(_snake_case						)						)
  _UpperCAmelCase         =					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.

  _UpperCAmelCase         =					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      ,)
  _UpperCAmelCase         =					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         =					AutoModelForTokenClassification.from_pretrained(
      model_args.model_name_or_path      ,from_tf=bool(""".ckpt""" in model_args.model_name_or_path						)      ,config=_snake_case      ,cache_dir=model_args.cache_dir      ,)

  # Get datasets
  _UpperCAmelCase         =					(
      TokenClassificationDataset(
          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
  )
  _UpperCAmelCase         =					(
      TokenClassificationDataset(
          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(lowercase      ,lowercase						) -> Tuple[List[int], List[int]]:
    _UpperCAmelCase         =					np.argmax(_snake_case      ,axis=2						)

    _UpperCAmelCase         =					preds.shape

    _UpperCAmelCase         =					[[] for _ in range(_snake_case						)]
    _UpperCAmelCase         =					[[] for _ in range(_snake_case						)]

    for i in range(_snake_case						):
      for j in range(_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(lowercase						) -> Dict:
    _UpperCAmelCase         =					align_predictions(p.predictions      ,p.label_ids						)
    return {
        "accuracy_score": accuracy_score(_snake_case      ,_snake_case						),
        "precision": precision_score(_snake_case      ,_snake_case						),
        "recall": recall_score(_snake_case      ,_snake_case						),
        "f1": fa_score(_snake_case      ,_snake_case						),
    }

  # Data collator
  _UpperCAmelCase         =					DataCollatorWithPadding(_snake_case      ,pad_to_multiple_of=8						) if training_args.fpaa else None

  # Initialize our Trainer
  _UpperCAmelCase         =					Trainer(
      model=_snake_case      ,args=_snake_case      ,train_dataset=_snake_case      ,eval_dataset=_snake_case      ,compute_metrics=_snake_case      ,data_collator=_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         =					{}
  if training_args.do_eval:
    logger.info("""*** Evaluate ***"""						)

    _UpperCAmelCase         =					trainer.evaluate()

    _UpperCAmelCase         =					os.path.join(training_args.output_dir      ,"""eval_results.txt"""						)
    if trainer.is_world_process_zero():
      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:
    _UpperCAmelCase         =					TokenClassificationDataset(
        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      ,)

    _UpperCAmelCase         =					trainer.predict(_snake_case						)
    _UpperCAmelCase         =					align_predictions(_snake_case      ,_snake_case						)

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

        # Save predictions
    _UpperCAmelCase         =					os.path.join(training_args.output_dir      ,"""test_predictions.txt"""						)
    if trainer.is_world_process_zero():
      with open(_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(_snake_case      ,_snake_case      ,_snake_case						)

  return results






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
    main()


"""simple docstring"""




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 XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('1.0.0a'):
					raise Exception('requires fairseq >= 1.0.0a')

logging.set_verbosity_info()
UpperCAmelCase__      :    Union[str, Any]    							= logging.get_logger(__name__)

UpperCAmelCase__      :    List[str]    							= 'Hello world! cécé herlolip'

def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:       int           =   FairseqRobertaModel.from_pretrained(_snake_case						)
							roberta.eval()  # disable dropout
							SCREAMING_SNAKE_CASE__						:       Any           =   roberta.model.encoder.sentence_encoder
							SCREAMING_SNAKE_CASE__						:       Any           =   XLMRobertaConfig(
							    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings   ,hidden_size=roberta.cfg.model.encoder_embed_dim   ,num_hidden_layers=roberta.cfg.model.encoder_layers   ,num_attention_heads=roberta.cfg.model.encoder_attention_heads   ,intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim   ,max_position_embeddings=514   ,type_vocab_size=1   ,layer_norm_eps=1E-5   ,)
							if classification_head:
														SCREAMING_SNAKE_CASE__						:       Dict           =   roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

							print("""Our RoBERTa config:"""   ,_snake_case						)

							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   XLMRobertaXLForSequenceClassification(_snake_case						) if classification_head else XLMRobertaXLForMaskedLM(_snake_case						)
							model.eval()

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

							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   roberta_sent_encoder.layer_norm.weight
							SCREAMING_SNAKE_CASE__						:       Dict           =   roberta_sent_encoder.layer_norm.bias

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

														SCREAMING_SNAKE_CASE__						:       RobertaAttention           =   layer.attention
														SCREAMING_SNAKE_CASE__						:       List[str]           =   roberta_layer.self_attn_layer_norm.weight
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   roberta_layer.self_attn_layer_norm.bias

														# self attention
														SCREAMING_SNAKE_CASE__						:       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)						)
														)

														SCREAMING_SNAKE_CASE__						:       List[str]           =   roberta_layer.self_attn.q_proj.weight
														SCREAMING_SNAKE_CASE__						:       Tuple           =   roberta_layer.self_attn.q_proj.bias
														SCREAMING_SNAKE_CASE__						:       Tuple           =   roberta_layer.self_attn.k_proj.weight
														SCREAMING_SNAKE_CASE__						:       int           =   roberta_layer.self_attn.k_proj.bias
														SCREAMING_SNAKE_CASE__						:       List[str]           =   roberta_layer.self_attn.v_proj.weight
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   roberta_layer.self_attn.v_proj.bias

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

														# this one is final layer norm
														SCREAMING_SNAKE_CASE__						:       Tuple           =   roberta_layer.final_layer_norm.weight
														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   roberta_layer.final_layer_norm.bias

														# intermediate
														SCREAMING_SNAKE_CASE__						:       BertIntermediate           =   layer.intermediate
														assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   roberta_layer.fca.weight
														SCREAMING_SNAKE_CASE__						:       Tuple           =   roberta_layer.fca.bias

														# output
														SCREAMING_SNAKE_CASE__						:       BertOutput           =   layer.output
														assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
														SCREAMING_SNAKE_CASE__						:       Tuple           =   roberta_layer.fca.weight
														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   roberta_layer.fca.bias
														# end of layer

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

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

							SCREAMING_SNAKE_CASE__						:       Tuple           =   model(_snake_case						)[0]
							if classification_head:
														SCREAMING_SNAKE_CASE__						:       Dict           =   roberta.model.classification_heads["""mnli"""](roberta.extract_features(_snake_case						)						)
							else:
														SCREAMING_SNAKE_CASE__						:       Tuple           =   roberta.model(_snake_case						)[0]
							print(our_output.shape   ,their_output.shape						)
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   torch.max(torch.abs(our_output - their_output						)						).item()
							print(f'''max_absolute_diff = {max_absolute_diff}'''						)  # ~ 1e-7
							SCREAMING_SNAKE_CASE__						:       Tuple           =   torch.allclose(_snake_case   ,_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(_snake_case						).mkdir(parents=_snake_case   ,exist_ok=_snake_case						)
							print(f'''Saving model to {pytorch_dump_folder_path}'''						)
							model.save_pretrained(_snake_case						)


if __name__ == "__main__":
					UpperCAmelCase__      :    Union[str, Any]    							= 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.'
					)
					UpperCAmelCase__      :    Any    							= parser.parse_args()
					convert_xlm_roberta_xl_checkpoint_to_pytorch(
					    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
					)




