






'''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


__A							:							List[Any]  							=		logging.getLogger(__name__)
@dataclass
class 				lowercase       :


				'''simple docstring'''

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


				'''simple docstring'''

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






def    lowerCamelCase_     (    ):
	lowerCamelCase__  =     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.
		lowerCamelCase__  =     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
	else:
		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(lowerCamelCase__				,     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 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"				,     lowerCamelCase__)

	# Set seed
	set_seed(training_args.seed)

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

	# 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=lowerCamelCase__				,     idalabel=lowerCamelCase__				,     labelaid={label: i for i, label in enumerate(lowerCamelCase__)}				,     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				,     )
	lowerCamelCase__  =     AutoModelForTokenClassification.from_pretrained(
	    model_args.model_name_or_path				,     from_tf=bool(".ckpt" in model_args.model_name_or_path)				,     config=lowerCamelCase__				,     cache_dir=model_args.cache_dir				,     )

	# Get datasets
	lowerCamelCase__  =     (
	    TokenClassificationDataset(
	        token_classification_task=lowerCamelCase__				,     data_dir=data_args.data_dir				,     tokenizer=lowerCamelCase__				,     labels=lowerCamelCase__				,     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__  =     (
	    TokenClassificationDataset(
	        token_classification_task=lowerCamelCase__				,     data_dir=data_args.data_dir				,     tokenizer=lowerCamelCase__				,     labels=lowerCamelCase__				,     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]]:
		lowerCamelCase__  =     np.argmax(lowerCamelCase__				,     axis=2)

		lowerCamelCase__  =     preds.shape

		lowerCamelCase__  =     [[] for _ in range(lowerCamelCase__)]
		lowerCamelCase__  =     [[] for _ in range(lowerCamelCase__)]

		for i in range(lowerCamelCase__):
			for j in range(lowerCamelCase__):
				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:
		lowerCamelCase__  =     align_predictions(p.predictions				,     p.label_ids)
		return {
		    "accuracy_score": accuracy_score(lowerCamelCase__				,     lowerCamelCase__),
		    "precision": precision_score(lowerCamelCase__				,     lowerCamelCase__),
		    "recall": recall_score(lowerCamelCase__				,     lowerCamelCase__),
		    "f1": fa_score(lowerCamelCase__				,     lowerCamelCase__),
		}

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

	# Initialize our Trainer
	lowerCamelCase__  =     Trainer(
	    model=lowerCamelCase__				,     args=lowerCamelCase__				,     train_dataset=lowerCamelCase__				,     eval_dataset=lowerCamelCase__				,     compute_metrics=lowerCamelCase__				,     data_collator=lowerCamelCase__				,     )

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

		lowerCamelCase__  =     trainer.evaluate()

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

			results.update(lowerCamelCase__)

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

		lowerCamelCase__  =     trainer.predict(lowerCamelCase__)
		lowerCamelCase__  =     align_predictions(lowerCamelCase__				,     lowerCamelCase__)

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

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

	return results






def    lowerCamelCase_     (    lowercase__):
	main()


if __name__ == "__main__":
	main()









'''simple docstring'''





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


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

__A							:							Tuple  							=		{
    """EleutherAI/gpt-neo-1.3B""": """https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json""",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							"gpt_neo"
    lowerCAmelCase__      				=							["past_key_values"]
    lowerCAmelCase__      				=							{"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}

    def __init__(					self     :     str				,     __lowerCamelCase     :     Optional[int]=50257				,     __lowerCamelCase     :     Optional[int]=2048				,     __lowerCamelCase     :     Optional[int]=2048				,     __lowerCamelCase     :     Any=24				,     __lowerCamelCase     :     List[Any]=[[["global", "local"], 12]]				,     __lowerCamelCase     :     Any=16				,     __lowerCamelCase     :     Any=None				,     __lowerCamelCase     :     Optional[Any]=256				,     __lowerCamelCase     :     List[Any]="gelu_new"				,     __lowerCamelCase     :     Any=0.0				,     __lowerCamelCase     :     List[Any]=0.0				,     __lowerCamelCase     :     Union[str, Any]=0.0				,     __lowerCamelCase     :     Any=0.1				,     __lowerCamelCase     :     Dict=1E-5				,     __lowerCamelCase     :     List[Any]=0.0_2				,     __lowerCamelCase     :     List[Any]=True				,     __lowerCamelCase     :     Optional[int]=50256				,     __lowerCamelCase     :     Tuple=50256				,     **__lowerCamelCase     :     Optional[int]				,     )							->							str:

     '''simple docstring'''




     lowerCamelCase__  =     vocab_size
     lowerCamelCase__  =     max_position_embeddings
     lowerCamelCase__  =     hidden_size
     lowerCamelCase__  =     num_layers
     lowerCamelCase__  =     num_heads
     lowerCamelCase__  =     intermediate_size
     lowerCamelCase__  =     window_size
     lowerCamelCase__  =     activation_function
     lowerCamelCase__  =     resid_dropout
     lowerCamelCase__  =     embed_dropout
     lowerCamelCase__  =     attention_dropout
     lowerCamelCase__  =     classifier_dropout
     lowerCamelCase__  =     layer_norm_epsilon
     lowerCamelCase__  =     initializer_range
     lowerCamelCase__  =     use_cache

     lowerCamelCase__  =     bos_token_id
     lowerCamelCase__  =     eos_token_id

     lowerCamelCase__  =     attention_types
     lowerCamelCase__  =     self.expand_attention_types_params(__lowerCamelCase     )

     if len(self.attention_layers     ) != self.num_layers:
      raise ValueError(
          "Configuration for convolutional module is incorrect. "
          "It is required that `len(config.attention_layers)` == `config.num_layers` "
          f'''but is `len(config.attention_layers) = {len(self.attention_layers     )}`, '''
          f'''`config.num_layers = {self.num_layers}`. '''
          "`config.attention_layers` is prepared using `config.attention_types`. "
          "Please verify the value of `config.attention_types` argument."     )

     super().__init__(bos_token_id=__lowerCamelCase				,     eos_token_id=__lowerCamelCase				,     **__lowerCamelCase     )




    @staticmethod
    def    a__				(					__lowerCamelCase     :     int     )							->							str:

     '''simple docstring'''




     lowerCamelCase__  =     []
     for item in attention_types:
      for _ in range(item[1]     ):
       attentions.extend(item[0]     )
     return attentions






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__				,     lowercase__):
 import torch

 lowerCamelCase__  =     input.size()
 lowerCamelCase__  =     len(lowercase__)
 lowerCamelCase__  =     shape[dimension]

 lowerCamelCase__  =     torch.arange(0				,     lowercase__				,     lowercase__)
 lowerCamelCase__  =     torch.div(sizedim - size				,     lowercase__				,     rounding_mode="floor") + 1
 lowerCamelCase__  =     torch.arange(lowercase__) + low_indices[:min_length][:, None]

 lowerCamelCase__  =     [slice(lowercase__)] * rank
 lowerCamelCase__  =     indices
 lowerCamelCase__  =     input[s]

 lowerCamelCase__  =     list(range(0				,     rank + 1))
 perm.append(perm.pop(dimension + 1))

 return sliced.permute(lowercase__)






def    lowerCamelCase_     (    lowercase__				,     lowercase__):
 import torch

 lowerCamelCase__  =     torch.arange(1				,     lowercase__)
 lowerCamelCase__  =     torch.remainder(lowercase__				,     lowercase__)
 lowerCamelCase__  =     remainders == 0
 lowerCamelCase__  =     candidates[divisor_indices]
 lowerCamelCase__  =     torch.max(lowercase__)
 return largest_divisor, torch.div(lowercase__				,     lowercase__				,     rounding_mode="floor")
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    @property
    def    a__				(					self     :     Any     )							->							Mapping[str, Mapping[int, str]]:

     '''simple docstring'''




     lowerCamelCase__  =     OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}     )
     if self.use_past:
      self.fill_with_past_key_values_(__lowerCamelCase				,     direction="inputs"     )
      lowerCamelCase__  =     {0: "batch", 1: "past_sequence + sequence"}
     else:
      lowerCamelCase__  =     {0: "batch", 1: "sequence"}

     return common_inputs

    @property
    def    a__				(					self     :     Any     )							->							int:

     '''simple docstring'''




     return self._config.num_heads

    def    a__				(					self     :     Optional[Any]				,     __lowerCamelCase     :     PreTrainedTokenizer				,     __lowerCamelCase     :     int = -1				,     __lowerCamelCase     :     int = -1				,     __lowerCamelCase     :     bool = False				,     __lowerCamelCase     :     Optional[TensorType] = None				,     )							->							Mapping[str, Any]:

     '''simple docstring'''




     lowerCamelCase__  =     super(__lowerCamelCase				,     self     ).generate_dummy_inputs(
         __lowerCamelCase				,     batch_size=__lowerCamelCase				,     seq_length=__lowerCamelCase				,     is_pair=__lowerCamelCase				,     framework=__lowerCamelCase     )

     # We need to order the input in the way they appears in the forward()
     lowerCamelCase__  =     OrderedDict({"input_ids": common_inputs["input_ids"]}     )

     # Need to add the past_keys
     if self.use_past:
      if not is_torch_available():
       raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."     )
      else:
       import torch

       lowerCamelCase__  ,	lowerCamelCase__  =     common_inputs["input_ids"].shape
       # Not using the same length for past_key_values
       lowerCamelCase__  =     seqlen + 2
       lowerCamelCase__  =     (
           batch,
           self.num_attention_heads,
           past_key_values_length,
           self._config.hidden_size // self.num_attention_heads,
       )
       lowerCamelCase__  =     [
           (torch.zeros(__lowerCamelCase     ), torch.zeros(__lowerCamelCase     )) for _ in range(self.num_layers     )
       ]

     lowerCamelCase__  =     common_inputs["attention_mask"]
     if self.use_past:
      lowerCamelCase__  =     ordered_inputs["attention_mask"].dtype
      lowerCamelCase__  =     torch.cat(
          [ordered_inputs["attention_mask"], torch.ones(__lowerCamelCase				,     __lowerCamelCase				,     dtype=__lowerCamelCase     )]				,     dim=1     )

     return ordered_inputs




    @property
    def    a__				(					self     :     Optional[Any]     )							->							int:

     '''simple docstring'''




     return 13

