



from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder






@dataclass
class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			"DiagonalGaussianDistribution"






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__			,						SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			Dict =				True






    @register_to_config
    def __init__(	self,		lowerCamelCase__ = 3,		lowerCamelCase__ = 3,		lowerCamelCase__ = ("DownEncoderBlock2D",),		lowerCamelCase__ = ("UpDecoderBlock2D",),		lowerCamelCase__ = (64,),		lowerCamelCase__ = 1,		lowerCamelCase__ = "silu",		lowerCamelCase__ = 4,		lowerCamelCase__ = 32,		lowerCamelCase__ = 32,		lowerCamelCase__ = 0.1_8215,		):
        super().__init__()

        # pass init params to Encoder
        A			:	Dict										=       Encoder(
            in_channels=lowerCamelCase__,		out_channels=lowerCamelCase__,		down_block_types=lowerCamelCase__,		block_out_channels=lowerCamelCase__,		layers_per_block=lowerCamelCase__,		act_fn=lowerCamelCase__,		norm_num_groups=lowerCamelCase__,		double_z=lowerCamelCase__,		)

        # pass init params to Decoder
        A			:	Any										=       Decoder(
            in_channels=lowerCamelCase__,		out_channels=lowerCamelCase__,		up_block_types=lowerCamelCase__,		block_out_channels=lowerCamelCase__,		layers_per_block=lowerCamelCase__,		norm_num_groups=lowerCamelCase__,		act_fn=lowerCamelCase__,		)

        A			:	str										=       nn.Convad(2 * latent_channels,		2 * latent_channels,		1		)
        A			:	Tuple										=       nn.Convad(lowerCamelCase__,		lowerCamelCase__,		1		)

        A			:	Optional[Any]										=       False
        A			:	Dict										=       False

        # only relevant if vae tiling is enabled
        A			:	List[str]										=       self.config.sample_size
        A			:	Union[str, Any]										=       (
            self.config.sample_size[0]
            if isinstance(self.config.sample_size,		(list, tuple)		)
            else self.config.sample_size
        )
        A			:	int										=       int(sample_size / (2 ** (len(self.config.block_out_channels		) - 1))		)
        A			:	str										=       0.25






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__=False		):
        if isinstance(lowerCamelCase__,		(Encoder, Decoder)		):
            A			:	Union[str, Any]										=       value






    def       _lowerCAmelCase (	self,		lowerCamelCase__ = True		):
        A			:	Optional[Any]										=       use_tiling






    def       _lowerCAmelCase (	self		):
        self.enable_tiling(lowerCamelCase__		)






    def       _lowerCAmelCase (	self		):
        A			:	Tuple										=       True






    def       _lowerCAmelCase (	self		):
        A			:	List[Any]										=       False






    @property
    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
    def       _lowerCAmelCase (	self		):
        A			:	Optional[Any]										=       {}

        def fn_recursive_add_processors(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
            if hasattr(lowerCamelCase__,		"""set_processor"""		):
                A			:	Optional[int]										=       module.processor

            for sub_name, child in module.named_children():
                fn_recursive_add_processors(f'''{name}.{sub_name}''',		lowerCamelCase__,		lowerCamelCase__		)

            return processors

        for name, module in self.named_children():
            fn_recursive_add_processors(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)

        return processors






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        A			:	Union[str, Any]										=       len(self.attn_processors.keys()		)

        if isinstance(lowerCamelCase__,		lowerCamelCase__		) and len(lowerCamelCase__		) != count:
            raise ValueError(
                f'''A dict of processors was passed, but the number of processors {len(lowerCamelCase__		)} does not match the'''
                f''' number of attention layers: {count}. Please make sure to pass {count} processor classes.'''		)

        def fn_recursive_attn_processor(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
            if hasattr(lowerCamelCase__,		"""set_processor"""		):
                if not isinstance(lowerCamelCase__,		lowerCamelCase__		):
                    module.set_processor(lowerCamelCase__		)
                else:
                    module.set_processor(processor.pop(f'''{name}.processor'''		)		)

            for sub_name, child in module.named_children():
                fn_recursive_attn_processor(f'''{name}.{sub_name}''',		lowerCamelCase__,		lowerCamelCase__		)

        for name, module in self.named_children():
            fn_recursive_attn_processor(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)






    def       _lowerCAmelCase (	self		):
        self.set_attn_processor(AttnProcessor()		)






    @apply_forward_hook
    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__ = True		):
        if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
            return self.tiled_encode(lowerCamelCase__,		return_dict=lowerCamelCase__		)

        if self.use_slicing and x.shape[0] > 1:
            A			:	str										=       [self.encoder(lowerCamelCase__		) for x_slice in x.split(1		)]
            A			:	int										=       torch.cat(lowerCamelCase__		)
        else:
            A			:	List[str]										=       self.encoder(lowerCamelCase__		)

        A			:	str										=       self.quant_conv(lowerCamelCase__		)
        A			:	List[str]										=       DiagonalGaussianDistribution(lowerCamelCase__		)

        if not return_dict:
            return (posterior,)

        return AutoencoderKLOutput(latent_dist=lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__ = True		):
        if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
            return self.tiled_decode(lowerCamelCase__,		return_dict=lowerCamelCase__		)

        A			:	str										=       self.post_quant_conv(lowerCamelCase__		)
        A			:	str										=       self.decoder(lowerCamelCase__		)

        if not return_dict:
            return (dec,)

        return DecoderOutput(sample=lowerCamelCase__		)






    @apply_forward_hook
    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__ = True		):
        if self.use_slicing and z.shape[0] > 1:
            A			:	int										=       [self._decode(lowerCamelCase__		).sample for z_slice in z.split(1		)]
            A			:	Any										=       torch.cat(lowerCamelCase__		)
        else:
            A			:	Union[str, Any]										=       self._decode(lowerCamelCase__		).sample

        if not return_dict:
            return (decoded,)

        return DecoderOutput(sample=lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A			:	Tuple										=       min(a.shape[2],		b.shape[2],		lowerCamelCase__		)
        for y in range(lowerCamelCase__		):
            A			:	int										=       a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
        return b






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A			:	Optional[int]										=       min(a.shape[3],		b.shape[3],		lowerCamelCase__		)
        for x in range(lowerCamelCase__		):
            A			:	Optional[Any]										=       a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
        return b






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__ = True		):
        A			:	List[str]										=       int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)		)
        A			:	int										=       int(self.tile_latent_min_size * self.tile_overlap_factor		)
        A			:	List[str]										=       self.tile_latent_min_size - blend_extent

        # Split the image into 512x512 tiles and encode them separately.
        A			:	Union[str, Any]										=       []
        for i in range(0,		x.shape[2],		lowerCamelCase__		):
            A			:	Optional[int]										=       []
            for j in range(0,		x.shape[3],		lowerCamelCase__		):
                A			:	Tuple										=       x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
                A			:	Dict										=       self.encoder(lowerCamelCase__		)
                A			:	Optional[Any]										=       self.quant_conv(lowerCamelCase__		)
                row.append(lowerCamelCase__		)
            rows.append(lowerCamelCase__		)
        A			:	Any										=       []
        for i, row in enumerate(lowerCamelCase__		):
            A			:	Dict										=       []
            for j, tile in enumerate(lowerCamelCase__		):
                # blend the above tile and the left tile
                # to the current tile and add the current tile to the result row
                if i > 0:
                    A			:	Dict										=       self.blend_v(rows[i - 1][j],		lowerCamelCase__,		lowerCamelCase__		)
                if j > 0:
                    A			:	List[Any]										=       self.blend_h(row[j - 1],		lowerCamelCase__,		lowerCamelCase__		)
                result_row.append(tile[:, :, :row_limit, :row_limit]		)
            result_rows.append(torch.cat(lowerCamelCase__,		dim=3		)		)

        A			:	Any										=       torch.cat(lowerCamelCase__,		dim=2		)
        A			:	Optional[int]										=       DiagonalGaussianDistribution(lowerCamelCase__		)

        if not return_dict:
            return (posterior,)

        return AutoencoderKLOutput(latent_dist=lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__ = True		):
        A			:	List[Any]										=       int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)		)
        A			:	Any										=       int(self.tile_sample_min_size * self.tile_overlap_factor		)
        A			:	int										=       self.tile_sample_min_size - blend_extent

        # Split z into overlapping 64x64 tiles and decode them separately.
        # The tiles have an overlap to avoid seams between tiles.
        A			:	Any										=       []
        for i in range(0,		z.shape[2],		lowerCamelCase__		):
            A			:	Union[str, Any]										=       []
            for j in range(0,		z.shape[3],		lowerCamelCase__		):
                A			:	List[str]										=       z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
                A			:	Union[str, Any]										=       self.post_quant_conv(lowerCamelCase__		)
                A			:	Union[str, Any]										=       self.decoder(lowerCamelCase__		)
                row.append(lowerCamelCase__		)
            rows.append(lowerCamelCase__		)
        A			:	Tuple										=       []
        for i, row in enumerate(lowerCamelCase__		):
            A			:	Tuple										=       []
            for j, tile in enumerate(lowerCamelCase__		):
                # blend the above tile and the left tile
                # to the current tile and add the current tile to the result row
                if i > 0:
                    A			:	Any										=       self.blend_v(rows[i - 1][j],		lowerCamelCase__,		lowerCamelCase__		)
                if j > 0:
                    A			:	List[str]										=       self.blend_h(row[j - 1],		lowerCamelCase__,		lowerCamelCase__		)
                result_row.append(tile[:, :, :row_limit, :row_limit]		)
            result_rows.append(torch.cat(lowerCamelCase__,		dim=3		)		)

        A			:	str										=       torch.cat(lowerCamelCase__,		dim=2		)
        if not return_dict:
            return (dec,)

        return DecoderOutput(sample=lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__ = False,		lowerCamelCase__ = True,		lowerCamelCase__ = None,		):
        A			:	List[str]										=       sample
        A			:	Optional[int]										=       self.encode(lowerCamelCase__		).latent_dist
        if sample_posterior:
            A			:	Optional[int]										=       posterior.sample(generator=lowerCamelCase__		)
        else:
            A			:	int										=       posterior.mode()
        A			:	Tuple										=       self.decode(lowerCamelCase__		).sample

        if not return_dict:
            return (dec,)

        return DecoderOutput(sample=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__		)


