






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


__UpperCamelCase   :   List[str]      =    logging.get_logger(__name__)


@dataclass
class 							__UpperCamelCase     :
	__snake_case						:str =							field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys() )} )
	__snake_case						:str =							field(
	    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'} )
	__snake_case						:int =							field(
	    default=1_2_8 ,      metadata={
	        'help': (
	            'The maximum total input sequence length after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded.'
	        )
	    } ,      )
	__snake_case						:bool =							field(
	    default=_lowerCAmelCase ,      metadata={'help': 'Overwrite the cached training and evaluation sets'} )







	def 			_a (	self   :				List[str]      )		->			Optional[int]:
		"""simple docstring"""







		__lowercase   							=	self.task_name.lower()


class 							__UpperCamelCase     (  _lowerCAmelCase ):
	__snake_case						:Optional[Any] =							'train'
	__snake_case						:int =							'dev'
	__snake_case						:Optional[int] =							'test'


class 							__UpperCamelCase     (  _lowerCAmelCase ):
	__snake_case						:GlueDataTrainingArguments
	__snake_case						:str
	__snake_case						:List[InputFeatures]







	def __init__(	self   :				int							,					_lowerCAmelCase   :				GlueDataTrainingArguments							,					_lowerCAmelCase   :				PreTrainedTokenizerBase							,					_lowerCAmelCase   :				Optional[int] = None							,					_lowerCAmelCase   :				Union[str, Split] = Split.train							,					_lowerCAmelCase   :				Optional[str] = None							,					)		->			str:
		"""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"""							,					_lowerCAmelCase							,					)
		__lowercase   							=	args
		__lowercase   							=	glue_processors[args.task_name]()
		__lowercase   							=	glue_output_modes[args.task_name]
		if isinstance(_lowerCAmelCase							,					_lowerCAmelCase      ):
			try:
				__lowercase   							=	Split[mode]
			except KeyError:
				raise KeyError("""mode is not a valid split name"""      )
        # Load data features from cache or dataset file
		__lowercase   							=	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}'							,					)
		__lowercase   							=	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)
			__lowercase							,    __lowercase   							=	label_list[2], label_list[1]
		__lowercase   							=	label_list

		# Make sure only the first process in distributed training processes the dataset,
		# and the others will use the cache.
		__lowercase   							=	cached_features_file + """.lock"""
		with FileLock(_lowerCAmelCase      ):
			if os.path.exists(_lowerCAmelCase      ) and not args.overwrite_cache:
				__lowercase   							=	time.time()
				__lowercase   							=	torch.load(_lowerCAmelCase      )
				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:
					__lowercase   							=	self.processor.get_dev_examples(args.data_dir      )
				elif mode == Split.test:
					__lowercase   							=	self.processor.get_test_examples(args.data_dir      )
				else:
					__lowercase   							=	self.processor.get_train_examples(args.data_dir      )
				if limit_length is not None:
					__lowercase   							=	examples[:limit_length]
				__lowercase   							=	glue_convert_examples_to_features(
				    _lowerCAmelCase							,					_lowerCAmelCase							,					max_length=args.max_seq_length							,					label_list=_lowerCAmelCase							,					output_mode=self.output_mode							,					)
				__lowercase   							=	time.time()
				torch.save(self.features							,					_lowerCAmelCase      )
				# ^ 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   :				Any      )		->			Optional[Any]:
		"""simple docstring"""







		return len(self.features      )







	def __getitem__(	self   :				Any							,					_lowerCAmelCase   :				Optional[Any]      )		->			InputFeatures:
		"""simple docstring"""







		return self.features[i]







	def 			_a (	self   :				int      )		->			Optional[Any]:
		"""simple docstring"""







		return self.label_list









import unittest

import numpy as np
import requests

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

    from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
    _SCREAMING_SNAKE_CASE   =  False

if is_vision_available():
    from PIL import Image

    from transformers import PixaStructImageProcessor






class 					SCREAMING_SNAKE_CASE_  (       unittest.TestCase ):
       """simple docstring"""


       def __init__(				self	:str,      snake_case	:int,      snake_case	:Dict=7,      snake_case	:List[Any]=3,      snake_case	:int=18,      snake_case	:List[str]=30,      snake_case	:str=400,      snake_case	:int=None,      snake_case	:List[Any]=True,      snake_case	:Optional[int]=True,      snake_case	:Union[str, Any]=None,      ):



          """simple docstring"""

          _lowercase              =size if size is not None else {'height': 20, 'width': 20}
          _lowercase              =parent
          _lowercase              =batch_size
          _lowercase              =num_channels
          _lowercase              =image_size
          _lowercase              =min_resolution
          _lowercase              =max_resolution
          _lowercase              =size
          _lowercase              =do_normalize
          _lowercase              =do_convert_rgb
          _lowercase              =[512, 1024, 2048, 4096]
          _lowercase              =patch_size if patch_size is not None else {'height': 16, 'width': 16}





       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

          return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}





       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

          _lowercase              ='https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
          _lowercase              =Image.open(requests.get(snake_case,      stream=snake_case).raw).convert('RGB')
          return raw_image






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11  ,						reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''  ,						)
@require_torch
@require_vision
class 					SCREAMING_SNAKE_CASE_  (       _a  ,						unittest.TestCase ):
       """simple docstring"""


       __lowerCAmelCase    :		Tuple					=PixaStructImageProcessor if is_vision_available() else None





       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          _lowercase              =PixaStructImageProcessingTester(self)





       @property
       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

          return self.image_processor_tester.prepare_image_processor_dict()





       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(snake_case,      'do_normalize'))
          self.assertTrue(hasattr(snake_case,      'do_convert_rgb'))





       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

          _lowercase              =self.image_processor_tester.prepare_dummy_image()

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          _lowercase              =2048

          _lowercase              =image_processor(snake_case,      return_tensors='pt',      max_patches=snake_case)
          self.assertTrue(torch.allclose(inputs.flattened_patches.mean(),      torch.tensor(0.0_6_0_6),      atol=1e-3,      rtol=1e-3))





       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          _lowercase              =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
          _lowercase              =(
              (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
              * self.image_processor_tester.num_channels
          ) + 2

          for max_patch in self.image_processor_tester.max_patches:
             # Test not batched input
             _lowercase              =image_processor(
                 image_inputs[0],      return_tensors='pt',      max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (1, max_patch, expected_hidden_dim),      )

             # Test batched
             _lowercase              =image_processor(
                 snake_case,      return_tensors='pt',      max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),      )





       def 				UpperCamelCase__						(				self	:Union[str, Any]):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          _lowercase              =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
          _lowercase              =(
              (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
              * self.image_processor_tester.num_channels
          ) + 2

          _lowercase              =True

          for max_patch in self.image_processor_tester.max_patches:
             # Test not batched input
             with self.assertRaises(snake_case):
                _lowercase              =image_processor(
                    image_inputs[0],      return_tensors='pt',      max_patches=snake_case).flattened_patches

             _lowercase              ='Hello'

             _lowercase              =image_processor(
                 image_inputs[0],      return_tensors='pt',      max_patches=snake_case,      header_text=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (1, max_patch, expected_hidden_dim),      )

             # Test batched
             _lowercase              =image_processor(
                 snake_case,      return_tensors='pt',      max_patches=snake_case,      header_text=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),      )





       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          # create random numpy tensors
          _lowercase              =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)

          _lowercase              =(
              (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
              * self.image_processor_tester.num_channels
          ) + 2

          for max_patch in self.image_processor_tester.max_patches:
             # Test not batched input
             _lowercase              =image_processor(
                 image_inputs[0],      return_tensors='pt',      max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (1, max_patch, expected_hidden_dim),      )

             # Test batched
             _lowercase              =image_processor(
                 snake_case,      return_tensors='pt',      max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),      )





       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          # create random PyTorch tensors
          _lowercase              =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
          _lowercase              =(
              (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
              * self.image_processor_tester.num_channels
          ) + 2

          for max_patch in self.image_processor_tester.max_patches:
             # Test not batched input
             _lowercase              =image_processor(
                 image_inputs[0],      return_tensors='pt',      max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (1, max_patch, expected_hidden_dim),      )

             # Test batched
             _lowercase              =image_processor(
                 snake_case,      return_tensors='pt',      max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),      )






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11  ,						reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''  ,						)
@require_torch
@require_vision
class 					SCREAMING_SNAKE_CASE_  (       _a  ,						unittest.TestCase ):
       """simple docstring"""


       __lowerCAmelCase    :		Optional[Any]					=PixaStructImageProcessor if is_vision_available() else None





       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

          _lowercase              =PixaStructImageProcessingTester(self,      num_channels=4)
          _lowercase              =3





       @property
       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          return self.image_processor_tester.prepare_image_processor_dict()





       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(snake_case,      'do_normalize'))
          self.assertTrue(hasattr(snake_case,      'do_convert_rgb'))





       def 				UpperCamelCase__						(				self	:Optional[Any]):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          _lowercase              =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
          _lowercase              =(
              (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
              * (self.image_processor_tester.num_channels - 1)
          ) + 2

          for max_patch in self.image_processor_tester.max_patches:
             # Test not batched input
             _lowercase              =image_processor(
                 image_inputs[0],      return_tensors='pt',      max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (1, max_patch, expected_hidden_dim),      )

             # Test batched
             _lowercase              =image_processor(
                 snake_case,      return_tensors='pt',      max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),      )


