

import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE		:Optional[Any]						=     logging.get_logger(__name__)




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_=False			)->						List[str]:



    """simple docstring"""






    UpperCamelCase_			  =						OrderedDict()
    for key, value in state_dict.items():
        if encoder_only and not key.startswith("head"			):
            UpperCamelCase_			  =						"segformer.encoder." + key
        if key.startswith("backbone"			):
            UpperCamelCase_			  =						key.replace("backbone"			,						"segformer.encoder"			)
        if "patch_embed" in key:
            # replace for example patch_embed1 by patch_embeddings.0
            UpperCamelCase_			  =						key[key.find("patch_embed"			) + len("patch_embed"			)]
            UpperCamelCase_			  =						key.replace(f"patch_embed{idx}"			,						f"patch_embeddings.{int(SCREAMING_SNAKE_CASE_			)-1}"			)
        if "norm" in key:
            UpperCamelCase_			  =						key.replace("norm"			,						"layer_norm"			)
        if "segformer.encoder.layer_norm" in key:
            # replace for example layer_norm1 by layer_norm.0
            UpperCamelCase_			  =						key[key.find("segformer.encoder.layer_norm"			) + len("segformer.encoder.layer_norm"			)]
            UpperCamelCase_			  =						key.replace(f"layer_norm{idx}"			,						f"layer_norm.{int(SCREAMING_SNAKE_CASE_			)-1}"			)
        if "layer_norm1" in key:
            UpperCamelCase_			  =						key.replace("layer_norm1"			,						"layer_norm_1"			)
        if "layer_norm2" in key:
            UpperCamelCase_			  =						key.replace("layer_norm2"			,						"layer_norm_2"			)
        if "block" in key:
            # replace for example block1 by block.0
            UpperCamelCase_			  =						key[key.find("block"			) + len("block"			)]
            UpperCamelCase_			  =						key.replace(f"block{idx}"			,						f"block.{int(SCREAMING_SNAKE_CASE_			)-1}"			)
        if "attn.q" in key:
            UpperCamelCase_			  =						key.replace("attn.q"			,						"attention.self.query"			)
        if "attn.proj" in key:
            UpperCamelCase_			  =						key.replace("attn.proj"			,						"attention.output.dense"			)
        if "attn" in key:
            UpperCamelCase_			  =						key.replace("attn"			,						"attention.self"			)
        if "fc1" in key:
            UpperCamelCase_			  =						key.replace("fc1"			,						"dense1"			)
        if "fc2" in key:
            UpperCamelCase_			  =						key.replace("fc2"			,						"dense2"			)
        if "linear_pred" in key:
            UpperCamelCase_			  =						key.replace("linear_pred"			,						"classifier"			)
        if "linear_fuse" in key:
            UpperCamelCase_			  =						key.replace("linear_fuse.conv"			,						"linear_fuse"			)
            UpperCamelCase_			  =						key.replace("linear_fuse.bn"			,						"batch_norm"			)
        if "linear_c" in key:
            # replace for example linear_c4 by linear_c.3
            UpperCamelCase_			  =						key[key.find("linear_c"			) + len("linear_c"			)]
            UpperCamelCase_			  =						key.replace(f"linear_c{idx}"			,						f"linear_c.{int(SCREAMING_SNAKE_CASE_			)-1}"			)
        if key.startswith("head"			):
            UpperCamelCase_			  =						key.replace("head"			,						"classifier"			)
        UpperCamelCase_			  =						value

    return new_state_dict




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Optional[int]:



    """simple docstring"""






    for i in range(config.num_encoder_blocks			):
        for j in range(config.depths[i]			):
            # read in weights + bias of keys and values (which is a single matrix in the original implementation)
            UpperCamelCase_			  =						state_dict.pop(f"segformer.encoder.block.{i}.{j}.attention.self.kv.weight"			)
            UpperCamelCase_			  =						state_dict.pop(f"segformer.encoder.block.{i}.{j}.attention.self.kv.bias"			)
            # next, add keys and values (in that order) to the state dict
            UpperCamelCase_			  =						kv_weight[
                : config.hidden_sizes[i], :
            ]
            UpperCamelCase_			  =						kv_bias[: config.hidden_sizes[i]]
            UpperCamelCase_			  =						kv_weight[
                config.hidden_sizes[i] :, :
            ]
            UpperCamelCase_			  =						kv_bias[
                config.hidden_sizes[i] :
            ]




def 			lowerCAmelCase(      )->						Tuple:



    """simple docstring"""






    UpperCamelCase_			  =						"http://images.cocodataset.org/val2017/000000039769.jpg"
    UpperCamelCase_			  =						Image.open(requests.get(SCREAMING_SNAKE_CASE_			,						stream=SCREAMING_SNAKE_CASE_			).raw			)

    return image




@torch.no_grad()
def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Optional[Any]:



    """simple docstring"""






    UpperCamelCase_			  =						SegformerConfig()
    UpperCamelCase_			  =						False

    # set attributes based on model_name
    UpperCamelCase_			  =						"huggingface/label-files"
    if "segformer" in model_name:
        UpperCamelCase_			  =						model_name[len("segformer."			) : len("segformer."			) + 2]
        if "ade" in model_name:
            UpperCamelCase_			  =						1_5_0
            UpperCamelCase_			  =						"ade20k-id2label.json"
            UpperCamelCase_			  =						(1, 1_5_0, 1_2_8, 1_2_8)
        elif "city" in model_name:
            UpperCamelCase_			  =						1_9
            UpperCamelCase_			  =						"cityscapes-id2label.json"
            UpperCamelCase_			  =						(1, 1_9, 1_2_8, 1_2_8)
        else:
            raise ValueError(f"Model {model_name} not supported"			)
    elif "mit" in model_name:
        UpperCamelCase_			  =						True
        UpperCamelCase_			  =						model_name[4:6]
        UpperCamelCase_			  =						1_0_0_0
        UpperCamelCase_			  =						"imagenet-1k-id2label.json"
        UpperCamelCase_			  =						(1, 1_0_0_0)
    else:
        raise ValueError(f"Model {model_name} not supported"			)

    # set config attributes
    UpperCamelCase_			  =						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						repo_type="dataset"			)			,						"r"			)			)
    UpperCamelCase_			  =						{int(SCREAMING_SNAKE_CASE_			): v for k, v in idalabel.items()}
    UpperCamelCase_			  =						idalabel
    UpperCamelCase_			  =						{v: k for k, v in idalabel.items()}
    if size == "b0":
        pass
    elif size == "b1":
        UpperCamelCase_			  =						[6_4, 1_2_8, 3_2_0, 5_1_2]
        UpperCamelCase_			  =						2_5_6
    elif size == "b2":
        UpperCamelCase_			  =						[6_4, 1_2_8, 3_2_0, 5_1_2]
        UpperCamelCase_			  =						7_6_8
        UpperCamelCase_			  =						[3, 4, 6, 3]
    elif size == "b3":
        UpperCamelCase_			  =						[6_4, 1_2_8, 3_2_0, 5_1_2]
        UpperCamelCase_			  =						7_6_8
        UpperCamelCase_			  =						[3, 4, 1_8, 3]
    elif size == "b4":
        UpperCamelCase_			  =						[6_4, 1_2_8, 3_2_0, 5_1_2]
        UpperCamelCase_			  =						7_6_8
        UpperCamelCase_			  =						[3, 8, 2_7, 3]
    elif size == "b5":
        UpperCamelCase_			  =						[6_4, 1_2_8, 3_2_0, 5_1_2]
        UpperCamelCase_			  =						7_6_8
        UpperCamelCase_			  =						[3, 6, 4_0, 3]
    else:
        raise ValueError(f"Size {size} not supported"			)

    # load image processor (only resize + normalize)
    UpperCamelCase_			  =						SegformerImageProcessor(
        image_scale=(5_1_2, 5_1_2)			,						keep_ratio=SCREAMING_SNAKE_CASE_			,						align=SCREAMING_SNAKE_CASE_			,						do_random_crop=SCREAMING_SNAKE_CASE_			)

    # prepare image
    UpperCamelCase_			  =						prepare_img()
    UpperCamelCase_			  =						image_processor(images=SCREAMING_SNAKE_CASE_			,						return_tensors="pt"			).pixel_values

    logger.info(f"Converting model {model_name}..."			)

    # load original state dict
    if encoder_only:
        UpperCamelCase_			  =						torch.load(SCREAMING_SNAKE_CASE_			,						map_location=torch.device("cpu"			)			)
    else:
        UpperCamelCase_			  =						torch.load(SCREAMING_SNAKE_CASE_			,						map_location=torch.device("cpu"			)			)["state_dict"]

    # rename keys
    UpperCamelCase_			  =						rename_keys(SCREAMING_SNAKE_CASE_			,						encoder_only=SCREAMING_SNAKE_CASE_			)
    if not encoder_only:
        del state_dict["decode_head.conv_seg.weight"]
        del state_dict["decode_head.conv_seg.bias"]

    # key and value matrices need special treatment
    read_in_k_v(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)

    # create HuggingFace model and load state dict
    if encoder_only:
        UpperCamelCase_			  =						False
        UpperCamelCase_			  =						SegformerForImageClassification(SCREAMING_SNAKE_CASE_			)
    else:
        UpperCamelCase_			  =						SegformerForSemanticSegmentation(SCREAMING_SNAKE_CASE_			)
    model.load_state_dict(SCREAMING_SNAKE_CASE_			)
    model.eval()

    # forward pass
    UpperCamelCase_			  =						model(SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						outputs.logits

    # set expected_slice based on model name
    # ADE20k checkpoints
    if model_name == "segformer.b0.512x512.ade.160k":
        UpperCamelCase_			  =						torch.tensor(
            [
                [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
                [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
                [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
            ]			)
    elif model_name == "segformer.b1.512x512.ade.160k":
        UpperCamelCase_			  =						torch.tensor(
            [
                [[-7.5820, -8.7231, -8.3215], [-8.0600, -10.3529, -10.0304], [-7.5208, -9.4103, -9.6239]],
                [[-12.6918, -13.8994, -13.7137], [-13.3196, -15.7523, -15.4789], [-12.9343, -14.8757, -14.9689]],
                [[-11.1911, -11.9421, -11.3243], [-11.3342, -13.6839, -13.3581], [-10.3909, -12.1832, -12.4858]],
            ]			)
    elif model_name == "segformer.b2.512x512.ade.160k":
        UpperCamelCase_			  =						torch.tensor(
            [
                [[-11.8173, -14.3850, -16.3128], [-14.5648, -16.5804, -18.6568], [-14.7223, -15.7387, -18.4218]],
                [[-15.7290, -17.9171, -19.4423], [-18.3105, -19.9448, -21.4661], [-17.9296, -18.6497, -20.7910]],
                [[-15.0783, -17.0336, -18.2789], [-16.8771, -18.6870, -20.1612], [-16.2454, -17.1426, -19.5055]],
            ]			)
    elif model_name == "segformer.b3.512x512.ade.160k":
        UpperCamelCase_			  =						torch.tensor(
            [
                [[-9.0878, -10.2081, -10.1891], [-9.3144, -10.7941, -10.9843], [-9.2294, -10.3855, -10.5704]],
                [[-12.2316, -13.9068, -13.6102], [-12.9161, -14.3702, -14.3235], [-12.5233, -13.7174, -13.7932]],
                [[-14.6275, -15.2490, -14.9727], [-14.3400, -15.9687, -16.2827], [-14.1484, -15.4033, -15.8937]],
            ]			)
    elif model_name == "segformer.b4.512x512.ade.160k":
        UpperCamelCase_			  =						torch.tensor(
            [
                [[-12.3144, -13.2447, -14.0802], [-13.3614, -14.5816, -15.6117], [-13.3340, -14.4433, -16.2219]],
                [[-19.2781, -20.4128, -20.7506], [-20.6153, -21.6566, -22.0998], [-19.9800, -21.0430, -22.1494]],
                [[-18.8739, -19.7804, -21.1834], [-20.1233, -21.6765, -23.2944], [-20.0315, -21.2641, -23.6944]],
            ]			)
    elif model_name == "segformer.b5.640x640.ade.160k":
        UpperCamelCase_			  =						torch.tensor(
            [
                [[-9.5524, -12.0835, -11.7348], [-10.5229, -13.6446, -14.5662], [-9.5842, -12.8851, -13.9414]],
                [[-15.3432, -17.5323, -17.0818], [-16.3330, -18.9255, -19.2101], [-15.1340, -17.7848, -18.3971]],
                [[-12.6072, -14.9486, -14.6631], [-13.7629, -17.0907, -17.7745], [-12.7899, -16.1695, -17.1671]],
            ]			)
    # Cityscapes checkpoints
    elif model_name == "segformer.b0.1024x1024.city.160k":
        UpperCamelCase_			  =						torch.tensor(
            [
                [[-11.9295, -13.4057, -14.8106], [-13.3431, -14.8179, -15.3781], [-14.2836, -15.5942, -16.1588]],
                [[-11.4906, -12.8067, -13.6564], [-13.1189, -14.0500, -14.1543], [-13.8748, -14.5136, -14.8789]],
                [[0.5374, 0.1067, -0.4742], [0.1141, -0.2255, -0.7099], [-0.3000, -0.5924, -1.3105]],
            ]			)
    elif model_name == "segformer.b0.512x1024.city.160k":
        UpperCamelCase_			  =						torch.tensor(
            [
                [[-7.8217, -9.8767, -10.1717], [-9.4438, -10.9058, -11.4047], [-9.7939, -12.3495, -12.1079]],
                [[-7.1514, -9.5336, -10.0860], [-9.7776, -11.6822, -11.8439], [-10.1411, -12.7655, -12.8972]],
                [[0.3021, 0.0805, -0.2310], [-0.0328, -0.1605, -0.2714], [-0.1408, -0.5477, -0.6976]],
            ]			)
    elif model_name == "segformer.b0.640x1280.city.160k":
        UpperCamelCase_			  =						torch.tensor(
            [
                [
                    [-1.13_72E01, -1.27_87E01, -1.34_77E01],
                    [-1.25_36E01, -1.41_94E01, -1.44_09E01],
                    [-1.32_17E01, -1.48_88E01, -1.53_27E01],
                ],
                [
                    [-1.47_91E01, -1.71_22E01, -1.82_77E01],
                    [-1.71_63E01, -1.91_92E01, -1.95_33E01],
                    [-1.78_97E01, -1.99_91E01, -2.03_15E01],
                ],
                [
                    [7.67_23E-01, 4.19_21E-01, -7.78_78E-02],
                    [4.77_72E-01, 9.55_57E-03, -2.80_82E-01],
                    [3.60_32E-01, -2.48_26E-01, -5.11_68E-01],
                ],
            ]			)
    elif model_name == "segformer.b0.768x768.city.160k":
        UpperCamelCase_			  =						torch.tensor(
            [
                [[-9.4959, -11.3087, -11.7479], [-11.0025, -12.6540, -12.3319], [-11.4064, -13.0487, -12.9905]],
                [[-9.8905, -11.3084, -12.0854], [-11.1726, -12.7698, -12.9583], [-11.5985, -13.3278, -14.1774]],
                [[0.2213, 0.0192, -0.2466], [-0.1731, -0.4213, -0.4874], [-0.3126, -0.6541, -1.1389]],
            ]			)
    elif model_name == "segformer.b1.1024x1024.city.160k":
        UpperCamelCase_			  =						torch.tensor(
            [
                [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
                [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
                [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
            ]			)
    elif model_name == "segformer.b2.1024x1024.city.160k":
        UpperCamelCase_			  =						torch.tensor(
            [
                [[-16.0976, -16.4856, -17.3962], [-16.6234, -19.0342, -19.7685], [-16.0900, -18.0661, -19.1180]],
                [[-18.4750, -18.8488, -19.5074], [-19.4030, -22.1570, -22.5977], [-19.1191, -20.8486, -22.3783]],
                [[-4.5178, -5.5037, -6.5109], [-5.0884, -7.2174, -8.0334], [-4.4156, -5.8117, -7.2970]],
            ]			)
    elif model_name == "segformer.b3.1024x1024.city.160k":
        UpperCamelCase_			  =						torch.tensor(
            [
                [[-14.2081, -14.4732, -14.1977], [-14.5867, -16.4423, -16.6356], [-13.4441, -14.9685, -16.8696]],
                [[-14.4576, -14.7073, -15.0451], [-15.0816, -17.6237, -17.9873], [-14.4213, -16.0199, -18.5992]],
                [[-4.7349, -4.9588, -5.0966], [-4.3210, -6.9325, -7.2591], [-3.4312, -4.7484, -7.1917]],
            ]			)
    elif model_name == "segformer.b4.1024x1024.city.160k":
        UpperCamelCase_			  =						torch.tensor(
            [
                [[-11.7737, -11.9526, -11.3273], [-13.6692, -14.4574, -13.8878], [-13.8937, -14.6924, -15.9345]],
                [[-14.6706, -14.5330, -14.1306], [-16.1502, -16.8180, -16.4269], [-16.8338, -17.8939, -20.1746]],
                [[1.0491, 0.8289, 1.0310], [1.1044, 0.5219, 0.8055], [1.0899, 0.6926, 0.5590]],
            ]			)
    elif model_name == "segformer.b5.1024x1024.city.160k":
        UpperCamelCase_			  =						torch.tensor(
            [
                [[-12.5641, -13.4777, -13.0684], [-13.9587, -15.8983, -16.6557], [-13.3109, -15.7350, -16.3141]],
                [[-14.7074, -15.4352, -14.5944], [-16.6353, -18.1663, -18.6120], [-15.1702, -18.0329, -18.1547]],
                [[-1.7990, -2.0951, -1.7784], [-2.6397, -3.8245, -3.9686], [-1.5264, -2.8126, -2.9316]],
            ]			)
    else:
        UpperCamelCase_			  =						logits.argmax(-1			).item()
        print("Predicted class:"			,						model.config.idalabel[predicted_class_idx]			)

    # verify logits
    if not encoder_only:
        assert logits.shape == expected_shape
        assert torch.allclose(logits[0, :3, :3, :3]			,						SCREAMING_SNAKE_CASE_			,						atol=1E-2			)

    # finally, save model and image processor
    logger.info(f"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."			)
    Path(SCREAMING_SNAKE_CASE_			).mkdir(exist_ok=SCREAMING_SNAKE_CASE_			)
    model.save_pretrained(SCREAMING_SNAKE_CASE_			)
    image_processor.save_pretrained(SCREAMING_SNAKE_CASE_			)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE		:List[Any]						=     argparse.ArgumentParser()

 parser.add_argument(
     """--model_name""",
     default="""segformer.b0.512x512.ade.160k""",
     type=str,
     help="""Name of the model you'd like to convert.""",
 )
 parser.add_argument(
     """--checkpoint_path""", default=None, type=str, help="""Path to the original PyTorch checkpoint (.pth file)."""
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
 )
 SCREAMING_SNAKE_CASE		:int						=     parser.parse_args()
 convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)



import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
 from PIL import Image
else:






 class 						__magic_name__					:






      @staticmethod
      def 	UpperCAmelCase_				(				*_lowercase		,						**_lowercase	)->     Optional[int]:
          pass






@is_pipeline_test
@require_vision
@require_torch
class 						__magic_name__					(							unittest.TestCase				):
     UpperCamelCase_	:Optional[Any]										=      MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase		,						_lowercase	)->     Union[str, Any]:
         UpperCamelCase_			  =						pipeline(
             "zero-shot-object-detection"		,						model="hf-internal-testing/tiny-random-owlvit-object-detection"	)

         UpperCamelCase_			  =						[
             {
                 "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
                 "candidate_labels": ["cat", "remote", "couch"],
             }
         ]
         return object_detector, examples






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase	)->     Union[str, Any]:
         UpperCamelCase_			  =						object_detector(examples[0]		,						threshold=0.0	)

         UpperCamelCase_			  =						len(_lowercase	)
         self.assertGreater(_lowercase		,						0	)
         self.assertEqual(
             _lowercase		,						[
                 {
                     "score": ANY(_lowercase	),
                     "label": ANY(_lowercase	),
                     "box": {"xmin": ANY(_lowercase	), "ymin": ANY(_lowercase	), "xmax": ANY(_lowercase	), "ymax": ANY(_lowercase	)},
                 }
                 for i in range(_lowercase	)
             ]		,						)






     @require_tf
     @unittest.skip("Zero Shot Object Detection not implemented in TF"	)
     def 	UpperCAmelCase_				(				self	)->     str:
         pass






     @require_torch
     def 	UpperCAmelCase_				(				self	)->     List[Any]:
         UpperCamelCase_			  =						pipeline(
             "zero-shot-object-detection"		,						model="hf-internal-testing/tiny-random-owlvit-object-detection"	)

         UpperCamelCase_			  =						object_detector(
             "./tests/fixtures/tests_samples/COCO/000000039769.png"		,						candidate_labels=["cat", "remote", "couch"]		,						threshold=0.64		,						)

         self.assertEqual(
             nested_simplify(_lowercase		,						decimals=4	)		,						[
                 {"score": 0.7_235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                 {"score": 0.7_218, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                 {"score": 0.7_184, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                 {"score": 0.6_748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                 {"score": 0.6_656, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                 {"score": 0.6_614, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                 {"score": 0.6_456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
                 {"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
                 {"score": 0.6_419, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
             ]		,						)

         UpperCamelCase_			  =						object_detector(
             [
                 {
                     "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
                     "candidate_labels": ["cat", "remote", "couch"],
                 }
             ]		,						threshold=0.64		,						)

         self.assertEqual(
             nested_simplify(_lowercase		,						decimals=4	)		,						[
                 [
                     {"score": 0.7_235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                     {"score": 0.7_218, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                     {"score": 0.7_184, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                     {"score": 0.6_748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                     {"score": 0.6_656, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                     {"score": 0.6_614, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                     {"score": 0.6_456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
                     {"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
                     {"score": 0.6_419, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
                 ]
             ]		,						)






     @require_torch
     @slow
     def 	UpperCAmelCase_				(				self	)->     Any:
         UpperCamelCase_			  =						pipeline("zero-shot-object-detection"	)

         UpperCamelCase_			  =						object_detector(
             "http://images.cocodataset.org/val2017/000000039769.jpg"		,						candidate_labels=["cat", "remote", "couch"]		,						)
         self.assertEqual(
             nested_simplify(_lowercase		,						decimals=4	)		,						[
                 {"score": 0.2_868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                 {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                 {"score": 0.2_537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                 {"score": 0.1_474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                 {"score": 0.1_208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
             ]		,						)

         UpperCamelCase_			  =						object_detector(
             [
                 {
                     "image": "http://images.cocodataset.org/val2017/000000039769.jpg",
                     "candidate_labels": ["cat", "remote", "couch"],
                 },
                 {
                     "image": "http://images.cocodataset.org/val2017/000000039769.jpg",
                     "candidate_labels": ["cat", "remote", "couch"],
                 },
             ]		,						)
         self.assertEqual(
             nested_simplify(_lowercase		,						decimals=4	)		,						[
                 [
                     {"score": 0.2_868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                     {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                     {"score": 0.2_537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                     {"score": 0.1_474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                     {"score": 0.1_208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
                 ],
                 [
                     {"score": 0.2_868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                     {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                     {"score": 0.2_537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                     {"score": 0.1_474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                     {"score": 0.1_208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
                 ],
             ]		,						)






     @require_tf
     @unittest.skip("Zero Shot Object Detection not implemented in TF"	)
     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         pass






     @require_torch
     @slow
     def 	UpperCAmelCase_				(				self	)->     Optional[Any]:
         UpperCamelCase_			  =						0.2
         UpperCamelCase_			  =						pipeline("zero-shot-object-detection"	)

         UpperCamelCase_			  =						object_detector(
             "http://images.cocodataset.org/val2017/000000039769.jpg"		,						candidate_labels=["cat", "remote", "couch"]		,						threshold=_lowercase		,						)
         self.assertEqual(
             nested_simplify(_lowercase		,						decimals=4	)		,						[
                 {"score": 0.2_868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                 {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                 {"score": 0.2_537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
             ]		,						)






     @require_torch
     @slow
     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         UpperCamelCase_			  =						2
         UpperCamelCase_			  =						pipeline("zero-shot-object-detection"	)

         UpperCamelCase_			  =						object_detector(
             "http://images.cocodataset.org/val2017/000000039769.jpg"		,						candidate_labels=["cat", "remote", "couch"]		,						top_k=_lowercase		,						)
         self.assertEqual(
             nested_simplify(_lowercase		,						decimals=4	)		,						[
                 {"score": 0.2_868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                 {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
             ]		,						)
