





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 :  Any										=  logging.getLogger(__name__)



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



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



def 	A_       (  )		->				int:
 UpperCamelCase			:      Union[str, 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			:      str 			=     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
 else:
  UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			:      List[str] 			=     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			:      Tuple 			=     import_module("tasks"  )
 try:
  UpperCamelCase			:      Optional[int] 			=     getattr(_lowerCAmelCase							,     model_args.task_type  )
  UpperCamelCase			:      Tuple 			=     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			:      int 			=     token_classification_task.get_labels(data_args.labels  )
 UpperCamelCase			:      Tuple 			=     dict(enumerate(_lowerCAmelCase  )  )
 UpperCamelCase			:      Dict 			=     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			:      Optional[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			:      Optional[Any] 			=     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			:      str 			=     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			:      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							,     _lowerCAmelCase  ) -> Tuple[List[int], List[int]]:
  UpperCamelCase			:      str 			=     np.argmax(_lowerCAmelCase							,     axis=2  )

  UpperCamelCase			,					UpperCamelCase			:      List[Any] 			=     preds.shape

  UpperCamelCase			:      Any 			=     [[] for _ in range(_lowerCAmelCase  )]
  UpperCamelCase			:      Optional[int] 			=     [[] 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  ) -> Dict:
  UpperCamelCase			,					UpperCamelCase			:      Tuple 			=     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			:      Optional[Any] 			=     DataCollatorWithPadding(_lowerCAmelCase							,     pad_to_multiple_of=8  ) if training_args.fpaa else None

 # Initialize our Trainer
 UpperCamelCase			:      Optional[int] 			=     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			:      List[Any] 			=     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			:      int 			=     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			:      List[str] 			=     trainer.predict(_lowerCAmelCase  )
  UpperCamelCase			,					UpperCamelCase			:      Optional[int] 			=     align_predictions(_lowerCAmelCase							,     _lowerCAmelCase  )

  UpperCamelCase			:      Union[str, Any] 			=     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			:      Union[str, Any] 			=     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 	A_       (  _lowerCAmelCase  )		->				int:
 main()


if __name__ == "__main__":
    main()







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_video_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import VivitImageProcessor



class       A__     ( unittest.TestCase    ):





      def __init__(				self ,							A_ ,							A_=7 ,							A_=3 ,							A_=10 ,							A_=18 ,							A_=30 ,							A_=400 ,							A_=True ,							A_=None ,							A_=True ,							A_=[0.5, 0.5, 0.5] ,							A_=[0.5, 0.5, 0.5] ,							A_=None ,							):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     size if size is not None else {"shortest_edge": 18}
       UpperCamelCase			:      Tuple 			=     crop_size if crop_size is not None else {"height": 18, "width": 18}

       UpperCamelCase			:      Optional[Any] 			=     parent
       UpperCamelCase			:      Optional[int] 			=     batch_size
       UpperCamelCase			:      List[Any] 			=     num_channels
       UpperCamelCase			:      Union[str, Any] 			=     num_frames
       UpperCamelCase			:      Any 			=     image_size
       UpperCamelCase			:      Tuple 			=     min_resolution
       UpperCamelCase			:      Optional[Any] 			=     max_resolution
       UpperCamelCase			:      Any 			=     do_resize
       UpperCamelCase			:      Tuple 			=     size
       UpperCamelCase			:      List[Any] 			=     do_normalize
       UpperCamelCase			:      Optional[int] 			=     image_mean
       UpperCamelCase			:      Any 			=     image_std
       UpperCamelCase			:      str 			=     crop_size





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "crop_size": self.crop_size,
       }



@require_torch
@require_vision
class       A__     ( __snake_case ,     unittest.TestCase    ):
      _UpperCAmelCase			:List[str]							=     VivitImageProcessor if is_vision_available() else None





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     VivitImageProcessingTester(self     )





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return self.image_processor_tester.prepare_image_processor_dict()





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     self.image_processing_class(**self.image_processor_dict     )
       self.assertTrue(hasattr(A_ ,							"image_mean"     )     )
       self.assertTrue(hasattr(A_ ,							"image_std"     )     )
       self.assertTrue(hasattr(A_ ,							"do_normalize"     )     )
       self.assertTrue(hasattr(A_ ,							"do_resize"     )     )
       self.assertTrue(hasattr(A_ ,							"do_center_crop"     )     )
       self.assertTrue(hasattr(A_ ,							"size"     )     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     self.image_processing_class.from_dict(self.image_processor_dict     )
       self.assertEqual(image_processor.size ,							{"shortest_edge": 18}     )
       self.assertEqual(image_processor.crop_size ,							{"height": 18, "width": 18}     )

       UpperCamelCase			:      Dict 			=     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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     self.image_processing_class(**self.image_processor_dict     )
       # create random PIL videos
       UpperCamelCase			:      Union[str, Any] 			=     prepare_video_inputs(self.image_processor_tester ,							equal_resolution=A_     )
       for video in video_inputs:
        self.assertIsInstance(A_ ,							A_     )
        self.assertIsInstance(video[0] ,							Image.Image     )

       # Test not batched input
       UpperCamelCase			:      Any 			=     image_processing(video_inputs[0] ,							return_tensors="pt"     ).pixel_values
       self.assertEqual(
           encoded_videos.shape ,							(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           ) ,							)

       # Test batched
       UpperCamelCase			:      str 			=     image_processing(A_ ,							return_tensors="pt"     ).pixel_values
       self.assertEqual(
           encoded_videos.shape ,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           ) ,							)





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     self.image_processing_class(**self.image_processor_dict     )
       # create random numpy tensors
       UpperCamelCase			:      str 			=     prepare_video_inputs(self.image_processor_tester ,							equal_resolution=A_ ,							numpify=A_     )
       for video in video_inputs:
        self.assertIsInstance(A_ ,							A_     )
        self.assertIsInstance(video[0] ,							np.ndarray     )

       # Test not batched input
       UpperCamelCase			:      Tuple 			=     image_processing(video_inputs[0] ,							return_tensors="pt"     ).pixel_values
       self.assertEqual(
           encoded_videos.shape ,							(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           ) ,							)

       # Test batched
       UpperCamelCase			:      Any 			=     image_processing(A_ ,							return_tensors="pt"     ).pixel_values
       self.assertEqual(
           encoded_videos.shape ,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           ) ,							)





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      str 			=     self.image_processing_class(**self.image_processor_dict     )
       # create random PyTorch tensors
       UpperCamelCase			:      Union[str, Any] 			=     prepare_video_inputs(self.image_processor_tester ,							equal_resolution=A_ ,							torchify=A_     )
       for video in video_inputs:
        self.assertIsInstance(A_ ,							A_     )
        self.assertIsInstance(video[0] ,							torch.Tensor     )

       # Test not batched input
       UpperCamelCase			:      Tuple 			=     image_processing(video_inputs[0] ,							return_tensors="pt"     ).pixel_values
       self.assertEqual(
           encoded_videos.shape ,							(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           ) ,							)

       # Test batched
       UpperCamelCase			:      List[Any] 			=     image_processing(A_ ,							return_tensors="pt"     ).pixel_values
       self.assertEqual(
           encoded_videos.shape ,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           ) ,							)
