





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:
			_a						=   False

if is_vision_available():
			from PIL import Image

			from transformers import PixaStructImageProcessor






class       __A					(					unittest.TestCase				):

			'''simple docstring'''







			def __init__(					self ,						__lowerCAmelCase ,						__lowerCAmelCase=7 ,						__lowerCAmelCase=3 ,						__lowerCAmelCase=1_8 ,						__lowerCAmelCase=3_0 ,						__lowerCAmelCase=4_0_0 ,						__lowerCAmelCase=None ,						__lowerCAmelCase=True ,						__lowerCAmelCase=True ,						__lowerCAmelCase=None ,						):



				'''simple docstring'''







				lowerCamelCase__              =	size if size is not None else {'''height''': 2_0, '''width''': 2_0}
				lowerCamelCase__              =	parent
				lowerCamelCase__              =	batch_size
				lowerCamelCase__              =	num_channels
				lowerCamelCase__              =	image_size
				lowerCamelCase__              =	min_resolution
				lowerCamelCase__              =	max_resolution
				lowerCamelCase__              =	size
				lowerCamelCase__              =	do_normalize
				lowerCamelCase__              =	do_convert_rgb
				lowerCamelCase__              =	[5_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
				lowerCamelCase__              =	patch_size if patch_size is not None else {'''height''': 1_6, '''width''': 1_6}





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	'''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
				lowerCamelCase__              =	Image.open(requests.get(__lowerCAmelCase ,						stream=__lowerCAmelCase							).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       __A					(					lowerCAmelCase    ,       unittest.TestCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		PixaStructImageProcessor if is_vision_available() else None





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	PixaStructImageProcessingTester(self							)





			@property
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				return self.image_processor_tester.prepare_image_processor_dict()





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.image_processing_class(**self.image_processor_dict							)
				self.assertTrue(hasattr(__lowerCAmelCase ,						'''do_normalize'''							)							)
				self.assertTrue(hasattr(__lowerCAmelCase ,						'''do_convert_rgb'''							)							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.image_processor_tester.prepare_dummy_image()

				lowerCamelCase__              =	self.image_processing_class(**self.image_processor_dict							)
				lowerCamelCase__              =	2_0_4_8

				lowerCamelCase__              =	image_processor(__lowerCAmelCase ,						return_tensors='''pt''' ,						max_patches=__lowerCAmelCase							)
				self.assertTrue(torch.allclose(inputs.flattened_patches.mean() ,						torch.tensor(0.0606							) ,						atol=1E-3 ,						rtol=1E-3							)							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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

				# Test not batched input
				lowerCamelCase__              =	(
				    (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
					lowerCamelCase__              =	image_processor(
					    image_inputs[0] ,						return_tensors='''pt''' ,						max_patches=__lowerCAmelCase							).flattened_patches
					self.assertEqual(
					    encoded_images.shape ,						(1, max_patch, expected_hidden_dim) ,						)

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





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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

				# Test not batched input
				lowerCamelCase__              =	(
				    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
				    * self.image_processor_tester.num_channels
				) + 2

				lowerCamelCase__              =	True

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

					lowerCamelCase__              =	'''Hello'''

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

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





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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

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

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





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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

				# Test not batched input
				lowerCamelCase__              =	(
				    (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
					lowerCamelCase__              =	image_processor(
					    image_inputs[0] ,						return_tensors='''pt''' ,						max_patches=__lowerCAmelCase							).flattened_patches
					self.assertEqual(
					    encoded_images.shape ,						(1, max_patch, expected_hidden_dim) ,						)

					# Test batched
					lowerCamelCase__              =	image_processor(
					    __lowerCAmelCase ,						return_tensors='''pt''' ,						max_patches=__lowerCAmelCase							).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       __A					(					lowerCAmelCase    ,       unittest.TestCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		PixaStructImageProcessor if is_vision_available() else None





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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





			@property
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				return self.image_processor_tester.prepare_image_processor_dict()





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.image_processing_class(**self.image_processor_dict							)
				self.assertTrue(hasattr(__lowerCAmelCase ,						'''do_normalize'''							)							)
				self.assertTrue(hasattr(__lowerCAmelCase ,						'''do_convert_rgb'''							)							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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

				# Test not batched input
				lowerCamelCase__              =	(
				    (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
					lowerCamelCase__              =	image_processor(
					    image_inputs[0] ,						return_tensors='''pt''' ,						max_patches=__lowerCAmelCase							).flattened_patches
					self.assertEqual(
					    encoded_images.shape ,						(1, max_patch, expected_hidden_dim) ,						)

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










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


_a						=   logging.get_logger(__name__)






@dataclass
class       __A					:

			'''simple docstring'''







			lowerCAmelCase_							  =		field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()				)}				)
			lowerCAmelCase_							  =		field(
			    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}				)
			lowerCAmelCase_							  =		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."""
			        )
			    }    ,       )
			lowerCAmelCase_							  =		field(
			    default=lowerCAmelCase    ,       metadata={"""help""": """Overwrite the cached training and evaluation sets"""}				)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.task_name.lower()






class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		"""train"""
			lowerCAmelCase_							  =		"""dev"""
			lowerCAmelCase_							  =		"""test"""






class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		42
			lowerCAmelCase_							  =		42
			lowerCAmelCase_							  =		42





			def __init__(					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase = None ,						__lowerCAmelCase = Split.train ,						__lowerCAmelCase = None ,						):



				'''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 ,						)
				lowerCamelCase__              =	args
				lowerCamelCase__              =	glue_processors[args.task_name]()
				lowerCamelCase__              =	glue_output_modes[args.task_name]
				if isinstance(__lowerCAmelCase ,						__lowerCAmelCase							):
					try:
						lowerCamelCase__              =	Split[mode]
					except KeyError:
						raise KeyError('''mode is not a valid split name'''							)
        # Load data features from cache or dataset file
				lowerCamelCase__              =	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}' ,						)
				lowerCamelCase__              =	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)
					lowerCamelCase__       ,			lowerCamelCase__              =	label_list[2], label_list[1]
				lowerCamelCase__              =	label_list

				# Make sure only the first process in distributed training processes the dataset,
				# and the others will use the cache.
				lowerCamelCase__              =	cached_features_file + '''.lock'''
				with FileLock(__lowerCAmelCase							):
					if os.path.exists(__lowerCAmelCase							) and not args.overwrite_cache:
						lowerCamelCase__              =	time.time()
						lowerCamelCase__              =	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:
							lowerCamelCase__              =	self.processor.get_dev_examples(args.data_dir							)
						elif mode == Split.test:
							lowerCamelCase__              =	self.processor.get_test_examples(args.data_dir							)
						else:
							lowerCamelCase__              =	self.processor.get_train_examples(args.data_dir							)
						if limit_length is not None:
							lowerCamelCase__              =	examples[:limit_length]
						lowerCamelCase__              =	glue_convert_examples_to_features(
						    __lowerCAmelCase ,						__lowerCAmelCase ,						max_length=args.max_seq_length ,						label_list=__lowerCAmelCase ,						output_mode=self.output_mode ,						)
						lowerCamelCase__              =	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							):



				'''simple docstring'''







				return len(self.features							)





			def __getitem__(					self ,						__lowerCAmelCase							):



				'''simple docstring'''







				return self.features[i]





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				return self.label_list



