




from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				for param, grad_param in zip(model_a.parameters()						,  model_b.parameters() ):
								if not param.requires_grad:
												continue
								if not did_step:
												# Grads should not be in sync
												assert (
												    torch.allclose(param.grad						,  grad_param.grad ) is False
												), F"Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"
								else:
												# Grads should be in sync
												assert (
												    torch.allclose(param.grad						,  grad_param.grad ) is True
												), F"Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase=True ):
				model.train()
				__magic_name__						:						Dict										=model(lowerCamelCase )
				__magic_name__						:						int										=F.mse_loss(lowerCamelCase						,  target.to(output.device ) )
				if not do_backward:
								loss /= accelerator.gradient_accumulation_steps
								loss.backward()
				else:
								accelerator.backward(lowerCamelCase )




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase=False ):
				set_seed(42 )
				__magic_name__						:						Optional[int]										=RegressionModel()
				__magic_name__						:						Optional[Any]										=deepcopy(lowerCamelCase )
				__magic_name__						:						List[str]										=RegressionDataset(length=80 )
				__magic_name__						:						Dict										=DataLoader(lowerCamelCase						,  batch_size=16 )
				model.to(accelerator.device )
				if sched:
								__magic_name__						:						Optional[Any]										=AdamW(params=model.parameters()						,  lr=1E-3 )
								__magic_name__						:						Optional[Any]										=AdamW(params=ddp_model.parameters()						,  lr=1E-3 )
								__magic_name__						:						Optional[int]										=LambdaLR(lowerCamelCase						,  lr_lambda=lambda lowerCamelCase : epoch**0.6_5 )
								__magic_name__						:						Dict										=LambdaLR(lowerCamelCase						,  lr_lambda=lambda lowerCamelCase : epoch**0.6_5 )
				# Make a copy of `model`
				if sched:
								__magic_name__					,			__magic_name__					,			__magic_name__					,			__magic_name__						:						Dict										=accelerator.prepare(lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase )
				else:
								__magic_name__					,			__magic_name__						:						int										=accelerator.prepare(lowerCamelCase						,  lowerCamelCase )
				if sched:
								return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
				return model, ddp_model, dataloader




def 		lowerCAmelCase_ (						lowerCamelCase ):
				# Test when on a single CPU or GPU that the context manager does nothing
				__magic_name__					,			__magic_name__					,			__magic_name__						:						Dict										=get_training_setup(lowerCamelCase )
				# Use a single batch
				__magic_name__					,			__magic_name__						:						Optional[Any]										=next(iter(lowerCamelCase ) ).values()
				for iteration in range(3 ):
								# Gather the distributed inputs and targs for the base model
								__magic_name__					,			__magic_name__						:						Any										=accelerator.gather((ddp_input, ddp_target) )
								__magic_name__					,			__magic_name__						:						str										=input.to(accelerator.device ), target.to(accelerator.device )
								# Perform our initial ground truth step in non "DDP"
								step_model(lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase )
								# Do "gradient accumulation" (noop)
								if iteration % 2 == 0:
												# Accumulate grads locally
												with accelerator.no_sync(lowerCamelCase ):
																step_model(lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase )
								else:
												# Sync grads
												step_model(lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase )

								# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
								check_model_parameters(lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase )
								for param, ddp_param in zip(model.parameters()						,  ddp_model.parameters() ):
												if not param.requires_grad:
																continue
												assert torch.allclose(
												    param.grad						,  ddp_param.grad ), F"Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"

								# Shuffle ddp_input on each iteration
								torch.manual_seed(1337 + iteration )
								__magic_name__						:						Dict										=ddp_input[torch.randperm(len(lowerCamelCase ) )]




def 		lowerCAmelCase_ (						lowerCamelCase ):
				# Test on distributed setup that context manager behaves properly
				__magic_name__					,			__magic_name__					,			__magic_name__						:						Dict										=get_training_setup(lowerCamelCase )
				# Use a single batch
				__magic_name__					,			__magic_name__						:						Any										=next(iter(lowerCamelCase ) ).values()
				for iteration in range(3 ):
								# Gather the distributed inputs and targs for the base model
								__magic_name__					,			__magic_name__						:						Tuple										=accelerator.gather((ddp_input, ddp_target) )
								__magic_name__					,			__magic_name__						:						Any										=input.to(accelerator.device ), target.to(accelerator.device )
								# Perform our initial ground truth step in non "DDP"
								step_model(lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase )
								# Do "gradient accumulation" (noop)
								if iteration % 2 == 0:
												# Accumulate grads locally
												with accelerator.no_sync(lowerCamelCase ):
																step_model(lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase )
								else:
												# Sync grads
												step_model(lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase )

								# DDP model and model should only be in sync when not (iteration % 2 == 0)
								for param, ddp_param in zip(model.parameters()						,  ddp_model.parameters() ):
												if not param.requires_grad:
																continue
												if iteration % 2 == 0:
																# Grads should not be in sync
																assert (
																    torch.allclose(param.grad						,  ddp_param.grad ) is False
																), F"Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"
												else:
																# Grads should be in sync
																assert (
																    torch.allclose(param.grad						,  ddp_param.grad ) is True
																), F"Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"

        # Shuffle ddp_input on each iteration
								torch.manual_seed(1337 + iteration )
								__magic_name__						:						Optional[Any]										=ddp_input[torch.randperm(len(lowerCamelCase ) )]




def 		lowerCAmelCase_ (						lowerCamelCase=False						,  lowerCamelCase=False ):
				__magic_name__						:						List[Any]										=Accelerator(
				    split_batches=lowerCamelCase						,  dispatch_batches=lowerCamelCase						,  gradient_accumulation_steps=2 )
				# Test that context manager behaves properly
				__magic_name__					,			__magic_name__					,			__magic_name__						:						str										=get_training_setup(lowerCamelCase )
				for iteration, batch in enumerate(lowerCamelCase ):
								__magic_name__					,			__magic_name__						:						List[Any]										=batch.values()
								# Gather the distributed inputs and targs for the base model
								__magic_name__					,			__magic_name__						:						Optional[Any]										=accelerator.gather((ddp_input, ddp_target) )
								__magic_name__					,			__magic_name__						:						str										=input.to(accelerator.device ), target.to(accelerator.device )
								# Perform our initial ground truth step in non "DDP"
								step_model(lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase )
								# Do "gradient accumulation" (noop)
								with accelerator.accumulate(lowerCamelCase ):
												step_model(lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase )

								# DDP model and model should only be in sync when not (iteration % 2 == 0)
								for param, ddp_param in zip(model.parameters()						,  ddp_model.parameters() ):
												if not param.requires_grad:
																continue
												if ((iteration + 1) % 2 == 0) or (iteration == len(lowerCamelCase ) - 1):
																# Grads should be in sync
																assert (
																    torch.allclose(param.grad						,  ddp_param.grad ) is True
																), F"Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"
												else:
																# Grads should not be in sync
																assert (
																    torch.allclose(param.grad						,  ddp_param.grad ) is False
																), F"Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"

        # Shuffle ddp_input on each iteration
								torch.manual_seed(1337 + iteration )
								__magic_name__						:						List[Any]										=ddp_input[torch.randperm(len(lowerCamelCase ) )]
				GradientState._reset_state()




def 		lowerCAmelCase_ (						lowerCamelCase=False						,  lowerCamelCase=False ):
				__magic_name__						:						Union[str, Any]										=Accelerator(
				    split_batches=lowerCamelCase						,  dispatch_batches=lowerCamelCase						,  gradient_accumulation_steps=2 )
				# Test that context manager behaves properly
				__magic_name__					,			__magic_name__					,			__magic_name__					,			__magic_name__					,			__magic_name__					,			__magic_name__					,			__magic_name__						:						Any										=get_training_setup(lowerCamelCase						,  lowerCamelCase )
				for iteration, batch in enumerate(lowerCamelCase ):
								__magic_name__					,			__magic_name__						:						str										=batch.values()
								# Gather the distributed inputs and targs for the base model
								__magic_name__					,			__magic_name__						:						List[Any]										=accelerator.gather((ddp_input, ddp_target) )
								__magic_name__					,			__magic_name__						:						List[Any]										=input.to(accelerator.device ), target.to(accelerator.device )
								# Perform our initial ground truth step in non "DDP"
								model.train()
								ddp_model.train()
								step_model(lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase )
								opt.step()

								if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(lowerCamelCase )):
												if split_batches:
																sched.step()
												else:
																for _ in range(accelerator.num_processes ):
																				sched.step()
								opt.zero_grad()
								# Perform gradient accumulation under wrapper
								with accelerator.accumulate(lowerCamelCase ):
												step_model(lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase )
												ddp_opt.step()
												ddp_sched.step()
												ddp_opt.zero_grad()

								# Learning rates should be the same
								assert (
								    opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
								), F"Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]['lr']}\nDDP opt: {ddp_opt.param_groups[0]['lr']}\n"
								__magic_name__						:						Union[str, Any]										=(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(lowerCamelCase ))
								if accelerator.num_processes > 1:
												check_model_parameters(lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase )
								# Shuffle ddp_input on each iteration
								torch.manual_seed(1337 + iteration )
				GradientState._reset_state()




def 		lowerCAmelCase_ (						):
				__magic_name__						:						Optional[int]										=Accelerator()

				__magic_name__						:						List[Any]										=RegressionDataset(length=80 )
				__magic_name__						:						Union[str, Any]										=DataLoader(lowerCamelCase						,  batch_size=16 )
				__magic_name__						:						Tuple										=RegressionDataset(length=96 )
				__magic_name__						:						Dict										=DataLoader(lowerCamelCase						,  batch_size=16 )
				__magic_name__					,			__magic_name__						:						Union[str, Any]										=accelerator.prepare(lowerCamelCase						,  lowerCamelCase )
				assert accelerator.gradient_state.active_dataloader is None
				for iteration, _ in enumerate(lowerCamelCase ):
								assert id(accelerator.gradient_state.active_dataloader ) == id(lowerCamelCase )
								if iteration < len(lowerCamelCase ) - 1:
												assert not accelerator.gradient_state.end_of_dataloader
												if iteration == 1:
																for batch_num, _ in enumerate(lowerCamelCase ):
																				assert id(accelerator.gradient_state.active_dataloader ) == id(lowerCamelCase )
																				if batch_num < len(lowerCamelCase ) - 1:
																								assert not accelerator.gradient_state.end_of_dataloader
																				else:
																								assert accelerator.gradient_state.end_of_dataloader
								else:
												assert accelerator.gradient_state.end_of_dataloader
				assert accelerator.gradient_state.active_dataloader is None




def 		lowerCAmelCase_ (						):
				__magic_name__						:						str										=Accelerator()
				__magic_name__						:						str										=accelerator.state
				if state.local_process_index == 0:
								print("""**Test `accumulate` gradient accumulation with dataloader break**""" )
				test_dataloader_break()
				if state.distributed_type == DistributedType.NO:
								if state.local_process_index == 0:
												print("""**Test NOOP `no_sync` context manager**""" )
								test_noop_sync(lowerCamelCase )
				if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
								if state.local_process_index == 0:
												print("""**Test Distributed `no_sync` context manager**""" )
								test_distributed_sync(lowerCamelCase )
				if state.distributed_type == DistributedType.MULTI_GPU:
								for split_batch in [True, False]:
												for dispatch_batches in [True, False]:
																if state.local_process_index == 0:
																				print(
																				    """**Test `accumulate` gradient accumulation, """						,  F"`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"						,  )
																test_gradient_accumulation(lowerCamelCase						,  lowerCamelCase )

    # Currently will break on torch 2.0 +, need to investigate why
				if is_torch_version("""<"""						,  """2.0""" ) or state.distributed_type == DistributedType.NO:
								if state.local_process_index == 0:
												print(
												    """**Test `accumulate` gradient accumulation with optimizer and scheduler, """						,  """`split_batches=False`, `dispatch_batches=False`**"""						,  )
								test_gradient_accumulation_with_opt_and_scheduler()
								if state.distributed_type == DistributedType.MULTI_GPU:
												for split_batch in [True, False]:
																for dispatch_batches in [True, False]:
																				if not split_batch and not dispatch_batches:
																								continue
																				if state.local_process_index == 0:
																								print(
																								    """**Test `accumulate` gradient accumulation with optimizer and scheduler, """						,  F"`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"						,  )
																				test_gradient_accumulation_with_opt_and_scheduler(lowerCamelCase						,  lowerCamelCase )




def 		lowerCAmelCase_ (						lowerCamelCase ):
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
							main()







'''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       :			str     	= field(
      metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}			)
  __lowerCAmelCase       :			Optional[str]     	= field(
      default=a			,							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=a			,							metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}			)
  __lowerCAmelCase       :			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.
  __lowerCAmelCase       :			Optional[str]     	= field(
      default=a			,							metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}			,							)




@dataclass
class 							lowercase_						:

  '''simple docstring'''





  __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=a			,							metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}			,							)
  __lowerCAmelCase       :			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."
          )
      }			,							)
  __lowerCAmelCase       :			bool     	= field(
      default=a			,							metadata={"help": "Overwrite the cached training and evaluation sets"}			)




def    lowerCamelCase__		(    ):
  # 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				,   UpperCAmelCase				,   UpperCAmelCase    				=						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
  else:
    UpperCAmelCase				,   UpperCAmelCase				,   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(SCREAMING_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'							, SCREAMING_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(SCREAMING_SNAKE_CASE    )    )
  UpperCAmelCase    				=						len(SCREAMING_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=SCREAMING_SNAKE_CASE							, idalabel=SCREAMING_SNAKE_CASE							, labelaid={label: i for i, label in enumerate(SCREAMING_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=SCREAMING_SNAKE_CASE							, cache_dir=model_args.cache_dir							, )

  # Get datasets
  UpperCAmelCase    				=						(
      TokenClassificationDataset(
          token_classification_task=SCREAMING_SNAKE_CASE							, data_dir=data_args.data_dir							, tokenizer=SCREAMING_SNAKE_CASE							, labels=SCREAMING_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=SCREAMING_SNAKE_CASE							, data_dir=data_args.data_dir							, tokenizer=SCREAMING_SNAKE_CASE							, labels=SCREAMING_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(SCREAMING_SNAKE_CASE   : np.ndarray							, SCREAMING_SNAKE_CASE   : np.ndarray    ) -> Tuple[List[int], List[int]]:
    UpperCAmelCase    				=						np.argmax(SCREAMING_SNAKE_CASE							, axis=2    )

    UpperCAmelCase				,   UpperCAmelCase    				=						preds.shape

    UpperCAmelCase    				=						[[] for _ in range(SCREAMING_SNAKE_CASE    )]
    UpperCAmelCase    				=						[[] for _ in range(SCREAMING_SNAKE_CASE    )]

    for i in range(SCREAMING_SNAKE_CASE    ):
      for j in range(SCREAMING_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(SCREAMING_SNAKE_CASE   : EvalPrediction    ) -> Dict:
    UpperCAmelCase				,   UpperCAmelCase    				=						align_predictions(p.predictions							, p.label_ids    )
    return {
        "accuracy_score": accuracy_score(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    ),
        "precision": precision_score(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    ),
        "recall": recall_score(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    ),
        "f1": fa_score(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    ),
    }

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

  # Initialize our Trainer
  UpperCAmelCase    				=						Trainer(
      model=SCREAMING_SNAKE_CASE							, args=SCREAMING_SNAKE_CASE							, train_dataset=SCREAMING_SNAKE_CASE							, eval_dataset=SCREAMING_SNAKE_CASE							, compute_metrics=SCREAMING_SNAKE_CASE							, data_collator=SCREAMING_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(SCREAMING_SNAKE_CASE							, 'w'    ) as writer:
        logger.info('***** Eval results *****'    )
        for key, value in result.items():
          logger.info('  %s = %s'							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
          writer.write('%s = %s\n' % (key, value)    )

      results.update(SCREAMING_SNAKE_CASE    )

    # Predict
  if training_args.do_predict:
    UpperCAmelCase    				=						TokenClassificationDataset(
        token_classification_task=SCREAMING_SNAKE_CASE							, data_dir=data_args.data_dir							, tokenizer=SCREAMING_SNAKE_CASE							, labels=SCREAMING_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				,   UpperCAmelCase				,   UpperCAmelCase    				=						trainer.predict(SCREAMING_SNAKE_CASE    )
    UpperCAmelCase				,   UpperCAmelCase    				=						align_predictions(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )

    UpperCAmelCase    				=						os.path.join(training_args.output_dir							, 'test_results.txt'    )
    if trainer.is_world_process_zero():
      with open(SCREAMING_SNAKE_CASE							, 'w'    ) as writer:
        for key, value in metrics.items():
          logger.info('  %s = %s'							, SCREAMING_SNAKE_CASE							, SCREAMING_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(SCREAMING_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(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )

  return results


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Dict    ):
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
  main()


