
"""simple docstring"""






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()


_lowercase     :   Dict							 =							logging.getLogger(__name__)

@dataclass
class 				__a    :



							'''simple docstring'''







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





@dataclass
class 				__a    :



							'''simple docstring'''







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





def     lowerCAmelCase__ (		)	->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, TFTrainingArguments)			)
			__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(lowercase_ ,      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" ,      lowercase_			)

			# Prepare Token Classification task
			__lowercase						      =					token_classification_task.get_labels(data_args.labels			)
			__lowercase						      =					dict(enumerate(lowercase_			)			)
			__lowercase						      =					len(lowercase_			)

			# 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=lowercase_ ,      idalabel=lowercase_ ,      labelaid={label: i for i, label in enumerate(lowercase_			)} ,      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=lowercase_ ,      cache_dir=model_args.cache_dir ,      )

			# Get datasets
			__lowercase						      =					(
			    TFTokenClassificationDataset(
			        token_classification_task=lowercase_ ,      data_dir=data_args.data_dir ,      tokenizer=lowercase_ ,      labels=lowercase_ ,      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=lowercase_ ,      data_dir=data_args.data_dir ,      tokenizer=lowercase_ ,      labels=lowercase_ ,      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(__magic_name__ ,      __magic_name__			) -> Tuple[List[int], List[int]]:
						__lowercase						      =					np.argmax(lowercase_ ,      axis=2			)
						__lowercase						      =					preds.shape
						__lowercase						      =					[[] for _ in range(lowercase_			)]
						__lowercase						      =					[[] for _ in range(lowercase_			)]

						for i in range(lowercase_			):
									for j in range(lowercase_			):
												if label_ids[i, j] != -1_0_0:
															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(__magic_name__			) -> Dict:
						__lowercase						      =					align_predictions(p.predictions ,      p.label_ids			)

						return {
						    "precision": precision_score(lowercase_ ,      lowercase_			),
						    "recall": recall_score(lowercase_ ,      lowercase_			),
						    "f1": fa_score(lowercase_ ,      lowercase_			),
						}

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

			# 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(lowercase_ ,      "w"			) as writer:
									logger.info("***** Eval results *****"			)

									for key, value in result.items():
												logger.info("  %s = %s" ,      lowercase_ ,      lowercase_			)
												writer.write("%s = %s\n" % (key, value)			)

									results.update(lowercase_			)

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

						__lowercase						      =					trainer.predict(test_dataset.get_dataset()			)
						__lowercase						      =					align_predictions(lowercase_ ,      lowercase_			)
						__lowercase						      =					classification_report(lowercase_ ,      lowercase_			)

						logger.info("\n%s" ,      lowercase_			)

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

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

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

						with open(lowercase_ ,      "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(lowercase_			)

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

			return results


if __name__ == "__main__":
					main()







"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase							 =							{'''configuration_sew_d''': ['''SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWDConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							 =							[
					    '''SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''SEWDForCTC''',
					    '''SEWDForSequenceClassification''',
					    '''SEWDModel''',
					    '''SEWDPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_sew_d import (
										    SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SEWDForCTC,
										    SEWDForSequenceClassification,
										    SEWDModel,
										    SEWDPreTrainedModel,
										)


else:
					import sys

					_lowercase							 =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





