"""simple docstring"""
import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


SCREAMING_SNAKE_CASE__      						=  logging.getLogger(__name__)



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    str			  =						"""sequence-classification"""

				def __init__(				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										if type(lowerCAmelCase    ) == dict:
																snake_case								=			Namespace(**lowerCAmelCase    )
										snake_case								=			glue_output_modes[hparams.task]
										snake_case								=			glue_tasks_num_labels[hparams.task]

										super().__init__(lowerCAmelCase		,    lowerCAmelCase		,    self.mode    )

				def 		snake_case (				self		,    **lowerCAmelCase    ):






										"""simple docstring"""

										return self.model(**lowerCAmelCase    )

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			{'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

										if self.config.model_type not in ["distilbert", "bart"]:
																snake_case								=			batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None

										snake_case								=			self(**lowerCAmelCase    )
										snake_case								=			outputs[0]

										snake_case								=			self.trainer.lr_schedulers[0]['scheduler']
										snake_case								=			{'loss': loss, 'rate': lr_scheduler.get_last_lr()[-1]}
										return {"loss": loss, "log": tensorboard_logs}

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.hparams
										snake_case								=			processors[args.task]()
										snake_case								=			processor.get_labels()

										for mode in ["train", "dev"]:
																snake_case								=			self._feature_file(lowerCAmelCase    )
																if os.path.exists(lowerCAmelCase    ) and not args.overwrite_cache:
																						logger.info('Loading features from cached file %s'		,    lowerCAmelCase    )
																else:
																						logger.info('Creating features from dataset file at %s'		,    args.data_dir    )
																						snake_case								=			(
																						    processor.get_dev_examples(args.data_dir    )
																						    if mode == 'dev'
																						    else processor.get_train_examples(args.data_dir    )
																						)
																						snake_case								=			convert_examples_to_features(
																						    lowerCAmelCase		,    self.tokenizer		,    max_length=args.max_seq_length		,    label_list=self.labels		,    output_mode=args.glue_output_mode		,    )
																						logger.info('Saving features into cached file %s'		,    lowerCAmelCase    )
																						torch.save(lowerCAmelCase		,    lowerCAmelCase    )

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase = False    ):






										"""simple docstring"""

										snake_case								=			'dev' if mode == 'test' else mode

										snake_case								=			self._feature_file(lowerCAmelCase    )
										logger.info('Loading features from cached file %s'		,    lowerCAmelCase    )
										snake_case								=			torch.load(lowerCAmelCase    )
										snake_case								=			torch.tensor([f.input_ids for f in features]		,    dtype=torch.long    )
										snake_case								=			torch.tensor([f.attention_mask for f in features]		,    dtype=torch.long    )
										snake_case								=			torch.tensor([f.token_type_ids for f in features]		,    dtype=torch.long    )
										if self.hparams.glue_output_mode == "classification":
																snake_case								=			torch.tensor([f.label for f in features]		,    dtype=torch.long    )
										elif self.hparams.glue_output_mode == "regression":
																snake_case								=			torch.tensor([f.label for f in features]		,    dtype=torch.float    )

										return DataLoader(
										    TensorDataset(lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    )		,    batch_size=lowerCAmelCase		,    shuffle=lowerCAmelCase		,    )

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			{'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

										if self.config.model_type not in ["distilbert", "bart"]:
																snake_case								=			batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None

										snake_case								=			self(**lowerCAmelCase    )
										snake_case					,snake_case								=			outputs[:2]
										snake_case								=			logits.detach().cpu().numpy()
										snake_case								=			inputs['labels'].detach().cpu().numpy()

										return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			torch.stack([x['val_loss'] for x in outputs]    ).mean().detach().cpu().item()
										snake_case								=			np.concatenate([x['pred'] for x in outputs]		,    axis=0    )

										if self.hparams.glue_output_mode == "classification":
																snake_case								=			np.argmax(lowerCAmelCase		,    axis=1    )
										elif self.hparams.glue_output_mode == "regression":
																snake_case								=			np.squeeze(lowerCAmelCase    )

										snake_case								=			np.concatenate([x['target'] for x in outputs]		,    axis=0    )
										snake_case								=			[[] for _ in range(out_label_ids.shape[0]    )]
										snake_case								=			[[] for _ in range(out_label_ids.shape[0]    )]

										snake_case								=			{**{'val_loss': val_loss_mean}, **compute_metrics(self.hparams.task		,    lowerCAmelCase		,    lowerCAmelCase    )}

										snake_case								=			dict(results.items()    )
										snake_case								=			results
										return ret, preds_list, out_label_list

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case					,snake_case					,snake_case								=			self._eval_end(lowerCAmelCase    )
										snake_case								=			ret['log']
										return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case					,snake_case					,snake_case								=			self._eval_end(lowerCAmelCase    )
										snake_case								=			ret['log']
										# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
										return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

				@staticmethod
				def 		snake_case (				lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										BaseTransformer.add_model_specific_args(lowerCAmelCase		,    lowerCAmelCase    )
										parser.add_argument(
										    '--max_seq_length'		,    default=1_28		,    type=lowerCAmelCase		,    help=(
										        'The maximum total input sequence length after tokenization. Sequences longer '
										        'than this will be truncated, sequences shorter will be padded.'
										    )		,    )

										parser.add_argument(
										    '--task'		,    default=''		,    type=lowerCAmelCase		,    required=lowerCAmelCase		,    help='The GLUE task to run'		,    )
										parser.add_argument(
										    '--gpus'		,    default=0		,    type=lowerCAmelCase		,    help='The number of GPUs allocated for this, it is by default 0 meaning none'		,    )

										parser.add_argument(
										    '--overwrite_cache'		,    action='store_true'		,    help='Overwrite the cached training and evaluation sets'    )

										return parser





def    lowerCAmelCase__    (			)					->				Optional[Any]:





						"""simple docstring"""






						snake_case								=			argparse.ArgumentParser()
						add_generic_args(_UpperCamelCase ,			os.getcwd()					)
						snake_case								=			GLUETransformer.add_model_specific_args(_UpperCamelCase ,			os.getcwd()					)
						snake_case								=			parser.parse_args()

						# If output_dir not provided, a folder will be generated in pwd
						if args.output_dir is None:
												snake_case								=			os.path.join(
												    './results' ,			f"""{args.task}_{time.strftime("%Y%m%d_%H%M%S"					)}""" ,			)
												os.makedirs(args.output_dir					)

						snake_case								=			GLUETransformer(_UpperCamelCase					)
						snake_case								=			generic_train(_UpperCamelCase ,			_UpperCamelCase					)

						# Optionally, predict on dev set and write to output_dir
						if args.do_predict:
												snake_case								=			sorted(glob.glob(os.path.join(args.output_dir ,			'checkpoint-epoch=*.ckpt'					) ,			recursive=_UpperCamelCase					)					)
												snake_case								=			model.load_from_checkpoint(checkpoints[-1]					)
												return trainer.test(_UpperCamelCase					)


if __name__ == "__main__":
					main()



"""simple docstring"""
import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import DonutImageProcessor



class   lowerCAmelCase_    (			unittest.TestCase					):



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase		,    lowerCAmelCase=7		,    lowerCAmelCase=3		,    lowerCAmelCase=18		,    lowerCAmelCase=30		,    lowerCAmelCase=4_00		,    lowerCAmelCase=True		,    lowerCAmelCase=None		,    lowerCAmelCase=True		,    lowerCAmelCase=False		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=[0.5, 0.5, 0.5]		,    lowerCAmelCase=[0.5, 0.5, 0.5]		,    ):






										"""simple docstring"""

										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 if size is not None else {'height': 18, 'width': 20}
										snake_case								=			do_thumbnail
										snake_case								=			do_align_axis
										snake_case								=			do_pad
										snake_case								=			do_normalize
										snake_case								=			image_mean
										snake_case								=			image_std

				def 		snake_case (				self    ):






										"""simple docstring"""

										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_thumbnail": self.do_thumbnail,
										    "do_align_long_axis": self.do_align_axis,
										    "do_pad": self.do_pad,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										}





@require_torch
@require_vision
class   lowerCAmelCase_    (			lowerCAmelCase     ,    unittest.TestCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    str			  =						DonutImageProcessor if is_vision_available() else None

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			DonutImageProcessingTester(self    )

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return self.image_processor_tester.prepare_image_processor_dict()

				def 		snake_case (				self    ):






										"""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_thumbnail'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'do_align_long_axis'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'do_pad'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'do_normalize'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'image_mean'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'image_std'    )    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.image_processing_class.from_dict(self.image_processor_dict    )
										self.assertEqual(image_processor.size		,    {'height': 18, 'width': 20}    )

										snake_case								=			self.image_processing_class.from_dict(self.image_processor_dict		,    size=42    )
										self.assertEqual(image_processor.size		,    {'height': 42, 'width': 42}    )

										# Previous config had dimensions in (width, height) order
										snake_case								=			self.image_processing_class.from_dict(self.image_processor_dict		,    size=(42, 84)    )
										self.assertEqual(image_processor.size		,    {'height': 84, 'width': 42}    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										pass

				@is_flaky()
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.image_processing_class(**self.image_processor_dict    )
										# create random PIL images
										snake_case								=			prepare_image_inputs(self.image_processor_tester		,    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.size['height'],
										        self.image_processor_tester.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.size['height'],
										        self.image_processor_tester.size['width'],
										    )		,    )

				@is_flaky()
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.image_processing_class(**self.image_processor_dict    )
										# create random numpy tensors
										snake_case								=			prepare_image_inputs(self.image_processor_tester		,    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.size['height'],
										        self.image_processor_tester.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.size['height'],
										        self.image_processor_tester.size['width'],
										    )		,    )

				@is_flaky()
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.image_processing_class(**self.image_processor_dict    )
										# create random PyTorch tensors
										snake_case								=			prepare_image_inputs(self.image_processor_tester		,    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.size['height'],
										        self.image_processor_tester.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.size['height'],
										        self.image_processor_tester.size['width'],
										    )		,    )


