

"""simple docstring"""






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


lowerCamelCase__ :     List[str]							  =     logging.getLogger(__name__)






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






@dataclass
class 			_UpperCAmelCase	:
			__a       :				str     =							field(
			    metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""})
			__a       :				Optional[str]     =							field(
			    default=lowerCAmelCase_			,   metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}			,   )
			__a       :				int     =							field(
			    default=1_2_8			,   metadata={
			        """help""": (
			            """The maximum total input sequence length after tokenization. Sequences longer """
			            """than this will be truncated, sequences shorter will be padded."""
			        )
			    }			,   )
			__a       :				bool     =							field(
			    default=lowerCAmelCase_			,   metadata={"""help""": """Overwrite the cached training and evaluation sets"""})
def 					UpperCamelCase    (	) ->	str:
	_UpperCAmelCase			:							Any	  =    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.
		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase			:							Union[str, Any]	  =    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
	else:
		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase			:							Union[str, Any]	  =    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			:							List[Any]	  =    import_module("""tasks"""       )
	try:
		_UpperCAmelCase			:							str	  =    getattr(_lowerCAmelCase,     model_args.task_type       )
		_UpperCAmelCase			:							List[Any]	  =    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""",     _lowerCAmelCase       )

	# Set seed
	set_seed(training_args.seed       )

	# Prepare CONLL-2003 task
	_UpperCAmelCase			:							Tuple	  =    token_classification_task.get_labels(data_args.labels       )
	_UpperCAmelCase			:							Union[str, Any]	  =    dict(enumerate(_lowerCAmelCase       )       )
	_UpperCAmelCase			:							int	  =    len(_lowerCAmelCase       )

	# Load pretrained model and tokenizer
	#
	# Distributed training:
	# The .from_pretrained methods guarantee that only one local process can concurrently
	# download model & vocab.

	_UpperCAmelCase			:							List[Any]	  =    AutoConfig.from_pretrained(
	    model_args.config_name if model_args.config_name else model_args.model_name_or_path,     num_labels=_lowerCAmelCase,     idalabel=_lowerCAmelCase,     labelaid={label: i for i, label in enumerate(_lowerCAmelCase       )},     cache_dir=model_args.cache_dir,     )
	_UpperCAmelCase			:							str	  =    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,     )
	_UpperCAmelCase			:							List[Any]	  =    AutoModelForTokenClassification.from_pretrained(
	    model_args.model_name_or_path,     from_tf=bool(""".ckpt""" in model_args.model_name_or_path       ),     config=_lowerCAmelCase,     cache_dir=model_args.cache_dir,     )

	# Get datasets
	_UpperCAmelCase			:							Any	  =    (
	    TokenClassificationDataset(
	        token_classification_task=_lowerCAmelCase,     data_dir=data_args.data_dir,     tokenizer=_lowerCAmelCase,     labels=_lowerCAmelCase,     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			:							List[str]	  =    (
	    TokenClassificationDataset(
	        token_classification_task=_lowerCAmelCase,     data_dir=data_args.data_dir,     tokenizer=_lowerCAmelCase,     labels=_lowerCAmelCase,     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   :  Tuple,     _lowerCAmelCase   :  Union[str, Any]       ) -> Tuple[List[int], List[int]]:
		_UpperCAmelCase			:							Optional[Any]	  =    np.argmax(_lowerCAmelCase,     axis=2       )

		_UpperCAmelCase  ,      _UpperCAmelCase			:							Union[str, Any]	  =    preds.shape

		_UpperCAmelCase			:							Union[str, Any]	  =    [[] for _ in range(_lowerCAmelCase       )]
		_UpperCAmelCase			:							Optional[Any]	  =    [[] for _ in range(_lowerCAmelCase       )]

		for i in range(_lowerCAmelCase       ):
			for j in range(_lowerCAmelCase       ):
				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(_lowerCAmelCase   :  Tuple       ) -> Dict:
		_UpperCAmelCase  ,      _UpperCAmelCase			:							List[str]	  =    align_predictions(p.predictions,     p.label_ids       )
		return {
		    "accuracy_score": accuracy_score(_lowerCAmelCase,     _lowerCAmelCase       ),
		    "precision": precision_score(_lowerCAmelCase,     _lowerCAmelCase       ),
		    "recall": recall_score(_lowerCAmelCase,     _lowerCAmelCase       ),
		    "f1": fa_score(_lowerCAmelCase,     _lowerCAmelCase       ),
		}

	# Data collator
	_UpperCAmelCase			:							int	  =    DataCollatorWithPadding(_lowerCAmelCase,     pad_to_multiple_of=8       ) if training_args.fpaa else None

	# Initialize our Trainer
	_UpperCAmelCase			:							List[Any]	  =    Trainer(
	    model=_lowerCAmelCase,     args=_lowerCAmelCase,     train_dataset=_lowerCAmelCase,     eval_dataset=_lowerCAmelCase,     compute_metrics=_lowerCAmelCase,     data_collator=_lowerCAmelCase,     )

	# 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
	_UpperCAmelCase			:							Optional[int]	  =    {}
	if training_args.do_eval:
		logger.info("""*** Evaluate ***"""       )

		_UpperCAmelCase			:							Tuple	  =    trainer.evaluate()

		_UpperCAmelCase			:							Tuple	  =    os.path.join(training_args.output_dir,     """eval_results.txt"""       )
		if trainer.is_world_process_zero():
			with open(_lowerCAmelCase,     """w"""       ) as writer:
				logger.info("""***** Eval results *****"""       )
				for key, value in result.items():
					logger.info("""  %s = %s""",     _lowerCAmelCase,     _lowerCAmelCase       )
					writer.write("""%s = %s\n""" % (key, value)       )

			results.update(_lowerCAmelCase       )

		# Predict
	if training_args.do_predict:
		_UpperCAmelCase			:							Optional[Any]	  =    TokenClassificationDataset(
		    token_classification_task=_lowerCAmelCase,     data_dir=data_args.data_dir,     tokenizer=_lowerCAmelCase,     labels=_lowerCAmelCase,     model_type=config.model_type,     max_seq_length=data_args.max_seq_length,     overwrite_cache=data_args.overwrite_cache,     mode=Split.test,     )

		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase			:							int	  =    trainer.predict(_lowerCAmelCase       )
		_UpperCAmelCase  ,      _UpperCAmelCase			:							str	  =    align_predictions(_lowerCAmelCase,     _lowerCAmelCase       )

		_UpperCAmelCase			:							int	  =    os.path.join(training_args.output_dir,     """test_results.txt"""       )
		if trainer.is_world_process_zero():
			with open(_lowerCAmelCase,     """w"""       ) as writer:
				for key, value in metrics.items():
					logger.info("""  %s = %s""",     _lowerCAmelCase,     _lowerCAmelCase       )
					writer.write("""%s = %s\n""" % (key, value)       )

				# Save predictions
		_UpperCAmelCase			:							Optional[int]	  =    os.path.join(training_args.output_dir,     """test_predictions.txt"""       )
		if trainer.is_world_process_zero():
			with open(_lowerCAmelCase,     """w"""       ) as writer:
				with open(os.path.join(data_args.data_dir,     """test.txt"""       ),     """r"""       ) as f:
					token_classification_task.write_predictions_to_file(_lowerCAmelCase,     _lowerCAmelCase,     _lowerCAmelCase       )

	return results
def 					UpperCamelCase    (	_lowerCAmelCase   :  Tuple       ) ->	Tuple:
	main()


if __name__ == "__main__":
						main()






"""simple docstring"""


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import MobileViTImageProcessor


class 				a (						unittest.TestCase      ):


    def __init__(    self		:     Dict      , __lowerCAmelCase		:     Tuple      , __lowerCAmelCase		:     Optional[Any]=7      , __lowerCAmelCase		:     Optional[Any]=3      , __lowerCAmelCase		:     Optional[Any]=18      , __lowerCAmelCase		:     str=30      , __lowerCAmelCase		:     List[str]=400      , __lowerCAmelCase		:     Union[str, Any]=True      , __lowerCAmelCase		:     str=None      , __lowerCAmelCase		:     Optional[int]=True      , __lowerCAmelCase		:     int=None      , __lowerCAmelCase		:     List[str]=True      , ):
      _UpperCAmelCase         =					size if size is not None else {"""shortest_edge""": 20}
      _UpperCAmelCase         =					crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
      _UpperCAmelCase         =					parent
      _UpperCAmelCase         =					batch_size
      _UpperCAmelCase         =					num_channels
      _UpperCAmelCase         =					image_size
      _UpperCAmelCase         =					min_resolution
      _UpperCAmelCase         =					max_resolution
      _UpperCAmelCase         =					do_resize
      _UpperCAmelCase         =					size
      _UpperCAmelCase         =					do_center_crop
      _UpperCAmelCase         =					crop_size
      _UpperCAmelCase         =					do_flip_channel_order


    def    lowerCAmelCase_     (    self		:     List[str]    ):
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_flip_channel_order": self.do_flip_channel_order,
      }



@require_torch
@require_vision
class 				a (						lowerCAmelCase_		,							unittest.TestCase      ):
    _snake_case   :     Optional[int]   							=							MobileViTImageProcessor if is_vision_available() else None


    def    lowerCAmelCase_     (    self		:     Optional[Any]    ):
      _UpperCAmelCase         =					MobileViTImageProcessingTester(self    )


    @property
    def    lowerCAmelCase_     (    self		:     Tuple    ):
      return self.image_processor_tester.prepare_image_processor_dict()


    def    lowerCAmelCase_     (    self		:     Dict    ):
      _UpperCAmelCase         =					self.image_processing_class(**self.image_processor_dict    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """do_resize"""    )    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """size"""    )    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """do_center_crop"""    )    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """center_crop"""    )    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """do_flip_channel_order"""    )    )


    def    lowerCAmelCase_     (    self		:     Union[str, Any]    ):
      _UpperCAmelCase         =					self.image_processing_class.from_dict(self.image_processor_dict    )
      self.assertEqual(image_processor.size      , {"""shortest_edge""": 20}    )
      self.assertEqual(image_processor.crop_size      , {"""height""": 18, """width""": 18}    )

      _UpperCAmelCase         =					self.image_processing_class.from_dict(self.image_processor_dict      , size=42      , crop_size=84    )
      self.assertEqual(image_processor.size      , {"""shortest_edge""": 42}    )
      self.assertEqual(image_processor.crop_size      , {"""height""": 84, """width""": 84}    )


    def    lowerCAmelCase_     (    self		:     List[str]    ):
      pass


    def    lowerCAmelCase_     (    self		:     Dict    ):
      # Initialize image_processing
      _UpperCAmelCase         =					self.image_processing_class(**self.image_processor_dict    )
      # create random PIL images
      _UpperCAmelCase         =					prepare_image_inputs(self.image_processor_tester      , equal_resolution=__lowerCAmelCase    )
      for image in image_inputs:
        self.assertIsInstance(__lowerCAmelCase      , Image.Image    )

      # Test not batched input
      _UpperCAmelCase         =					image_processing(image_inputs[0]      , return_tensors="""pt"""    ).pixel_values
      self.assertEqual(
          encoded_images.shape      , (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )      , )

      # Test batched
      _UpperCAmelCase         =					image_processing(__lowerCAmelCase      , return_tensors="""pt"""    ).pixel_values
      self.assertEqual(
          encoded_images.shape      , (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )      , )


    def    lowerCAmelCase_     (    self		:     str    ):
      # Initialize image_processing
      _UpperCAmelCase         =					self.image_processing_class(**self.image_processor_dict    )
      # create random numpy tensors
      _UpperCAmelCase         =					prepare_image_inputs(self.image_processor_tester      , equal_resolution=__lowerCAmelCase      , numpify=__lowerCAmelCase    )
      for image in image_inputs:
        self.assertIsInstance(__lowerCAmelCase      , np.ndarray    )

      # Test not batched input
      _UpperCAmelCase         =					image_processing(image_inputs[0]      , return_tensors="""pt"""    ).pixel_values
      self.assertEqual(
          encoded_images.shape      , (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )      , )

      # Test batched
      _UpperCAmelCase         =					image_processing(__lowerCAmelCase      , return_tensors="""pt"""    ).pixel_values
      self.assertEqual(
          encoded_images.shape      , (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )      , )


    def    lowerCAmelCase_     (    self		:     Optional[int]    ):
      # Initialize image_processing
      _UpperCAmelCase         =					self.image_processing_class(**self.image_processor_dict    )
      # create random PyTorch tensors
      _UpperCAmelCase         =					prepare_image_inputs(self.image_processor_tester      , equal_resolution=__lowerCAmelCase      , torchify=__lowerCAmelCase    )
      for image in image_inputs:
        self.assertIsInstance(__lowerCAmelCase      , torch.Tensor    )

      # Test not batched input
      _UpperCAmelCase         =					image_processing(image_inputs[0]      , return_tensors="""pt"""    ).pixel_values
      self.assertEqual(
          encoded_images.shape      , (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )      , )

      # Test batched
      _UpperCAmelCase         =					image_processing(__lowerCAmelCase      , return_tensors="""pt"""    ).pixel_values
      self.assertEqual(
          encoded_images.shape      , (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )      , )
