


'''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()


_UpperCAmelCase					:			List[Any]				     =					logging.getLogger(__name__)


@dataclass
class    a__					:







				"""simple docstring"""



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


@dataclass
class    a__					:







				"""simple docstring"""



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

def 				__magic_name__(    ):
						# 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(lowerCamelCase,					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''',					lowerCamelCase)

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

						# 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=lowerCamelCase,					idalabel=lowerCamelCase,					labelaid={label: i for i, label in enumerate(lowerCamelCase)},					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=lowerCamelCase,					cache_dir=model_args.cache_dir,					)

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

												for i in range(lowerCamelCase):
																		for j in range(lowerCamelCase):
																								if label_ids[i, j] != -1_0_0:
																														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:
												__lowerCAmelCase							,       __lowerCAmelCase											=					align_predictions(p.predictions,					p.label_ids)

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

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

						# 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(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:
												__lowerCAmelCase											=					TFTokenClassificationDataset(
												    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,					)

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

												logger.info('''\n%s''',					lowerCamelCase)

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

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

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

												with open(lowerCamelCase,					'''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(lowerCamelCase)

																																				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(lowerCamelCase)
																														else:
																																				logger.warning('''Maximum sequence length exceeded: No prediction for \'%s\'.''',					line.split()[0])

						return results


if __name__ == "__main__":
	main()





'''simple docstring'''

import argparse
import json
import logging
import os
import sys
from unittest import skip
from unittest.mock import patch

import tensorflow as tf

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


_UpperCAmelCase					:			str				     =					[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        """text-generation""",
        """text-classification""",
        """token-classification""",
        """language-modeling""",
        """multiple-choice""",
        """question-answering""",
        """summarization""",
        """translation""",
        """image-classification""",
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
	import run_clm
	import run_image_classification
	import run_mlm
	import run_ner
	import run_qa as run_squad
	import run_summarization
	import run_swag
	import run_text_classification
	import run_translation


logging.basicConfig(level=logging.DEBUG)

_UpperCAmelCase					:			Optional[Any]				     =					logging.getLogger()

def 				__magic_name__(    ):
						__lowerCAmelCase											=					argparse.ArgumentParser()
						parser.add_argument('''-f''')
						__lowerCAmelCase											=					parser.parse_args()
						return args.f

def 				__magic_name__(    lowerCamelCase):
						__lowerCAmelCase											=					{}
						__lowerCAmelCase											=					os.path.join(lowerCamelCase,					'''all_results.json''')
						if os.path.exists(lowerCamelCase):
												with open(lowerCamelCase,					'''r''') as f:
																		__lowerCAmelCase											=					json.load(lowerCamelCase)
						else:
												raise ValueError(F"""can't find {path}""")
						return results

def 				__magic_name__(    ):
						return bool(tf.config.list_physical_devices('''GPU'''))


_UpperCAmelCase					:			str				     =					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




class    a__					(			__A   ):







				"""simple docstring"""



				@skip('''Skipping until shape inference for to_tf_dataset PR is merged.'''		)
				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_auto_remove_tmp_dir()
										__lowerCAmelCase											=					F"""
            run_text_classification.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

										if is_cuda_available():
																testargs.append('''--fp16'''		)

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_text_classification.main()
																# Reset the mixed precision policy so we don't break other tests
																tf.keras.mixed_precision.set_global_policy('''float32'''		)
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertGreaterEqual(result['''eval_accuracy''']							,      0.7_5		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_auto_remove_tmp_dir()
										__lowerCAmelCase											=					F"""
            run_clm.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

										if len(tf.config.list_physical_devices('''GPU'''		)		) > 1:
																# Skipping because there are not enough batches to train the model + would need a drop_last to work.
																return

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_clm.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertLess(result['''eval_perplexity''']							,      1_00		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_auto_remove_tmp_dir()
										__lowerCAmelCase											=					F"""
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --max_seq_length 64
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --prediction_loss_only
            --num_train_epochs=1
            --learning_rate=1e-4
        """.split()

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_mlm.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertLess(result['''eval_perplexity''']							,      42		)




				def        _snake_case	(self		):
										# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
										__lowerCAmelCase											=					7 if get_gpu_count() > 1 else 2

										__lowerCAmelCase											=					self.get_auto_remove_tmp_dir()
										__lowerCAmelCase											=					F"""
            run_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        """.split()

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_ner.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertGreaterEqual(result['''accuracy''']							,      0.7_5		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_auto_remove_tmp_dir()
										__lowerCAmelCase											=					F"""
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=10
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_squad.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertGreaterEqual(result['''f1''']							,      30		)
																self.assertGreaterEqual(result['''exact''']							,      30		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_auto_remove_tmp_dir()
										__lowerCAmelCase											=					F"""
            run_swag.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=20
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_swag.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertGreaterEqual(result['''val_accuracy''']							,      0.8		)




				@slow
				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_auto_remove_tmp_dir()
										__lowerCAmelCase											=					F"""
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=50
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_summarization.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertGreaterEqual(result['''rouge1''']							,      10		)
																self.assertGreaterEqual(result['''rouge2''']							,      2		)
																self.assertGreaterEqual(result['''rougeL''']							,      7		)
																self.assertGreaterEqual(result['''rougeLsum''']							,      7		)




				@slow
				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_auto_remove_tmp_dir()
										__lowerCAmelCase											=					F"""
            run_translation.py
            --model_name_or_path Rocketknight1/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=3e-3
            --num_train_epochs 12
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
        """.split()

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_translation.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertGreaterEqual(result['''bleu''']							,      30		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_auto_remove_tmp_dir()
										__lowerCAmelCase											=					F"""
            run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --model_name_or_path microsoft/resnet-18
            --do_train
            --do_eval
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --dataloader_num_workers 16
            --num_train_epochs 2
            --train_val_split 0.1
            --seed 42
            --ignore_mismatched_sizes True
            """.split()

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_image_classification.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertGreaterEqual(result['''accuracy''']							,      0.7		)

