

'''simple docstring'''




import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Image
from datasets.packaged_modules.text.text import Text

from ..utils import require_pil




@pytest.fixture
def 						UpperCamelCase       ( a )      ->	Tuple:






     '''simple docstring'''
     __magic_name__						   =				tmp_path / '''text.txt'''
     __magic_name__						   =				textwrap.dedent(
         '''\
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

        Second paragraph:
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
        ''' )
     with open(a		,						'''w'''		,						encoding='''utf-8''' ) as f:
          f.write(a )
     return str(a )




@pytest.fixture
def 						UpperCamelCase       ( a		,						a )      ->	Any:






     '''simple docstring'''
     __magic_name__						   =				tmp_path / '''text_with_image.txt'''
     with open(a		,						'''w'''		,						encoding='''utf-8''' ) as f:
          f.write(a )
     return str(a )




@pytest.mark.parametrize('''keep_linebreaks'''		,						[True, False] )
def 						UpperCamelCase       ( a		,						a )      ->	Tuple:






     '''simple docstring'''
     with open(a		,						encoding='''utf-8''' ) as f:
          __magic_name__						   =				f.read().splitlines(keepends=a )
     __magic_name__						   =				Text(keep_linebreaks=a		,						encoding='''utf-8''' )
     __magic_name__						   =				text._generate_tables([[text_file]] )
     __magic_name__						   =				pa.concat_tables([table for _, table in generator] ).to_pydict()['''text''']
     assert generated_content == expected_content




@require_pil
def 						UpperCamelCase       ( a )      ->	Optional[Any]:






     '''simple docstring'''
     with open(a		,						encoding='''utf-8''' ) as f:
          __magic_name__						   =				f.read().splitlines()[0]
     __magic_name__						   =				Text(encoding='''utf-8'''		,						features=Features({'''image''': Image()} ) )
     __magic_name__						   =				text._generate_tables([[text_file_with_image]] )
     __magic_name__						   =				pa.concat_tables([table for _, table in generator] )
     assert pa_table.schema.field('''image''' ).type == Image()()
     __magic_name__						   =				pa_table.to_pydict()['''image''']
     assert generated_content == [{"path": image_file, "bytes": None}]




@pytest.mark.parametrize('''sample_by'''		,						['''line''', '''paragraph''', '''document'''] )
def 						UpperCamelCase       ( a		,						a )      ->	List[str]:






     '''simple docstring'''
     with open(a		,						encoding='''utf-8''' ) as f:
          __magic_name__						   =				f.read()
     if sample_by == "line":
          __magic_name__						   =				expected_content.splitlines()
     elif sample_by == "paragraph":
          __magic_name__						   =				expected_content.split('''\n\n''' )
     elif sample_by == "document":
          __magic_name__						   =				[expected_content]
     __magic_name__						   =				Text(sample_by=a		,						encoding='''utf-8'''		,						chunksize=100 )
     __magic_name__						   =				text._generate_tables([[text_file]] )
     __magic_name__						   =				pa.concat_tables([table for _, table in generator] ).to_pydict()['''text''']
     assert generated_content == expected_content













'''simple docstring'''







import logging
import os
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 classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


_a					: 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(
		    metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}   )
		a   :   int     =field(
		    default=1_28	,					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    _lowerCAmelCase						(						)					->					List[str]:
			# 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.
			__lowerCAmelCase											=				HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)	)
			__lowerCAmelCase						,       __lowerCAmelCase						,       __lowerCAmelCase											=				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."""	)

			__lowerCAmelCase											=				import_module("""tasks"""	)

			try:
						__lowerCAmelCase											=				getattr(lowercase					,	model_args.task_type	)
						__lowerCAmelCase											=				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					,	)
			logger.info(
			    """n_replicas: %s, distributed training: %s, 16-bits training: %s"""					,	training_args.n_replicas					,	bool(training_args.n_replicas > 1	)					,	training_args.fpaa					,	)
			logger.info("""Training/evaluation parameters %s"""					,	lowercase	)

			# Prepare Token Classification task
			__lowerCAmelCase											=				token_classification_task.get_labels(data_args.labels	)
			__lowerCAmelCase											=				dict(enumerate(lowercase	)	)
			__lowerCAmelCase											=				len(lowercase	)

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

			__lowerCAmelCase											=				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					,	)
			__lowerCAmelCase											=				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					,	)

			with training_args.strategy.scope():
						__lowerCAmelCase											=				TFAutoModelForTokenClassification.from_pretrained(
						    model_args.model_name_or_path					,	from_pt=bool(""".bin""" in model_args.model_name_or_path	)					,	config=lowercase					,	cache_dir=model_args.cache_dir					,	)

			# Get datasets
			__lowerCAmelCase											=				(
			    TFTokenClassificationDataset(
			        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
			)
			__lowerCAmelCase											=				(
			    TFTokenClassificationDataset(
			        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]]:
						__lowerCAmelCase											=				np.argmax(lowercase					,	axis=2	)
						__lowerCAmelCase						,       __lowerCAmelCase											=				preds.shape
						__lowerCAmelCase											=				[[] for _ in range(lowercase	)]
						__lowerCAmelCase											=				[[] for _ in range(lowercase	)]

						for i in range(lowercase	):
									for j in range(lowercase	):
												if label_ids[i, j] != -100:
															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:
						__lowerCAmelCase						,       __lowerCAmelCase											=				align_predictions(p.predictions					,	p.label_ids	)

						return {
						    "precision": precision_score(lowercase					,	lowercase	),
						    "recall": recall_score(lowercase					,	lowercase	),
						    "f1": fa_score(lowercase					,	lowercase	),
						}

			# Initialize our Trainer
			__lowerCAmelCase											=				TFTrainer(
			    model=lowercase					,	args=lowercase					,	train_dataset=train_dataset.get_dataset() if train_dataset else None					,	eval_dataset=eval_dataset.get_dataset() if eval_dataset else None					,	compute_metrics=lowercase					,	)

			# Training
			if training_args.do_train:
						trainer.train()
						trainer.save_model()
						tokenizer.save_pretrained(training_args.output_dir	)

			# Evaluation
			__lowerCAmelCase											=				{}
			if training_args.do_eval:
						logger.info("""*** Evaluate ***"""	)

						__lowerCAmelCase											=				trainer.evaluate()
						__lowerCAmelCase											=				os.path.join(training_args.output_dir					,	"""eval_results.txt"""	)

						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:
						__lowerCAmelCase											=				TFTokenClassificationDataset(
						    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					,	)

						__lowerCAmelCase						,       __lowerCAmelCase						,       __lowerCAmelCase											=				trainer.predict(test_dataset.get_dataset()	)
						__lowerCAmelCase						,       __lowerCAmelCase											=				align_predictions(lowercase					,	lowercase	)
						__lowerCAmelCase											=				classification_report(lowercase					,	lowercase	)

						logger.info("""\n%s"""					,	lowercase	)

						__lowerCAmelCase											=				os.path.join(training_args.output_dir					,	"""test_results.txt"""	)

						with open(lowercase					,	"""w"""	) as writer:
									writer.write("""%s\n""" % report	)

						# Save predictions
						__lowerCAmelCase											=				os.path.join(training_args.output_dir					,	"""test_predictions.txt"""	)

						with open(lowercase					,	"""w"""	) as writer:
									with open(os.path.join(data_args.data_dir					,	"""test.txt"""	)					,	"""r"""	) as f:
												__lowerCAmelCase											=				0

												for line in f:
															if line.startswith("""-DOCSTART-"""	) or line == "" or line == "\n":
																		writer.write(lowercase	)

																		if not preds_list[example_id]:
																					example_id += 1
															elif preds_list[example_id]:
																		__lowerCAmelCase											=				line.split()[0] + """ """ + preds_list[example_id].pop(0	) + """\n"""

																		writer.write(lowercase	)
															else:
																		logger.warning("""Maximum sequence length exceeded: No prediction for '%s'."""					,	line.split()[0]	)

			return results


if __name__ == "__main__":
	main()

