
import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


lowercase        =  random.Random()



def      __lowerCAmelCase						(							UpperCAmelCase__				:      List[str]			,				UpperCAmelCase__				:      Dict=1.0			,				UpperCAmelCase__				:      Any=None			,				UpperCAmelCase__				:      Optional[int]=None       )  ->    Any:
							if rng is None:
														lowerCamelCase_							  =					global_rng

							lowerCamelCase_							  =					[]
							for batch_idx in range(shape[0]       ):
														values.append([]       )
														for _ in range(shape[1]       ):
																					values[-1].append(rng.random() * scale       )

							return values





class       __A(				unittest.TestCase       ):

					def __init__(	self					:     Tuple   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     List[str]=7   ,					__UpperCamelCase					:     Dict=4_0_0   ,					__UpperCamelCase					:     List[Any]=2_0_0_0   ,					__UpperCamelCase					:     Union[str, Any]=1   ,					__UpperCamelCase					:     Union[str, Any]=0.0   ,					__UpperCamelCase					:     Optional[int]=1_6_0_0_0   ,					__UpperCamelCase					:     str=True   ,					__UpperCamelCase					:     Union[str, Any]=True   ,					):
												lowerCamelCase_							  =					parent
												lowerCamelCase_							  =					batch_size
												lowerCamelCase_							  =					min_seq_length
												lowerCamelCase_							  =					max_seq_length
												lowerCamelCase_							  =					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
												lowerCamelCase_							  =					feature_size
												lowerCamelCase_							  =					padding_value
												lowerCamelCase_							  =					sampling_rate
												lowerCamelCase_							  =					return_attention_mask
												lowerCamelCase_							  =					do_normalize

					def    lowercase__      (	self					:     Tuple   ):
												return {
												    "feature_size": self.feature_size,
												    "padding_value": self.padding_value,
												    "sampling_rate": self.sampling_rate,
												    "return_attention_mask": self.return_attention_mask,
												    "do_normalize": self.do_normalize,
												}



					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     Optional[int]=False   ,					__UpperCamelCase					:     List[str]=False   ):
												def _flatten(__UpperCamelCase					:     List[Any]   ):
																			return list(itertools.chain(*__UpperCamelCase   )   )

												if equal_length:
																			lowerCamelCase_							  =					floats_list((self.batch_size, self.max_seq_length)   )
												else:
																			# make sure that inputs increase in size
																			lowerCamelCase_							  =					[
																			    _flatten(floats_list((x, self.feature_size)   )   )
																			    for x in range(self.min_seq_length   ,					self.max_seq_length   ,					self.seq_length_diff   )
																			]

												if numpify:
																			lowerCamelCase_							  =					[np.asarray(__UpperCamelCase   ) for x in speech_inputs]

												return speech_inputs





class       __A(				UpperCAmelCase     ,		unittest.TestCase       ):
					SCREAMING_SNAKE_CASE			    =    WavaVecaFeatureExtractor

					def    lowercase__      (	self					:     Tuple   ):
												lowerCamelCase_							  =					WavaVecaFeatureExtractionTester(self   )

					def    lowercase__      (	self					:     Optional[Any]   ,					__UpperCamelCase					:     List[str]   ):
												self.assertTrue(np.all(np.mean(__UpperCamelCase   ,					axis=0   ) < 1E-3   )   )
												self.assertTrue(np.all(np.abs(np.var(__UpperCamelCase   ,					axis=0   ) - 1   ) < 1E-3   )   )

					def    lowercase__      (	self					:     Any   ):
												# Tests that all call wrap to encode_plus and batch_encode_plus
												lowerCamelCase_							  =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
												# create three inputs of length 800, 1000, and 1200
												lowerCamelCase_							  =					[floats_list((1, x)   )[0] for x in range(8_0_0   ,					1_4_0_0   ,					2_0_0   )]
												lowerCamelCase_							  =					[np.asarray(__UpperCamelCase   ) for speech_input in speech_inputs]

												# Test not batched input
												lowerCamelCase_							  =					feat_extract(speech_inputs[0]   ,					return_tensors="""np"""   ).input_values
												lowerCamelCase_							  =					feat_extract(np_speech_inputs[0]   ,					return_tensors="""np"""   ).input_values
												self.assertTrue(np.allclose(__UpperCamelCase   ,					__UpperCamelCase   ,					atol=1E-3   )   )

												# Test batched
												lowerCamelCase_							  =					feat_extract(__UpperCamelCase   ,					return_tensors="""np"""   ).input_values
												lowerCamelCase_							  =					feat_extract(__UpperCamelCase   ,					return_tensors="""np"""   ).input_values
												for enc_seq_a, enc_seq_a in zip(__UpperCamelCase   ,					__UpperCamelCase   ):
																			self.assertTrue(np.allclose(__UpperCamelCase   ,					__UpperCamelCase   ,					atol=1E-3   )   )

												# Test 2-D numpy arrays are batched.
												lowerCamelCase_							  =					[floats_list((1, x)   )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
												lowerCamelCase_							  =					np.asarray(__UpperCamelCase   )
												lowerCamelCase_							  =					feat_extract(__UpperCamelCase   ,					return_tensors="""np"""   ).input_values
												lowerCamelCase_							  =					feat_extract(__UpperCamelCase   ,					return_tensors="""np"""   ).input_values
												for enc_seq_a, enc_seq_a in zip(__UpperCamelCase   ,					__UpperCamelCase   ):
																			self.assertTrue(np.allclose(__UpperCamelCase   ,					__UpperCamelCase   ,					atol=1E-3   )   )

					def    lowercase__      (	self					:     List[Any]   ):
												lowerCamelCase_							  =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
												lowerCamelCase_							  =					[floats_list((1, x)   )[0] for x in range(8_0_0   ,					1_4_0_0   ,					2_0_0   )]

												lowerCamelCase_							  =					["""longest""", """max_length""", """do_not_pad"""]
												lowerCamelCase_							  =					[None, 1_6_0_0, None]
												for max_length, padding in zip(__UpperCamelCase   ,					__UpperCamelCase   ):
																			lowerCamelCase_							  =					feat_extract(__UpperCamelCase   ,					padding=__UpperCamelCase   ,					max_length=__UpperCamelCase   ,					return_tensors="""np"""   )
																			lowerCamelCase_							  =					processed.input_values

																			self._check_zero_mean_unit_variance(input_values[0][:8_0_0]   )
																			self.assertTrue(input_values[0][8_0_0:].sum() < 1E-6   )
																			self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]   )
																			self.assertTrue(input_values[0][1_0_0_0:].sum() < 1E-6   )
																			self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]   )

					def    lowercase__      (	self					:     List[str]   ):
												lowerCamelCase_							  =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
												lowerCamelCase_							  =					range(8_0_0   ,					1_4_0_0   ,					2_0_0   )
												lowerCamelCase_							  =					[floats_list((1, x)   )[0] for x in lengths]

												lowerCamelCase_							  =					["""longest""", """max_length""", """do_not_pad"""]
												lowerCamelCase_							  =					[None, 1_6_0_0, None]

												for max_length, padding in zip(__UpperCamelCase   ,					__UpperCamelCase   ):
																			lowerCamelCase_							  =					feat_extract(__UpperCamelCase   ,					max_length=__UpperCamelCase   ,					padding=__UpperCamelCase   )
																			lowerCamelCase_							  =					processed.input_values

																			self._check_zero_mean_unit_variance(input_values[0][:8_0_0]   )
																			self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]   )
																			self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]   )

					def    lowercase__      (	self					:     Union[str, Any]   ):
												lowerCamelCase_							  =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
												lowerCamelCase_							  =					[floats_list((1, x)   )[0] for x in range(8_0_0   ,					1_4_0_0   ,					2_0_0   )]
												lowerCamelCase_							  =					feat_extract(
												    __UpperCamelCase   ,					truncation=__UpperCamelCase   ,					max_length=1_0_0_0   ,					padding="""max_length"""   ,					return_tensors="""np"""   )
												lowerCamelCase_							  =					processed.input_values

												self._check_zero_mean_unit_variance(input_values[0, :8_0_0]   )
												self._check_zero_mean_unit_variance(input_values[1]   )
												self._check_zero_mean_unit_variance(input_values[2]   )

					def    lowercase__      (	self					:     Tuple   ):
												lowerCamelCase_							  =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
												lowerCamelCase_							  =					[floats_list((1, x)   )[0] for x in range(8_0_0   ,					1_4_0_0   ,					2_0_0   )]
												lowerCamelCase_							  =					feat_extract(
												    __UpperCamelCase   ,					truncation=__UpperCamelCase   ,					max_length=1_0_0_0   ,					padding="""longest"""   ,					return_tensors="""np"""   )
												lowerCamelCase_							  =					processed.input_values

												self._check_zero_mean_unit_variance(input_values[0, :8_0_0]   )
												self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]   )
												self._check_zero_mean_unit_variance(input_values[2]   )

												# make sure that if max_length < longest -> then pad to max_length
												self.assertTrue(input_values.shape == (3, 1_0_0_0)   )

												lowerCamelCase_							  =					[floats_list((1, x)   )[0] for x in range(8_0_0   ,					1_4_0_0   ,					2_0_0   )]
												lowerCamelCase_							  =					feat_extract(
												    __UpperCamelCase   ,					truncation=__UpperCamelCase   ,					max_length=2_0_0_0   ,					padding="""longest"""   ,					return_tensors="""np"""   )
												lowerCamelCase_							  =					processed.input_values

												self._check_zero_mean_unit_variance(input_values[0, :8_0_0]   )
												self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]   )
												self._check_zero_mean_unit_variance(input_values[2]   )

												# make sure that if max_length > longest -> then pad to longest
												self.assertTrue(input_values.shape == (3, 1_2_0_0)   )

					@require_torch
					def    lowercase__      (	self					:     Union[str, Any]   ):
												import torch

												lowerCamelCase_							  =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
												lowerCamelCase_							  =					np.random.rand(1_0_0   ).astype(np.floataa   )
												lowerCamelCase_							  =					np_speech_inputs.tolist()

												for inputs in [py_speech_inputs, np_speech_inputs]:
																			lowerCamelCase_							  =					feature_extractor.pad([{"""input_values""": inputs}]   ,					return_tensors="""np"""   )
																			self.assertTrue(np_processed.input_values.dtype == np.floataa   )
																			lowerCamelCase_							  =					feature_extractor.pad([{"""input_values""": inputs}]   ,					return_tensors="""pt"""   )
																			self.assertTrue(pt_processed.input_values.dtype == torch.floataa   )



					@slow
					@require_torch
					def    lowercase__      (	self					:     Any   ):
												# this test makes sure that models that are using
												# group norm don't have their feature extractor return the
												# attention_mask
												for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
																			lowerCamelCase_							  =					WavaVecaConfig.from_pretrained(__UpperCamelCase   )
																			lowerCamelCase_							  =					WavaVecaFeatureExtractor.from_pretrained(__UpperCamelCase   )

																			# only "layer" feature extraction norm should make use of
																			# attention_mask
																			self.assertEqual(feat_extract.return_attention_mask   ,					config.feat_extract_norm == """layer"""   )







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       __A(				unittest.TestCase       ):

					def    lowercase__      (	self					:     Union[str, Any]   ):
												lowerCamelCase_							  =					tempfile.mkdtemp()

												# fmt: off
												lowerCamelCase_							  =					["""""", """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
												lowerCamelCase_							  =					dict(zip(__UpperCamelCase   ,					range(len(__UpperCamelCase   )   )   )   )
												lowerCamelCase_							  =					["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
												lowerCamelCase_							  =					{"""unk_token""": """<unk>"""}

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

												lowerCamelCase_							  =					{
												    """do_resize""": True,
												    """size""": 2_0,
												    """do_center_crop""": True,
												    """crop_size""": 1_8,
												    """do_normalize""": True,
												    """image_mean""": [0.48145466, 0.4578275, 0.40821073],
												    """image_std""": [0.26862954, 0.26130258, 0.27577711],
												}
												lowerCamelCase_							  =					os.path.join(self.tmpdirname   ,					__UpperCamelCase   )
												with open(self.image_processor_file   ,					"""w"""   ,					encoding="""utf-8"""   ) as fp:
																			json.dump(__UpperCamelCase   ,					__UpperCamelCase   )

					def    lowercase__      (	self					:     str   ,					**__UpperCamelCase					:     Optional[Any]   ):
												return CLIPTokenizer.from_pretrained(self.tmpdirname   ,					pad_token="""!"""   ,					**__UpperCamelCase   )

					def    lowercase__      (	self					:     Any   ,					**__UpperCamelCase					:     List[Any]   ):
												return CLIPTokenizerFast.from_pretrained(self.tmpdirname   ,					pad_token="""!"""   ,					**__UpperCamelCase   )

					def    lowercase__      (	self					:     List[Any]   ,					**__UpperCamelCase					:     Optional[int]   ):
												return OwlViTImageProcessor.from_pretrained(self.tmpdirname   ,					**__UpperCamelCase   )

					def    lowercase__      (	self					:     Union[str, Any]   ):
												shutil.rmtree(self.tmpdirname   )

					def    lowercase__      (	self					:     str   ):
												lowerCamelCase_							  =					[np.random.randint(2_5_5   ,					size=(3, 3_0, 4_0_0)   ,					dtype=np.uinta   )]

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

												return image_inputs

					def    lowercase__      (	self					:     Optional[int]   ):
												lowerCamelCase_							  =					self.get_tokenizer()
												lowerCamelCase_							  =					self.get_rust_tokenizer()
												lowerCamelCase_							  =					self.get_image_processor()

												lowerCamelCase_							  =					OwlViTProcessor(tokenizer=__UpperCamelCase   ,					image_processor=__UpperCamelCase   )
												processor_slow.save_pretrained(self.tmpdirname   )
												lowerCamelCase_							  =					OwlViTProcessor.from_pretrained(self.tmpdirname   ,					use_fast=__UpperCamelCase   )

												lowerCamelCase_							  =					OwlViTProcessor(tokenizer=__UpperCamelCase   ,					image_processor=__UpperCamelCase   )
												processor_fast.save_pretrained(self.tmpdirname   )
												lowerCamelCase_							  =					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   ,					__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    lowercase__      (	self					:     Optional[int]   ):
												lowerCamelCase_							  =					OwlViTProcessor(tokenizer=self.get_tokenizer()   ,					image_processor=self.get_image_processor()   )
												processor.save_pretrained(self.tmpdirname   )

												lowerCamelCase_							  =					self.get_tokenizer(bos_token="""(BOS)"""   ,					eos_token="""(EOS)"""   )
												lowerCamelCase_							  =					self.get_image_processor(do_normalize=__UpperCamelCase   )

												lowerCamelCase_							  =					OwlViTProcessor.from_pretrained(
												    self.tmpdirname   ,					bos_token="""(BOS)"""   ,					eos_token="""(EOS)"""   ,					do_normalize=__UpperCamelCase   )

												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    lowercase__      (	self					:     Any   ):
												lowerCamelCase_							  =					self.get_image_processor()
												lowerCamelCase_							  =					self.get_tokenizer()

												lowerCamelCase_							  =					OwlViTProcessor(tokenizer=__UpperCamelCase   ,					image_processor=__UpperCamelCase   )

												lowerCamelCase_							  =					self.prepare_image_inputs()

												lowerCamelCase_							  =					image_processor(__UpperCamelCase   ,					return_tensors="""np"""   )
												lowerCamelCase_							  =					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    lowercase__      (	self					:     Union[str, Any]   ):
												lowerCamelCase_							  =					self.get_image_processor()
												lowerCamelCase_							  =					self.get_tokenizer()

												lowerCamelCase_							  =					OwlViTProcessor(tokenizer=__UpperCamelCase   ,					image_processor=__UpperCamelCase   )

												lowerCamelCase_							  =					"""lower newer"""

												lowerCamelCase_							  =					processor(text=__UpperCamelCase   ,					return_tensors="""np"""   )

												lowerCamelCase_							  =					tokenizer(__UpperCamelCase   ,					return_tensors="""np"""   )

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

					def    lowercase__      (	self					:     List[str]   ):
												lowerCamelCase_							  =					self.get_image_processor()
												lowerCamelCase_							  =					self.get_tokenizer()

												lowerCamelCase_							  =					OwlViTProcessor(tokenizer=__UpperCamelCase   ,					image_processor=__UpperCamelCase   )

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

												lowerCamelCase_							  =					processor(text=__UpperCamelCase   ,					images=__UpperCamelCase   )

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

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

					def    lowercase__      (	self					:     List[Any]   ):
												lowerCamelCase_							  =					"""google/owlvit-base-patch32"""
												lowerCamelCase_							  =					OwlViTProcessor.from_pretrained(__UpperCamelCase   )

												lowerCamelCase_							  =					["""cat""", """nasa badge"""]
												lowerCamelCase_							  =					processor(text=__UpperCamelCase   )

												lowerCamelCase_							  =					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(__UpperCamelCase   ):
																			processor()

					def    lowercase__      (	self					:     Optional[int]   ):
												lowerCamelCase_							  =					"""google/owlvit-base-patch32"""
												lowerCamelCase_							  =					OwlViTProcessor.from_pretrained(__UpperCamelCase   )

												lowerCamelCase_							  =					[["""cat""", """nasa badge"""], ["""person"""]]
												lowerCamelCase_							  =					processor(text=__UpperCamelCase   )

												lowerCamelCase_							  =					1_6
												lowerCamelCase_							  =					len(__UpperCamelCase   )
												lowerCamelCase_							  =					max([len(__UpperCamelCase   ) 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(__UpperCamelCase   ):
																			processor()

					def    lowercase__      (	self					:     str   ):
												lowerCamelCase_							  =					"""google/owlvit-base-patch32"""
												lowerCamelCase_							  =					OwlViTProcessor.from_pretrained(__UpperCamelCase   )

												lowerCamelCase_							  =					["""cat""", """nasa badge"""]
												lowerCamelCase_							  =					processor(text=__UpperCamelCase   )

												lowerCamelCase_							  =					1_6
												lowerCamelCase_							  =					inputs["""input_ids"""]
												lowerCamelCase_							  =					[
												    [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    lowercase__      (	self					:     int   ):
												lowerCamelCase_							  =					self.get_image_processor()
												lowerCamelCase_							  =					self.get_tokenizer()

												lowerCamelCase_							  =					OwlViTProcessor(tokenizer=__UpperCamelCase   ,					image_processor=__UpperCamelCase   )

												lowerCamelCase_							  =					self.prepare_image_inputs()
												lowerCamelCase_							  =					self.prepare_image_inputs()

												lowerCamelCase_							  =					processor(images=__UpperCamelCase   ,					query_images=__UpperCamelCase   )

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

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



					def    lowercase__      (	self					:     Dict   ):
												lowerCamelCase_							  =					self.get_image_processor()
												lowerCamelCase_							  =					self.get_tokenizer()

												lowerCamelCase_							  =					OwlViTProcessor(tokenizer=__UpperCamelCase   ,					image_processor=__UpperCamelCase   )

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

												lowerCamelCase_							  =					processor.batch_decode(__UpperCamelCase   )
												lowerCamelCase_							  =					tokenizer.batch_decode(__UpperCamelCase   )

												self.assertListEqual(__UpperCamelCase   ,					__UpperCamelCase   )





