


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:
			__snake_case        =   False

if is_vision_available():
			from PIL import Image

			from transformers import PixaStructImageProcessor




class    UpperCAmelCase					(       unittest.TestCase    ):




			def __init__(						self							:		Optional[int]       ,       __magic_name__							:		Union[str, Any]       ,       __magic_name__							:		str=7       ,       __magic_name__							:		List[str]=3       ,       __magic_name__							:		List[str]=1_8       ,       __magic_name__							:		List[Any]=3_0       ,       __magic_name__							:		Optional[int]=4_0_0       ,       __magic_name__							:		List[Any]=None       ,       __magic_name__							:		Optional[Any]=True       ,       __magic_name__							:		List[Any]=True       ,       __magic_name__							:		List[Any]=None       ,       ):







						"""simple docstring"""







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




			def   lowerCamelCase_					(						self							:		Tuple						):







						"""simple docstring"""







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





			def   lowerCamelCase_					(						self							:		Optional[int]						):







						"""simple docstring"""







						UpperCamelCase         =					"""https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"""
						UpperCamelCase         =					Image.open(requests.get(__magic_name__       ,       stream=__magic_name__						).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    UpperCAmelCase					(       __snake_case      , unittest.TestCase    ):
			lowercase						= PixaStructImageProcessor if is_vision_available() else None




			def   lowerCamelCase_					(						self							:		Any						):







						"""simple docstring"""







						UpperCamelCase         =					PixaStructImageProcessingTester(self						)




			@property
			def   lowerCamelCase_					(						self							:		Union[str, Any]						):







						"""simple docstring"""







						return self.image_processor_tester.prepare_image_processor_dict()




			def   lowerCamelCase_					(						self							:		List[str]						):







						"""simple docstring"""







						UpperCamelCase         =					self.image_processing_class(**self.image_processor_dict						)
						self.assertTrue(hasattr(__magic_name__       ,       """do_normalize"""						)						)
						self.assertTrue(hasattr(__magic_name__       ,       """do_convert_rgb"""						)						)




			def   lowerCamelCase_					(						self							:		Optional[int]						):







						"""simple docstring"""







						UpperCamelCase         =					self.image_processor_tester.prepare_dummy_image()

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

						UpperCamelCase         =					image_processor(__magic_name__       ,       return_tensors="""pt"""       ,       max_patches=__magic_name__						)
						self.assertTrue(torch.allclose(inputs.flattened_patches.mean()       ,       torch.tensor(0.0_606						)       ,       atol=1e-3       ,       rtol=1e-3						)						)




			def   lowerCamelCase_					(						self							:		Optional[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=__magic_name__						)
						for image in image_inputs:
									self.assertIsInstance(__magic_name__       ,       Image.Image						)

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

									# Test batched
									UpperCamelCase         =					image_processor(
									    __magic_name__       ,       return_tensors="""pt"""       ,       max_patches=__magic_name__						).flattened_patches
									self.assertEqual(
									    encoded_images.shape       ,       (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,       )




			def   lowerCamelCase_					(						self							:		Union[str, 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=__magic_name__						)
						for image in image_inputs:
									self.assertIsInstance(__magic_name__       ,       Image.Image						)

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

						UpperCamelCase         =					True

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

									UpperCamelCase         =					"""Hello"""

									UpperCamelCase         =					image_processor(
									    image_inputs[0]       ,       return_tensors="""pt"""       ,       max_patches=__magic_name__       ,       header_text=__magic_name__						).flattened_patches
									self.assertEqual(
									    encoded_images.shape       ,       (1, max_patch, expected_hidden_dim)       ,       )

									# Test batched
									UpperCamelCase         =					image_processor(
									    __magic_name__       ,       return_tensors="""pt"""       ,       max_patches=__magic_name__       ,       header_text=__magic_name__						).flattened_patches
									self.assertEqual(
									    encoded_images.shape       ,       (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,       )




			def   lowerCamelCase_					(						self							:		Optional[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=__magic_name__       ,       numpify=__magic_name__						)
						for image in image_inputs:
									self.assertIsInstance(__magic_name__       ,       np.ndarray						)

						UpperCamelCase         =					(
						    (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
									UpperCamelCase         =					image_processor(
									    image_inputs[0]       ,       return_tensors="""pt"""       ,       max_patches=__magic_name__						).flattened_patches
									self.assertEqual(
									    encoded_images.shape       ,       (1, max_patch, expected_hidden_dim)       ,       )

									# Test batched
									UpperCamelCase         =					image_processor(
									    __magic_name__       ,       return_tensors="""pt"""       ,       max_patches=__magic_name__						).flattened_patches
									self.assertEqual(
									    encoded_images.shape       ,       (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,       )





			def   lowerCamelCase_					(						self							:		Any						):







						"""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=__magic_name__       ,       torchify=__magic_name__						)
						for image in image_inputs:
									self.assertIsInstance(__magic_name__       ,       torch.Tensor						)

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

									# Test batched
									UpperCamelCase         =					image_processor(
									    __magic_name__       ,       return_tensors="""pt"""       ,       max_patches=__magic_name__						).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    UpperCAmelCase					(       __snake_case      , unittest.TestCase    ):
			lowercase						= PixaStructImageProcessor if is_vision_available() else None




			def   lowerCamelCase_					(						self							:		str						):







						"""simple docstring"""







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




			@property
			def   lowerCamelCase_					(						self							:		str						):







						"""simple docstring"""







						return self.image_processor_tester.prepare_image_processor_dict()




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







						"""simple docstring"""







						UpperCamelCase         =					self.image_processing_class(**self.image_processor_dict						)
						self.assertTrue(hasattr(__magic_name__       ,       """do_normalize"""						)						)
						self.assertTrue(hasattr(__magic_name__       ,       """do_convert_rgb"""						)						)





			def   lowerCamelCase_					(						self							:		Dict						):







						"""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=__magic_name__						)
						for image in image_inputs:
									self.assertIsInstance(__magic_name__       ,       Image.Image						)

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

									# Test batched
									UpperCamelCase         =					image_processor(
									    __magic_name__       ,       return_tensors="""pt"""       ,       max_patches=__magic_name__						).flattened_patches
									self.assertEqual(
									    encoded_images.shape       ,       (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,       )








import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip 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 OwlViTImageProcessor, OwlViTProcessor




@require_vision
class    UpperCAmelCase					(       unittest.TestCase    ):




			def   lowerCamelCase_					(						self							:		Optional[Any]						):







						"""simple docstring"""







						UpperCamelCase         =					tempfile.mkdtemp()

						# fmt: off
						UpperCamelCase         =					["""""", """l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
						# fmt: on
						UpperCamelCase         =					dict(zip(__magic_name__       ,       range(len(__magic_name__						)						)						)						)
						UpperCamelCase         =					["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
						UpperCamelCase         =					{"""unk_token""": """<unk>"""}

						UpperCamelCase         =					os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES["""vocab_file"""]						)
						UpperCamelCase         =					os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES["""merges_file"""]						)
						with open(self.vocab_file       ,       """w"""       ,       encoding="""utf-8"""						) as fp:
									fp.write(json.dumps(__magic_name__						) + """\n"""						)
						with open(self.merges_file       ,       """w"""       ,       encoding="""utf-8"""						) as fp:
									fp.write("""\n""".join(__magic_name__						)						)

						UpperCamelCase         =					{
						    """do_resize""": True,
						    """size""": 2_0,
						    """do_center_crop""": True,
						    """crop_size""": 1_8,
						    """do_normalize""": True,
						    """image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
						    """image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
						}
						UpperCamelCase         =					os.path.join(self.tmpdirname       ,       __magic_name__						)
						with open(self.image_processor_file       ,       """w"""       ,       encoding="""utf-8"""						) as fp:
									json.dump(__magic_name__       ,       __magic_name__						)




			def   lowerCamelCase_					(						self							:		str       ,       **__magic_name__							:		Tuple						):







						"""simple docstring"""







						return CLIPTokenizer.from_pretrained(self.tmpdirname       ,       pad_token="""!"""       ,       **__magic_name__						)




			def   lowerCamelCase_					(						self							:		Optional[int]       ,       **__magic_name__							:		str						):







						"""simple docstring"""







						return CLIPTokenizerFast.from_pretrained(self.tmpdirname       ,       pad_token="""!"""       ,       **__magic_name__						)




			def   lowerCamelCase_					(						self							:		Optional[int]       ,       **__magic_name__							:		Optional[Any]						):







						"""simple docstring"""







						return OwlViTImageProcessor.from_pretrained(self.tmpdirname       ,       **__magic_name__						)




			def   lowerCamelCase_					(						self							:		int						):







						"""simple docstring"""







						shutil.rmtree(self.tmpdirname						)




			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						UpperCamelCase         =					[np.random.randint(2_5_5       ,       size=(3, 3_0, 4_0_0)       ,       dtype=np.uinta						)]

						UpperCamelCase         =					[Image.fromarray(np.moveaxis(__magic_name__       ,       0       ,       -1						)						) for x in image_inputs]

						return image_inputs




			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						UpperCamelCase         =					self.get_tokenizer()
						UpperCamelCase         =					self.get_rust_tokenizer()
						UpperCamelCase         =					self.get_image_processor()

						UpperCamelCase         =					OwlViTProcessor(tokenizer=__magic_name__       ,       image_processor=__magic_name__						)
						processor_slow.save_pretrained(self.tmpdirname						)
						UpperCamelCase         =					OwlViTProcessor.from_pretrained(self.tmpdirname       ,       use_fast=__magic_name__						)

						UpperCamelCase         =					OwlViTProcessor(tokenizer=__magic_name__       ,       image_processor=__magic_name__						)
						processor_fast.save_pretrained(self.tmpdirname						)
						UpperCamelCase         =					OwlViTProcessor.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       ,       __magic_name__						)
						self.assertIsInstance(processor_fast.tokenizer       ,       __magic_name__						)

						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       ,       __magic_name__						)
						self.assertIsInstance(processor_fast.image_processor       ,       __magic_name__						)




			def   lowerCamelCase_					(						self							:		int						):







						"""simple docstring"""







						UpperCamelCase         =					OwlViTProcessor(tokenizer=self.get_tokenizer()       ,       image_processor=self.get_image_processor()						)
						processor.save_pretrained(self.tmpdirname						)

						UpperCamelCase         =					self.get_tokenizer(bos_token="""(BOS)"""       ,       eos_token="""(EOS)"""						)
						UpperCamelCase         =					self.get_image_processor(do_normalize=__magic_name__						)

						UpperCamelCase         =					OwlViTProcessor.from_pretrained(
						    self.tmpdirname       ,       bos_token="""(BOS)"""       ,       eos_token="""(EOS)"""       ,       do_normalize=__magic_name__						)

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

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




			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						UpperCamelCase         =					self.get_image_processor()
						UpperCamelCase         =					self.get_tokenizer()

						UpperCamelCase         =					OwlViTProcessor(tokenizer=__magic_name__       ,       image_processor=__magic_name__						)

						UpperCamelCase         =					self.prepare_image_inputs()

						UpperCamelCase         =					image_processor(__magic_name__       ,       return_tensors="""np"""						)
						UpperCamelCase         =					processor(images=__magic_name__       ,       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   lowerCamelCase_					(						self							:		Any						):







						"""simple docstring"""







						UpperCamelCase         =					self.get_image_processor()
						UpperCamelCase         =					self.get_tokenizer()

						UpperCamelCase         =					OwlViTProcessor(tokenizer=__magic_name__       ,       image_processor=__magic_name__						)

						UpperCamelCase         =					"""lower newer"""

						UpperCamelCase         =					processor(text=__magic_name__       ,       return_tensors="""np"""						)

						UpperCamelCase         =					tokenizer(__magic_name__       ,       return_tensors="""np"""						)

						for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key][0].tolist()       ,       encoded_processor[key][0].tolist()						)




			def   lowerCamelCase_					(						self							:		Optional[Any]						):







						"""simple docstring"""







						UpperCamelCase         =					self.get_image_processor()
						UpperCamelCase         =					self.get_tokenizer()

						UpperCamelCase         =					OwlViTProcessor(tokenizer=__magic_name__       ,       image_processor=__magic_name__						)

						UpperCamelCase         =					"""lower newer"""
						UpperCamelCase         =					self.prepare_image_inputs()

						UpperCamelCase         =					processor(text=__magic_name__       ,       images=__magic_name__						)

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

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




			def   lowerCamelCase_					(						self							:		int						):







						"""simple docstring"""







						UpperCamelCase         =					"""google/owlvit-base-patch32"""
						UpperCamelCase         =					OwlViTProcessor.from_pretrained(__magic_name__						)

						UpperCamelCase         =					["""cat""", """nasa badge"""]
						UpperCamelCase         =					processor(text=__magic_name__						)

						UpperCamelCase         =					1_6
						self.assertListEqual(list(inputs.keys()						)       ,       ["""input_ids""", """attention_mask"""]						)
						self.assertEqual(inputs["""input_ids"""].shape       ,       (2, seq_length)						)

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




			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						UpperCamelCase         =					"""google/owlvit-base-patch32"""
						UpperCamelCase         =					OwlViTProcessor.from_pretrained(__magic_name__						)

						UpperCamelCase         =					[["""cat""", """nasa badge"""], ["""person"""]]
						UpperCamelCase         =					processor(text=__magic_name__						)

						UpperCamelCase         =					1_6
						UpperCamelCase         =					len(__magic_name__						)
						UpperCamelCase         =					max([len(__magic_name__						) for texts in input_texts]						)

						self.assertListEqual(list(inputs.keys()						)       ,       ["""input_ids""", """attention_mask"""]						)
						self.assertEqual(inputs["""input_ids"""].shape       ,       (batch_size * num_max_text_queries, seq_length)						)

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




			def   lowerCamelCase_					(						self							:		List[str]						):







						"""simple docstring"""







						UpperCamelCase         =					"""google/owlvit-base-patch32"""
						UpperCamelCase         =					OwlViTProcessor.from_pretrained(__magic_name__						)

						UpperCamelCase         =					["""cat""", """nasa badge"""]
						UpperCamelCase         =					processor(text=__magic_name__						)

						UpperCamelCase         =					1_6
						UpperCamelCase         =					inputs["""input_ids"""]
						UpperCamelCase         =					[
						    [4_9_4_0_6, 2_3_6_8, 4_9_4_0_7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
						    [4_9_4_0_6, 6_8_4_1, 1_1_3_0_1, 4_9_4_0_7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
						]

						self.assertListEqual(list(inputs.keys()						)       ,       ["""input_ids""", """attention_mask"""]						)
						self.assertEqual(inputs["""input_ids"""].shape       ,       (2, seq_length)						)
						self.assertListEqual(list(input_ids[0]						)       ,       predicted_ids[0]						)
						self.assertListEqual(list(input_ids[1]						)       ,       predicted_ids[1]						)




			def   lowerCamelCase_					(						self							:		str						):







						"""simple docstring"""







						UpperCamelCase         =					self.get_image_processor()
						UpperCamelCase         =					self.get_tokenizer()

						UpperCamelCase         =					OwlViTProcessor(tokenizer=__magic_name__       ,       image_processor=__magic_name__						)

						UpperCamelCase         =					self.prepare_image_inputs()
						UpperCamelCase         =					self.prepare_image_inputs()

						UpperCamelCase         =					processor(images=__magic_name__       ,       query_images=__magic_name__						)

						self.assertListEqual(list(inputs.keys()						)       ,       ["""query_pixel_values""", """pixel_values"""]						)

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





			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						UpperCamelCase         =					self.get_image_processor()
						UpperCamelCase         =					self.get_tokenizer()

						UpperCamelCase         =					OwlViTProcessor(tokenizer=__magic_name__       ,       image_processor=__magic_name__						)

						UpperCamelCase         =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						UpperCamelCase         =					processor.batch_decode(__magic_name__						)
						UpperCamelCase         =					tokenizer.batch_decode(__magic_name__						)

						self.assertListEqual(__magic_name__       ,       __magic_name__						)




