



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






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





def  UpperCamelCase						(       )->       Any:





						"""simple docstring"""





						A__							=       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__							=       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
						else:
												A__						,       A__						,       A__							=       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__							=       import_module("tasks"    )
						try:
												A__							=       getattr(snake_case__			,							model_args.task_type    )
												A__							=       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
						A__							=       token_classification_task.get_labels(data_args.labels    )
						A__							=       dict(enumerate(snake_case__    )    )
						A__							=       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.

						A__							=       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			,							)
						A__							=       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__							=       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
						A__							=       (
						    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
						)
						A__							=       (
						    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(_A			:				np.ndarray			,							_A			:				np.ndarray    ) -> Tuple[List[int], List[int]]:
												A__							=       np.argmax(snake_case__			,							axis=2    )

												A__						,       A__							=       preds.shape

												A__							=       [[] for _ in range(snake_case__    )]
												A__							=       [[] 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(_A			:				EvalPrediction    ) -> Dict:
												A__						,       A__							=       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
						A__							=       DataCollatorWithPadding(snake_case__			,							pad_to_multiple_of=8    ) if training_args.fpaa else None

						# Initialize our Trainer
						A__							=       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
						A__							=       {}
						if training_args.do_eval:
												logger.info("*** Evaluate ***"    )

												A__							=       trainer.evaluate()

												A__							=       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:
												A__							=       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			,							)

												A__						,       A__						,       A__							=       trainer.predict(snake_case__    )
												A__						,       A__							=       align_predictions(snake_case__			,							snake_case__    )

												A__							=       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
												A__							=       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						(       _A			:				Optional[Any]    )->       int:





						"""simple docstring"""





						main()


if __name__ == "__main__":
			main()










import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_							:      Union[str, Any]      =  logging.get_logger(__name__)
def  UpperCamelCase						(       _A			:				str			,							_A			:				str    )->       Any:





						"""simple docstring"""





						A__							=       RobertaPreLayerNormConfig.from_pretrained(
						    _A			,							architectures=["RobertaPreLayerNormForMaskedLM"]    )

						# convert state_dict
						A__							=       torch.load(hf_hub_download(repo_id=_A			,							filename="pytorch_model.bin"    )    )
						A__							=       {}
						for tensor_key, tensor_value in original_state_dict.items():
												# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
												if tensor_key.startswith("roberta."    ):
																		A__							=       "roberta_prelayernorm." + tensor_key[len("roberta."    ) :]

												# The original implementation contains weights which are not used, remove them from the state_dict
												if tensor_key.endswith(".self.LayerNorm.weight"    ) or tensor_key.endswith(".self.LayerNorm.bias"    ):
																		continue

												A__							=       tensor_value

						A__							=       RobertaPreLayerNormForMaskedLM.from_pretrained(
						    pretrained_model_name_or_path=_A			,							config=_A			,							state_dict=_A    )
						model.save_pretrained(_A    )

						# convert tokenizer
						A__							=       AutoTokenizer.from_pretrained(_A    )
						tokenizer.save_pretrained(_A    )


if __name__ == "__main__":
						UpperCAmelCase_							:      str      =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--checkpoint-repo",
						    default=None,
						    type=str,
						    required=True,
						    help="Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						UpperCAmelCase_							:      List[Any]      =  parser.parse_args()
						convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)





