


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       =							logging.getLogger(__name__)


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


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


		"""simple docstring"""




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

		lowercase__            =      import_module('''tasks'''     )
		try:
				lowercase__            =      getattr(__UpperCAmelCase						,				model_args.task_type     )
				lowercase__            =      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'''						,				__UpperCAmelCase     )

		# Set seed
		set_seed(training_args.seed     )

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

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

		lowercase__            =      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						,				)
		lowercase__            =      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						,				)
		lowercase__            =      AutoModelForTokenClassification.from_pretrained(
		    model_args.model_name_or_path						,				from_tf=bool('''.ckpt''' in model_args.model_name_or_path     )						,				config=__UpperCAmelCase						,				cache_dir=model_args.cache_dir						,				)

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

				lowercase__     ,		lowercase__            =      preds.shape

				lowercase__            =      [[] for _ in range(__UpperCAmelCase     )]
				lowercase__            =      [[] for _ in range(__UpperCAmelCase     )]

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

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

		# Initialize our Trainer
		lowercase__            =      Trainer(
		    model=__UpperCAmelCase						,				args=__UpperCAmelCase						,				train_dataset=__UpperCAmelCase						,				eval_dataset=__UpperCAmelCase						,				compute_metrics=__UpperCAmelCase						,				data_collator=__UpperCAmelCase						,				)

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

				lowercase__            =      trainer.evaluate()

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

				lowercase__     ,		lowercase__     ,		lowercase__            =      trainer.predict(__UpperCAmelCase     )
				lowercase__     ,		lowercase__            =      align_predictions(__UpperCAmelCase						,				__UpperCAmelCase     )

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

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

		return results
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		main()


if __name__ == "__main__":
			main()








import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402





class        lowerCamelCase_							(      unittest.TestCase					):


 '''simple docstring'''







 def       lowerCAmelCase_  (							self  :		Any						):
       SCREAMING_SNAKE_CASE_							=			0



 def       lowerCAmelCase_  (							self  :		int						):
       SCREAMING_SNAKE_CASE_							=			AutoImageProcessor.from_pretrained('openai/clip-vit-base-patch32'						)
       self.assertIsInstance(_lowerCAmelCase				,				_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		Optional[int]						):
       with tempfile.TemporaryDirectory() as tmpdirname:
             SCREAMING_SNAKE_CASE_							=			Path(_lowerCAmelCase						) / 'preprocessor_config.json'
             SCREAMING_SNAKE_CASE_							=			Path(_lowerCAmelCase						) / 'config.json'
             json.dump(
                 {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}				,				open(_lowerCAmelCase				,				'w'						)				,				)
             json.dump({'model_type': 'clip'}				,				open(_lowerCAmelCase				,				'w'						)						)

             SCREAMING_SNAKE_CASE_							=			AutoImageProcessor.from_pretrained(_lowerCAmelCase						)
             self.assertIsInstance(_lowerCAmelCase				,				_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		str						):
       # Ensure we can load the image processor from the feature extractor config
       with tempfile.TemporaryDirectory() as tmpdirname:
             SCREAMING_SNAKE_CASE_							=			Path(_lowerCAmelCase						) / 'preprocessor_config.json'
             SCREAMING_SNAKE_CASE_							=			Path(_lowerCAmelCase						) / 'config.json'
             json.dump(
                 {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'}				,				open(_lowerCAmelCase				,				'w'						)				,				)
             json.dump({'model_type': 'clip'}				,				open(_lowerCAmelCase				,				'w'						)						)

             SCREAMING_SNAKE_CASE_							=			AutoImageProcessor.from_pretrained(_lowerCAmelCase						)
             self.assertIsInstance(_lowerCAmelCase				,				_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		Union[str, Any]						):
       with tempfile.TemporaryDirectory() as tmpdirname:
             SCREAMING_SNAKE_CASE_							=			CLIPConfig()

             # Create a dummy config file with image_proceesor_type
             SCREAMING_SNAKE_CASE_							=			Path(_lowerCAmelCase						) / 'preprocessor_config.json'
             SCREAMING_SNAKE_CASE_							=			Path(_lowerCAmelCase						) / 'config.json'
             json.dump(
                 {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}				,				open(_lowerCAmelCase				,				'w'						)				,				)
             json.dump({'model_type': 'clip'}				,				open(_lowerCAmelCase				,				'w'						)						)

             # remove image_processor_type to make sure config.json alone is enough to load image processor locally
             SCREAMING_SNAKE_CASE_							=			AutoImageProcessor.from_pretrained(_lowerCAmelCase						).to_dict()

             config_dict.pop('image_processor_type'						)
             SCREAMING_SNAKE_CASE_							=			CLIPImageProcessor(**_lowerCAmelCase						)

             # save in new folder
             model_config.save_pretrained(_lowerCAmelCase						)
             config.save_pretrained(_lowerCAmelCase						)

             SCREAMING_SNAKE_CASE_							=			AutoImageProcessor.from_pretrained(_lowerCAmelCase						)

             # make sure private variable is not incorrectly saved
             SCREAMING_SNAKE_CASE_							=			json.loads(config.to_json_string()						)
             self.assertTrue('_processor_class' not in dict_as_saved						)

       self.assertIsInstance(_lowerCAmelCase				,				_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		str						):
       with tempfile.TemporaryDirectory() as tmpdirname:
             SCREAMING_SNAKE_CASE_							=			Path(_lowerCAmelCase						) / 'preprocessor_config.json'
             json.dump(
                 {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}				,				open(_lowerCAmelCase				,				'w'						)				,				)

             SCREAMING_SNAKE_CASE_							=			AutoImageProcessor.from_pretrained(_lowerCAmelCase						)
             self.assertIsInstance(_lowerCAmelCase				,				_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		List[Any]						):
       with self.assertRaisesRegex(
           _lowerCAmelCase				,				'clip-base is not a local folder and is not a valid model identifier'						):
             SCREAMING_SNAKE_CASE_							=			AutoImageProcessor.from_pretrained('clip-base'						)



 def       lowerCAmelCase_  (							self  :		List[Any]						):
       with self.assertRaisesRegex(
           _lowerCAmelCase				,				R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'						):
             SCREAMING_SNAKE_CASE_							=			AutoImageProcessor.from_pretrained(_lowerCAmelCase				,				revision='aaaaaa'						)



 def       lowerCAmelCase_  (							self  :		str						):
       with self.assertRaisesRegex(
           _lowerCAmelCase				,				'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'				,				):
             SCREAMING_SNAKE_CASE_							=			AutoImageProcessor.from_pretrained('hf-internal-testing/config-no-model'						)



 def       lowerCAmelCase_  (							self  :		Tuple						):
       # If remote code is not set, we will time out when asking whether to load the model.
       with self.assertRaises(_lowerCAmelCase						):
             SCREAMING_SNAKE_CASE_							=			AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor'						)
       # If remote code is disabled, we can't load this config.
       with self.assertRaises(_lowerCAmelCase						):
             SCREAMING_SNAKE_CASE_							=			AutoImageProcessor.from_pretrained(
                 'hf-internal-testing/test_dynamic_image_processor'				,				trust_remote_code=_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_							=			AutoImageProcessor.from_pretrained(
           'hf-internal-testing/test_dynamic_image_processor'				,				trust_remote_code=_lowerCAmelCase						)
       self.assertEqual(image_processor.__class__.__name__				,				'NewImageProcessor'						)

       # Test image processor can be reloaded.
       with tempfile.TemporaryDirectory() as tmp_dir:
             image_processor.save_pretrained(_lowerCAmelCase						)
             SCREAMING_SNAKE_CASE_							=			AutoImageProcessor.from_pretrained(_lowerCAmelCase				,				trust_remote_code=_lowerCAmelCase						)
       self.assertEqual(reloaded_image_processor.__class__.__name__				,				'NewImageProcessor'						)



 def       lowerCAmelCase_  (							self  :		Optional[Any]						):
       try:
             AutoConfig.register('custom'				,				_lowerCAmelCase						)
             AutoImageProcessor.register(_lowerCAmelCase				,				_lowerCAmelCase						)
             # Trying to register something existing in the Transformers library will raise an error
             with self.assertRaises(_lowerCAmelCase						):
                   AutoImageProcessor.register(_lowerCAmelCase				,				_lowerCAmelCase						)

             with tempfile.TemporaryDirectory() as tmpdirname:
                   SCREAMING_SNAKE_CASE_							=			Path(_lowerCAmelCase						) / 'preprocessor_config.json'
                   SCREAMING_SNAKE_CASE_							=			Path(_lowerCAmelCase						) / 'config.json'
                   json.dump(
                       {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'}				,				open(_lowerCAmelCase				,				'w'						)				,				)
                   json.dump({'model_type': 'clip'}				,				open(_lowerCAmelCase				,				'w'						)						)

                   SCREAMING_SNAKE_CASE_							=			CustomImageProcessor.from_pretrained(_lowerCAmelCase						)

             # Now that the config is registered, it can be used as any other config with the auto-API
             with tempfile.TemporaryDirectory() as tmp_dir:
                   image_processor.save_pretrained(_lowerCAmelCase						)
                   SCREAMING_SNAKE_CASE_							=			AutoImageProcessor.from_pretrained(_lowerCAmelCase						)
                   self.assertIsInstance(_lowerCAmelCase				,				_lowerCAmelCase						)

       finally:
             if "custom" in CONFIG_MAPPING._extra_content:
                   del CONFIG_MAPPING._extra_content["custom"]
             if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
                   del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]




 def       lowerCAmelCase_  (							self  :		Union[str, Any]						):





       class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE					):


        '''simple docstring'''







        lowercase_				    =    True

       try:
             AutoConfig.register('custom'				,				_lowerCAmelCase						)
             AutoImageProcessor.register(_lowerCAmelCase				,				_lowerCAmelCase						)
             # If remote code is not set, the default is to use local
             SCREAMING_SNAKE_CASE_							=			AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor'						)
             self.assertEqual(image_processor.__class__.__name__				,				'NewImageProcessor'						)
             self.assertTrue(image_processor.is_local						)

             # If remote code is disabled, we load the local one.
             SCREAMING_SNAKE_CASE_							=			AutoImageProcessor.from_pretrained(
                 'hf-internal-testing/test_dynamic_image_processor'				,				trust_remote_code=_lowerCAmelCase						)
             self.assertEqual(image_processor.__class__.__name__				,				'NewImageProcessor'						)
             self.assertTrue(image_processor.is_local						)

             # If remote is enabled, we load from the Hub
             SCREAMING_SNAKE_CASE_							=			AutoImageProcessor.from_pretrained(
                 'hf-internal-testing/test_dynamic_image_processor'				,				trust_remote_code=_lowerCAmelCase						)
             self.assertEqual(image_processor.__class__.__name__				,				'NewImageProcessor'						)
             self.assertTrue(not hasattr(_lowerCAmelCase				,				'is_local'						)						)

       finally:
             if "custom" in CONFIG_MAPPING._extra_content:
                   del CONFIG_MAPPING._extra_content["custom"]
             if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
                   del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]