



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      (						snake_case__  ):







     '''simple docstring'''







     lowerCAmelCase_				:    Any									=  '''sequence-classification'''



     def __init__(			self    ,	lowerCAmelCase     ):
       if type(_A     ) == dict:
         UpperCAmelCase_						=				Namespace(**_A     )
       UpperCAmelCase_						=				glue_output_modes[hparams.task]
       UpperCAmelCase_						=				glue_tasks_num_labels[hparams.task]

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



     def    A__		(			self    ,	**lowerCAmelCase     ):
       return self.model(**_A     )



     def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
       UpperCAmelCase_						=				{'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

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

       UpperCAmelCase_						=				self(**_A     )
       UpperCAmelCase_						=				outputs[0]

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



     def    A__		(			self     ):
       UpperCAmelCase_						=				self.hparams
       UpperCAmelCase_						=				processors[args.task]()
       UpperCAmelCase_						=				processor.get_labels()

       for mode in ["train", "dev"]:
         UpperCAmelCase_						=				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     )
           UpperCAmelCase_						=				(
               processor.get_dev_examples(args.data_dir     )
               if mode == 'dev'
               else processor.get_train_examples(args.data_dir     )
           )
           UpperCAmelCase_						=				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    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = False     ):
       UpperCAmelCase_						=				'dev' if mode == 'test' else mode

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

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



     def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
       UpperCAmelCase_						=				{'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

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

       UpperCAmelCase_						=				self(**_A     )
       UpperCAmelCase_						=				outputs[:2]
       UpperCAmelCase_						=				logits.detach().cpu().numpy()
       UpperCAmelCase_						=				inputs['labels'].detach().cpu().numpy()

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



     def    A__		(			self    ,	lowerCAmelCase     ):
       UpperCAmelCase_						=				torch.stack([x["val_loss"] for x in outputs]     ).mean().detach().cpu().item()
       UpperCAmelCase_						=				np.concatenate([x["pred"] for x in outputs]    ,	axis=0     )

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

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

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

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



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



     def    A__		(			self    ,	lowerCAmelCase     ):
       UpperCAmelCase_						=				self._eval_end(_A     )
       UpperCAmelCase_						=				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    A__		(			lowerCAmelCase    ,	lowerCAmelCase     ):
       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 snake_case__  (			)		->							Tuple:
  UpperCAmelCase_						=				argparse.ArgumentParser()
  add_generic_args(__SCREAMING_SNAKE_CASE			,     os.getcwd() )
  UpperCAmelCase_						=				GLUETransformer.add_model_specific_args(__SCREAMING_SNAKE_CASE			,     os.getcwd() )
  UpperCAmelCase_						=				parser.parse_args()

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

  UpperCAmelCase_						=				GLUETransformer(__SCREAMING_SNAKE_CASE )
  UpperCAmelCase_						=				generic_train(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

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


if __name__ == "__main__":
   main()






import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch
			from torch import nn

			from transformers import SwiftFormerForImageClassification, SwiftFormerModel
			from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import ViTImageProcessor



class 	lowerCamelCase      :







					'''simple docstring'''







					def __init__(			self    ,	lowerCAmelCase    ,	lowerCAmelCase=13    ,	lowerCAmelCase=3    ,	lowerCAmelCase=True    ,	lowerCAmelCase=True    ,	lowerCAmelCase=0.1    ,	lowerCAmelCase=0.1    ,	lowerCAmelCase=224    ,	lowerCAmelCase=1000    ,	lowerCAmelCase=[3, 3, 6, 4]    ,	lowerCAmelCase=[48, 56, 112, 220]    ,	):
							UpperCAmelCase_						=				parent
							UpperCAmelCase_						=				batch_size
							UpperCAmelCase_						=				num_channels
							UpperCAmelCase_						=				is_training
							UpperCAmelCase_						=				use_labels
							UpperCAmelCase_						=				hidden_dropout_prob
							UpperCAmelCase_						=				attention_probs_dropout_prob
							UpperCAmelCase_						=				num_labels
							UpperCAmelCase_						=				image_size
							UpperCAmelCase_						=				layer_depths
							UpperCAmelCase_						=				embed_dims



					def    A__		(			self     ):
							UpperCAmelCase_						=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

							UpperCAmelCase_						=				None
							if self.use_labels:
									UpperCAmelCase_						=				ids_tensor([self.batch_size]    ,	self.num_labels     )

							UpperCAmelCase_						=				self.get_config()

							return config, pixel_values, labels



					def    A__		(			self     ):
							return SwiftFormerConfig(
							    depths=self.layer_depths    ,	embed_dims=self.embed_dims    ,	mlp_ratio=4    ,	downsamples=[True, True, True, True]    ,	hidden_act="gelu"    ,	num_labels=self.num_labels    ,	down_patch_size=3    ,	down_stride=2    ,	down_pad=1    ,	drop_rate=0.0    ,	drop_path_rate=0.0    ,	use_layer_scale=lowerCAmelCase    ,	layer_scale_init_value=1e-5    ,	)



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				SwiftFormerModel(config=lowerCAmelCase     )
							model.to(lowerCAmelCase     )
							model.eval()
							UpperCAmelCase_						=				model(lowerCAmelCase     )
							self.parent.assertEqual(result.last_hidden_state.shape    ,	(self.batch_size, self.embed_dims[-1], 7, 7)     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				self.num_labels
							UpperCAmelCase_						=				SwiftFormerForImageClassification(lowerCAmelCase     )
							model.to(lowerCAmelCase     )
							model.eval()
							UpperCAmelCase_						=				model(lowerCAmelCase    ,	labels=lowerCAmelCase     )
							self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.num_labels)     )

							UpperCAmelCase_						=				SwiftFormerForImageClassification(lowerCAmelCase     )
							model.to(lowerCAmelCase     )
							model.eval()

							UpperCAmelCase_						=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )
							UpperCAmelCase_						=				model(lowerCAmelCase     )
							self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.num_labels)     )



					def    A__		(			self     ):
							((UpperCAmelCase_)					,						(UpperCAmelCase_)					,						(UpperCAmelCase_))						=				self.prepare_config_and_inputs()
							UpperCAmelCase_						=				{"pixel_values": pixel_values}
							return config, inputs_dict





@require_torch
class 	lowerCamelCase      (						lowercase__,     lowercase__,     unittest.TestCase  ):







					'''simple docstring'''







					lowerCAmelCase_				:    Optional[Any]									=  (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
					lowerCAmelCase_				:    int									=  (
					    {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
					    if is_torch_available()
					    else {}
					)

					lowerCAmelCase_				:    List[Any]									=  False
					lowerCAmelCase_				:    Dict									=  False
					lowerCAmelCase_				:    int									=  False
					lowerCAmelCase_				:    str									=  False
					lowerCAmelCase_				:    Optional[Any]									=  False



					def    A__		(			self     ):
							UpperCAmelCase_						=				SwiftFormerModelTester(self     )
							UpperCAmelCase_						=				ConfigTester(
							    self    ,	config_class=lowerCAmelCase    ,	has_text_modality=lowerCAmelCase    ,	hidden_size=37    ,	num_attention_heads=12    ,	num_hidden_layers=12    ,	)



					def    A__		(			self     ):
							self.config_tester.run_common_tests()



					@unittest.skip(reason="SwiftFormer does not use inputs_embeds"     )
					def    A__		(			self     ):
							pass



					def    A__		(			self     ):
							UpperCAmelCase_					,						UpperCAmelCase_						=				self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									UpperCAmelCase_						=				model_class(lowerCAmelCase     )
									UpperCAmelCase_						=				model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(lowerCAmelCase    ,	nn.Linear     )     )



					def    A__		(			self     ):
							UpperCAmelCase_					,						UpperCAmelCase_						=				self.model_tester.prepare_config_and_inputs_for_common()

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

									UpperCAmelCase_						=				["pixel_values"]
									self.assertListEqual(arg_names[:1]    ,	lowerCAmelCase     )



					def    A__		(			self     ):
							UpperCAmelCase_						=				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCAmelCase     )



					def    A__		(			self     ):
							UpperCAmelCase_						=				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase     )



					@slow
					def    A__		(			self     ):
							for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									UpperCAmelCase_						=				SwiftFormerModel.from_pretrained(lowerCAmelCase     )
									self.assertIsNotNone(lowerCAmelCase     )



					@unittest.skip(reason="SwiftFormer does not output attentions"     )
					def    A__		(			self     ):
							pass



					def    A__		(			self     ):
							def check_hidden_states_output(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
									UpperCAmelCase_						=				model_class(lowerCAmelCase     )
									model.to(lowerCAmelCase     )
									model.eval()

									with torch.no_grad():
											UpperCAmelCase_						=				model(**self._prepare_for_class(lowerCAmelCase    ,	lowerCAmelCase     )     )

									UpperCAmelCase_						=				outputs.hidden_states

									UpperCAmelCase_						=				8
									self.assertEqual(len(lowerCAmelCase     )    ,	lowerCAmelCase     )  # TODO

									# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
									# with the width and height being successively divided by 2, after every 2 blocks
									for i in range(len(lowerCAmelCase     )     ):
											self.assertEqual(
											    hidden_states[i].shape    ,	torch.Size(
											        [
											            self.model_tester.batch_size,
											            self.model_tester.embed_dims[i // 2],
											            (self.model_tester.image_size // 4) // 2 ** (i // 2),
											            (self.model_tester.image_size // 4) // 2 ** (i // 2),
											        ]     )    ,	)

							UpperCAmelCase_					,						UpperCAmelCase_						=				self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									UpperCAmelCase_						=				True
									check_hidden_states_output(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     )

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									UpperCAmelCase_						=				True

									check_hidden_states_output(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     )



					def    A__		(			self     ):
							def _config_zero_init(lowerCAmelCase     ):
									UpperCAmelCase_						=				copy.deepcopy(lowerCAmelCase     )
									for key in configs_no_init.__dict__.keys():
											if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
													setattr(lowerCAmelCase    ,	lowerCAmelCase    ,	1e-1_0     )
											if isinstance(getattr(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     )    ,	lowerCAmelCase     ):
													UpperCAmelCase_						=				_config_zero_init(getattr(lowerCAmelCase    ,	lowerCAmelCase     )     )
													setattr(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     )
									return configs_no_init

							UpperCAmelCase_					,						UpperCAmelCase_						=				self.model_tester.prepare_config_and_inputs_for_common()

							UpperCAmelCase_						=				_config_zero_init(lowerCAmelCase     )
							for model_class in self.all_model_classes:
									UpperCAmelCase_						=				model_class(config=lowerCAmelCase     )
									for name, param in model.named_parameters():
											if param.requires_grad:
													self.assertIn(
													    ((param.data.mean() * 1e9) / 1e9).round().item()    ,	[0.0, 1.0]    ,	msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''    ,	)



					@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."     )
					def    A__		(			self     ):
							pass







def snake_case__  (			)		->							str:
		UpperCAmelCase_						=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
		return image



@require_torch
@require_vision
class 	lowerCamelCase      (						unittest.TestCase  ):







					'''simple docstring'''







					@cached_property
					def    A__		(			self     ):
							return ViTImageProcessor.from_pretrained("MBZUAI/swiftformer-xs"     ) if is_vision_available() else None



					@slow
					def    A__		(			self     ):
							UpperCAmelCase_						=				SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs"     ).to(lowerCAmelCase     )

							UpperCAmelCase_						=				self.default_image_processor
							UpperCAmelCase_						=				prepare_img()
							UpperCAmelCase_						=				image_processor(images=lowerCAmelCase    ,	return_tensors="pt"     ).to(lowerCAmelCase     )

							# forward pass
							with torch.no_grad():
									UpperCAmelCase_						=				model(**lowerCAmelCase     )

							# verify the logits
							UpperCAmelCase_						=				torch.Size((1, 1000)     )
							self.assertEqual(outputs.logits.shape    ,	lowerCAmelCase     )

							UpperCAmelCase_						=				torch.tensor([[-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0]]     ).to(lowerCAmelCase     )
							self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,	lowerCAmelCase    ,	atol=1e-4     )     )

