





import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import 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 transformers import OneFormerImageProcessor
        from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
        from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
    from PIL import Image


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase="shi-labs/oneformer_demo" )			->     List[Any]:
    with open(hf_hub_download(_UpperCAmelCase						,	_UpperCAmelCase						,	repo_type='dataset' )						,	'r' ) as f:
        lowerCamelCase__    :      str					 =		json.load(_UpperCAmelCase )
    lowerCamelCase__    :      Tuple					 =		{}
    lowerCamelCase__    :      List[Any]					 =		[]
    lowerCamelCase__    :      str					 =		[]
    for key, info in class_info.items():
        lowerCamelCase__    :      Union[str, Any]					 =		info['name']
        class_names.append(info['name'] )
        if info["isthing"]:
            thing_ids.append(int(_UpperCAmelCase ) )
    lowerCamelCase__    :      Optional[int]					 =		thing_ids
    lowerCamelCase__    :      Union[str, Any]					 =		class_names
    return metadata

class    lowerCAmelCase							(					unittest.TestCase			):
 def __init__(      self  :      List[str]			,     UpperCAmelCase  :      Optional[int]			,     UpperCAmelCase  :      Optional[int]=7			,     UpperCAmelCase  :      Union[str, Any]=3			,     UpperCAmelCase  :      int=30			,     UpperCAmelCase  :      List[str]=400			,     UpperCAmelCase  :      List[Any]=None			,     UpperCAmelCase  :      Any=True			,     UpperCAmelCase  :      List[Any]=True			,     UpperCAmelCase  :      Dict=[0.5, 0.5, 0.5]			,     UpperCAmelCase  :      List[str]=[0.5, 0.5, 0.5]			,     UpperCAmelCase  :      Optional[Any]=10			,     UpperCAmelCase  :      Tuple=False			,     UpperCAmelCase  :      Optional[int]=255			,     UpperCAmelCase  :      Any="shi-labs/oneformer_demo"			,     UpperCAmelCase  :      Any="ade20k_panoptic.json"			,     UpperCAmelCase  :      List[Any]=10			,     )			->	Union[str, Any]:
     lowerCamelCase__    :      Tuple					 =		parent
     lowerCamelCase__    :      Tuple					 =		batch_size
     lowerCamelCase__    :      str					 =		num_channels
     lowerCamelCase__    :      Union[str, Any]					 =		min_resolution
     lowerCamelCase__    :      int					 =		max_resolution
     lowerCamelCase__    :      Dict					 =		do_resize
     lowerCamelCase__    :      Optional[int]					 =		{'shortest_edge': 32, 'longest_edge': 1333} if size is None else size
     lowerCamelCase__    :      Dict					 =		do_normalize
     lowerCamelCase__    :      Tuple					 =		image_mean
     lowerCamelCase__    :      List[str]					 =		image_std
     lowerCamelCase__    :      Any					 =		class_info_file
     lowerCamelCase__    :      Any					 =		prepare_metadata(UpperCAmelCase			,     UpperCAmelCase	)
     lowerCamelCase__    :      Optional[int]					 =		num_text
     lowerCamelCase__    :      List[str]					 =		repo_path

     # for the post_process_functions
     lowerCamelCase__    :      Any					 =		2
     lowerCamelCase__    :      str					 =		10
     lowerCamelCase__    :      str					 =		10
     lowerCamelCase__    :      Any					 =		3
     lowerCamelCase__    :      Union[str, Any]					 =		4
     lowerCamelCase__    :      Any					 =		num_labels
     lowerCamelCase__    :      str					 =		do_reduce_labels
     lowerCamelCase__    :      str					 =		ignore_index
 def      A_	(      self  :      Union[str, Any]	)			->	Any:
     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "num_labels": self.num_labels,
         "do_reduce_labels": self.do_reduce_labels,
         "ignore_index": self.ignore_index,
         "class_info_file": self.class_info_file,
         "metadata": self.metadata,
         "num_text": self.num_text,
     }
 def      A_	(      self  :      Optional[int]			,     UpperCAmelCase  :      List[str]			,     UpperCAmelCase  :      List[Any]=False	)			->	int:
     if not batched:
         lowerCamelCase__    :      List[str]					 =		image_inputs[0]
         if isinstance(UpperCAmelCase			,     Image.Image	):
             lowerCamelCase__ ,							lowerCamelCase__    :      Tuple					 =		image.size
         else:
             lowerCamelCase__ ,							lowerCamelCase__    :      Dict					 =		image.shape[1], image.shape[2]
         if w < h:
             lowerCamelCase__    :      Dict					 =		int(self.size['shortest_edge'] * h / w	)
             lowerCamelCase__    :      List[Any]					 =		self.size['shortest_edge']
         elif w > h:
             lowerCamelCase__    :      Optional[Any]					 =		self.size['shortest_edge']
             lowerCamelCase__    :      str					 =		int(self.size['shortest_edge'] * w / h	)
         else:
             lowerCamelCase__    :      str					 =		self.size['shortest_edge']
             lowerCamelCase__    :      Union[str, Any]					 =		self.size['shortest_edge']

     else:
         lowerCamelCase__    :      Any					 =		[]
         for image in image_inputs:
             lowerCamelCase__ ,							lowerCamelCase__    :      List[str]					 =		self.get_expected_values([image]	)
             expected_values.append((expected_height, expected_width)	)
         lowerCamelCase__    :      Optional[Any]					 =		max(UpperCAmelCase			,     key=lambda UpperCAmelCase	: item[0]	)[0]
         lowerCamelCase__    :      str					 =		max(UpperCAmelCase			,     key=lambda UpperCAmelCase	: item[1]	)[1]

     return expected_height, expected_width







 def      A_	(      self  :      Tuple	)			->	Tuple:
     return OneFormerForUniversalSegmentationOutput(
         # +1 for null class
         class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)	)			,     masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)	)			,     )


@require_torch
@require_vision
class    lowerCAmelCase							(					__UpperCamelCase,  unittest.TestCase			):
 UpperCAmelCase__													=		OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
 # only for test_image_processing_common.test_image_proc_to_json_string
 UpperCAmelCase__													=		image_processing_class
 def      A_	(      self  :      Any	)			->	int:
     lowerCamelCase__    :      Union[str, Any]					 =		OneFormerImageProcessorTester(self	)
 @property
 def      A_	(      self  :      str	)			->	int:
     return self.image_processing_tester.prepare_image_processor_dict()
 def      A_	(      self  :      int	)			->	Any:
     lowerCamelCase__    :      Optional[Any]					 =		self.image_processing_class(**self.image_processor_dict	)
     self.assertTrue(hasattr(UpperCAmelCase			,     'image_mean'	)	)
     self.assertTrue(hasattr(UpperCAmelCase			,     'image_std'	)	)
     self.assertTrue(hasattr(UpperCAmelCase			,     'do_normalize'	)	)
     self.assertTrue(hasattr(UpperCAmelCase			,     'do_resize'	)	)
     self.assertTrue(hasattr(UpperCAmelCase			,     'size'	)	)
     self.assertTrue(hasattr(UpperCAmelCase			,     'ignore_index'	)	)
     self.assertTrue(hasattr(UpperCAmelCase			,     'class_info_file'	)	)
     self.assertTrue(hasattr(UpperCAmelCase			,     'num_text'	)	)
     self.assertTrue(hasattr(UpperCAmelCase			,     'repo_path'	)	)
     self.assertTrue(hasattr(UpperCAmelCase			,     'metadata'	)	)
     self.assertTrue(hasattr(UpperCAmelCase			,     'do_reduce_labels'	)	)
 def      A_	(      self  :      str	)			->	List[Any]:
     pass
 def      A_	(      self  :      Tuple	)			->	Union[str, Any]:
     # Initialize image_processor
     lowerCamelCase__    :      Optional[int]					 =		self.image_processing_class(**self.image_processor_dict	)
     # create random PIL images
     lowerCamelCase__    :      int					 =		prepare_image_inputs(self.image_processing_tester			,     equal_resolution=UpperCAmelCase	)
     for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase			,     Image.Image	)

     # Test not batched input
     lowerCamelCase__    :      List[str]					 =		image_processor(image_inputs[0]			,     ['semantic']			,     return_tensors='pt'	).pixel_values

     lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		self.image_processing_tester.get_expected_values(UpperCAmelCase	)

     self.assertEqual(
         encoded_images.shape			,     (1, self.image_processing_tester.num_channels, expected_height, expected_width)			,     )

     # Test batched
     lowerCamelCase__ ,							lowerCamelCase__    :      Tuple					 =		self.image_processing_tester.get_expected_values(UpperCAmelCase			,     batched=UpperCAmelCase	)

     lowerCamelCase__    :      List[str]					 =		image_processor(
         UpperCAmelCase			,     ['semantic'] * len(UpperCAmelCase	)			,     return_tensors='pt'	).pixel_values
     self.assertEqual(
         encoded_images.shape			,     (
             self.image_processing_tester.batch_size,
             self.image_processing_tester.num_channels,
             expected_height,
             expected_width,
         )			,     )
 def      A_	(      self  :      Tuple	)			->	str:
     # Initialize image_processor
     lowerCamelCase__    :      List[Any]					 =		self.image_processing_class(**self.image_processor_dict	)
     # create random numpy tensors
     lowerCamelCase__    :      Union[str, Any]					 =		prepare_image_inputs(self.image_processing_tester			,     equal_resolution=UpperCAmelCase			,     numpify=UpperCAmelCase	)
     for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase			,     np.ndarray	)

     # Test not batched input
     lowerCamelCase__    :      List[str]					 =		image_processor(image_inputs[0]			,     ['semantic']			,     return_tensors='pt'	).pixel_values

     lowerCamelCase__ ,							lowerCamelCase__    :      List[str]					 =		self.image_processing_tester.get_expected_values(UpperCAmelCase	)

     self.assertEqual(
         encoded_images.shape			,     (1, self.image_processing_tester.num_channels, expected_height, expected_width)			,     )

     # Test batched
     lowerCamelCase__ ,							lowerCamelCase__    :      List[str]					 =		self.image_processing_tester.get_expected_values(UpperCAmelCase			,     batched=UpperCAmelCase	)

     lowerCamelCase__    :      str					 =		image_processor(
         UpperCAmelCase			,     ['semantic'] * len(UpperCAmelCase	)			,     return_tensors='pt'	).pixel_values
     self.assertEqual(
         encoded_images.shape			,     (
             self.image_processing_tester.batch_size,
             self.image_processing_tester.num_channels,
             expected_height,
             expected_width,
         )			,     )
 def      A_	(      self  :      Optional[int]	)			->	Union[str, Any]:
     # Initialize image_processor
     lowerCamelCase__    :      Optional[Any]					 =		self.image_processing_class(**self.image_processor_dict	)
     # create random PyTorch tensors
     lowerCamelCase__    :      Union[str, Any]					 =		prepare_image_inputs(self.image_processing_tester			,     equal_resolution=UpperCAmelCase			,     torchify=UpperCAmelCase	)
     for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase			,     torch.Tensor	)

     # Test not batched input
     lowerCamelCase__    :      Union[str, Any]					 =		image_processor(image_inputs[0]			,     ['semantic']			,     return_tensors='pt'	).pixel_values

     lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		self.image_processing_tester.get_expected_values(UpperCAmelCase	)

     self.assertEqual(
         encoded_images.shape			,     (1, self.image_processing_tester.num_channels, expected_height, expected_width)			,     )

     # Test batched
     lowerCamelCase__ ,							lowerCamelCase__    :      int					 =		self.image_processing_tester.get_expected_values(UpperCAmelCase			,     batched=UpperCAmelCase	)

     lowerCamelCase__    :      int					 =		image_processor(
         UpperCAmelCase			,     ['semantic'] * len(UpperCAmelCase	)			,     return_tensors='pt'	).pixel_values
     self.assertEqual(
         encoded_images.shape			,     (
             self.image_processing_tester.batch_size,
             self.image_processing_tester.num_channels,
             expected_height,
             expected_width,
         )			,     )
 def      A_	(      self  :      int			,     UpperCAmelCase  :      List[str]=False			,     UpperCAmelCase  :      List[Any]=False			,     UpperCAmelCase  :      Union[str, Any]="np"	)			->	str:
     lowerCamelCase__    :      List[str]					 =		self.image_processing_class(**self.image_processor_dict	)
     # prepare image and target
     lowerCamelCase__    :      Dict					 =		self.image_processing_tester.num_labels
     lowerCamelCase__    :      List[str]					 =		None
     lowerCamelCase__    :      Optional[int]					 =		None
     lowerCamelCase__    :      str					 =		prepare_image_inputs(self.image_processing_tester			,     equal_resolution=UpperCAmelCase	)
     if with_segmentation_maps:
         lowerCamelCase__    :      Tuple					 =		num_labels
         if is_instance_map:
             lowerCamelCase__    :      Dict					 =		list(range(UpperCAmelCase	)	) * 2
             lowerCamelCase__    :      Optional[int]					 =		dict(enumerate(UpperCAmelCase	)	)
         lowerCamelCase__    :      int					 =		[
             np.random.randint(0			,     high * 2			,     (img.size[1], img.size[0])	).astype(np.uinta	) for img in image_inputs
         ]
         if segmentation_type == "pil":
             lowerCamelCase__    :      Optional[int]					 =		[Image.fromarray(UpperCAmelCase	) for annotation in annotations]

     lowerCamelCase__    :      List[str]					 =		image_processor(
         UpperCAmelCase			,     ['semantic'] * len(UpperCAmelCase	)			,     UpperCAmelCase			,     return_tensors='pt'			,     instance_id_to_semantic_id=UpperCAmelCase			,     pad_and_return_pixel_mask=UpperCAmelCase			,     )

     return inputs
 def      A_	(      self  :      str	)			->	Any:
     pass
 def      A_	(      self  :      Tuple	)			->	List[Any]:
     def common(UpperCAmelCase  :      Union[str, Any]=False			,     UpperCAmelCase  :      Optional[Any]=None	):
         lowerCamelCase__    :      Any					 =		self.comm_get_image_processor_inputs(
             with_segmentation_maps=UpperCAmelCase			,     is_instance_map=UpperCAmelCase			,     segmentation_type=UpperCAmelCase	)

         lowerCamelCase__    :      Tuple					 =		inputs['mask_labels']
         lowerCamelCase__    :      Union[str, Any]					 =		inputs['class_labels']
         lowerCamelCase__    :      Optional[Any]					 =		inputs['pixel_values']
         lowerCamelCase__    :      List[Any]					 =		inputs['text_inputs']

         # check the batch_size
         for mask_label, class_label, text_input in zip(UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase	):
             self.assertEqual(mask_label.shape[0]			,     class_label.shape[0]	)
             # this ensure padding has happened
             self.assertEqual(mask_label.shape[1:]			,     pixel_values.shape[2:]	)
             self.assertEqual(len(UpperCAmelCase	)			,     self.image_processing_tester.num_text	)

     common()
     common(is_instance_map=UpperCAmelCase	)
     common(is_instance_map=UpperCAmelCase			,     segmentation_type='pil'	)
     common(is_instance_map=UpperCAmelCase			,     segmentation_type='pil'	)
 def      A_	(      self  :      Optional[int]	)			->	Any:
     lowerCamelCase__    :      Dict					 =		np.zeros((20, 50)	)
     lowerCamelCase__    :      List[Any]					 =		1
     lowerCamelCase__    :      Dict					 =		1
     lowerCamelCase__    :      Optional[int]					 =		1

     lowerCamelCase__    :      Union[str, Any]					 =		binary_mask_to_rle(UpperCAmelCase	)
     self.assertEqual(len(UpperCAmelCase	)			,     4	)
     self.assertEqual(rle[0]			,     21	)
     self.assertEqual(rle[1]			,     45	)
 def      A_	(      self  :      Union[str, Any]	)			->	str:
     lowerCamelCase__    :      str					 =		self.image_processing_class(
         num_labels=self.image_processing_tester.num_classes			,     max_seq_length=77			,     task_seq_length=77			,     class_info_file='ade20k_panoptic.json'			,     num_text=self.image_processing_tester.num_text			,     repo_path='shi-labs/oneformer_demo'			,     )
     lowerCamelCase__    :      Tuple					 =		self.image_processing_tester.get_fake_oneformer_outputs()

     lowerCamelCase__    :      Any					 =		fature_extractor.post_process_semantic_segmentation(UpperCAmelCase	)

     self.assertEqual(len(UpperCAmelCase	)			,     self.image_processing_tester.batch_size	)
     self.assertEqual(
         segmentation[0].shape			,     (
             self.image_processing_tester.height,
             self.image_processing_tester.width,
         )			,     )

     lowerCamelCase__    :      Any					 =		[(1, 4) for i in range(self.image_processing_tester.batch_size	)]
     lowerCamelCase__    :      Dict					 =		fature_extractor.post_process_semantic_segmentation(UpperCAmelCase			,     target_sizes=UpperCAmelCase	)

     self.assertEqual(segmentation[0].shape			,     target_sizes[0]	)
 def      A_	(      self  :      List[str]	)			->	List[str]:
     lowerCamelCase__    :      Tuple					 =		self.image_processing_class(
         num_labels=self.image_processing_tester.num_classes			,     max_seq_length=77			,     task_seq_length=77			,     class_info_file='ade20k_panoptic.json'			,     num_text=self.image_processing_tester.num_text			,     repo_path='shi-labs/oneformer_demo'			,     )
     lowerCamelCase__    :      Union[str, Any]					 =		self.image_processing_tester.get_fake_oneformer_outputs()
     lowerCamelCase__    :      str					 =		image_processor.post_process_instance_segmentation(UpperCAmelCase			,     threshold=0	)

     self.assertTrue(len(UpperCAmelCase	) == self.image_processing_tester.batch_size	)
     for el in segmentation:
         self.assertTrue('segmentation' in el	)
         self.assertTrue('segments_info' in el	)
         self.assertEqual(type(el['segments_info']	)			,     UpperCAmelCase	)
         self.assertEqual(
             el['segmentation'].shape			,     (self.image_processing_tester.height, self.image_processing_tester.width)	)







 def      A_	(      self  :      Any	)			->	Union[str, Any]:
     lowerCamelCase__    :      int					 =		self.image_processing_class(
         num_labels=self.image_processing_tester.num_classes			,     max_seq_length=77			,     task_seq_length=77			,     class_info_file='ade20k_panoptic.json'			,     num_text=self.image_processing_tester.num_text			,     repo_path='shi-labs/oneformer_demo'			,     )
     lowerCamelCase__    :      int					 =		self.image_processing_tester.get_fake_oneformer_outputs()
     lowerCamelCase__    :      Tuple					 =		image_processor.post_process_panoptic_segmentation(UpperCAmelCase			,     threshold=0	)

     self.assertTrue(len(UpperCAmelCase	) == self.image_processing_tester.batch_size	)
     for el in segmentation:
         self.assertTrue('segmentation' in el	)
         self.assertTrue('segments_info' in el	)
         self.assertEqual(type(el['segments_info']	)			,     UpperCAmelCase	)
         self.assertEqual(
             el['segmentation'].shape			,     (self.image_processing_tester.height, self.image_processing_tester.width)	)







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


UpperCAmelCase	      = logging.getLogger(__name__)







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     Optional[int]							 =       """sequence-classification"""





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

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





	def 	SCREAMING_SNAKE_CASE__						(       self		,    **snake_case       ):
		return self.model(**snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case       ):
		lowercase       =    {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

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

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

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





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.hparams
		lowercase       =    processors[args.task]()
		lowercase       =    processor.get_labels()

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





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case = False       ):
		lowercase       =    'dev' if mode == 'test' else mode

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

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





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case       ):
		lowercase       =    {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

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

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

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





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

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

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

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

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





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





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		lowercase  ,					lowercase  ,					lowercase       =    self._eval_end(snake_case       )
		lowercase       =    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__						(       snake_case		,    snake_case       ):
		BaseTransformer.add_model_specific_args(snake_case		,    snake_case       )
		parser.add_argument(
		    '--max_seq_length'		,    default=128		,    type=snake_case		,    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=snake_case		,    required=snake_case		,    help='The GLUE task to run'		,    )
		parser.add_argument(
		    '--gpus'		,    default=0		,    type=snake_case		,    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_		(							):
	lowercase       =    argparse.ArgumentParser()
	add_generic_args(__SCREAMING_SNAKE_CASE ,							os.getcwd()							)
	lowercase       =    GLUETransformer.add_model_specific_args(__SCREAMING_SNAKE_CASE ,							os.getcwd()							)
	lowercase       =    parser.parse_args()

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

	lowercase       =    GLUETransformer(__SCREAMING_SNAKE_CASE							)
	lowercase       =    generic_train(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)

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


if __name__ == "__main__":
	main()

