



import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
     from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE_:Union[str, Any]					=						get_tests_dir("""fixtures/test_sentencepiece.model""")

SCREAMING_SNAKE_CASE_:int					=						{"""target_lang""": """fi""", """source_lang""": """en"""}
SCREAMING_SNAKE_CASE_:Tuple					=						""">>zh<<"""
SCREAMING_SNAKE_CASE_:int					=						"""Helsinki-NLP/"""

if is_torch_available():
     SCREAMING_SNAKE_CASE_:int					=						"""pt"""
elif is_tf_available():
     SCREAMING_SNAKE_CASE_:str					=						"""tf"""
else:
     SCREAMING_SNAKE_CASE_:List[str]					=						"""jax"""






@require_sentencepiece
class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__			,						unittest.TestCase   ):




    '''simple docstring'''






    __lowerCamelCase      :			Union[str, Any] =				MarianTokenizer
    __lowerCamelCase      :			Dict =				False
    __lowerCamelCase      :			List[str] =				True






    def       _lowerCAmelCase (	self		):
        super().setUp()
        A			:	List[Any]										=       ["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
        A			:	List[Any]										=       dict(zip(lowerCamelCase__,		range(len(lowerCamelCase__		)		)		)		)
        A			:	List[str]										=       Path(self.tmpdirname		)
        save_json(lowerCamelCase__,		save_dir / VOCAB_FILES_NAMES["""vocab"""]		)
        save_json(lowerCamelCase__,		save_dir / VOCAB_FILES_NAMES["""tokenizer_config_file"""]		)
        if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
            copyfile(lowerCamelCase__,		save_dir / VOCAB_FILES_NAMES["""source_spm"""]		)
            copyfile(lowerCamelCase__,		save_dir / VOCAB_FILES_NAMES["""target_spm"""]		)

        A			:	Optional[Any]										=       MarianTokenizer.from_pretrained(self.tmpdirname		)
        tokenizer.save_pretrained(self.tmpdirname		)






    def       _lowerCAmelCase (	self,		**lowerCamelCase__		):
        return MarianTokenizer.from_pretrained(self.tmpdirname,		**lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        return (
            "This is a test",
            "This is a test",
        )






    def       _lowerCAmelCase (	self		):
        A			:	Any										=       """</s>"""
        A			:	str										=       0

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase__		),		lowerCamelCase__		)
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase__		),		lowerCamelCase__		)






    def       _lowerCAmelCase (	self		):
        A			:	Tuple										=       list(self.get_tokenizer().get_vocab().keys()		)

        self.assertEqual(vocab_keys[0],		"""</s>"""		)
        self.assertEqual(vocab_keys[1],		"""<unk>"""		)
        self.assertEqual(vocab_keys[-1],		"""<pad>"""		)
        self.assertEqual(len(lowerCamelCase__		),		9		)






    def       _lowerCAmelCase (	self		):
        self.assertEqual(self.get_tokenizer().vocab_size,		9		)






    def       _lowerCAmelCase (	self		):
        A			:	Any										=       MarianTokenizer.from_pretrained(f'''{ORG_NAME}opus-mt-en-de'''		)
        A			:	Tuple										=       en_de_tokenizer(["""I am a small frog"""],		return_tensors=lowerCamelCase__		)
        self.assertIsInstance(lowerCamelCase__,		lowerCamelCase__		)
        A			:	List[str]										=       [38, 121, 14, 697, 3_8848, 0]
        self.assertListEqual(lowerCamelCase__,		batch.input_ids[0]		)

        A			:	Any										=       tempfile.mkdtemp()
        en_de_tokenizer.save_pretrained(lowerCamelCase__		)
        A			:	int										=       [x.name for x in Path(lowerCamelCase__		).glob("""*"""		)]
        self.assertIn("""source.spm""",		lowerCamelCase__		)
        MarianTokenizer.from_pretrained(lowerCamelCase__		)






    def       _lowerCAmelCase (	self		):
        A			:	Tuple										=       self.get_tokenizer()

        A			:	Optional[int]										=       tok(
            ["""I am a small frog""" * 1000, """I am a small frog"""],		padding=lowerCamelCase__,		truncation=lowerCamelCase__,		return_tensors=lowerCamelCase__		)
        self.assertIsInstance(lowerCamelCase__,		lowerCamelCase__		)
        self.assertEqual(batch.input_ids.shape,		(2, 512)		)






    def       _lowerCAmelCase (	self		):
        A			:	Tuple										=       self.get_tokenizer()
        A			:	int										=       tok(["""I am a tiny frog""", """I am a small frog"""],		padding=lowerCamelCase__,		return_tensors=lowerCamelCase__		)
        self.assertIsInstance(lowerCamelCase__,		lowerCamelCase__		)
        self.assertEqual(batch_smaller.input_ids.shape,		(2, 10)		)






    @slow
    def       _lowerCAmelCase (	self		):
        # fmt: off
        A			:	Dict										=       {"""input_ids""": [[4_3495, 462, 20, 4_2164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 3_8999, 6, 8, 464, 132, 1703, 492, 13, 4669, 3_7867, 13, 7525, 27, 1593, 988, 13, 3_3972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 1_2338, 2, 1_3958, 387, 2, 3629, 6953, 188, 2900, 2, 1_3958, 8011, 1_1501, 23, 8460, 4073, 3_4009, 20, 435, 1_1439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 3_7867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 2_6453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 1_0767, 6, 316, 304, 4239, 3, 0], [148, 1_5722, 19, 1839, 12, 1350, 13, 2_2327, 5082, 5418, 4_7567, 3_5938, 59, 318, 1_9552, 108, 2183, 54, 1_4976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 1_9088, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100], [36, 6395, 1_2570, 3_9147, 1_1597, 6, 266, 4, 4_5405, 7296, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
        # fmt: on

        self.tokenizer_integration_test_util(
            expected_encoding=lowerCamelCase__,		model_name="""Helsinki-NLP/opus-mt-en-de""",		revision="""1a8c2263da11e68e50938f97e10cd57820bd504c""",		decode_kwargs={"""use_source_tokenizer""": True},		)






    def       _lowerCAmelCase (	self		):
        A			:	Optional[Any]										=       MarianTokenizer.from_pretrained("""hf-internal-testing/test-marian-two-vocabs"""		)

        A			:	Optional[Any]										=       """Tämä on testi"""
        A			:	int										=       """This is a test"""

        A			:	Optional[int]										=       [76, 7, 2047, 2]
        A			:	Union[str, Any]										=       [69, 12, 11, 940, 2]

        A			:	str										=       tokenizer(lowerCamelCase__		).input_ids
        self.assertListEqual(lowerCamelCase__,		lowerCamelCase__		)

        A			:	int										=       tokenizer(text_target=lowerCamelCase__		).input_ids
        self.assertListEqual(lowerCamelCase__,		lowerCamelCase__		)

        A			:	str										=       tokenizer.decode(lowerCamelCase__,		skip_special_tokens=lowerCamelCase__		)
        self.assertEqual(lowerCamelCase__,		lowerCamelCase__		)







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 SCREAMING_SNAKE_CASE__					(		unittest.TestCase   ):




    '''simple docstring'''






    def       _lowerCAmelCase (	self		):
        A			:	str										=       tempfile.mkdtemp()

        # fmt: off
        A			:	List[Any]										=       ["""""", """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
        A			:	Optional[int]										=       dict(zip(lowerCamelCase__,		range(len(lowerCamelCase__		)		)		)		)
        A			:	Optional[Any]										=       ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
        A			:	Union[str, Any]										=       {"""unk_token""": """<unk>"""}

        A			:	List[Any]										=       os.path.join(self.tmpdirname,		VOCAB_FILES_NAMES["""vocab_file"""]		)
        A			:	int										=       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(lowerCamelCase__		) + """\n"""		)
        with open(self.merges_file,		"""w""",		encoding="""utf-8"""		) as fp:
            fp.write("""\n""".join(lowerCamelCase__		)		)

        A			:	int										=       {
            """do_resize""": True,
            """size""": 20,
            """do_center_crop""": True,
            """crop_size""": 18,
            """do_normalize""": True,
            """image_mean""": [0.4814_5466, 0.457_8275, 0.4082_1073],
            """image_std""": [0.2686_2954, 0.2613_0258, 0.2757_7711],
        }
        A			:	List[Any]										=       os.path.join(self.tmpdirname,		lowerCamelCase__		)
        with open(self.image_processor_file,		"""w""",		encoding="""utf-8"""		) as fp:
            json.dump(lowerCamelCase__,		lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		**lowerCamelCase__		):
        return CLIPTokenizer.from_pretrained(self.tmpdirname,		pad_token="""!""",		**lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		**lowerCamelCase__		):
        return CLIPTokenizerFast.from_pretrained(self.tmpdirname,		pad_token="""!""",		**lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		**lowerCamelCase__		):
        return OwlViTImageProcessor.from_pretrained(self.tmpdirname,		**lowerCamelCase__		)






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






    def       _lowerCAmelCase (	self		):
        A			:	str										=       [np.random.randint(255,		size=(3, 30, 400),		dtype=np.uinta		)]

        A			:	Optional[int]										=       [Image.fromarray(np.moveaxis(lowerCamelCase__,		0,		-1		)		) for x in image_inputs]

        return image_inputs






    def       _lowerCAmelCase (	self		):
        A			:	Optional[Any]										=       self.get_tokenizer()
        A			:	Optional[Any]										=       self.get_rust_tokenizer()
        A			:	Optional[int]										=       self.get_image_processor()

        A			:	List[str]										=       OwlViTProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__		)
        processor_slow.save_pretrained(self.tmpdirname		)
        A			:	Optional[int]										=       OwlViTProcessor.from_pretrained(self.tmpdirname,		use_fast=lowerCamelCase__		)

        A			:	Tuple										=       OwlViTProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__		)
        processor_fast.save_pretrained(self.tmpdirname		)
        A			:	str										=       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,		lowerCamelCase__		)
        self.assertIsInstance(processor_fast.tokenizer,		lowerCamelCase__		)

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






    def       _lowerCAmelCase (	self		):
        A			:	List[str]										=       OwlViTProcessor(tokenizer=self.get_tokenizer(),		image_processor=self.get_image_processor()		)
        processor.save_pretrained(self.tmpdirname		)

        A			:	Optional[int]										=       self.get_tokenizer(bos_token="""(BOS)""",		eos_token="""(EOS)"""		)
        A			:	Tuple										=       self.get_image_processor(do_normalize=lowerCamelCase__		)

        A			:	Optional[Any]										=       OwlViTProcessor.from_pretrained(
            self.tmpdirname,		bos_token="""(BOS)""",		eos_token="""(EOS)""",		do_normalize=lowerCamelCase__		)

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

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






    def       _lowerCAmelCase (	self		):
        A			:	List[Any]										=       self.get_image_processor()
        A			:	str										=       self.get_tokenizer()

        A			:	List[str]										=       OwlViTProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__		)

        A			:	Optional[Any]										=       self.prepare_image_inputs()

        A			:	Optional[Any]										=       image_processor(lowerCamelCase__,		return_tensors="""np"""		)
        A			:	Any										=       processor(images=lowerCamelCase__,		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		):
        A			:	int										=       self.get_image_processor()
        A			:	Optional[Any]										=       self.get_tokenizer()

        A			:	Optional[int]										=       OwlViTProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__		)

        A			:	Any										=       """lower newer"""

        A			:	Union[str, Any]										=       processor(text=lowerCamelCase__,		return_tensors="""np"""		)

        A			:	str										=       tokenizer(lowerCamelCase__,		return_tensors="""np"""		)

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






    def       _lowerCAmelCase (	self		):
        A			:	Tuple										=       self.get_image_processor()
        A			:	int										=       self.get_tokenizer()

        A			:	str										=       OwlViTProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__		)

        A			:	List[str]										=       """lower newer"""
        A			:	Any										=       self.prepare_image_inputs()

        A			:	Tuple										=       processor(text=lowerCamelCase__,		images=lowerCamelCase__		)

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

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






    def       _lowerCAmelCase (	self		):
        A			:	str										=       """google/owlvit-base-patch32"""
        A			:	Dict										=       OwlViTProcessor.from_pretrained(lowerCamelCase__		)

        A			:	str										=       ["""cat""", """nasa badge"""]
        A			:	Optional[int]										=       processor(text=lowerCamelCase__		)

        A			:	Any										=       16
        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(lowerCamelCase__		):
            processor()






    def       _lowerCAmelCase (	self		):
        A			:	Tuple										=       """google/owlvit-base-patch32"""
        A			:	Any										=       OwlViTProcessor.from_pretrained(lowerCamelCase__		)

        A			:	int										=       [["""cat""", """nasa badge"""], ["""person"""]]
        A			:	List[Any]										=       processor(text=lowerCamelCase__		)

        A			:	Dict										=       16
        A			:	List[str]										=       len(lowerCamelCase__		)
        A			:	List[str]										=       max([len(lowerCamelCase__		) 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(lowerCamelCase__		):
            processor()






    def       _lowerCAmelCase (	self		):
        A			:	Dict										=       """google/owlvit-base-patch32"""
        A			:	int										=       OwlViTProcessor.from_pretrained(lowerCamelCase__		)

        A			:	str										=       ["""cat""", """nasa badge"""]
        A			:	Optional[Any]										=       processor(text=lowerCamelCase__		)

        A			:	int										=       16
        A			:	Optional[Any]										=       inputs["""input_ids"""]
        A			:	Optional[int]										=       [
            [4_9406, 2368, 4_9407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
            [4_9406, 6841, 1_1301, 4_9407, 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		):
        A			:	Tuple										=       self.get_image_processor()
        A			:	Dict										=       self.get_tokenizer()

        A			:	Optional[Any]										=       OwlViTProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__		)

        A			:	Any										=       self.prepare_image_inputs()
        A			:	Optional[Any]										=       self.prepare_image_inputs()

        A			:	List[str]										=       processor(images=lowerCamelCase__,		query_images=lowerCamelCase__		)

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

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






    def       _lowerCAmelCase (	self		):
        A			:	Any										=       self.get_image_processor()
        A			:	Optional[Any]										=       self.get_tokenizer()

        A			:	List[str]										=       OwlViTProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__		)

        A			:	Any										=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        A			:	Optional[Any]										=       processor.batch_decode(lowerCamelCase__		)
        A			:	Union[str, Any]										=       tokenizer.batch_decode(lowerCamelCase__		)

        self.assertListEqual(lowerCamelCase__,		lowerCamelCase__		)


