'''simple docstring'''

from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
							from PIL import Image

							from transformers import ViTImageProcessor
class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A=13		,						__A=30		,						__A=2		,						__A=3		,						__A=True		,						__A=True		,						__A=32		,						__A=2		,						__A=4		,						__A=37		,						__A="gelu"		,						__A=0.1		,						__A=0.1		,						__A=10		,						__A=0.02		,						__A=3		,						__A=0.6		,						__A=None		,						) ->     Any:
								_lowerCAmelCase										=parent
								_lowerCAmelCase										=batch_size
								_lowerCAmelCase										=image_size
								_lowerCAmelCase										=patch_size
								_lowerCAmelCase										=num_channels
								_lowerCAmelCase										=is_training
								_lowerCAmelCase										=use_labels
								_lowerCAmelCase										=hidden_size
								_lowerCAmelCase										=num_hidden_layers
								_lowerCAmelCase										=num_attention_heads
								_lowerCAmelCase										=intermediate_size
								_lowerCAmelCase										=hidden_act
								_lowerCAmelCase										=hidden_dropout_prob
								_lowerCAmelCase										=attention_probs_dropout_prob
								_lowerCAmelCase										=type_sequence_label_size
								_lowerCAmelCase										=initializer_range
								_lowerCAmelCase										=mask_ratio
								_lowerCAmelCase										=scope

								# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
								# (we add 1 for the [CLS] token)
								_lowerCAmelCase										=(image_size // patch_size) ** 2
								_lowerCAmelCase										=int(math.ceil((1 - mask_ratio) * (num_patches + 1)				)				)






		def 		UpperCamelCase__       (   self				) ->     str:
								_lowerCAmelCase										=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

								_lowerCAmelCase										=None
								if self.use_labels:
														_lowerCAmelCase										=ids_tensor([self.batch_size]		,						self.type_sequence_label_size				)

								_lowerCAmelCase										=self.get_config()

								return config, pixel_values, labels






		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								return ViTMAEConfig(
								    image_size=self.image_size		,						patch_size=self.patch_size		,						num_channels=self.num_channels		,						hidden_size=self.hidden_size		,						num_hidden_layers=self.num_hidden_layers		,						num_attention_heads=self.num_attention_heads		,						intermediate_size=self.intermediate_size		,						decoder_hidden_size=self.hidden_size		,						decoder_num_hidden_layers=self.num_hidden_layers		,						decoder_num_attention_heads=self.num_attention_heads		,						decoder_intermediate_size=self.intermediate_size		,						hidden_act=self.hidden_act		,						hidden_dropout_prob=self.hidden_dropout_prob		,						attention_probs_dropout_prob=self.attention_probs_dropout_prob		,						is_decoder=snake_case_		,						initializer_range=self.initializer_range		,						mask_ratio=self.mask_ratio		,						)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A				) ->     int:
								_lowerCAmelCase										=TFViTMAEModel(config=snake_case_				)
								_lowerCAmelCase										=model(snake_case_		,						training=snake_case_				)
								self.parent.assertEqual(result.last_hidden_state.shape		,						(self.batch_size, self.seq_length, self.hidden_size)				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A				) ->     str:
								_lowerCAmelCase										=TFViTMAEForPreTraining(snake_case_				)
								_lowerCAmelCase										=model(snake_case_		,						training=snake_case_				)
								# expected sequence length = num_patches
								_lowerCAmelCase										=(self.image_size // self.patch_size) ** 2
								_lowerCAmelCase										=self.patch_size**2 * self.num_channels
								self.parent.assertEqual(result.logits.shape		,						(self.batch_size, num_patches, expected_num_channels)				)

								# test greyscale images
								_lowerCAmelCase										=1
								_lowerCAmelCase										=TFViTMAEForPreTraining(snake_case_				)

								_lowerCAmelCase										=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
								_lowerCAmelCase										=model(snake_case_		,						training=snake_case_				)
								_lowerCAmelCase										=self.patch_size**2
								self.parent.assertEqual(result.logits.shape		,						(self.batch_size, num_patches, expected_num_channels)				)






		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								_lowerCAmelCase										=self.prepare_config_and_inputs()
								((_lowerCAmelCase)						,				(_lowerCAmelCase)						,				(_lowerCAmelCase))										=config_and_inputs
								_lowerCAmelCase										={'pixel_values': pixel_values}
								return config, inputs_dict

@require_tf
class   SCREAMING_SNAKE_CASE			(						_a    ,			_a    ,			unittest.TestCase):







		"""simple docstring"""
		lowercase   :      int       =							(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
		lowercase   :      List[str]       =							{"""feature-extraction""": TFViTMAEModel} if is_tf_available() else {}

		lowercase   :      List[Any]       =							False
		lowercase   :      str       =							False
		lowercase   :      Dict       =							False
		lowercase   :      Dict       =							False






		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								_lowerCAmelCase										=TFViTMAEModelTester(self				)
								_lowerCAmelCase										=ConfigTester(self		,						config_class=snake_case_		,						has_text_modality=snake_case_		,						hidden_size=37				)






		def 		UpperCamelCase__       (   self				) ->     Tuple:
								self.config_tester.run_common_tests()






		@unittest.skip(reason='ViTMAE does not use inputs_embeds'				)
		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								pass






		def 		UpperCamelCase__       (   self				) ->     Any:
								_lowerCAmelCase						,				_lowerCAmelCase										=self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														_lowerCAmelCase										=model_class(snake_case_				)
														self.assertIsInstance(model.get_input_embeddings()		,						(tf.keras.layers.Layer)				)
														_lowerCAmelCase										=model.get_output_embeddings()
														self.assertTrue(x is None or isinstance(snake_case_		,						tf.keras.layers.Layer				)				)






		def 		UpperCamelCase__       (   self				) ->     Tuple:
								_lowerCAmelCase						,				_lowerCAmelCase										=self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														_lowerCAmelCase										=model_class(snake_case_				)
														_lowerCAmelCase										=inspect.signature(model.call				)
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														_lowerCAmelCase										=[*signature.parameters.keys()]

														_lowerCAmelCase										=['pixel_values']
														self.assertListEqual(arg_names[:1]		,						snake_case_				)






		def 		UpperCamelCase__       (   self				) ->     str:
								_lowerCAmelCase										=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*snake_case_				)






		def 		UpperCamelCase__       (   self				) ->     List[Any]:
								_lowerCAmelCase										=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_pretraining(*snake_case_				)






		def 		UpperCamelCase__       (   self				) ->     str:
								# make the mask reproducible
								np.random.seed(2				)

								_lowerCAmelCase						,				_lowerCAmelCase										=self.model_tester.prepare_config_and_inputs_for_common()

								_lowerCAmelCase										=int((config.image_size // config.patch_size) ** 2				)
								_lowerCAmelCase										=np.random.uniform(size=(self.model_tester.batch_size, num_patches)				)

								for model_class in self.all_model_classes:
														_lowerCAmelCase										=model_class(snake_case_				)
														_lowerCAmelCase										=self._prepare_for_class(snake_case_		,						snake_case_				)

														_lowerCAmelCase										=model(snake_case_		,						noise=snake_case_				)

														_lowerCAmelCase										=copy.deepcopy(self._prepare_for_class(snake_case_		,						snake_case_				)				)
														_lowerCAmelCase										=model(**snake_case_		,						noise=snake_case_				)
														_lowerCAmelCase										=outputs_dict[0].numpy()
														_lowerCAmelCase										=outputs_keywords[0].numpy()

														self.assertLess(np.sum(np.abs(output_dict - output_keywords				)				)		,						1E-6				)






		def 		UpperCamelCase__       (   self				) ->     str:
								# make the mask reproducible
								np.random.seed(2				)

								_lowerCAmelCase						,				_lowerCAmelCase										=self.model_tester.prepare_config_and_inputs_for_common()

								_lowerCAmelCase										=int((config.image_size // config.patch_size) ** 2				)
								_lowerCAmelCase										=np.random.uniform(size=(self.model_tester.batch_size, num_patches)				)

								def prepare_numpy_arrays(__A				):
														_lowerCAmelCase										={}
														for k, v in inputs_dict.items():
																				if tf.is_tensor(snake_case_				):
																										_lowerCAmelCase										=v.numpy()
																				else:
																										_lowerCAmelCase										=np.array(snake_case_				)

														return inputs_np_dict

								for model_class in self.all_model_classes:
														_lowerCAmelCase										=model_class(snake_case_				)

														_lowerCAmelCase										=self._prepare_for_class(snake_case_		,						snake_case_				)
														_lowerCAmelCase										=prepare_numpy_arrays(snake_case_				)

														_lowerCAmelCase										=model(snake_case_		,						noise=snake_case_				)
														_lowerCAmelCase										=model(**snake_case_		,						noise=snake_case_				)
														self.assert_outputs_same(snake_case_		,						snake_case_				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A				) ->     Any:
								# make masks reproducible
								np.random.seed(2				)

								_lowerCAmelCase										=int((tf_model.config.image_size // tf_model.config.patch_size) ** 2				)
								_lowerCAmelCase										=np.random.uniform(size=(self.model_tester.batch_size, num_patches)				)
								_lowerCAmelCase										=tf.constant(snake_case_				)

								# Add `noise` argument.
								# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
								_lowerCAmelCase										=tf_noise

								super().check_pt_tf_models(snake_case_		,						snake_case_		,						snake_case_				)






		def 		UpperCamelCase__       (   self				) ->     int:
								# make mask reproducible
								np.random.seed(2				)

								_lowerCAmelCase						,				_lowerCAmelCase										=self.model_tester.prepare_config_and_inputs_for_common()

								_lowerCAmelCase										={
								    module_member
								    for model_class in self.all_model_classes
								    for module in (import_module(model_class.__module__				),)
								    for module_member_name in dir(snake_case_				)
								    if module_member_name.endswith('MainLayer'				)
								    # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
								    and module_member_name[: -len('MainLayer'				)] == model_class.__name__[: -len('Model'				)]
								    for module_member in (getattr(snake_case_		,						snake_case_				),)
								    if isinstance(snake_case_		,						snake_case_				)
								    and tf.keras.layers.Layer in module_member.__bases__
								    and getattr(snake_case_		,						'_keras_serializable'		,						snake_case_				)
								}

								_lowerCAmelCase										=int((config.image_size // config.patch_size) ** 2				)
								_lowerCAmelCase										=np.random.uniform(size=(self.model_tester.batch_size, num_patches)				)
								_lowerCAmelCase										=tf.convert_to_tensor(snake_case_				)
								inputs_dict.update({'noise': noise}				)

								for main_layer_class in tf_main_layer_classes:
														_lowerCAmelCase										=main_layer_class(snake_case_				)

														_lowerCAmelCase										={
														    name: tf.keras.Input(tensor.shape[1:]		,						dtype=tensor.dtype				) for name, tensor in inputs_dict.items()
														}

														_lowerCAmelCase										=tf.keras.Model(snake_case_		,						outputs=main_layer(snake_case_				)				)
														_lowerCAmelCase										=model(snake_case_				)

														with tempfile.TemporaryDirectory() as tmpdirname:
																				_lowerCAmelCase										=os.path.join(snake_case_		,						'keras_model.h5'				)
																				model.save(snake_case_				)
																				_lowerCAmelCase										=tf.keras.models.load_model(
																				    snake_case_		,						custom_objects={main_layer_class.__name__: main_layer_class}				)
																				assert isinstance(snake_case_		,						tf.keras.Model				)
																				_lowerCAmelCase										=model(snake_case_				)
																				self.assert_outputs_same(snake_case_		,						snake_case_				)






		@slow
		def 		UpperCamelCase__       (   self				) ->     Any:
								# make mask reproducible
								np.random.seed(2				)

								_lowerCAmelCase						,				_lowerCAmelCase										=self.model_tester.prepare_config_and_inputs_for_common()

								_lowerCAmelCase										=int((config.image_size // config.patch_size) ** 2				)
								_lowerCAmelCase										=np.random.uniform(size=(self.model_tester.batch_size, num_patches)				)

								for model_class in self.all_model_classes:
														_lowerCAmelCase										=model_class(snake_case_				)
														_lowerCAmelCase										=self._prepare_for_class(snake_case_		,						snake_case_				)
														_lowerCAmelCase										=model(snake_case_		,						noise=snake_case_				)

														if model_class.__name__ == "TFViTMAEModel":
																				_lowerCAmelCase										=outputs.last_hidden_state.numpy()
																				_lowerCAmelCase										=0
														else:
																				_lowerCAmelCase										=outputs.logits.numpy()
																				_lowerCAmelCase										=0

														with tempfile.TemporaryDirectory() as tmpdirname:
																				model.save_pretrained(snake_case_		,						saved_model=snake_case_				)
																				_lowerCAmelCase										=model_class.from_pretrained(snake_case_				)
																				_lowerCAmelCase										=model(snake_case_		,						noise=snake_case_				)

																				if model_class.__name__ == "TFViTMAEModel":
																										_lowerCAmelCase										=after_outputs['last_hidden_state'].numpy()
																										_lowerCAmelCase										=0
																				else:
																										_lowerCAmelCase										=after_outputs['logits'].numpy()
																										_lowerCAmelCase										=0

																				_lowerCAmelCase										=np.amax(np.abs(out_a - out_a				)				)
																				self.assertLessEqual(snake_case_		,						1E-5				)






		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								# make mask reproducible
								np.random.seed(2				)

								_lowerCAmelCase						,				_lowerCAmelCase										=self.model_tester.prepare_config_and_inputs_for_common()

								_lowerCAmelCase										=int((config.image_size // config.patch_size) ** 2				)
								_lowerCAmelCase										=np.random.uniform(size=(self.model_tester.batch_size, num_patches)				)

								for model_class in self.all_model_classes:
														_lowerCAmelCase										=model_class(snake_case_				)
														_lowerCAmelCase										=self._prepare_for_class(snake_case_		,						snake_case_				)

														_lowerCAmelCase										=model(snake_case_		,						noise=snake_case_				)
														_lowerCAmelCase										=model.get_config()
														# make sure that returned config is jsonifiable, which is required by keras
														json.dumps(snake_case_				)
														_lowerCAmelCase										=model_class.from_config(model.get_config()				)
														# make sure it also accepts a normal config
														_lowerCAmelCase										=model_class.from_config(model.config				)
														_lowerCAmelCase										=new_model(snake_case_				)  # Build model
														new_model.set_weights(model.get_weights()				)
														_lowerCAmelCase										=new_model(snake_case_		,						noise=snake_case_				)

														self.assert_outputs_same(snake_case_		,						snake_case_				)






		@unittest.skip(
		    reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'				)
		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								pass






		@unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'				)
		def 		UpperCamelCase__       (   self				) ->     List[Any]:
								pass






		@slow
		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								_lowerCAmelCase										=TFViTMAEModel.from_pretrained('google/vit-base-patch16-224'				)
								self.assertIsNotNone(snake_case_				)



def 							UpperCamelCase__			(			):


						'''simple docstring'''






						_lowerCAmelCase										=Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
						return image


@require_tf
@require_vision
class   SCREAMING_SNAKE_CASE			(						unittest.TestCase):







		"""simple docstring"""






		@cached_property
		def 		UpperCamelCase__       (   self				) ->     int:
								return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'				) if is_vision_available() else None






		@slow
		def 		UpperCamelCase__       (   self				) ->     Dict:
								# make random mask reproducible across the PT and TF model
								np.random.seed(2				)

								_lowerCAmelCase										=TFViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'				)

								_lowerCAmelCase										=self.default_image_processor
								_lowerCAmelCase										=prepare_img()
								_lowerCAmelCase										=image_processor(images=snake_case_		,						return_tensors='tf'				)

								# prepare a noise vector that will be also used for testing the TF model
								# (this way we can ensure that the PT and TF models operate on the same inputs)
								_lowerCAmelCase										=ViTMAEConfig()
								_lowerCAmelCase										=int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2				)
								_lowerCAmelCase										=np.random.uniform(size=(1, num_patches)				)

								# forward pass
								_lowerCAmelCase										=model(**snake_case_		,						noise=snake_case_				)

								# verify the logits
								_lowerCAmelCase										=tf.convert_to_tensor([1, 196, 768]				)
								self.assertEqual(outputs.logits.shape		,						snake_case_				)

								_lowerCAmelCase										=tf.convert_to_tensor(
								    [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]				)

								tf.debugging.assert_near(outputs.logits[0, :3, :3]		,						snake_case_		,						atol=1E-4				)
'''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


lowercase_     		= logging.getLogger(__name__)
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      int       =							'sequence-classification'






		def __init__(   self		,						__A				) ->     List[Any]:
								if type(__A				) == dict:
														_lowerCAmelCase										=Namespace(**__A				)
								_lowerCAmelCase										=glue_output_modes[hparams.task]
								_lowerCAmelCase										=glue_tasks_num_labels[hparams.task]

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






		def 		UpperCamelCase__       (   self		,						**__A				) ->     Any:
								return self.model(**__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A				) ->     Union[str, Any]:
								_lowerCAmelCase										={'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

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

								_lowerCAmelCase										=self(**__A				)
								_lowerCAmelCase										=outputs[0]

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






		def 		UpperCamelCase__       (   self				) ->     Any:
								_lowerCAmelCase										=self.hparams
								_lowerCAmelCase										=processors[args.task]()
								_lowerCAmelCase										=processor.get_labels()

								for mode in ["train", "dev"]:
														_lowerCAmelCase										=self._feature_file(__A				)
														if os.path.exists(__A				) and not args.overwrite_cache:
																				logger.info('Loading features from cached file %s'		,						__A				)
														else:
																				logger.info('Creating features from dataset file at %s'		,						args.data_dir				)
																				_lowerCAmelCase										=(
																				    processor.get_dev_examples(args.data_dir				)
																				    if mode == 'dev'
																				    else processor.get_train_examples(args.data_dir				)
																				)
																				_lowerCAmelCase										=convert_examples_to_features(
																				    __A		,						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'		,						__A				)
																				torch.save(__A		,						__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A = False				) ->     DataLoader:
								_lowerCAmelCase										='dev' if mode == 'test' else mode

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

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






		def 		UpperCamelCase__       (   self		,						__A		,						__A				) ->     List[str]:
								_lowerCAmelCase										={'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

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

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

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






		def 		UpperCamelCase__       (   self		,						__A				) ->     tuple:
								_lowerCAmelCase										=torch.stack([x['val_loss'] for x in outputs]				).mean().detach().cpu().item()
								_lowerCAmelCase										=np.concatenate([x['pred'] for x in outputs]		,						axis=0				)

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

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

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

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






		def 		UpperCamelCase__       (   self		,						__A				) ->     dict:
								_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=self._eval_end(__A				)
								_lowerCAmelCase										=ret['log']
								return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






		def 		UpperCamelCase__       (   self		,						__A				) ->     dict:
								_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=self._eval_end(__A				)
								_lowerCAmelCase										=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 		UpperCamelCase__       (   __A		,						__A				) ->     Any:
								BaseTransformer.add_model_specific_args(__A		,						__A				)
								parser.add_argument(
								    '--max_seq_length'		,						default=128		,						type=__A		,						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=__A		,						required=__A		,						help='The GLUE task to run'		,						)
								parser.add_argument(
								    '--gpus'		,						default=0		,						type=__A		,						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 							UpperCamelCase__			(			):


						'''simple docstring'''






						_lowerCAmelCase										=argparse.ArgumentParser()
						add_generic_args(a__			,      os.getcwd()      )
						_lowerCAmelCase										=GLUETransformer.add_model_specific_args(a__			,      os.getcwd()      )
						_lowerCAmelCase										=parser.parse_args()

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

						_lowerCAmelCase										=GLUETransformer(a__      )
						_lowerCAmelCase										=generic_train(a__			,      a__      )

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


if __name__ == "__main__":
							main()





