



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_:List[str]					=						logging.get_logger(__name__)




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  List[Any]:



    """simple docstring"""






    A			:	Tuple										=       DPTConfig()

    if "large" in checkpoint_url:
        A			:	Any										=       1024
        A			:	Optional[int]										=       4096
        A			:	Dict										=       24
        A			:	Union[str, Any]										=       16
        A			:	Union[str, Any]										=       [5, 11, 17, 23]
        A			:	List[Any]										=       [256, 512, 1024, 1024]
        A			:	Any										=       (1, 384, 384)

    if "ade" in checkpoint_url:
        A			:	Tuple										=       True

        A			:	str										=       150
        A			:	List[str]										=       """huggingface/label-files"""
        A			:	int										=       """ade20k-id2label.json"""
        A			:	Union[str, Any]										=       json.load(open(cached_download(hf_hub_url(snake_case_				,					snake_case_				,					repo_type="""dataset"""		)		)				,					"""r"""		)		)
        A			:	Union[str, Any]										=       {int(snake_case_		): v for k, v in idalabel.items()}
        A			:	Tuple										=       idalabel
        A			:	int										=       {v: k for k, v in idalabel.items()}
        A			:	Any										=       [1, 150, 480, 480]

    return config, expected_shape




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Dict:



    """simple docstring"""






    A			:	str										=       ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
    for k in ignore_keys:
        state_dict.pop(snake_case_				,					snake_case_		)




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Optional[int]:



    """simple docstring"""






    if (
        "pretrained.model" in name
        and "cls_token" not in name
        and "pos_embed" not in name
        and "patch_embed" not in name
    ):
        A			:	Dict										=       name.replace("""pretrained.model"""				,					"""dpt.encoder"""		)
    if "pretrained.model" in name:
        A			:	Union[str, Any]										=       name.replace("""pretrained.model"""				,					"""dpt.embeddings"""		)
    if "patch_embed" in name:
        A			:	str										=       name.replace("""patch_embed"""				,					"""patch_embeddings"""		)
    if "pos_embed" in name:
        A			:	str										=       name.replace("""pos_embed"""				,					"""position_embeddings"""		)
    if "attn.proj" in name:
        A			:	Optional[int]										=       name.replace("""attn.proj"""				,					"""attention.output.dense"""		)
    if "proj" in name and "project" not in name:
        A			:	str										=       name.replace("""proj"""				,					"""projection"""		)
    if "blocks" in name:
        A			:	List[str]										=       name.replace("""blocks"""				,					"""layer"""		)
    if "mlp.fc1" in name:
        A			:	Optional[Any]										=       name.replace("""mlp.fc1"""				,					"""intermediate.dense"""		)
    if "mlp.fc2" in name:
        A			:	Dict										=       name.replace("""mlp.fc2"""				,					"""output.dense"""		)
    if "norm1" in name:
        A			:	Optional[int]										=       name.replace("""norm1"""				,					"""layernorm_before"""		)
    if "norm2" in name:
        A			:	int										=       name.replace("""norm2"""				,					"""layernorm_after"""		)
    if "scratch.output_conv" in name:
        A			:	Union[str, Any]										=       name.replace("""scratch.output_conv"""				,					"""head"""		)
    if "scratch" in name:
        A			:	Dict										=       name.replace("""scratch"""				,					"""neck"""		)
    if "layer1_rn" in name:
        A			:	Any										=       name.replace("""layer1_rn"""				,					"""convs.0"""		)
    if "layer2_rn" in name:
        A			:	Any										=       name.replace("""layer2_rn"""				,					"""convs.1"""		)
    if "layer3_rn" in name:
        A			:	Any										=       name.replace("""layer3_rn"""				,					"""convs.2"""		)
    if "layer4_rn" in name:
        A			:	List[str]										=       name.replace("""layer4_rn"""				,					"""convs.3"""		)
    if "refinenet" in name:
        A			:	Optional[Any]										=       int(name[len("""neck.refinenet"""		) : len("""neck.refinenet"""		) + 1]		)
        # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
        A			:	Optional[Any]										=       name.replace(f'''refinenet{layer_idx}'''				,					f'''fusion_stage.layers.{abs(layer_idx-4		)}'''		)
    if "out_conv" in name:
        A			:	List[str]										=       name.replace("""out_conv"""				,					"""projection"""		)
    if "resConfUnit1" in name:
        A			:	str										=       name.replace("""resConfUnit1"""				,					"""residual_layer1"""		)
    if "resConfUnit2" in name:
        A			:	int										=       name.replace("""resConfUnit2"""				,					"""residual_layer2"""		)
    if "conv1" in name:
        A			:	int										=       name.replace("""conv1"""				,					"""convolution1"""		)
    if "conv2" in name:
        A			:	Union[str, Any]										=       name.replace("""conv2"""				,					"""convolution2"""		)
    # readout blocks
    if "pretrained.act_postprocess1.0.project.0" in name:
        A			:	List[str]										=       name.replace("""pretrained.act_postprocess1.0.project.0"""				,					"""neck.reassemble_stage.readout_projects.0.0"""		)
    if "pretrained.act_postprocess2.0.project.0" in name:
        A			:	int										=       name.replace("""pretrained.act_postprocess2.0.project.0"""				,					"""neck.reassemble_stage.readout_projects.1.0"""		)
    if "pretrained.act_postprocess3.0.project.0" in name:
        A			:	Tuple										=       name.replace("""pretrained.act_postprocess3.0.project.0"""				,					"""neck.reassemble_stage.readout_projects.2.0"""		)
    if "pretrained.act_postprocess4.0.project.0" in name:
        A			:	str										=       name.replace("""pretrained.act_postprocess4.0.project.0"""				,					"""neck.reassemble_stage.readout_projects.3.0"""		)
    # resize blocks
    if "pretrained.act_postprocess1.3" in name:
        A			:	str										=       name.replace("""pretrained.act_postprocess1.3"""				,					"""neck.reassemble_stage.layers.0.projection"""		)
    if "pretrained.act_postprocess1.4" in name:
        A			:	List[str]										=       name.replace("""pretrained.act_postprocess1.4"""				,					"""neck.reassemble_stage.layers.0.resize"""		)
    if "pretrained.act_postprocess2.3" in name:
        A			:	List[str]										=       name.replace("""pretrained.act_postprocess2.3"""				,					"""neck.reassemble_stage.layers.1.projection"""		)
    if "pretrained.act_postprocess2.4" in name:
        A			:	int										=       name.replace("""pretrained.act_postprocess2.4"""				,					"""neck.reassemble_stage.layers.1.resize"""		)
    if "pretrained.act_postprocess3.3" in name:
        A			:	Union[str, Any]										=       name.replace("""pretrained.act_postprocess3.3"""				,					"""neck.reassemble_stage.layers.2.projection"""		)
    if "pretrained.act_postprocess4.3" in name:
        A			:	int										=       name.replace("""pretrained.act_postprocess4.3"""				,					"""neck.reassemble_stage.layers.3.projection"""		)
    if "pretrained.act_postprocess4.4" in name:
        A			:	str										=       name.replace("""pretrained.act_postprocess4.4"""				,					"""neck.reassemble_stage.layers.3.resize"""		)
    if "pretrained" in name:
        A			:	Any										=       name.replace("""pretrained"""				,					"""dpt"""		)
    if "bn" in name:
        A			:	Optional[Any]										=       name.replace("""bn"""				,					"""batch_norm"""		)
    if "head" in name:
        A			:	Optional[Any]										=       name.replace("""head"""				,					"""head.head"""		)
    if "encoder.norm" in name:
        A			:	str										=       name.replace("""encoder.norm"""				,					"""layernorm"""		)
    if "auxlayer" in name:
        A			:	List[Any]										=       name.replace("""auxlayer"""				,					"""auxiliary_head.head"""		)

    return name




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  List[str]:



    """simple docstring"""






    for i in range(config.num_hidden_layers		):
        # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
        A			:	List[Any]										=       state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight'''		)
        A			:	Any										=       state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.bias'''		)
        # next, add query, keys and values (in that order) to the state dict
        A			:	Tuple										=       in_proj_weight[: config.hidden_size, :]
        A			:	int										=       in_proj_bias[: config.hidden_size]
        A			:	int										=       in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        A			:	Optional[Any]										=       in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        A			:	Tuple										=       in_proj_weight[
            -config.hidden_size :, :
        ]
        A			:	Tuple										=       in_proj_bias[-config.hidden_size :]




def      __UpperCamelCase     (  )	->  Dict:



    """simple docstring"""






    A			:	List[str]										=       """http://images.cocodataset.org/val2017/000000039769.jpg"""
    A			:	List[Any]										=       Image.open(requests.get(snake_case_				,					stream=snake_case_		).raw		)
    return im




@torch.no_grad()
def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)	->  Optional[int]:



    """simple docstring"""






    A					,							A			:	Tuple										=       get_dpt_config(snake_case_		)
    # load original state_dict from URL
    A			:	Union[str, Any]										=       torch.hub.load_state_dict_from_url(snake_case_				,					map_location="""cpu"""		)
    # remove certain keys
    remove_ignore_keys_(snake_case_		)
    # rename keys
    for key in state_dict.copy().keys():
        A			:	str										=       state_dict.pop(snake_case_		)
        A			:	Optional[int]										=       val
    # read in qkv matrices
    read_in_q_k_v(snake_case_				,					snake_case_		)

    # load HuggingFace model
    A			:	Tuple										=       DPTForSemanticSegmentation(snake_case_		) if """ade""" in checkpoint_url else DPTForDepthEstimation(snake_case_		)
    model.load_state_dict(snake_case_		)
    model.eval()

    # Check outputs on an image
    A			:	Union[str, Any]										=       480 if """ade""" in checkpoint_url else 384
    A			:	List[str]										=       DPTImageProcessor(size=snake_case_		)

    A			:	Optional[Any]										=       prepare_img()
    A			:	str										=       image_processor(snake_case_				,					return_tensors="""pt"""		)

    # forward pass
    A			:	Optional[Any]										=       model(**snake_case_		).logits if """ade""" in checkpoint_url else model(**snake_case_		).predicted_depth

    # Assert logits
    A			:	List[str]										=       torch.tensor([[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]]		)
    if "ade" in checkpoint_url:
        A			:	Tuple										=       torch.tensor([[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]]		)
    assert outputs.shape == torch.Size(snake_case_		)
    assert (
        torch.allclose(outputs[0, 0, :3, :3]				,					snake_case_				,					atol=1e-4		)
        if "ade" in checkpoint_url
        else torch.allclose(outputs[0, :3, :3]				,					snake_case_		)
    )

    Path(snake_case_		).mkdir(exist_ok=snake_case_		)
    print(f'''Saving model to {pytorch_dump_folder_path}'''		)
    model.save_pretrained(snake_case_		)
    print(f'''Saving image processor to {pytorch_dump_folder_path}'''		)
    image_processor.save_pretrained(snake_case_		)

    if push_to_hub:
        print("""Pushing model to hub..."""		)
        model.push_to_hub(
            repo_path_or_name=Path(snake_case_				,					snake_case_		)				,					organization="""nielsr"""				,					commit_message="""Add model"""				,					use_temp_dir=snake_case_				,					)
        image_processor.push_to_hub(
            repo_path_or_name=Path(snake_case_				,					snake_case_		)				,					organization="""nielsr"""				,					commit_message="""Add image processor"""				,					use_temp_dir=snake_case_				,					)


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_:List[str]					=						argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--checkpoint_url""",
         default="""https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt""",
         type=str,
         help="""URL of the original DPT checkpoint you\'d like to convert.""",
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""",
         default=None,
         type=str,
         required=True,
         help="""Path to the output PyTorch model directory.""",
     )
     parser.add_argument(
         """--push_to_hub""",
         action="""store_true""",
     )
     parser.add_argument(
         """--model_name""",
         default="""dpt-large""",
         type=str,
         help="""Name of the model, in case you\'re pushing to the hub.""",
     )

     SCREAMING_SNAKE_CASE_:Union[str, Any]					=						parser.parse_args()
     convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)




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__		)


