






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 YolosImageProcessor


class 							__a							(  unittest.TestCase						):





		'''simple docstring'''



		def __init__(						self ,    UpperCamelCase__ ,    UpperCamelCase__=7 ,    UpperCamelCase__=3 ,    UpperCamelCase__=30 ,    UpperCamelCase__=400 ,    UpperCamelCase__=True ,    UpperCamelCase__=None ,    UpperCamelCase__=True ,    UpperCamelCase__=[0.5, 0.5, 0.5] ,    UpperCamelCase__=[0.5, 0.5, 0.5] ,    UpperCamelCase__=True ,    UpperCamelCase__=1 / 255 ,    UpperCamelCase__=True ,    ):
						# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
						SCREAMING_SNAKE_CASE_   :	List[str]													=   parent
						SCREAMING_SNAKE_CASE_   :	str													=   batch_size
						SCREAMING_SNAKE_CASE_   :	Any													=   num_channels
						SCREAMING_SNAKE_CASE_   :	Any													=   min_resolution
						SCREAMING_SNAKE_CASE_   :	Tuple													=   max_resolution
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   do_resize
						SCREAMING_SNAKE_CASE_   :	Dict													=   size
						SCREAMING_SNAKE_CASE_   :	str													=   do_normalize
						SCREAMING_SNAKE_CASE_   :	List[str]													=   image_mean
						SCREAMING_SNAKE_CASE_   :	int													=   image_std
						SCREAMING_SNAKE_CASE_   :	Any													=   do_rescale
						SCREAMING_SNAKE_CASE_   :	Dict													=   rescale_factor
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   do_pad


		def   __snake_case			(						self       ):
						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_rescale": self.do_rescale,
						    "rescale_factor": self.rescale_factor,
						    "do_pad": self.do_pad,
						}






		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__=False       ):
						if not batched:
										SCREAMING_SNAKE_CASE_   :	Any													=   image_inputs[0]
										if isinstance(UpperCamelCase__ ,    Image.Image       ):
														SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Optional[int]													=   image.size
										else:
														SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	List[Any]													=   image.shape[1], image.shape[2]
										if w < h:
														SCREAMING_SNAKE_CASE_   :	int													=   int(self.size['shortest_edge'] * h / w       )
														SCREAMING_SNAKE_CASE_   :	Optional[int]													=   self.size['shortest_edge']
										elif w > h:
														SCREAMING_SNAKE_CASE_   :	int													=   self.size['shortest_edge']
														SCREAMING_SNAKE_CASE_   :	Any													=   int(self.size['shortest_edge'] * w / h       )
										else:
														SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.size['shortest_edge']
														SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.size['shortest_edge']

						else:
										SCREAMING_SNAKE_CASE_   :	str													=   []
										for image in image_inputs:
														SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	int													=   self.get_expected_values([image]       )
														expected_values.append((expected_height, expected_width)       )
										SCREAMING_SNAKE_CASE_   :	List[str]													=   max(UpperCamelCase__ ,    key=lambda UpperCamelCase__       : item[0]       )[0]
										SCREAMING_SNAKE_CASE_   :	Tuple													=   max(UpperCamelCase__ ,    key=lambda UpperCamelCase__       : item[1]       )[1]

						return expected_height, expected_width






@require_torch
@require_vision
class 							__a							(  __A ,	unittest.TestCase						):





		'''simple docstring'''



		UpperCAmelCase__      :				Union[str, Any]				    =						YolosImageProcessor if is_vision_available() else None


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   YolosImageProcessingTester(self       )


		@property
		def   __snake_case			(						self       ):
						return self.image_processor_tester.prepare_image_processor_dict()


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	int													=   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'       )       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	int													=   self.image_processing_class.from_dict(self.image_processor_dict       )
						self.assertEqual(image_processor.size ,    {'shortest_edge': 18, 'longest_edge': 1333}       )
						self.assertEqual(image_processor.do_pad ,    UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.image_processing_class.from_dict(
						    self.image_processor_dict ,    size=42 ,    max_size=84 ,    pad_and_return_pixel_mask=UpperCamelCase__       )
						self.assertEqual(image_processor.size ,    {'shortest_edge': 42, 'longest_edge': 84}       )
						self.assertEqual(image_processor.do_pad ,    UpperCamelCase__       )


		def   __snake_case			(						self       ):
						pass


		def   __snake_case			(						self       ):
						# Initialize image_processing
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.image_processing_class(**self.image_processor_dict       )
						# create random PIL images
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   prepare_image_inputs(self.image_processor_tester ,    equal_resolution=UpperCamelCase__       )
						for image in image_inputs:
										self.assertIsInstance(UpperCamelCase__ ,    Image.Image       )

						# Test not batched input
						SCREAMING_SNAKE_CASE_   :	str													=   image_processing(image_inputs[0] ,    return_tensors='pt'       ).pixel_values

						SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	List[str]													=   self.image_processor_tester.get_expected_values(UpperCamelCase__       )

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

						# Test batched
						SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Tuple													=   self.image_processor_tester.get_expected_values(UpperCamelCase__ ,    batched=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	int													=   image_processing(UpperCamelCase__ ,    return_tensors='pt'       ).pixel_values
						self.assertEqual(
						    encoded_images.shape ,    (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    ) ,    )


		def   __snake_case			(						self       ):
						# Initialize image_processing
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.image_processing_class(**self.image_processor_dict       )
						# create random numpy tensors
						SCREAMING_SNAKE_CASE_   :	Tuple													=   prepare_image_inputs(self.image_processor_tester ,    equal_resolution=UpperCamelCase__ ,    numpify=UpperCamelCase__       )
						for image in image_inputs:
										self.assertIsInstance(UpperCamelCase__ ,    np.ndarray       )

						# Test not batched input
						SCREAMING_SNAKE_CASE_   :	Tuple													=   image_processing(image_inputs[0] ,    return_tensors='pt'       ).pixel_values

						SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Any													=   self.image_processor_tester.get_expected_values(UpperCamelCase__       )

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

						# Test batched
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   image_processing(UpperCamelCase__ ,    return_tensors='pt'       ).pixel_values

						SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	int													=   self.image_processor_tester.get_expected_values(UpperCamelCase__ ,    batched=UpperCamelCase__       )

						self.assertEqual(
						    encoded_images.shape ,    (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    ) ,    )


		def   __snake_case			(						self       ):
						# Initialize image_processing
						SCREAMING_SNAKE_CASE_   :	Tuple													=   self.image_processing_class(**self.image_processor_dict       )
						# create random PyTorch tensors
						SCREAMING_SNAKE_CASE_   :	str													=   prepare_image_inputs(self.image_processor_tester ,    equal_resolution=UpperCamelCase__ ,    torchify=UpperCamelCase__       )
						for image in image_inputs:
										self.assertIsInstance(UpperCamelCase__ ,    torch.Tensor       )

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

						SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.image_processor_tester.get_expected_values(UpperCamelCase__       )

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

						# Test batched
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   image_processing(UpperCamelCase__ ,    return_tensors='pt'       ).pixel_values

						SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Tuple													=   self.image_processor_tester.get_expected_values(UpperCamelCase__ ,    batched=UpperCamelCase__       )

						self.assertEqual(
						    encoded_images.shape ,    (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    ) ,    )


		def   __snake_case			(						self       ):
						# Initialize image_processings
						SCREAMING_SNAKE_CASE_   :	Tuple													=   self.image_processing_class(**self.image_processor_dict       )
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.image_processing_class(do_resize=UpperCamelCase__ ,    do_normalize=UpperCamelCase__ ,    do_rescale=UpperCamelCase__       )
						# create random PyTorch tensors
						SCREAMING_SNAKE_CASE_   :	List[str]													=   prepare_image_inputs(self.image_processor_tester ,    equal_resolution=UpperCamelCase__ ,    torchify=UpperCamelCase__       )
						for image in image_inputs:
										self.assertIsInstance(UpperCamelCase__ ,    torch.Tensor       )

						# Test whether the method "pad" and calling the image processor return the same tensors
						SCREAMING_SNAKE_CASE_   :	Any													=   image_processing_a.pad(UpperCamelCase__ ,    return_tensors='pt'       )
						SCREAMING_SNAKE_CASE_   :	str													=   image_processing_a(UpperCamelCase__ ,    return_tensors='pt'       )

						self.assertTrue(
						    torch.allclose(encoded_images_with_method['pixel_values'] ,    encoded_images['pixel_values'] ,    atol=1E-4       )       )


		@slow
		def   __snake_case			(						self       ):
						# prepare image and target
						SCREAMING_SNAKE_CASE_   :	Dict													=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
						with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt' ,    'r'       ) as f:
										SCREAMING_SNAKE_CASE_   :	List[Any]													=   json.loads(f.read()       )

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   {'image_id': 39769, 'annotations': target}

						# encode them
						SCREAMING_SNAKE_CASE_   :	str													=   YolosImageProcessor.from_pretrained('hustvl/yolos-small'       )
						SCREAMING_SNAKE_CASE_   :	str													=   image_processing(images=UpperCamelCase__ ,    annotations=UpperCamelCase__ ,    return_tensors='pt'       )

						# verify pixel values
						SCREAMING_SNAKE_CASE_   :	str													=   torch.Size([1, 3, 800, 1066]       )
						self.assertEqual(encoding['pixel_values'].shape ,    UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   torch.tensor([0.27_96, 0.31_38, 0.34_81]       )
						self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] ,    UpperCamelCase__ ,    atol=1E-4       )       )

						# verify area
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]       )
						self.assertTrue(torch.allclose(encoding['labels'][0]['area'] ,    UpperCamelCase__       )       )
						# verify boxes
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   torch.Size([6, 4]       )
						self.assertEqual(encoding['labels'][0]['boxes'].shape ,    UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	List[str]													=   torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]       )
						self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] ,    UpperCamelCase__ ,    atol=1E-3       )       )
						# verify image_id
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   torch.tensor([39769]       )
						self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] ,    UpperCamelCase__       )       )
						# verify is_crowd
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   torch.tensor([0, 0, 0, 0, 0, 0]       )
						self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] ,    UpperCamelCase__       )       )
						# verify class_labels
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   torch.tensor([75, 75, 63, 65, 17, 17]       )
						self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] ,    UpperCamelCase__       )       )
						# verify orig_size
						SCREAMING_SNAKE_CASE_   :	Any													=   torch.tensor([480, 640]       )
						self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] ,    UpperCamelCase__       )       )
						# verify size
						SCREAMING_SNAKE_CASE_   :	Dict													=   torch.tensor([800, 1066]       )
						self.assertTrue(torch.allclose(encoding['labels'][0]['size'] ,    UpperCamelCase__       )       )






		@slow
		def   __snake_case			(						self       ):
						# prepare image, target and masks_path
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
						with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt' ,    'r'       ) as f:
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   json.loads(f.read()       )

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   {'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'       )

						# encode them
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   YolosImageProcessor(format='coco_panoptic'       )
						SCREAMING_SNAKE_CASE_   :	Tuple													=   image_processing(images=UpperCamelCase__ ,    annotations=UpperCamelCase__ ,    masks_path=UpperCamelCase__ ,    return_tensors='pt'       )

						# verify pixel values
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   torch.Size([1, 3, 800, 1066]       )
						self.assertEqual(encoding['pixel_values'].shape ,    UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Tuple													=   torch.tensor([0.27_96, 0.31_38, 0.34_81]       )
						self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] ,    UpperCamelCase__ ,    atol=1E-4       )       )

						# verify area
						SCREAMING_SNAKE_CASE_   :	int													=   torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]       )
						self.assertTrue(torch.allclose(encoding['labels'][0]['area'] ,    UpperCamelCase__       )       )
						# verify boxes
						SCREAMING_SNAKE_CASE_   :	List[str]													=   torch.Size([6, 4]       )
						self.assertEqual(encoding['labels'][0]['boxes'].shape ,    UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	List[str]													=   torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]       )
						self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] ,    UpperCamelCase__ ,    atol=1E-3       )       )
						# verify image_id
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   torch.tensor([39769]       )
						self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] ,    UpperCamelCase__       )       )
						# verify is_crowd
						SCREAMING_SNAKE_CASE_   :	str													=   torch.tensor([0, 0, 0, 0, 0, 0]       )
						self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] ,    UpperCamelCase__       )       )
						# verify class_labels
						SCREAMING_SNAKE_CASE_   :	List[str]													=   torch.tensor([17, 17, 63, 75, 75, 93]       )
						self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] ,    UpperCamelCase__       )       )
						# verify masks
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   822873
						self.assertEqual(encoding['labels'][0]['masks'].sum().item() ,    UpperCamelCase__       )
						# verify orig_size
						SCREAMING_SNAKE_CASE_   :	int													=   torch.tensor([480, 640]       )
						self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] ,    UpperCamelCase__       )       )
						# verify size
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   torch.tensor([800, 1066]       )
						self.assertTrue(torch.allclose(encoding['labels'][0]['size'] ,    UpperCamelCase__       )       )







import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
				from PIL import Image

				from transformers import AlignProcessor, EfficientNetImageProcessor


@require_vision
class 							__a							(  unittest.TestCase						):





		'''simple docstring'''



		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   tempfile.mkdtemp()

						SCREAMING_SNAKE_CASE_   :	List[str]													=   [
						    '[UNK]',
						    '[CLS]',
						    '[SEP]',
						    '[PAD]',
						    '[MASK]',
						    'want',
						    '##want',
						    '##ed',
						    'wa',
						    'un',
						    'runn',
						    '##ing',
						    ',',
						    'low',
						    'lowest',
						]
						SCREAMING_SNAKE_CASE_   :	Dict													=   os.path.join(self.tmpdirname ,    VOCAB_FILES_NAMES['vocab_file']       )
						with open(self.vocab_file ,    'w' ,    encoding='utf-8'       ) as vocab_writer:
										vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]       )       )

						SCREAMING_SNAKE_CASE_   :	Tuple													=   {
						    'do_resize': True,
						    'size': 20,
						    'do_center_crop': True,
						    'crop_size': 18,
						    'do_normalize': True,
						    'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
						    'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
						}
						SCREAMING_SNAKE_CASE_   :	str													=   os.path.join(self.tmpdirname ,    UpperCamelCase__       )
						with open(self.image_processor_file ,    'w' ,    encoding='utf-8'       ) as fp:
										json.dump(UpperCamelCase__ ,    UpperCamelCase__       )


		def   __snake_case			(						self ,    **UpperCamelCase__       ):
						return BertTokenizer.from_pretrained(self.tmpdirname ,    **UpperCamelCase__       )


		def   __snake_case			(						self ,    **UpperCamelCase__       ):
						return BertTokenizerFast.from_pretrained(self.tmpdirname ,    **UpperCamelCase__       )


		def   __snake_case			(						self ,    **UpperCamelCase__       ):
						return EfficientNetImageProcessor.from_pretrained(self.tmpdirname ,    **UpperCamelCase__       )


		def   __snake_case			(						self       ):
						shutil.rmtree(self.tmpdirname       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   [np.random.randint(255 ,    size=(3, 30, 400) ,    dtype=np.uinta       )]
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   [Image.fromarray(np.moveaxis(UpperCamelCase__ ,    0 ,    -1       )       ) for x in image_inputs]
						return image_inputs


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Tuple													=   self.get_tokenizer()
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.get_rust_tokenizer()
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.get_image_processor()

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )
						processor_slow.save_pretrained(self.tmpdirname       )
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   AlignProcessor.from_pretrained(self.tmpdirname ,    use_fast=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )
						processor_fast.save_pretrained(self.tmpdirname       )
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   AlignProcessor.from_pretrained(self.tmpdirname       )

						self.assertEqual(processor_slow.tokenizer.get_vocab() ,    tokenizer_slow.get_vocab()       )
						self.assertEqual(processor_fast.tokenizer.get_vocab() ,    tokenizer_fast.get_vocab()       )
						self.assertEqual(tokenizer_slow.get_vocab() ,    tokenizer_fast.get_vocab()       )
						self.assertIsInstance(processor_slow.tokenizer ,    UpperCamelCase__       )
						self.assertIsInstance(processor_fast.tokenizer ,    UpperCamelCase__       )

						self.assertEqual(processor_slow.image_processor.to_json_string() ,    image_processor.to_json_string()       )
						self.assertEqual(processor_fast.image_processor.to_json_string() ,    image_processor.to_json_string()       )
						self.assertIsInstance(processor_slow.image_processor ,    UpperCamelCase__       )
						self.assertIsInstance(processor_fast.image_processor ,    UpperCamelCase__       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   AlignProcessor(tokenizer=self.get_tokenizer() ,    image_processor=self.get_image_processor()       )
						processor.save_pretrained(self.tmpdirname       )

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.get_tokenizer(bos_token='(BOS)' ,    eos_token='(EOS)'       )
						SCREAMING_SNAKE_CASE_   :	int													=   self.get_image_processor(do_normalize=UpperCamelCase__ ,    padding_value=1.0       )

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   AlignProcessor.from_pretrained(
						    self.tmpdirname ,    bos_token='(BOS)' ,    eos_token='(EOS)' ,    do_normalize=UpperCamelCase__ ,    padding_value=1.0       )

						self.assertEqual(processor.tokenizer.get_vocab() ,    tokenizer_add_kwargs.get_vocab()       )
						self.assertIsInstance(processor.tokenizer ,    UpperCamelCase__       )

						self.assertEqual(processor.image_processor.to_json_string() ,    image_processor_add_kwargs.to_json_string()       )
						self.assertIsInstance(processor.image_processor ,    UpperCamelCase__       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	int													=   self.get_image_processor()
						SCREAMING_SNAKE_CASE_   :	str													=   self.get_tokenizer()

						SCREAMING_SNAKE_CASE_   :	str													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.prepare_image_inputs()

						SCREAMING_SNAKE_CASE_   :	int													=   image_processor(UpperCamelCase__ ,    return_tensors='np'       )
						SCREAMING_SNAKE_CASE_   :	Dict													=   processor(images=UpperCamelCase__ ,    return_tensors='np'       )

						for key in input_image_proc.keys():
										self.assertAlmostEqual(input_image_proc[key].sum() ,    input_processor[key].sum() ,    delta=1E-2       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.get_image_processor()
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.get_tokenizer()

						SCREAMING_SNAKE_CASE_   :	List[str]													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Tuple													=   'lower newer'

						SCREAMING_SNAKE_CASE_   :	Tuple													=   processor(text=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	int													=   tokenizer(UpperCamelCase__ ,    padding='max_length' ,    max_length=64       )

						for key in encoded_tok.keys():
										self.assertListEqual(encoded_tok[key] ,    encoded_processor[key]       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.get_image_processor()
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.get_tokenizer()

						SCREAMING_SNAKE_CASE_   :	str													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	str													=   'lower newer'
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   self.prepare_image_inputs()

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   processor(text=UpperCamelCase__ ,    images=UpperCamelCase__       )

						self.assertListEqual(list(inputs.keys()       ) ,    ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']       )

						# test if it raises when no input is passed
						with pytest.raises(UpperCamelCase__       ):
										processor()


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.get_image_processor()
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.get_tokenizer()

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Tuple													=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						SCREAMING_SNAKE_CASE_   :	List[str]													=   processor.batch_decode(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Any													=   tokenizer.batch_decode(UpperCamelCase__       )

						self.assertListEqual(UpperCamelCase__ ,    UpperCamelCase__       )






		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.get_image_processor()
						SCREAMING_SNAKE_CASE_   :	int													=   self.get_tokenizer()

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	str													=   'lower newer'
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.prepare_image_inputs()

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   processor(text=UpperCamelCase__ ,    images=UpperCamelCase__       )

						self.assertListEqual(list(inputs.keys()       ) ,    processor.model_input_names       )