




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      :				Any				      =logging.getLogger(__name__)






@dataclass
class      A     :
					"""simple docstring"""

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






@dataclass
class      A     :
					"""simple docstring"""

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

def      lowerCAmelCase_     (					)   ->     List[Any]:


						'''simple docstring'''




						lowerCamelCase__ 					=			HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)  )
						lowerCamelCase__ 					=			parser.parse_args_into_dataclasses()

						if (
						    os.path.exists(training_args.output_dir  )
						    and os.listdir(training_args.output_dir  )
						    and training_args.do_train
						    and not training_args.overwrite_output_dir
						):
												raise ValueError(
												    f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
												    ' --overwrite_output_dir to overcome.'  )

						lowerCamelCase__ 					=			import_module('tasks'  )

						try:
												lowerCamelCase__ 					=			getattr(SCREAMING_SNAKE_CASE_       ,			model_args.task_type  )
												lowerCamelCase__ 					=			token_classification_task_clazz()
						except AttributeError:
												raise ValueError(
												    f"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
												    f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""  )

						# Setup logging
						logging.basicConfig(
						    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'       ,			datefmt='%m/%d/%Y %H:%M:%S'       ,			level=logging.INFO       ,			)
						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
						lowerCamelCase__ 					=			token_classification_task.get_labels(data_args.labels  )
						lowerCamelCase__ 					=			dict(enumerate(SCREAMING_SNAKE_CASE_  )  )
						lowerCamelCase__ 					=			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.

						lowerCamelCase__ 					=			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       ,			)
						lowerCamelCase__ 					=			AutoTokenizer.from_pretrained(
						    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path       ,			cache_dir=model_args.cache_dir       ,			use_fast=model_args.use_fast       ,			)

						with training_args.strategy.scope():
												lowerCamelCase__ 					=			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
						lowerCamelCase__ 					=			(
						    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
						)
						lowerCamelCase__ 					=			(
						    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(lowerCamelCase:    Dict       ,			lowerCamelCase:    int  ) -> Tuple[List[int], List[int]]:
												lowerCamelCase__ 					=			np.argmax(SCREAMING_SNAKE_CASE_       ,			axis=2  )
												lowerCamelCase__ 					=			preds.shape
												lowerCamelCase__ 					=			[[] for _ in range(SCREAMING_SNAKE_CASE_  )]
												lowerCamelCase__ 					=			[[] 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] != -1_00:
																														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(lowerCamelCase:    Any  ) -> Dict:
												lowerCamelCase__ 					=			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
						lowerCamelCase__ 					=			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
						lowerCamelCase__ 					=			{}
						if training_args.do_eval:
												logger.info('*** Evaluate ***'  )

												lowerCamelCase__ 					=			trainer.evaluate()
												lowerCamelCase__ 					=			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:
												lowerCamelCase__ 					=			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       ,			)

												lowerCamelCase__ 					=			trainer.predict(test_dataset.get_dataset()  )
												lowerCamelCase__ 					=			align_predictions(SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_  )
												lowerCamelCase__ 					=			classification_report(SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_  )

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

												lowerCamelCase__ 					=			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
												lowerCamelCase__ 					=			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:
																								lowerCamelCase__ 					=			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]:
																																				lowerCamelCase__ 					=			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()





# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
      import tensorflow as tf






class      A     (						TensorFormatter[Mapping, "tf.Tensor", Mapping]						):
     """simple docstring"""

     def __init__(      self					:				int		,					A__					:				Tuple=None		,					**A__					:				Tuple     )			->    Dict:
           super().__init__(features=A__     )
           lowerCamelCase__ 					=			tf_tensor_kwargs
           import tensorflow as tf  # noqa: import tf at initialization




     def 	__snake_case				(      self					:				List[Any]		,					A__					:				List[str]     )			->    Dict:
           import tensorflow as tf

           if isinstance(A__		,					A__     ) and column:
                 if all(
                     isinstance(A__		,					tf.Tensor     ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column     ):
                       return tf.stack(A__     )
                 elif all(
                     isinstance(A__		,					(tf.Tensor, tf.RaggedTensor)     ) and x.ndim == 1 and x.dtype == column[0].dtype
                     for x in column     ):
                       # only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
                       return tf.ragged.stack(A__     )

           return column




     def 	__snake_case				(      self					:				Tuple		,					A__					:				Optional[int]     )			->    int:
           import tensorflow as tf

           if value is None:
                 return value

           lowerCamelCase__ 					=			{}

           if isinstance(A__		,					(np.number, np.ndarray)     ) and np.issubdtype(value.dtype		,					np.integer     ):
                 lowerCamelCase__ 					=			{'dtype': tf.intaa}
           elif isinstance(A__		,					(np.number, np.ndarray)     ) and np.issubdtype(value.dtype		,					np.floating     ):
                 lowerCamelCase__ 					=			{'dtype': tf.floataa}
           elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                 import PIL.Image

                 if isinstance(A__		,					PIL.Image.Image     ):
                       lowerCamelCase__ 					=			np.asarray(A__     )

           return tf.convert_to_tensor(A__		,					**{**default_dtype, **self.tf_tensor_kwargs}     )




     def 	__snake_case				(      self					:				Optional[int]		,					A__					:				Dict     )			->    List[str]:
           import tensorflow as tf

           # support for torch, tf, jax etc.
           if config.TORCH_AVAILABLE and "torch" in sys.modules:
                 import torch

                 if isinstance(A__		,					torch.Tensor     ):
                       return self._tensorize(data_struct.detach().cpu().numpy()[()]     )
           if hasattr(A__		,					'__array__'     ) and not isinstance(A__		,					tf.Tensor     ):
                 lowerCamelCase__ 					=			data_struct.__array__()
           # support for nested types like struct of list of struct
           if isinstance(A__		,					np.ndarray     ):
                 if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
                       return self._consolidate([self.recursive_tensorize(A__     ) for substruct in data_struct]     )
           elif isinstance(A__		,					(list, tuple)     ):
                 return self._consolidate([self.recursive_tensorize(A__     ) for substruct in data_struct]     )
           return self._tensorize(A__     )




     def 	__snake_case				(      self					:				List[Any]		,					A__					:				dict     )			->    List[str]:
           return map_nested(self._recursive_tensorize		,					A__		,					map_list=A__     )




     def 	__snake_case				(      self					:				Union[str, Any]		,					A__					:				pa.Table     )			->    Mapping:
           lowerCamelCase__ 					=			self.numpy_arrow_extractor().extract_row(A__     )
           lowerCamelCase__ 					=			self.python_features_decoder.decode_row(A__     )
           return self.recursive_tensorize(A__     )




     def 	__snake_case				(      self					:				Optional[Any]		,					A__					:				pa.Table     )			->    "tf.Tensor":
           lowerCamelCase__ 					=			self.numpy_arrow_extractor().extract_column(A__     )
           lowerCamelCase__ 					=			self.python_features_decoder.decode_column(A__		,					pa_table.column_names[0]     )
           lowerCamelCase__ 					=			self.recursive_tensorize(A__     )
           lowerCamelCase__ 					=			self._consolidate(A__     )
           return column




     def 	__snake_case				(      self					:				int		,					A__					:				pa.Table     )			->    Mapping:
           lowerCamelCase__ 					=			self.numpy_arrow_extractor().extract_batch(A__     )
           lowerCamelCase__ 					=			self.python_features_decoder.decode_batch(A__     )
           lowerCamelCase__ 					=			self.recursive_tensorize(A__     )
           for column_name in batch:
                 lowerCamelCase__ 					=			self._consolidate(batch[column_name]     )
           return batch