


"""simple docstring"""






import argparse
import gc
import os
import sys
from pathlib import Path
from typing import List  # noqa: F401

import pytorch_lightning as pl
import torch
from finetune import SummarizationModule, TranslationModule
from finetune import main as ft_main
from make_student import create_student_by_copying_alternating_layers, get_layers_to_supervise
from torch import nn

from transformers import AutoModelForSeqaSeqLM, MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import calculate_bleu, check_output_dir, freeze_params, label_smoothed_nll_loss, use_task_specific_params


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import generic_train  # noqa


class 			SCREAMING_SNAKE_CASE_  (   _lowercase):




							'''simple docstring'''





							__magic_name__					:     int				  =  ['''loss''', '''ce_loss''', '''mlm_loss''', '''hid_loss_enc''', '''hid_loss_dec''']




							def __init__(						self	,					lowerCamelCase__)   ->	Union[str, Any]:







								'''simple docstring'''




								assert Path(hparams.data_dir).exists()
								snake_case__  :						List[str]		=      Path(hparams.output_dir)
								self.output_dir.mkdir(exist_ok=lowerCamelCase__)

								snake_case__  :						Dict		=      self.output_dir.joinpath("student")

								snake_case__  :						str		=      str(lowerCamelCase__)  # Tell lightning we are training the student
								snake_case__  :						List[Any]		=      AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher).eval()
								use_task_specific_params(lowerCamelCase__	,					hparams.task)  # We copy good generation parameters to student by default
								if hparams.student is not None:
									snake_case__  :						Optional[int]		=      AutoModelForSeqaSeqLM.from_pretrained(hparams.student)
									use_task_specific_params(lowerCamelCase__	,					hparams.task)
									snake_case__,					snake_case__  :						Optional[Any]		=      None, None
								else:
									snake_case__,					snake_case__,					snake_case__  :						Any		=      create_student_by_copying_alternating_layers(
									    lowerCamelCase__	,					e=hparams.student_encoder_layers	,					d=hparams.student_decoder_layers	,					save_path=lowerCamelCase__)

								if hparams.length_penalty != -1:
									snake_case__  :						List[str]		=      hparams.length_penalty
								snake_case__  :						List[Any]		=      hparams.teacher  # Use teacher's tokenizer
								super().__init__(lowerCamelCase__	,					model=lowerCamelCase__	,					config=student.config)
								assert student.config.model_type == teacher.config.model_type, (
								    f"""teacher, student model types should be the same, got {student.config.model_type} !="""
								    f""" {teacher.config.model_type}"""
								)

								if student.config.model_type == "t5":
									snake_case__  :						Optional[int]		=      len(student.get_encoder().block)
									snake_case__  :						Dict		=      len(student.get_decoder().block)
									snake_case__  :						Union[str, Any]		=      len(teacher.get_encoder().block)
									snake_case__  :						Union[str, Any]		=      len(teacher.get_decoder().block)
								else:
									snake_case__  :						List[str]		=      student.config.encoder_layers
									snake_case__  :						int		=      student.config.decoder_layers
									snake_case__  :						Tuple		=      teacher.config.encoder_layers
									snake_case__  :						List[Any]		=      teacher.config.decoder_layers

								snake_case__  :						List[str]		=      not (hparams.student is None or hparams.teacher == hparams.student)
								snake_case__  :						Any		=      (not self.different_base_models) and hparams.alpha_hid > 0
								snake_case__  :						Union[str, Any]		=      self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
								# self.different_encoder determines whether we need to run the teacher encoder
								snake_case__  :						Any		=      teacher
								freeze_params(self.teacher)

								if not self.different_encoder:  # To save RAM, delete teacher encoder and freeze student encoder.
									try:
										del self.teacher.model.encoder
									except AttributeError:  # T5
										del self.teacher.encoder

								if e_layer_ids is None:
									snake_case__  :						List[str]		=      list(range(lowerCamelCase__))
								if d_layer_ids is None:
									snake_case__  :						Union[str, Any]		=      list(range(lowerCamelCase__))

								snake_case__,					snake_case__  :						int		=      e_layer_ids, d_layer_ids  # type: List[int], List[int]

								if self.do_calc_hidden_loss:  # Intermediate supervision: Decide which layers to supervise
									if hparams.supervise_forward:
										snake_case__  :						int		=      get_layers_to_supervise(
										    n_student=len(self.e_layer_ids)	,					n_teacher=lowerCamelCase__)
										snake_case__  :						str		=      get_layers_to_supervise(
										    n_student=len(self.d_layer_ids)	,					n_teacher=lowerCamelCase__)
									else:  # student layer should emulate hidden states of the teacher layer it was copied from
										snake_case__  :						str		=      self.e_layer_ids
										snake_case__  :						List[Any]		=      self.d_layer_ids
								else:
									snake_case__  :						List[Any]		=      None
									snake_case__  :						List[str]		=      None

								snake_case__  :						Tuple		=      nn.KLDivLoss(reduction="batchmean")
								snake_case__  :						int		=      2.0
								snake_case__  :						Any		=      hparams.alpha_mlm
								snake_case__  :						Tuple		=      hparams.alpha_ce
								snake_case__  :						int		=      hparams.alpha_hid
								gc.collect()
								torch.cuda.empty_cache()




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__)   ->	List[Any]:







								'''simple docstring'''




								snake_case__  :						List[Any]		=      mask[:, :, None].expand_as(lowerCamelCase__)
								snake_case__  :						Tuple		=      s_logits.size(-1)
								snake_case__  :						Tuple		=      torch.masked_select(lowerCamelCase__	,					lowerCamelCase__)  # (bs * seq_length * voc_size) modulo the 1s in mask
								snake_case__  :						int		=      torch.masked_select(lowerCamelCase__	,					lowerCamelCase__)  # (bs * seq_length * voc_size) modulo the 1s in mask
								snake_case__  :						Optional[Any]		=      s_logits_slct.view(-1	,					lowerCamelCase__)  # (bs * seq_length, voc_size) modulo the 1s in mask
								snake_case__  :						Optional[Any]		=      t_logits_slct.view(-1	,					lowerCamelCase__)  # (bs * seq_length, voc_size) modulo the 1s in mask
								assert t_logits_slct.size() == s_logits_slct.size()
								snake_case__  :						List[str]		=      (
								    self.ce_loss_fct(
								        nn.functional.log_softmax(s_logits_slct / self.temperature	,					dim=-1)	,					nn.functional.softmax(t_logits_slct / self.temperature	,					dim=-1)	,					)
								    * (self.temperature) ** 2
								)
								return loss_ce




							@staticmethod
							def        UpperCAmelCase (						lowerCamelCase__	,					lowerCamelCase__)   ->	Optional[int]:







								'''simple docstring'''




								SummarizationModule.add_model_specific_args(lowerCamelCase__	,					lowerCamelCase__)
								add_distill_args(lowerCamelCase__)
								return parser




							def        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	tuple:







								'''simple docstring'''




								snake_case__  :						str		=      self.tokenizer.pad_token_id
								snake_case__,					snake_case__,					snake_case__  :						Dict		=      batch["input_ids"], batch["attention_mask"], batch["labels"]
								if isinstance(self.model	,					lowerCamelCase__):
									snake_case__  :						Optional[Any]		=      self.model._shift_right(lowerCamelCase__)
								else:
									snake_case__  :						int		=      shift_tokens_right(lowerCamelCase__	,					lowerCamelCase__)

								# noinspection PyCallingNonCallable
								snake_case__  :						List[str]		=      self(
								    lowerCamelCase__	,					attention_mask=lowerCamelCase__	,					decoder_input_ids=lowerCamelCase__	,					output_hidden_states=self.do_calc_hidden_loss	,					output_attentions=lowerCamelCase__	,					use_cache=lowerCamelCase__	,					)
								snake_case__  :						Dict		=      student_outputs["logits"]

								# Same cross entropy vs. label smoothing logic as finetune.py
								assert lm_logits.shape[-1] == self.model.config.vocab_size
								if self.hparams.label_smoothing == 0:
									# Same behavior as modeling_bart.py, besides ignoring pad_token_id
									snake_case__  :						List[str]		=      nn.CrossEntropyLoss(ignore_index=lowerCamelCase__)
									snake_case__  :						Tuple		=      loss_fct(lm_logits.view(-1	,					lm_logits.shape[-1])	,					labels.view(-1))
								else:
									snake_case__  :						Optional[int]		=      nn.functional.log_softmax(lowerCamelCase__	,					dim=-1)
									snake_case__,					snake_case__  :						List[str]		=      label_smoothed_nll_loss(
									    lowerCamelCase__	,					lowerCamelCase__	,					self.hparams.label_smoothing	,					ignore_index=lowerCamelCase__)

								def zero_tensor():
									return torch.tensor(0.0).type_as(lowerCamelCase__)

								snake_case__  :						Optional[int]		=      student_outputs[
								    "encoder_last_hidden_state"
								]  # use this unless self.different_base_models
								snake_case__,					snake_case__  :						Optional[int]		=      zero_tensor(), zero_tensor()
								if self.different_encoder:  # compute encoder hidden state loss
									snake_case__  :						str		=      self.teacher.get_encoder()(
									    lowerCamelCase__	,					attention_mask=lowerCamelCase__	,					output_hidden_states=self.do_calc_hidden_loss	,					)
									if self.different_base_models:
										snake_case__  :						Optional[int]		=      all_teacher_encoder_outputs["last_hidden_state"]
									elif self.do_calc_hidden_loss:
										snake_case__  :						int		=      self.calc_hidden_loss(
										    lowerCamelCase__	,					student_outputs["encoder_hidden_states"]	,					all_teacher_encoder_outputs["hidden_states"]	,					self.e_matches	,					normalize_hidden=self.hparams.normalize_hidden	,					)

								snake_case__  :						str		=      self.teacher(
								    lowerCamelCase__	,					attention_mask=lowerCamelCase__	,					encoder_outputs=(teacher_enc_outputs,)	,					decoder_input_ids=lowerCamelCase__	,					output_hidden_states=self.do_calc_hidden_loss	,					use_cache=lowerCamelCase__	,					)
								snake_case__  :						List[str]		=      decoder_input_ids.ne(lowerCamelCase__)
								snake_case__  :						Dict		=      self.calc_ce_loss(lowerCamelCase__	,					lowerCamelCase__	,					teacher_outputs["logits"])
								if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
									snake_case__  :						Any		=      self.calc_hidden_loss(
									    lowerCamelCase__	,					student_outputs["decoder_hidden_states"]	,					teacher_outputs["decoder_hidden_states"]	,					self.d_matches	,					normalize_hidden=self.hparams.normalize_hidden	,					)

								snake_case__  :						int		=      (
								    self.alpha_ce * loss_ce
								    + self.alpha_mlm * student_lm_loss
								    + self.hparams.alpha_hid * (hid_loss_enc + hid_loss_dec)
								)
								return blended_loss, loss_ce, student_lm_loss, hid_loss_enc, hid_loss_dec







							@staticmethod
							def        UpperCAmelCase (						lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__)   ->	List[Any]:







								'''simple docstring'''




								snake_case__  :						str		=      "expected list or tuple for hidden_states, got tensor of shape: "
								assert not isinstance(lowerCamelCase__	,					torch.Tensor), f"""{msg}{hidden_states.shape}"""
								assert not isinstance(lowerCamelCase__	,					torch.Tensor), f"""{msg}{hidden_states_T.shape}"""
								snake_case__  :						Any		=      attention_mask.to(hidden_states[0])
								snake_case__  :						Optional[Any]		=      mask.sum() * hidden_states[0].size(-1)
								snake_case__  :						Union[str, Any]		=      torch.stack([hidden_states[i] for i in range(len(lowerCamelCase__))])
								snake_case__  :						Any		=      torch.stack([hidden_states_T[j] for j in matches])
								assert student_states.shape == teacher_states.shape, f"""{student_states.shape} != {teacher_states.shape}"""
								if normalize_hidden:
									snake_case__  :						Any		=      nn.functional.layer_norm(lowerCamelCase__	,					student_states.shape[1:])
									snake_case__  :						Optional[Any]		=      nn.functional.layer_norm(lowerCamelCase__	,					teacher_states.shape[1:])
								snake_case__  :						Tuple		=      nn.functional.mse_loss(lowerCamelCase__	,					lowerCamelCase__	,					reduction="none")
								snake_case__  :						List[str]		=      (mse * mask.unsqueeze(0).unsqueeze(-1)).sum() / valid_count
								return masked_mse



def 			A__		(					_UpperCAmelCase    :      int	)							->	Union[str, Any]:

	'''simple docstring'''


	parser.add_argument("--teacher"				,    type=_UpperCAmelCase	)
	parser.add_argument("--alpha_ce"				,    default=0.8				,    type=_UpperCAmelCase	)
	parser.add_argument("--alpha_mlm"				,    default=0.2				,    type=_UpperCAmelCase	)
	parser.add_argument("--alpha_hid"				,    default=0.0				,    type=_UpperCAmelCase				,    required=_UpperCAmelCase	)
	parser.add_argument("--student"				,    type=_UpperCAmelCase				,    required=_UpperCAmelCase	)
	parser.add_argument("--student_decoder_layers"				,    default=12				,    type=_UpperCAmelCase				,    required=_UpperCAmelCase	)
	parser.add_argument("--student_encoder_layers"				,    default=12				,    type=_UpperCAmelCase				,    required=_UpperCAmelCase	)
	parser.add_argument("--no_teacher"				,    action="store_true"				,    default=_UpperCAmelCase	)
	parser.add_argument("--length_penalty"				,    type=_UpperCAmelCase				,    default=-1	)
	parser.add_argument("--supervise_forward"				,    action="store_true"				,    default=_UpperCAmelCase	)
	parser.add_argument("--normalize_hidden"				,    action="store_true"				,    default=_UpperCAmelCase	)


class 			SCREAMING_SNAKE_CASE_  (   _lowercase):




							'''simple docstring'''





							__magic_name__					:     Optional[Any]				  =  '''translation'''
							__magic_name__					:     Optional[int]				  =  ['''bleu''']
							__magic_name__					:     Dict				  =  '''bleu'''




							def __init__(						self	,					lowerCamelCase__	,					**lowerCamelCase__)   ->	Optional[int]:







								'''simple docstring'''




								super().__init__(lowerCamelCase__	,					**lowerCamelCase__)
								assert hparams.src_lang is not None
								assert hparams.tgt_lang is not None
								snake_case__  :						Union[str, Any]		=      hparams.src_lang
								snake_case__  :						List[Any]		=      hparams.tgt_lang
								if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer	,					lowerCamelCase__):
									snake_case__  :						List[str]		=      self.tokenizer.lang_code_to_id[hparams.tgt_lang]




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__)   ->	dict:







								'''simple docstring'''




								return calculate_bleu(lowerCamelCase__	,					lowerCamelCase__)







							@staticmethod
							def        UpperCAmelCase (						lowerCamelCase__	,					lowerCamelCase__)   ->	Tuple:







								'''simple docstring'''




								TranslationModule.add_model_specific_args(lowerCamelCase__	,					lowerCamelCase__)
								add_distill_args(lowerCamelCase__)
								return parser



def 			A__		(					_UpperCAmelCase    :      int	)							->	Union[str, Any]:

	'''simple docstring'''


	if args.no_teacher:
		snake_case__  :						Optional[int]		=      TranslationModule if "translation" in args.task else SummarizationModule
	else:  # DISTILL WITH TEACHER
		snake_case__  :						int		=      TranslationDistiller if "translation" in args.task else SummarizationDistiller
	snake_case__  :						str		=      module_cls.__name__
	print(F"""using module {args.setup_cls}"""	)
	snake_case__  :						int		=      module_cls(_UpperCAmelCase	)
	return model



def 			A__		(					_UpperCAmelCase    :      Any	)							->	int:

	'''simple docstring'''


	Path(args.output_dir	).mkdir(exist_ok=_UpperCAmelCase	)
	check_output_dir(_UpperCAmelCase				,    expected_items=3	)

	snake_case__  :						Optional[int]		=      create_module(_UpperCAmelCase	)
	return ft_main(_UpperCAmelCase				,    model=_UpperCAmelCase	)


if __name__ == "__main__":
					lowercase  		=			argparse.ArgumentParser()
					lowercase  		=			pl.Trainer.add_argparse_args(parser)
					lowercase  		=			SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
					lowercase  		=			parser.parse_args()

					distill_main(args)







import logging
import os
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 classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


SCREAMING_SNAKE_CASE__				=							logging.getLogger(__name__)
@dataclass
class       A__  :
							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       A__  :
							lowerCAmelCase__						:		str            =				field(
							    metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}							)
							lowerCAmelCase__						:		Optional[str]            =				field(
							    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 				__SCREAMING_SNAKE_CASE  (						)	->		int:
				# 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, TFTrainingArguments)     )
				__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       ,				)
				logger.info(
				    'n_replicas: %s, distributed training: %s, 16-bits training: %s'       ,				training_args.n_replicas       ,				bool(training_args.n_replicas > 1     )       ,				training_args.fpaa       ,				)
				logger.info('Training/evaluation parameters %s'       ,				SCREAMING_SNAKE_CASE     )

				# Prepare Token Classification 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       ,				)

				with training_args.strategy.scope():
								__lowercase								=   TFAutoModelForTokenClassification.from_pretrained(
								    model_args.model_name_or_path       ,				from_pt=bool('.bin' in model_args.model_name_or_path     )       ,				config=SCREAMING_SNAKE_CASE       ,				cache_dir=model_args.cache_dir       ,				)

				# Get datasets
				__lowercase								=   (
				    TFTokenClassificationDataset(
				        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								=   (
				    TFTokenClassificationDataset(
				        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]]:
								__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] != -100:
																				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:
								__lowercase						,   __lowercase								=   align_predictions(p.predictions       ,				p.label_ids     )

								return {
								    "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     ),
								}

				# Initialize our Trainer
				__lowercase								=   TFTrainer(
				    model=SCREAMING_SNAKE_CASE       ,				args=SCREAMING_SNAKE_CASE       ,				train_dataset=train_dataset.get_dataset() if train_dataset else None       ,				eval_dataset=eval_dataset.get_dataset() if eval_dataset else None       ,				compute_metrics=SCREAMING_SNAKE_CASE       ,				)

				# Training
				if training_args.do_train:
								trainer.train()
								trainer.save_model()
								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'     )

								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								=   TFTokenClassificationDataset(
								    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(test_dataset.get_dataset()     )
								__lowercase						,   __lowercase								=   align_predictions(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     )
								__lowercase								=   classification_report(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     )

								logger.info('\n%s'       ,				SCREAMING_SNAKE_CASE     )

								__lowercase								=   os.path.join(training_args.output_dir       ,				'test_results.txt'     )

								with open(SCREAMING_SNAKE_CASE       ,				'w'     ) as writer:
												writer.write('%s\n' % report     )

								# Save predictions
								__lowercase								=   os.path.join(training_args.output_dir       ,				'test_predictions.txt'     )

								with open(SCREAMING_SNAKE_CASE       ,				'w'     ) as writer:
												with open(os.path.join(data_args.data_dir       ,				'test.txt'     )       ,				'r'     ) as f:
																__lowercase								=   0

																for line in f:
																				if line.startswith('-DOCSTART-'     ) or line == "" or line == "\n":
																								writer.write(SCREAMING_SNAKE_CASE     )

																								if not preds_list[example_id]:
																												example_id += 1
																				elif preds_list[example_id]:
																								__lowercase								=   line.split()[0] + ' ' + preds_list[example_id].pop(0     ) + '\n'

																								writer.write(SCREAMING_SNAKE_CASE     )
																				else:
																								logger.warning('Maximum sequence length exceeded: No prediction for \'%s\'.'       ,				line.split()[0]     )

				return results


if __name__ == "__main__":
							main()





