





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()


__lowerCAmelCase     =			logging.getLogger(__name__)

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



@dataclass
class   _UpperCAmelCase	:
					__A          =   field(
					    metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}					)
					__A          =   field(
					    metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}					)
					__A          =   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."
					        )
					    } ,       )
					__A          =   field(
					    default=_lowercase ,       metadata={"help": "Overwrite the cached training and evaluation sets"}					)



def     a						(      )     ->Optional[int]:
		UpperCamelCase__         =	HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)       )
		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(_UpperCAmelCase ,						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 ,						)
		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""" ,						_UpperCAmelCase       )

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

		# 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=_UpperCAmelCase ,						idalabel=_UpperCAmelCase ,						labelaid={label: i for i, label in enumerate(_UpperCAmelCase       )} ,						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 ,						)

		with training_args.strategy.scope():
				UpperCamelCase__         =	TFAutoModelForTokenClassification.from_pretrained(
				    model_args.model_name_or_path ,						from_pt=bool(""".bin""" in model_args.model_name_or_path       ) ,						config=_UpperCAmelCase ,						cache_dir=model_args.cache_dir ,						)

		# Get datasets
		UpperCamelCase__         =	(
		    TFTokenClassificationDataset(
		        token_classification_task=_UpperCAmelCase ,						data_dir=data_args.data_dir ,						tokenizer=_UpperCAmelCase ,						labels=_UpperCAmelCase ,						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__         =	(
		    TFTokenClassificationDataset(
		        token_classification_task=_UpperCAmelCase ,						data_dir=data_args.data_dir ,						tokenizer=_UpperCAmelCase ,						labels=_UpperCAmelCase ,						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(_lowercase   :     List[str] ,						_lowercase   :     Union[str, Any]       ) -> Tuple[List[int], List[int]]:
				UpperCamelCase__         =	np.argmax(_UpperCAmelCase ,						axis=2       )
				UpperCamelCase__         =	preds.shape
				UpperCamelCase__         =	[[] for _ in range(_UpperCAmelCase       )]
				UpperCamelCase__         =	[[] for _ in range(_UpperCAmelCase       )]

				for i in range(_UpperCAmelCase       ):
						for j in range(_UpperCAmelCase       ):
								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(_lowercase   :     Union[str, Any]       ) -> Dict:
				UpperCamelCase__         =	align_predictions(p.predictions ,						p.label_ids       )

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

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

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

				with open(_UpperCAmelCase ,						"""w"""       ) as writer:
						logger.info("""***** Eval results *****"""       )

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

						results.update(_UpperCAmelCase       )

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

				UpperCamelCase__         =	trainer.predict(test_dataset.get_dataset()       )
				UpperCamelCase__         =	align_predictions(_UpperCAmelCase ,						_UpperCAmelCase       )
				UpperCamelCase__         =	classification_report(_UpperCAmelCase ,						_UpperCAmelCase       )

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

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

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

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

				with open(_UpperCAmelCase ,						"""w"""       ) as writer:
						with open(os.path.join(data_args.data_dir ,						"""test.txt"""       ) ,						"""r"""       ) as f:
								UpperCamelCase__         =	0

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

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

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

		return results


if __name__ == "__main__":
				main()












from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


__lowerCAmelCase     =			logging.get_logger(__name__)


if is_vision_available():
				import PIL




class   _UpperCAmelCase	(   _lowercase					):
					__A          =   ["pixel_values"]




					def __init__(						self    ,      __lowerCAmelCase = True    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = PILImageResampling.BILINEAR    ,      __lowerCAmelCase = True    ,      __lowerCAmelCase = False    ,      __lowerCAmelCase = True    ,      __lowerCAmelCase = True    ,      __lowerCAmelCase = 1 / 255    ,      __lowerCAmelCase = True    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      **__lowerCAmelCase    ,      )						->  None:
							super().__init__(**__lowerCAmelCase       )

							UpperCamelCase__         =	size if size is not None else {"""height""": 2_560, """width""": 1_920}
							if isinstance(__lowerCAmelCase    ,      (tuple, list)       ):
									# The previous feature extractor size parameter was in (width, height) format
									UpperCamelCase__         =	size[::-1]
							UpperCamelCase__         =	get_size_dict(__lowerCAmelCase       )

							UpperCamelCase__         =	do_resize
							UpperCamelCase__         =	size
							UpperCamelCase__         =	resample
							UpperCamelCase__         =	do_thumbnail
							UpperCamelCase__         =	do_align_long_axis
							UpperCamelCase__         =	do_pad
							UpperCamelCase__         =	do_rescale
							UpperCamelCase__         =	rescale_factor
							UpperCamelCase__         =	do_normalize
							UpperCamelCase__         =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							UpperCamelCase__         =	image_std if image_std is not None else IMAGENET_STANDARD_STD




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = None       )						->  np.ndarray:
							UpperCamelCase__  ,			UpperCamelCase__         =	get_image_size(__lowerCAmelCase       )
							UpperCamelCase__  ,			UpperCamelCase__         =	size["""height"""], size["""width"""]

							if (output_width < output_height and input_width > input_height) or (
							    output_width > output_height and input_width < input_height
							):
									UpperCamelCase__         =	np.rotaa(__lowerCAmelCase    ,      3       )

							if data_format is not None:
									UpperCamelCase__         =	to_channel_dimension_format(__lowerCAmelCase    ,      __lowerCAmelCase       )

							return image




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = False    ,      __lowerCAmelCase = None    ,      )						->  np.ndarray:
							UpperCamelCase__  ,			UpperCamelCase__         =	size["""height"""], size["""width"""]
							UpperCamelCase__  ,			UpperCamelCase__         =	get_image_size(__lowerCAmelCase       )

							UpperCamelCase__         =	output_width - input_width
							UpperCamelCase__         =	output_height - input_height

							if random_padding:
									UpperCamelCase__         =	np.random.randint(low=0    ,      high=delta_height + 1       )
									UpperCamelCase__         =	np.random.randint(low=0    ,      high=delta_width + 1       )
							else:
									UpperCamelCase__         =	delta_height // 2
									UpperCamelCase__         =	delta_width // 2

							UpperCamelCase__         =	delta_height - pad_top
							UpperCamelCase__         =	delta_width - pad_left

							UpperCamelCase__         =	((pad_top, pad_bottom), (pad_left, pad_right))
							return pad(__lowerCAmelCase    ,      __lowerCAmelCase    ,      data_format=__lowerCAmelCase       )




					def 	snake_case_		(						self    ,      *__lowerCAmelCase    ,      **__lowerCAmelCase       )						->  List[Any]:
							logger.info("""pad is deprecated and will be removed in version 4.27. Please use pad_image instead."""       )
							return self.pad_image(*__lowerCAmelCase    ,      **__lowerCAmelCase       )




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = PILImageResampling.BICUBIC    ,      __lowerCAmelCase = None    ,      **__lowerCAmelCase    ,      )						->  np.ndarray:
							UpperCamelCase__  ,			UpperCamelCase__         =	get_image_size(__lowerCAmelCase       )
							UpperCamelCase__  ,			UpperCamelCase__         =	size["""height"""], size["""width"""]

							# We always resize to the smallest of either the input or output size.
							UpperCamelCase__         =	min(__lowerCAmelCase    ,      __lowerCAmelCase       )
							UpperCamelCase__         =	min(__lowerCAmelCase    ,      __lowerCAmelCase       )

							if height == input_height and width == input_width:
									return image

							if input_height > input_width:
									UpperCamelCase__         =	int(input_width * height / input_height       )
							elif input_width > input_height:
									UpperCamelCase__         =	int(input_height * width / input_width       )

							return resize(
							    __lowerCAmelCase    ,      size=(height, width)    ,      resample=__lowerCAmelCase    ,      reducing_gap=2.0    ,      data_format=__lowerCAmelCase    ,      **__lowerCAmelCase       )




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = PILImageResampling.BICUBIC    ,      __lowerCAmelCase = None    ,      **__lowerCAmelCase    ,      )						->  np.ndarray:
							UpperCamelCase__         =	get_size_dict(__lowerCAmelCase       )
							UpperCamelCase__         =	min(size["""height"""]    ,      size["""width"""]       )
							UpperCamelCase__         =	get_resize_output_image_size(__lowerCAmelCase    ,      size=__lowerCAmelCase    ,      default_to_square=__lowerCAmelCase       )
							UpperCamelCase__         =	resize(__lowerCAmelCase    ,      size=__lowerCAmelCase    ,      resample=__lowerCAmelCase    ,      data_format=__lowerCAmelCase    ,      **__lowerCAmelCase       )
							return resized_image




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = None    ,      **__lowerCAmelCase    ,      )						->  List[str]:
							return rescale(__lowerCAmelCase    ,      scale=__lowerCAmelCase    ,      data_format=__lowerCAmelCase    ,      **__lowerCAmelCase       )




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = None    ,      **__lowerCAmelCase    ,      )						->  np.ndarray:
							return normalize(__lowerCAmelCase    ,      mean=__lowerCAmelCase    ,      std=__lowerCAmelCase    ,      data_format=__lowerCAmelCase    ,      **__lowerCAmelCase       )




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = False    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = ChannelDimension.FIRST    ,      **__lowerCAmelCase    ,      )						->  PIL.Image.Image:
							UpperCamelCase__         =	do_resize if do_resize is not None else self.do_resize
							UpperCamelCase__         =	size if size is not None else self.size
							if isinstance(__lowerCAmelCase    ,      (tuple, list)       ):
									# Previous feature extractor had size in (width, height) format
									UpperCamelCase__         =	size[::-1]
							UpperCamelCase__         =	get_size_dict(__lowerCAmelCase       )
							UpperCamelCase__         =	resample if resample is not None else self.resample
							UpperCamelCase__         =	do_thumbnail if do_thumbnail is not None else self.do_thumbnail
							UpperCamelCase__         =	do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
							UpperCamelCase__         =	do_pad if do_pad is not None else self.do_pad
							UpperCamelCase__         =	do_rescale if do_rescale is not None else self.do_rescale
							UpperCamelCase__         =	rescale_factor if rescale_factor is not None else self.rescale_factor
							UpperCamelCase__         =	do_normalize if do_normalize is not None else self.do_normalize
							UpperCamelCase__         =	image_mean if image_mean is not None else self.image_mean
							UpperCamelCase__         =	image_std if image_std is not None else self.image_std

							UpperCamelCase__         =	make_list_of_images(__lowerCAmelCase       )

							if not valid_images(__lowerCAmelCase       ):
									raise ValueError(
									    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
									    """torch.Tensor, tf.Tensor or jax.ndarray."""       )

							if do_resize and size is None:
									raise ValueError("""Size must be specified if do_resize is True."""       )

							if do_rescale and rescale_factor is None:
									raise ValueError("""Rescale factor must be specified if do_rescale is True."""       )

							if do_pad and size is None:
									raise ValueError("""Size must be specified if do_pad is True."""       )

							if do_normalize and (image_mean is None or image_std is None):
									raise ValueError("""Image mean and std must be specified if do_normalize is True."""       )

							# All transformations expect numpy arrays.
							UpperCamelCase__         =	[to_numpy_array(__lowerCAmelCase       ) for image in images]

							if do_align_long_axis:
									UpperCamelCase__         =	[self.align_long_axis(__lowerCAmelCase    ,      size=__lowerCAmelCase       ) for image in images]

							if do_resize:
									UpperCamelCase__         =	[self.resize(image=__lowerCAmelCase    ,      size=__lowerCAmelCase    ,      resample=__lowerCAmelCase       ) for image in images]

							if do_thumbnail:
									UpperCamelCase__         =	[self.thumbnail(image=__lowerCAmelCase    ,      size=__lowerCAmelCase       ) for image in images]

							if do_pad:
									UpperCamelCase__         =	[self.pad_image(image=__lowerCAmelCase    ,      size=__lowerCAmelCase    ,      random_padding=__lowerCAmelCase       ) for image in images]

							if do_rescale:
									UpperCamelCase__         =	[self.rescale(image=__lowerCAmelCase    ,      scale=__lowerCAmelCase       ) for image in images]

							if do_normalize:
									UpperCamelCase__         =	[self.normalize(image=__lowerCAmelCase    ,      mean=__lowerCAmelCase    ,      std=__lowerCAmelCase       ) for image in images]

							UpperCamelCase__         =	[to_channel_dimension_format(__lowerCAmelCase    ,      __lowerCAmelCase       ) for image in images]

							UpperCamelCase__         =	{"""pixel_values""": images}
							return BatchFeature(data=__lowerCAmelCase    ,      tensor_type=__lowerCAmelCase       )





