






"""simple docstring"""







import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


lowerCAmelCase_	:    Optional[Any]            =      logging.get_logger(__name__)





@dataclass
class   UpperCamelCase_      :
  _A       :					Optional[int]				=	field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys()  )}  )
  _A       :					Optional[Any]				=	field(
      metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}  )
  _A       :					Tuple				=	field(
      default=128   ,					metadata={
          'help': (
              'The maximum total input sequence length after tokenization. Sequences longer '
              'than this will be truncated, sequences shorter will be padded.'
          )
      }   ,					)
  _A       :					Optional[Any]				=	field(
      default=SCREAMING_SNAKE_CASE__   ,					metadata={'help': 'Overwrite the cached training and evaluation sets'}  )



  def 		UpperCamelCase_				(      self				)			->			Any:



         """simple docstring"""





         UpperCAmelCase	    =			self.task_name.lower()





class   UpperCamelCase_      (       SCREAMING_SNAKE_CASE__  ):
  _A       :					List[Any]				=	'train'
  _A       :					List[str]				=	'dev'
  _A       :					int				=	'test'





class   UpperCamelCase_      (       SCREAMING_SNAKE_CASE__  ):
  _A       :					List[str]				=	42
  _A       :					Optional[int]				=	42
  _A       :					List[str]				=	42



  def __init__(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ = None ,		snake_case__ = Split.train ,		snake_case__ = None ,		)			->			Optional[Any]:



         """simple docstring"""





         warnings.warn(
             """This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
             """library. You can have a look at this example script for pointers: """
             """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py""" ,		_lowercase ,		)
         UpperCAmelCase	    =			args
         UpperCAmelCase	    =			glue_processors[args.task_name]()
         UpperCAmelCase	    =			glue_output_modes[args.task_name]
         if isinstance(_lowercase ,		_lowercase				):
                try:
                       UpperCAmelCase	    =			Split[mode]
                except KeyError:
                       raise KeyError("""mode is not a valid split name"""				)
        # Load data features from cache or dataset file
         UpperCAmelCase	    =			os.path.join(
             cache_dir if cache_dir is not None else args.data_dir ,		f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}''' ,		)
         UpperCAmelCase	    =			self.processor.get_labels()
         if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
             "RobertaTokenizer",
             "RobertaTokenizerFast",
             "XLMRobertaTokenizer",
             "BartTokenizer",
             "BartTokenizerFast",
         ):
                # HACK(label indices are swapped in RoBERTa pretrained model)
                UpperCAmelCase	    =			label_list[2], label_list[1]
         UpperCAmelCase	    =			label_list

         # Make sure only the first process in distributed training processes the dataset,
         # and the others will use the cache.
         UpperCAmelCase	    =			cached_features_file + """.lock"""
         with FileLock(_lowercase				):
                if os.path.exists(_lowercase				) and not args.overwrite_cache:
                       UpperCAmelCase	    =			time.time()
                       UpperCAmelCase	    =			torch.load(_lowercase				)
                       logger.info(
                           f'''Loading features from cached file {cached_features_file} [took %.3f s]''' ,		time.time() - start				)
                else:
                       logger.info(f'''Creating features from dataset file at {args.data_dir}'''				)

                       if mode == Split.dev:
                              UpperCAmelCase	    =			self.processor.get_dev_examples(args.data_dir				)
                       elif mode == Split.test:
                              UpperCAmelCase	    =			self.processor.get_test_examples(args.data_dir				)
                       else:
                              UpperCAmelCase	    =			self.processor.get_train_examples(args.data_dir				)
                       if limit_length is not None:
                              UpperCAmelCase	    =			examples[:limit_length]
                       UpperCAmelCase	    =			glue_convert_examples_to_features(
                           _lowercase ,		_lowercase ,		max_length=args.max_seq_length ,		label_list=_lowercase ,		output_mode=self.output_mode ,		)
                       UpperCAmelCase	    =			time.time()
                       torch.save(self.features ,		_lowercase				)
                       # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
                       logger.info(
                           f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'''				)



  def __len__(      self				)			->			int:



         """simple docstring"""





         return len(self.features				)



  def __getitem__(      self ,		snake_case__				)			->			InputFeatures:



         """simple docstring"""





         return self.features[i]



  def 		UpperCamelCase_				(      self				)			->			Any:



         """simple docstring"""





         return self.label_list








"""simple docstring"""







import unittest
from typing import Dict, List, Optional, Union

import numpy as np

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 PIL import Image

 from transformers import BridgeTowerImageProcessor





class   UpperCamelCase_ (       unittest.TestCase  ):



  def __init__(      self ,		snake_case__ ,		snake_case__ = True ,		snake_case__ = None ,		snake_case__ = 32 ,		snake_case__ = True ,		snake_case__ = 1 / 2_55 ,		snake_case__ = True ,		snake_case__ = True ,		snake_case__ = [0.48_145_466, 0.4_578_275, 0.40_821_073] ,		snake_case__ = [0.26_862_954, 0.26_130_258, 0.27_577_711] ,		snake_case__ = True ,		snake_case__=7 ,		snake_case__=30 ,		snake_case__=4_00 ,		snake_case__=3 ,		)			->			Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase	    =			parent
         UpperCAmelCase	    =			do_resize
         UpperCAmelCase	    =			size if size is not None else {"""shortest_edge""": 2_88}
         UpperCAmelCase	    =			size_divisor
         UpperCAmelCase	    =			do_rescale
         UpperCAmelCase	    =			rescale_factor
         UpperCAmelCase	    =			do_normalize
         UpperCAmelCase	    =			do_center_crop
         UpperCAmelCase	    =			image_mean
         UpperCAmelCase	    =			image_std
         UpperCAmelCase	    =			do_pad
         UpperCAmelCase	    =			batch_size
         UpperCAmelCase	    =			num_channels
         UpperCAmelCase	    =			min_resolution
         UpperCAmelCase	    =			max_resolution



  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "size_divisor": self.size_divisor,
         }



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__=False				)			->			Optional[int]:



         """simple docstring"""





         if not batched:
                UpperCAmelCase	    =			self.size["""shortest_edge"""]
                UpperCAmelCase	    =			image_inputs[0]
                if isinstance(snake_case__ ,		Image.Image				):
                       UpperCAmelCase    ,       UpperCAmelCase	    =			image.size
                else:
                       UpperCAmelCase    ,       UpperCAmelCase	    =			image.shape[1], image.shape[2]
                UpperCAmelCase	    =			size / min(snake_case__ ,		snake_case__				)
                if h < w:
                       UpperCAmelCase    ,       UpperCAmelCase	    =			size, scale * w
                else:
                       UpperCAmelCase    ,       UpperCAmelCase	    =			scale * h, size

                UpperCAmelCase	    =			int((13_33 / 8_00) * size				)
                if max(snake_case__ ,		snake_case__				) > max_size:
                       UpperCAmelCase	    =			max_size / max(snake_case__ ,		snake_case__				)
                       UpperCAmelCase	    =			newh * scale
                       UpperCAmelCase	    =			neww * scale

                UpperCAmelCase    ,       UpperCAmelCase	    =			int(newh + 0.5				), int(neww + 0.5				)
                UpperCAmelCase    ,       UpperCAmelCase	    =			(
                    newh // self.size_divisor * self.size_divisor,
                    neww // self.size_divisor * self.size_divisor,
                )

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

         return expected_height, expected_width





@require_torch
@require_vision
class   UpperCamelCase_ (       a_   ,					unittest.TestCase  ):
  _A       :					int				=	BridgeTowerImageProcessor if is_vision_available() else None



  def 		UpperCamelCase_				(      self				)			->			str:



         """simple docstring"""





         UpperCAmelCase	    =			BridgeTowerImageProcessingTester(self				)



  @property
  def 		UpperCamelCase_				(      self				)			->			Dict:



         """simple docstring"""





         return self.image_processor_tester.prepare_image_processor_dict()



  def 		UpperCamelCase_				(      self				)			->			List[str]:



         """simple docstring"""





         UpperCAmelCase	    =			self.image_processing_class(**self.image_processor_dict				)
         self.assertTrue(hasattr(snake_case__ ,		"""image_mean"""				)				)
         self.assertTrue(hasattr(snake_case__ ,		"""image_std"""				)				)
         self.assertTrue(hasattr(snake_case__ ,		"""do_normalize"""				)				)
         self.assertTrue(hasattr(snake_case__ ,		"""do_resize"""				)				)
         self.assertTrue(hasattr(snake_case__ ,		"""size"""				)				)
         self.assertTrue(hasattr(snake_case__ ,		"""size_divisor"""				)				)



  def 		UpperCamelCase_				(      self				)			->			Union[str, Any]:



         """simple docstring"""





         pass



  def 		UpperCamelCase_				(      self				)			->			List[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			self.image_processing_class(**self.image_processor_dict				)
         # create random PIL images
         UpperCAmelCase	    =			prepare_image_inputs(self.image_processor_tester ,		equal_resolution=snake_case__				)
         for image in image_inputs:
                self.assertIsInstance(snake_case__ ,		Image.Image				)

         # Test not batched input
         UpperCAmelCase	    =			image_processing(image_inputs[0] ,		return_tensors="""pt"""				).pixel_values

         UpperCAmelCase    ,       UpperCAmelCase	    =			self.image_processor_tester.get_expected_values(snake_case__				)
         self.assertEqual(
             encoded_images.shape ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,		)

         # Test batched
         UpperCAmelCase	    =			image_processing(snake_case__ ,		return_tensors="""pt"""				).pixel_values

         UpperCAmelCase    ,       UpperCAmelCase	    =			self.image_processor_tester.get_expected_values(snake_case__ ,		batched=snake_case__				)
         self.assertEqual(
             encoded_images.shape ,		(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             ) ,		)



  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         UpperCAmelCase	    =			self.image_processing_class(**self.image_processor_dict				)
         # create random numpy tensors
         UpperCAmelCase	    =			prepare_image_inputs(self.image_processor_tester ,		equal_resolution=snake_case__ ,		numpify=snake_case__				)
         for image in image_inputs:
                self.assertIsInstance(snake_case__ ,		np.ndarray				)

         # Test not batched input
         UpperCAmelCase	    =			image_processing(image_inputs[0] ,		return_tensors="""pt"""				).pixel_values

         UpperCAmelCase    ,       UpperCAmelCase	    =			self.image_processor_tester.get_expected_values(snake_case__				)
         self.assertEqual(
             encoded_images.shape ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,		)

         # Test batched
         UpperCAmelCase	    =			image_processing(snake_case__ ,		return_tensors="""pt"""				).pixel_values

         UpperCAmelCase    ,       UpperCAmelCase	    =			self.image_processor_tester.get_expected_values(snake_case__ ,		batched=snake_case__				)
         self.assertEqual(
             encoded_images.shape ,		(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             ) ,		)



  def 		UpperCamelCase_				(      self				)			->			Dict:



         """simple docstring"""





         UpperCAmelCase	    =			self.image_processing_class(**self.image_processor_dict				)
         # create random PyTorch tensors
         UpperCAmelCase	    =			prepare_image_inputs(self.image_processor_tester ,		equal_resolution=snake_case__ ,		torchify=snake_case__				)
         for image in image_inputs:
                self.assertIsInstance(snake_case__ ,		torch.Tensor				)

         # Test not batched input
         UpperCAmelCase	    =			image_processing(image_inputs[0] ,		return_tensors="""pt"""				).pixel_values

         UpperCAmelCase    ,       UpperCAmelCase	    =			self.image_processor_tester.get_expected_values(snake_case__				)
         self.assertEqual(
             encoded_images.shape ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,		)

         # Test batched
         UpperCAmelCase	    =			image_processing(snake_case__ ,		return_tensors="""pt"""				).pixel_values

         UpperCAmelCase    ,       UpperCAmelCase	    =			self.image_processor_tester.get_expected_values(snake_case__ ,		batched=snake_case__				)
         self.assertEqual(
             encoded_images.shape ,		(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             ) ,		)
