
"""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


_lowercase				 =					logging.getLogger(__name__)







@dataclass
class 					lowerCAmelCase_					:



						'''simple docstring'''


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







@dataclass
class 					lowerCAmelCase_					:



						'''simple docstring'''


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






def    _snake_case					(		):
	# See all possible arguments in src/transformers/training_args.py
	# or by passing the --help flag to this script.
	# We now keep distinct sets of args, for a cleaner separation of concerns.

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

	A     = import_module('tasks'  )
	try:
		A     = getattr(snake_case__						,   model_args.task_type  )
		A     = 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'						,   snake_case__  )

	# Set seed
	set_seed(training_args.seed  )

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

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

	A     = AutoConfig.from_pretrained(
	    model_args.config_name if model_args.config_name else model_args.model_name_or_path						,   num_labels=snake_case__						,   idalabel=snake_case__						,   labelaid={label: i for i, label in enumerate(snake_case__  )}						,   cache_dir=model_args.cache_dir						,   )
	A     = 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						,   )
	A     = AutoModelForTokenClassification.from_pretrained(
	    model_args.model_name_or_path						,   from_tf=bool('.ckpt' in model_args.model_name_or_path  )						,   config=snake_case__						,   cache_dir=model_args.cache_dir						,   )

	# Get datasets
	A     = (
	    TokenClassificationDataset(
	        token_classification_task=snake_case__						,   data_dir=data_args.data_dir						,   tokenizer=snake_case__						,   labels=snake_case__						,   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
	)
	A     = (
	    TokenClassificationDataset(
	        token_classification_task=snake_case__						,   data_dir=data_args.data_dir						,   tokenizer=snake_case__						,   labels=snake_case__						,   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(snake_case__ :  np.ndarray						,   snake_case__ :  np.ndarray  ) -> Tuple[List[int], List[int]]:
		A     = np.argmax(snake_case__						,   axis=2  )

		A ,   A     = preds.shape

		A     = [[] for _ in range(snake_case__  )]
		A     = [[] for _ in range(snake_case__  )]

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

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

	# Initialize our Trainer
	A     = Trainer(
	    model=snake_case__						,   args=snake_case__						,   train_dataset=snake_case__						,   eval_dataset=snake_case__						,   compute_metrics=snake_case__						,   data_collator=snake_case__						,   )

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

		A     = trainer.evaluate()

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

			results.update(snake_case__  )

    # Predict
	if training_args.do_predict:
		A     = TokenClassificationDataset(
		    token_classification_task=snake_case__						,   data_dir=data_args.data_dir						,   tokenizer=snake_case__						,   labels=snake_case__						,   model_type=config.model_type						,   max_seq_length=data_args.max_seq_length						,   overwrite_cache=data_args.overwrite_cache						,   mode=Split.test						,   )

		A ,   A ,   A     = trainer.predict(snake_case__  )
		A ,   A     = align_predictions(snake_case__						,   snake_case__  )

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

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

	return results






def    _snake_case					(		snake_case__ :  Any  ):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
		main()

"""simple docstring"""





from datasets.utils.patching import _PatchedModuleObj, patch_submodule

from . import _test_patching



def  lowerCamelCase__						(     )				->						List[str]:


					"""simple docstring"""





					import os as original_os
					from os import path as original_path
					from os import rename as original_rename
					from os.path import dirname as original_dirname
					from os.path import join as original_join

					assert _test_patching.os is original_os
					assert _test_patching.path is original_path
					assert _test_patching.join is original_join

					assert _test_patching.renamed_os is original_os
					assert _test_patching.renamed_path is original_path
					assert _test_patching.renamed_join is original_join

					_UpperCamelCase									=    '''__test_patch_submodule_mock__'''
					with patch_submodule(_test_patching,	'''os.path.join''',	__snake_case      ):
										# Every way to access os.path.join must be patched, and the rest must stay untouched

										# check os.path.join
										assert isinstance(_test_patching.os,	_PatchedModuleObj      )
										assert isinstance(_test_patching.os.path,	_PatchedModuleObj      )
										assert _test_patching.os.path.join is mock

										# check path.join
										assert isinstance(_test_patching.path,	_PatchedModuleObj      )
										assert _test_patching.path.join is mock

										# check join
										assert _test_patching.join is mock

										# check that the other attributes are untouched
										assert _test_patching.os.rename is original_rename
										assert _test_patching.path.dirname is original_dirname
										assert _test_patching.os.path.dirname is original_dirname

										# Even renamed modules or objects must be patched

										# check renamed_os.path.join
										assert isinstance(_test_patching.renamed_os,	_PatchedModuleObj      )
										assert isinstance(_test_patching.renamed_os.path,	_PatchedModuleObj      )
										assert _test_patching.renamed_os.path.join is mock

										# check renamed_path.join
										assert isinstance(_test_patching.renamed_path,	_PatchedModuleObj      )
										assert _test_patching.renamed_path.join is mock

										# check renamed_join
										assert _test_patching.renamed_join is mock

										# check that the other attributes are untouched
										assert _test_patching.renamed_os.rename is original_rename
										assert _test_patching.renamed_path.dirname is original_dirname
										assert _test_patching.renamed_os.path.dirname is original_dirname

					# check that everthing is back to normal when the patch is over

					assert _test_patching.os is original_os
					assert _test_patching.path is original_path
					assert _test_patching.join is original_join

					assert _test_patching.renamed_os is original_os
					assert _test_patching.renamed_path is original_path
					assert _test_patching.renamed_join is original_join



def  lowerCamelCase__						(     )				->						List[str]:


					"""simple docstring"""





					assert _test_patching.open is open

					_UpperCamelCase									=    '''__test_patch_submodule_builtin_mock__'''
					# _test_patching has "open" in its globals
					assert _test_patching.open is open
					with patch_submodule(_test_patching,	'''open''',	__snake_case      ):
										assert _test_patching.open is mock

					# check that everthing is back to normal when the patch is over

					assert _test_patching.open is open



def  lowerCamelCase__						(     )				->						Union[str, Any]:


					"""simple docstring"""





					_UpperCamelCase									=    '''__test_patch_submodule_missing_mock__'''
					with patch_submodule(_test_patching,	'''pandas.read_csv''',	__snake_case      ):
										pass



def  lowerCamelCase__						(     )				->						Dict:


					"""simple docstring"""





					_UpperCamelCase									=    '''__test_patch_submodule_missing_builtin_mock__'''
					# _test_patching doesn't have "len" in its globals
					assert getattr(_test_patching,	'''len''',	__snake_case      ) is None
					with patch_submodule(_test_patching,	'''len''',	__snake_case      ):
										assert _test_patching.len is mock
					assert _test_patching.len is len



def  lowerCamelCase__						(     )				->						Tuple:


					"""simple docstring"""





					_UpperCamelCase									=    '''__test_patch_submodule_start_and_stop_mock__'''
					_UpperCamelCase									=    patch_submodule(_test_patching,	'''open''',	__snake_case      )
					assert _test_patching.open is open
					patch.start()
					assert _test_patching.open is mock
					patch.stop()
					assert _test_patching.open is open



def  lowerCamelCase__						(     )				->						Optional[int]:


					"""simple docstring"""





					from os import rename as original_rename
					from os.path import dirname as original_dirname
					from os.path import join as original_join

					_UpperCamelCase									=    '''__test_patch_submodule_successive_join__'''
					_UpperCamelCase									=    '''__test_patch_submodule_successive_dirname__'''
					_UpperCamelCase									=    '''__test_patch_submodule_successive_rename__'''
					assert _test_patching.os.path.join is original_join
					assert _test_patching.os.path.dirname is original_dirname
					assert _test_patching.os.rename is original_rename

					with patch_submodule(_test_patching,	'''os.path.join''',	__snake_case      ):
										with patch_submodule(_test_patching,	'''os.rename''',	__snake_case      ):
															with patch_submodule(_test_patching,	'''os.path.dirname''',	__snake_case      ):
																				assert _test_patching.os.path.join is mock_join
																				assert _test_patching.os.path.dirname is mock_dirname
																				assert _test_patching.os.rename is mock_rename

    # try another order
					with patch_submodule(_test_patching,	'''os.rename''',	__snake_case      ):
										with patch_submodule(_test_patching,	'''os.path.join''',	__snake_case      ):
															with patch_submodule(_test_patching,	'''os.path.dirname''',	__snake_case      ):
																				assert _test_patching.os.path.join is mock_join
																				assert _test_patching.os.path.dirname is mock_dirname
																				assert _test_patching.os.rename is mock_rename

					assert _test_patching.os.path.join is original_join
					assert _test_patching.os.path.dirname is original_dirname
					assert _test_patching.os.rename is original_rename



def  lowerCamelCase__						(     )				->						str:


					"""simple docstring"""





					_UpperCamelCase									=    '''__test_patch_submodule_doesnt_exist_mock__'''
					with patch_submodule(_test_patching,	'''__module_that_doesn_exist__.__attribute_that_doesn_exist__''',	__snake_case      ):
										pass
					with patch_submodule(_test_patching,	'''os.__attribute_that_doesn_exist__''',	__snake_case      ):
										pass






