





import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class     SCREAMING_SNAKE_CASE       (a__      ,     a__				):


			@register_to_config
			def __init__(   self      :    Optional[int]      ,					a      :    Optional[int] = 128      ,					a      :    Optional[Any] = 256      ,					a      :    List[str] = 2_000.0      ,					a      :    Tuple = 768      ,					a      :    Optional[int] = 12      ,					a      :    Optional[int] = 12      ,					a      :    List[str] = 64      ,					a      :    Union[str, Any] = 2_048      ,					a      :    List[Any] = 0.1      ,					)->       int:





					"""simple docstring"""







					super().__init__()

					lowercase__           =  nn.Sequential(
					    nn.Linear(_lowerCamelCase      ,					d_model * 4      ,					bias=_lowerCamelCase	)      ,					nn.SiLU()      ,					nn.Linear(d_model * 4      ,					d_model * 4      ,					bias=_lowerCamelCase	)      ,					nn.SiLU()      ,					)

					lowercase__           =  nn.Embedding(_lowerCamelCase      ,					_lowerCamelCase	)
					lowercase__           =  False

					lowercase__           =  nn.Linear(_lowerCamelCase      ,					_lowerCamelCase      ,					bias=_lowerCamelCase	)

					lowercase__           =  nn.Dropout(p=_lowerCamelCase	)

					lowercase__           =  nn.ModuleList()
					for lyr_num in range(_lowerCamelCase	):
							# FiLM conditional T5 decoder
							lowercase__           =  DecoderLayer(d_model=_lowerCamelCase      ,					d_kv=_lowerCamelCase      ,					num_heads=_lowerCamelCase      ,					d_ff=_lowerCamelCase      ,					dropout_rate=_lowerCamelCase	)
							self.decoders.append(_lowerCamelCase	)

					lowercase__           =  TaLayerNorm(_lowerCamelCase	)

					lowercase__           =  nn.Dropout(p=_lowerCamelCase	)
					lowercase__           =  nn.Linear(_lowerCamelCase      ,					_lowerCamelCase      ,					bias=_lowerCamelCase	)


			def 	SCREAMING_SNAKE_CASE_							(   self      :    int      ,					a      :    Dict      ,					a      :    str	)->       Optional[Any]:





					"""simple docstring"""







					lowercase__           =  torch.mul(query_input.unsqueeze(-1	)      ,					key_input.unsqueeze(-2	)	)
					return mask.unsqueeze(-3	)


			def 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]      ,					a      :    Union[str, Any]      ,					a      :    List[Any]      ,					a      :    int	)->       List[Any]:





					"""simple docstring"""







					lowercase__           =  decoder_input_tokens.shape
					assert decoder_noise_time.shape == (batch,)

					# decoder_noise_time is in [0, 1), so rescale to expected timing range.
					lowercase__           =  get_timestep_embedding(
					    decoder_noise_time * self.config.max_decoder_noise_time      ,					embedding_dim=self.config.d_model      ,					max_period=self.config.max_decoder_noise_time      ,					).to(dtype=self.dtype	)

					lowercase__           =  self.conditioning_emb(_lowerCamelCase	).unsqueeze(1	)

					assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

					lowercase__           =  decoder_input_tokens.shape[1]

					# If we want to use relative positions for audio context, we can just offset
					# this sequence by the length of encodings_and_masks.
					lowercase__           =  torch.broadcast_to(
					    torch.arange(_lowerCamelCase      ,					device=decoder_input_tokens.device	)      ,					(batch, seq_length)      ,					)

					lowercase__           =  self.position_encoding(_lowerCamelCase	)

					lowercase__           =  self.continuous_inputs_projection(_lowerCamelCase	)
					inputs += position_encodings
					lowercase__           =  self.dropout(_lowerCamelCase	)

					# decoder: No padding present.
					lowercase__           =  torch.ones(
					    decoder_input_tokens.shape[:2]      ,					device=decoder_input_tokens.device      ,					dtype=inputs.dtype	)

					# Translate encoding masks to encoder-decoder masks.
					lowercase__           =  [(x, self.encoder_decoder_mask(_lowerCamelCase      ,					_lowerCamelCase	)) for x, y in encodings_and_masks]

					# cross attend style: concat encodings
					lowercase__           =  torch.cat([x[0] for x in encodings_and_encdec_masks]      ,					dim=1	)
					lowercase__           =  torch.cat([x[1] for x in encodings_and_encdec_masks]      ,					dim=-1	)

					for lyr in self.decoders:
							lowercase__           =  lyr(
							    _lowerCamelCase      ,					conditioning_emb=_lowerCamelCase      ,					encoder_hidden_states=_lowerCamelCase      ,					encoder_attention_mask=_lowerCamelCase      ,					)[0]

					lowercase__           =  self.decoder_norm(_lowerCamelCase	)
					lowercase__           =  self.post_dropout(_lowerCamelCase	)

					lowercase__           =  self.spec_out(_lowerCamelCase	)
					return spec_out






class     SCREAMING_SNAKE_CASE       (nn.Module				):


			def __init__(   self      :    Dict      ,					a      :    Union[str, Any]      ,					a      :    Optional[int]      ,					a      :    Tuple      ,					a      :    List[Any]      ,					a      :    Any      ,					a      :    int=1E-6	)->       List[str]:





					"""simple docstring"""







					super().__init__()
					lowercase__           =  nn.ModuleList()

					# cond self attention: layer 0
					self.layer.append(
					    TaLayerSelfAttentionCond(d_model=_lowerCamelCase      ,					d_kv=_lowerCamelCase      ,					num_heads=_lowerCamelCase      ,					dropout_rate=_lowerCamelCase	)	)

					# cross attention: layer 1
					self.layer.append(
					    TaLayerCrossAttention(
					        d_model=_lowerCamelCase      ,					d_kv=_lowerCamelCase      ,					num_heads=_lowerCamelCase      ,					dropout_rate=_lowerCamelCase      ,					layer_norm_epsilon=_lowerCamelCase      ,					)	)

					# Film Cond MLP + dropout: last layer
					self.layer.append(
					    TaLayerFFCond(d_model=_lowerCamelCase      ,					d_ff=_lowerCamelCase      ,					dropout_rate=_lowerCamelCase      ,					layer_norm_epsilon=_lowerCamelCase	)	)


			def 	SCREAMING_SNAKE_CASE_							(   self      :    int      ,					a      :    Any      ,					a      :    Dict=None      ,					a      :    List[str]=None      ,					a      :    str=None      ,					a      :    Tuple=None      ,					a      :    Optional[int]=None      ,					)->       Optional[int]:





					"""simple docstring"""







					lowercase__           =  self.layer[0](
					    _lowerCamelCase      ,					conditioning_emb=_lowerCamelCase      ,					attention_mask=_lowerCamelCase      ,					)

					if encoder_hidden_states is not None:
							lowercase__           =  torch.where(encoder_attention_mask > 0      ,					0      ,					-1E1_0	).to(
							    encoder_hidden_states.dtype	)

							lowercase__           =  self.layer[1](
							    _lowerCamelCase      ,					key_value_states=_lowerCamelCase      ,					attention_mask=_lowerCamelCase      ,					)

					# Apply Film Conditional Feed Forward layer
					lowercase__           =  self.layer[-1](_lowerCamelCase      ,					_lowerCamelCase	)

					return (hidden_states,)






class     SCREAMING_SNAKE_CASE       (nn.Module				):


			def __init__(   self      :    List[str]      ,					a      :    str      ,					a      :    Tuple      ,					a      :    str      ,					a      :    Optional[int]	)->       List[str]:





					"""simple docstring"""







					super().__init__()
					lowercase__           =  TaLayerNorm(_lowerCamelCase	)
					lowercase__           =  TaFiLMLayer(in_features=d_model * 4      ,					out_features=_lowerCamelCase	)
					lowercase__           =  Attention(query_dim=_lowerCamelCase      ,					heads=_lowerCamelCase      ,					dim_head=_lowerCamelCase      ,					out_bias=_lowerCamelCase      ,					scale_qk=_lowerCamelCase	)
					lowercase__           =  nn.Dropout(_lowerCamelCase	)


			def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict      ,					a      :    str      ,					a      :    Tuple=None      ,					a      :    Tuple=None      ,					)->       List[str]:





					"""simple docstring"""







					lowercase__           =  self.layer_norm(_lowerCamelCase	)

					if conditioning_emb is not None:
							lowercase__           =  self.FiLMLayer(_lowerCamelCase      ,					_lowerCamelCase	)

					# Self-attention block
					lowercase__           =  self.attention(_lowerCamelCase	)

					lowercase__           =  hidden_states + self.dropout(_lowerCamelCase	)

					return hidden_states






class     SCREAMING_SNAKE_CASE       (nn.Module				):


			def __init__(   self      :    List[Any]      ,					a      :    str      ,					a      :    str      ,					a      :    Tuple      ,					a      :    Optional[int]      ,					a      :    str	)->       Any:





					"""simple docstring"""







					super().__init__()
					lowercase__           =  Attention(query_dim=_lowerCamelCase      ,					heads=_lowerCamelCase      ,					dim_head=_lowerCamelCase      ,					out_bias=_lowerCamelCase      ,					scale_qk=_lowerCamelCase	)
					lowercase__           =  TaLayerNorm(_lowerCamelCase      ,					eps=_lowerCamelCase	)
					lowercase__           =  nn.Dropout(_lowerCamelCase	)


			def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]      ,					a      :    Union[str, Any]      ,					a      :    Any=None      ,					a      :    Optional[Any]=None      ,					)->       Optional[int]:





					"""simple docstring"""







					lowercase__           =  self.layer_norm(_lowerCamelCase	)
					lowercase__           =  self.attention(
					    _lowerCamelCase      ,					encoder_hidden_states=_lowerCamelCase      ,					attention_mask=attention_mask.squeeze(1	)      ,					)
					lowercase__           =  hidden_states + self.dropout(_lowerCamelCase	)
					return layer_output






class     SCREAMING_SNAKE_CASE       (nn.Module				):


			def __init__(   self      :    List[str]      ,					a      :    str      ,					a      :    Union[str, Any]      ,					a      :    Tuple      ,					a      :    int	)->       int:





					"""simple docstring"""







					super().__init__()
					lowercase__           =  TaDenseGatedActDense(d_model=_lowerCamelCase      ,					d_ff=_lowerCamelCase      ,					dropout_rate=_lowerCamelCase	)
					lowercase__           =  TaFiLMLayer(in_features=d_model * 4      ,					out_features=_lowerCamelCase	)
					lowercase__           =  TaLayerNorm(_lowerCamelCase      ,					eps=_lowerCamelCase	)
					lowercase__           =  nn.Dropout(_lowerCamelCase	)


			def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple      ,					a      :    List[Any]      ,					a      :    Tuple=None	)->       Optional[int]:





					"""simple docstring"""







					lowercase__           =  self.layer_norm(_lowerCamelCase	)
					if conditioning_emb is not None:
							lowercase__           =  self.film(_lowerCamelCase      ,					_lowerCamelCase	)

					lowercase__           =  self.DenseReluDense(_lowerCamelCase	)
					lowercase__           =  hidden_states + self.dropout(_lowerCamelCase	)
					return hidden_states






class     SCREAMING_SNAKE_CASE       (nn.Module				):


			def __init__(   self      :    Tuple      ,					a      :    int      ,					a      :    List[str]      ,					a      :    Tuple	)->       int:





					"""simple docstring"""







					super().__init__()
					lowercase__           =  nn.Linear(_lowerCamelCase      ,					_lowerCamelCase      ,					bias=_lowerCamelCase	)
					lowercase__           =  nn.Linear(_lowerCamelCase      ,					_lowerCamelCase      ,					bias=_lowerCamelCase	)
					lowercase__           =  nn.Linear(_lowerCamelCase      ,					_lowerCamelCase      ,					bias=_lowerCamelCase	)
					lowercase__           =  nn.Dropout(_lowerCamelCase	)
					lowercase__           =  NewGELUActivation()


			def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]      ,					a      :    Tuple	)->       List[Any]:





					"""simple docstring"""







					lowercase__           =  self.act(self.wi_a(_lowerCamelCase	)	)
					lowercase__           =  self.wi_a(_lowerCamelCase	)
					lowercase__           =  hidden_gelu * hidden_linear
					lowercase__           =  self.dropout(_lowerCamelCase	)

					lowercase__           =  self.wo(_lowerCamelCase	)
					return hidden_states






class     SCREAMING_SNAKE_CASE       (nn.Module				):


			def __init__(   self      :    Union[str, Any]      ,					a      :    Optional[int]      ,					a      :    Tuple=1E-6	)->       List[str]:





					"""simple docstring"""







					super().__init__()
					lowercase__           =  nn.Parameter(torch.ones(_lowerCamelCase	)	)
					lowercase__           =  eps


			def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict      ,					a      :    Any	)->       Optional[Any]:





					"""simple docstring"""







					lowercase__           =  hidden_states.to(torch.floataa	).pow(2	).mean(-1      ,					keepdim=_lowerCamelCase	)
					lowercase__           =  hidden_states * torch.rsqrt(variance + self.variance_epsilon	)

					# convert into half-precision if necessary
					if self.weight.dtype in [torch.floataa, torch.bfloataa]:
							lowercase__           =  hidden_states.to(self.weight.dtype	)

					return self.weight * hidden_states






class     SCREAMING_SNAKE_CASE       (nn.Module				):


			def 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]      ,					a      :    List[Any]	)->       torch.Tensor:





					"""simple docstring"""







					return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi	) * (input + 0.044715 * torch.pow(_lowerCamelCase      ,					3.0	))	))






class     SCREAMING_SNAKE_CASE       (nn.Module				):


			def __init__(   self      :    Tuple      ,					a      :    str      ,					a      :    Tuple	)->       Any:





					"""simple docstring"""







					super().__init__()
					lowercase__           =  nn.Linear(_lowerCamelCase      ,					out_features * 2      ,					bias=_lowerCamelCase	)


			def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]      ,					a      :    Optional[int]      ,					a      :    Any	)->       List[str]:





					"""simple docstring"""







					lowercase__           =  self.scale_bias(_lowerCamelCase	)
					lowercase__           =  torch.chunk(_lowerCamelCase      ,					2      ,					-1	)
					lowercase__           =  x * (1 + scale) + shift
					return x









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


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






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






def       __UpperCamelCase  ()    ->       str:
  # 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.

  lowercase__           =  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.
    lowercase__      ,   lowercase__      ,   lowercase__           =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
  else:
    lowercase__      ,   lowercase__      ,   lowercase__           =  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.'		)

  lowercase__           =  import_module('tasks'		)
  try:
    lowercase__           =  getattr(_SCREAMING_SNAKE_CASE		,						model_args.task_type		)
    lowercase__           =  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
  lowercase__           =  token_classification_task.get_labels(data_args.labels		)
  lowercase__           =  dict(enumerate(_SCREAMING_SNAKE_CASE		)		)
  lowercase__           =  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.

  lowercase__           =  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		,						)
  lowercase__           =  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		,						)
  lowercase__           =  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
  lowercase__           =  (
      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
  )
  lowercase__           =  (
      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		,						_SCREAMING_SNAKE_CASE		) -> Tuple[List[int], List[int]]:
    lowercase__           =  np.argmax(_SCREAMING_SNAKE_CASE		,						axis=2		)

    lowercase__      ,   lowercase__           =  preds.shape

    lowercase__           =  [[] for _ in range(_SCREAMING_SNAKE_CASE		)]
    lowercase__           =  [[] 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		) -> Dict:
    lowercase__      ,   lowercase__           =  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
  lowercase__           =  DataCollatorWithPadding(_SCREAMING_SNAKE_CASE		,						pad_to_multiple_of=8		) if training_args.fpaa else None

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

    lowercase__           =  trainer.evaluate()

    lowercase__           =  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:
    lowercase__           =  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		,						)

    lowercase__      ,   lowercase__      ,   lowercase__           =  trainer.predict(_SCREAMING_SNAKE_CASE		)
    lowercase__      ,   lowercase__           =  align_predictions(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)

    lowercase__           =  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
    lowercase__           =  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       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Any:
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
      main()


