



"""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


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import ChineseCLIPImageProcessor



class     UpperCAmelCase    (	unittest.TestCase  ):



   def __init__(       self    :	Dict							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	Optional[Any]=7							,       __lowerCamelCase    :	Union[str, Any]=3							,       __lowerCamelCase    :	str=1_8							,       __lowerCamelCase    :	Any=3_0							,       __lowerCamelCase    :	Any=4_0_0							,       __lowerCamelCase    :	Tuple=True							,       __lowerCamelCase    :	List[Any]=None							,       __lowerCamelCase    :	Dict=True							,       __lowerCamelCase    :	Tuple=None							,       __lowerCamelCase    :	Tuple=True							,       __lowerCamelCase    :	List[str]=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]							,       __lowerCamelCase    :	List[Any]=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]							,       __lowerCamelCase    :	int=True							,       ):



     """simple docstring"""
     _snake_case   	=      size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
     _snake_case   	=      crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
     _snake_case   	=      parent
     _snake_case   	=      batch_size
     _snake_case   	=      num_channels
     _snake_case   	=      image_size
     _snake_case   	=      min_resolution
     _snake_case   	=      max_resolution
     _snake_case   	=      do_resize
     _snake_case   	=      size
     _snake_case   	=      do_center_crop
     _snake_case   	=      crop_size
     _snake_case   	=      do_normalize
     _snake_case   	=      image_mean
     _snake_case   	=      image_std
     _snake_case   	=      do_convert_rgb



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_center_crop": self.do_center_crop,
         "crop_size": self.crop_size,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_convert_rgb": self.do_convert_rgb,
     }





   def    __UpperCAmelCase    (       self    :	List[Any]							,       __lowerCamelCase    :	List[str]=False							,       __lowerCamelCase    :	int=False							,       __lowerCamelCase    :	Any=False				):



     """simple docstring"""
     assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

     if equal_resolution:
       _snake_case   	=      []
       for i in range(self.batch_size				):
         image_inputs.append(
             np.random.randint(
                 2_5_5							,       size=(self.num_channels, self.max_resolution, self.max_resolution)							,       dtype=np.uinta				)				)
     else:
       _snake_case   	=      []
       for i in range(self.batch_size				):
         _snake_case	,     _snake_case   	=      np.random.choice(np.arange(self.min_resolution							,       self.max_resolution				)							,       2				)
         image_inputs.append(np.random.randint(2_5_5							,       size=(self.num_channels, width, height)							,       dtype=np.uinta				)				)

     if not numpify and not torchify:
       # PIL expects the channel dimension as last dimension
       _snake_case   	=      [Image.fromarray(np.moveaxis(__lowerCamelCase							,       0							,       -1				)				) for x in image_inputs]

     if torchify:
       _snake_case   	=      [torch.from_numpy(__lowerCamelCase				) for x in image_inputs]

     return image_inputs



@require_torch
@require_vision
class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE,unittest.TestCase  ):
   A__		:	Dict           =  ChineseCLIPImageProcessor if is_vision_available() else None



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     _snake_case   	=      ChineseCLIPImageProcessingTester(self							,       do_center_crop=__lowerCamelCase				)



   @property
   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     return self.image_processor_tester.prepare_image_processor_dict()



   def    __UpperCAmelCase    (       self    :	Tuple				):



     """simple docstring"""
     _snake_case   	=      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_normalize'''				)				)
     self.assertTrue(hasattr(__lowerCamelCase							,       '''image_mean'''				)				)
     self.assertTrue(hasattr(__lowerCamelCase							,       '''image_std'''				)				)
     self.assertTrue(hasattr(__lowerCamelCase							,       '''do_convert_rgb'''				)				)



   def    __UpperCAmelCase    (       self    :	str				):



     """simple docstring"""
     _snake_case   	=      self.image_processing_class.from_dict(self.image_processor_dict				)
     self.assertEqual(image_processor.size							,       {'''height''': 2_2_4, '''width''': 2_2_4}				)
     self.assertEqual(image_processor.crop_size							,       {'''height''': 1_8, '''width''': 1_8}				)

     _snake_case   	=      self.image_processing_class.from_dict(self.image_processor_dict							,       size=4_2							,       crop_size=8_4				)
     self.assertEqual(image_processor.size							,       {'''shortest_edge''': 4_2}				)
     self.assertEqual(image_processor.crop_size							,       {'''height''': 8_4, '''width''': 8_4}				)



   def    __UpperCAmelCase    (       self    :	Tuple				):



     """simple docstring"""
     pass



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     # Initialize image_processing
     _snake_case   	=      self.image_processing_class(**self.image_processor_dict				)
     # create random PIL images
     _snake_case   	=      self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase				)
     for image in image_inputs:
       self.assertIsInstance(__lowerCamelCase							,       Image.Image				)

     # Test not batched input
     _snake_case   	=      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
     _snake_case   	=      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    __UpperCAmelCase    (       self    :	Tuple				):



     """simple docstring"""
     # Initialize image_processing
     _snake_case   	=      self.image_processing_class(**self.image_processor_dict				)
     # create random numpy tensors
     _snake_case   	=      self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase							,       numpify=__lowerCamelCase				)
     for image in image_inputs:
       self.assertIsInstance(__lowerCamelCase							,       np.ndarray				)

     # Test not batched input
     _snake_case   	=      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
     _snake_case   	=      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    __UpperCAmelCase    (       self    :	Tuple				):



     """simple docstring"""
     # Initialize image_processing
     _snake_case   	=      self.image_processing_class(**self.image_processor_dict				)
     # create random PyTorch tensors
     _snake_case   	=      self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase							,       torchify=__lowerCamelCase				)
     for image in image_inputs:
       self.assertIsInstance(__lowerCamelCase							,       torch.Tensor				)

     # Test not batched input
     _snake_case   	=      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
     _snake_case   	=      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'''],
         )							,       )



@require_torch
@require_vision
class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE,unittest.TestCase  ):
   A__		:	int           =  ChineseCLIPImageProcessor if is_vision_available() else None



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case   	=      ChineseCLIPImageProcessingTester(self							,       num_channels=4							,       do_center_crop=__lowerCamelCase				)
     _snake_case   	=      3



   @property
   def    __UpperCAmelCase    (       self    :	str				):



     """simple docstring"""
     return self.image_processor_tester.prepare_image_processor_dict()



   def    __UpperCAmelCase    (       self    :	str				):



     """simple docstring"""
     _snake_case   	=      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_normalize'''				)				)
     self.assertTrue(hasattr(__lowerCamelCase							,       '''image_mean'''				)				)
     self.assertTrue(hasattr(__lowerCamelCase							,       '''image_std'''				)				)
     self.assertTrue(hasattr(__lowerCamelCase							,       '''do_convert_rgb'''				)				)



   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



     """simple docstring"""
     pass





   def    __UpperCAmelCase    (       self    :	str				):



     """simple docstring"""
     # Initialize image_processing
     _snake_case   	=      self.image_processing_class(**self.image_processor_dict				)
     # create random PIL images
     _snake_case   	=      self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase				)
     for image in image_inputs:
       self.assertIsInstance(__lowerCamelCase							,       Image.Image				)

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

     # Test batched
     _snake_case   	=      image_processing(__lowerCamelCase							,       return_tensors='''pt'''				).pixel_values
     self.assertEqual(
         encoded_images.shape							,       (
             self.image_processor_tester.batch_size,
             self.expected_encoded_image_num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )							,       )









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


__snake_case   							=						logging.getLogger(__name__)



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



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






def 		_A		(		)					-> str:


     """simple docstring"""


     __UpperCamelCase =			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 =			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
     else:
          __UpperCamelCase,    __UpperCamelCase,    __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(_lowercase					,  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 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'					,  _lowercase )

     # Set seed
     set_seed(training_args.seed )

     # Prepare CONLL-2003 task
     __UpperCamelCase =			token_classification_task.get_labels(data_args.labels )
     __UpperCamelCase =			dict(enumerate(_lowercase ) )
     __UpperCamelCase =			len(_lowercase )

     # 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=_lowercase					,  idalabel=_lowercase					,  labelaid={label: i for i, label in enumerate(_lowercase )}					,  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					,  )
     __UpperCamelCase =			AutoModelForTokenClassification.from_pretrained(
         model_args.model_name_or_path					,  from_tf=bool('.ckpt' in model_args.model_name_or_path )					,  config=_lowercase					,  cache_dir=model_args.cache_dir					,  )

     # Get datasets
     __UpperCamelCase =			(
         TokenClassificationDataset(
             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
     )
     __UpperCamelCase =			(
         TokenClassificationDataset(
             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(_lowercase					,  _lowercase ) -> Tuple[List[int], List[int]]:
          __UpperCamelCase =			np.argmax(_lowercase					,  axis=2 )

          __UpperCamelCase,    __UpperCamelCase =			preds.shape

          __UpperCamelCase =			[[] for _ in range(_lowercase )]
          __UpperCamelCase =			[[] for _ in range(_lowercase )]

          for i in range(_lowercase ):
               for j in range(_lowercase ):
                    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(_lowercase ) -> Dict:
          __UpperCamelCase,    __UpperCamelCase =			align_predictions(p.predictions					,  p.label_ids )
          return {
              "accuracy_score": accuracy_score(_lowercase					,  _lowercase ),
              "precision": precision_score(_lowercase					,  _lowercase ),
              "recall": recall_score(_lowercase					,  _lowercase ),
              "f1": fa_score(_lowercase					,  _lowercase ),
          }

     # Data collator
     __UpperCamelCase =			DataCollatorWithPadding(_lowercase					,  pad_to_multiple_of=8 ) if training_args.fpaa else None

     # Initialize our Trainer
     __UpperCamelCase =			Trainer(
         model=_lowercase					,  args=_lowercase					,  train_dataset=_lowercase					,  eval_dataset=_lowercase					,  compute_metrics=_lowercase					,  data_collator=_lowercase					,  )

     # 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 =			{}
     if training_args.do_eval:
          logger.info('*** Evaluate ***' )

          __UpperCamelCase =			trainer.evaluate()

          __UpperCamelCase =			os.path.join(training_args.output_dir					,  'eval_results.txt' )
          if trainer.is_world_process_zero():
               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:
          __UpperCamelCase =			TokenClassificationDataset(
              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					,  )

          __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase =			trainer.predict(_lowercase )
          __UpperCamelCase,    __UpperCamelCase =			align_predictions(_lowercase					,  _lowercase )

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

        # Save predictions
          __UpperCamelCase =			os.path.join(training_args.output_dir					,  'test_predictions.txt' )
          if trainer.is_world_process_zero():
               with open(_lowercase					,  'w' ) as writer:
                    with open(os.path.join(data_args.data_dir					,  'test.txt' )					,  'r' ) as f:
                         token_classification_task.write_predictions_to_file(_lowercase					,  _lowercase					,  _lowercase )

     return results






def 		_A		(		_lowercase )					-> Dict:


     """simple docstring"""


     main()


if __name__ == "__main__":
    main()


