





'''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__    :      int						=    logging.getLogger(__name__)


class  SCREAMING_SNAKE_CASE    (a__							):
     lowerCAmelCase					=       '''sequence-classification'''


     def __init__(						self						,	_UpperCAmelCase):

          '''simple docstring'''






          if type(_UpperCAmelCase) == dict:
               __A						: List[Any]      =       Namespace(**_UpperCAmelCase)
          __A						: Optional[int]      =       glue_output_modes[hparams.task]
          __A						: Optional[Any]      =       glue_tasks_num_labels[hparams.task]

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


     def 							SCREAMING_SNAKE_CASE  (						self						,	**_UpperCAmelCase):

          '''simple docstring'''






          return self.model(**_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






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

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

          __A						: Tuple      =       self(**_UpperCAmelCase)
          __A						: Dict      =       outputs[0]

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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[int]      =       self.hparams
          __A						: List[str]      =       processors[args.task]()
          __A						: Optional[Any]      =       processor.get_labels()

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


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase = False):

          '''simple docstring'''






          __A						: int      =       'dev' if mode == 'test' else mode

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

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


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: Optional[int]      =       {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

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

          __A						: int      =       self(**_UpperCAmelCase)
          __A ,__A						: Union[str, Any]      =       outputs[:2]
          __A						: Optional[Any]      =       logits.detach().cpu().numpy()
          __A						: str      =       inputs['labels'].detach().cpu().numpy()

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


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase):

          '''simple docstring'''






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

          if self.hparams.glue_output_mode == "classification":
               __A						: Dict      =       np.argmax(_UpperCAmelCase						,	axis=1)
          elif self.hparams.glue_output_mode == "regression":
               __A						: Union[str, Any]      =       np.squeeze(_UpperCAmelCase)

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

          __A						: List[Any]      =       {**{'val_loss': val_loss_mean}, **compute_metrics(self.hparams.task						,	_UpperCAmelCase						,	_UpperCAmelCase)}

          __A						: str      =       dict(results.items())
          __A						: Optional[int]      =       results
          return ret, preds_list, out_label_list


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase):

          '''simple docstring'''






          __A ,__A ,__A						: Union[str, Any]      =       self._eval_end(_UpperCAmelCase)
          __A						: Dict      =       ret['log']
          return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase):

          '''simple docstring'''






          __A ,__A ,__A						: Tuple      =       self._eval_end(_UpperCAmelCase)
          __A						: str      =       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 							SCREAMING_SNAKE_CASE  (						_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          BaseTransformer.add_model_specific_args(_UpperCAmelCase						,	_UpperCAmelCase)
          parser.add_argument(
              '--max_seq_length'						,	default=128						,	type=_UpperCAmelCase						,	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=_UpperCAmelCase						,	required=_UpperCAmelCase						,	help='The GLUE task to run'						,	)
          parser.add_argument(
              '--gpus'						,	default=0						,	type=_UpperCAmelCase						,	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			(  )     ->     Tuple:
     __A						: Tuple      =       argparse.ArgumentParser()
     add_generic_args(__snake_case ,							os.getcwd()			)
     __A						: Union[str, Any]      =       GLUETransformer.add_model_specific_args(__snake_case ,							os.getcwd()			)
     __A						: str      =       parser.parse_args()

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

     __A						: Dict      =       GLUETransformer(__snake_case			)
     __A						: Tuple      =       generic_train(__snake_case ,							__snake_case			)

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


if __name__ == "__main__":
  main()






'''simple docstring'''




import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
  from PIL import Image
else:


  class  SCREAMING_SNAKE_CASE    :


       @staticmethod
       def 							SCREAMING_SNAKE_CASE  (						*_UpperCAmelCase						,	**_UpperCAmelCase):

            '''simple docstring'''






            pass






def        _lowerCAmelCase			(  __snake_case  : Image			)     ->     str:
     __A						: Dict      =       hashlib.mda(image.tobytes()			)
     return m.hexdigest()[:10]






def        _lowerCAmelCase			(  __snake_case  : Image			)     ->     Dict:
     __A						: Dict      =       np.array(__snake_case			)
     __A						: List[Any]      =       npimg.shape
     return {"hash": hashimage(__snake_case			), "shape": shape}







@is_pipeline_test
@require_vision
@require_torch
class  SCREAMING_SNAKE_CASE    (unittest.TestCase							):
     lowerCAmelCase					=       dict(
         (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()							) if MODEL_FOR_MASK_GENERATION_MAPPING else [])							)
     lowerCAmelCase					=       dict(
         (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()							) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])							)


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: Any      =       MaskGenerationPipeline(model=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)
          return image_segmenter, [
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
          ]


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          pass


     @require_tf
     @unittest.skip('Image segmentation not implemented in TF')
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass


     @slow
     @require_torch
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[int]      =       pipeline('mask-generation'						,	model='facebook/sam-vit-huge')

          __A						: Tuple      =       image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg'						,	points_per_batch=256)

          # Shortening by hashing
          __A						: int      =       []
          for i, o in enumerate(outputs['masks']):
               new_outupt += [{"mask": mask_to_test_readable(_UpperCAmelCase), "scores": outputs["scores"][i]}]

          # fmt: off
          self.assertEqual(
              nested_simplify(_UpperCAmelCase						,	decimals=4)						,	[
                  {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0444},
                  {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.021},
                  {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0167},
                  {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0132},
                  {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0053},
                  {'mask': {'hash': 'e2d0b7a0b7', 'shape': (480, 640)}, 'scores': 0.9967},
                  {'mask': {'hash': '453c7844bd', 'shape': (480, 640)}, 'scores': 0.993},
                  {'mask': {'hash': '3d44f2926d', 'shape': (480, 640)}, 'scores': 0.9909},
                  {'mask': {'hash': '64033ddc3f', 'shape': (480, 640)}, 'scores': 0.9879},
                  {'mask': {'hash': '801064ff79', 'shape': (480, 640)}, 'scores': 0.9834},
                  {'mask': {'hash': '6172f276ef', 'shape': (480, 640)}, 'scores': 0.9716},
                  {'mask': {'hash': 'b49e60e084', 'shape': (480, 640)}, 'scores': 0.9612},
                  {'mask': {'hash': 'a811e775fd', 'shape': (480, 640)}, 'scores': 0.9599},
                  {'mask': {'hash': 'a6a8ebcf4b', 'shape': (480, 640)}, 'scores': 0.9552},
                  {'mask': {'hash': '9d8257e080', 'shape': (480, 640)}, 'scores': 0.9532},
                  {'mask': {'hash': '32de6454a8', 'shape': (480, 640)}, 'scores': 0.9516},
                  {'mask': {'hash': 'af3d4af2c8', 'shape': (480, 640)}, 'scores': 0.9499},
                  {'mask': {'hash': '3c6db475fb', 'shape': (480, 640)}, 'scores': 0.9483},
                  {'mask': {'hash': 'c290813fb9', 'shape': (480, 640)}, 'scores': 0.9464},
                  {'mask': {'hash': 'b6f0b8f606', 'shape': (480, 640)}, 'scores': 0.943},
                  {'mask': {'hash': '92ce16bfdf', 'shape': (480, 640)}, 'scores': 0.943},
                  {'mask': {'hash': 'c749b25868', 'shape': (480, 640)}, 'scores': 0.9408},
                  {'mask': {'hash': 'efb6cab859', 'shape': (480, 640)}, 'scores': 0.9335},
                  {'mask': {'hash': '1ff2eafb30', 'shape': (480, 640)}, 'scores': 0.9326},
                  {'mask': {'hash': '788b798e24', 'shape': (480, 640)}, 'scores': 0.9262},
                  {'mask': {'hash': 'abea804f0e', 'shape': (480, 640)}, 'scores': 0.8999},
                  {'mask': {'hash': '7b9e8ddb73', 'shape': (480, 640)}, 'scores': 0.8986},
                  {'mask': {'hash': 'cd24047c8a', 'shape': (480, 640)}, 'scores': 0.8984},
                  {'mask': {'hash': '6943e6bcbd', 'shape': (480, 640)}, 'scores': 0.8873},
                  {'mask': {'hash': 'b5f47c9191', 'shape': (480, 640)}, 'scores': 0.8871}
              ]						,	)
          # fmt: on


     @require_torch
     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: int      =       'facebook/sam-vit-huge'
          __A						: Optional[Any]      =       pipeline('mask-generation'						,	model=_UpperCAmelCase)

          __A						: int      =       image_segmenter(
              'http://images.cocodataset.org/val2017/000000039769.jpg'						,	pred_iou_thresh=1						,	points_per_batch=256)

          # Shortening by hashing
          __A						: int      =       []
          for i, o in enumerate(outputs['masks']):
               new_outupt += [{"mask": mask_to_test_readable(_UpperCAmelCase), "scores": outputs["scores"][i]}]

          self.assertEqual(
              nested_simplify(_UpperCAmelCase						,	decimals=4)						,	[
                  {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0444},
                  {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0210},
                  {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0167},
                  {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0132},
                  {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0053},
              ]						,	)