






import argparse
import json
import pickle
from pathlib import Path

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

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
A_						:						Union[str, Any]						      =					logging.get_logger(__name__)

def     snake_case   (UpperCAmelCase__    )			->							List[str]:
  UpperCamelCase_:   Any        =							SwinConfig.from_pretrained(
      'microsoft/swin-tiny-patch4-window7-224'      ,       out_features=['stage1', 'stage2', 'stage3', 'stage4']    )
  UpperCamelCase_:   Dict        =							MaskFormerConfig(backbone_config=SCREAMING_SNAKE_CASE_    )

  UpperCamelCase_:   int        =							'huggingface/label-files'
  if "ade20k-full" in model_name:
    # this should be ok
    UpperCamelCase_:   Any        =							8_4_7
    UpperCamelCase_:   Union[str, Any]        =							'maskformer-ade20k-full-id2label.json'
  elif "ade" in model_name:
    # this should be ok
    UpperCamelCase_:   List[str]        =							1_5_0
    UpperCamelCase_:   Union[str, Any]        =							'ade20k-id2label.json'
  elif "coco-stuff" in model_name:
    # this should be ok
    UpperCamelCase_:   str        =							1_7_1
    UpperCamelCase_:   List[str]        =							'maskformer-coco-stuff-id2label.json'
  elif "coco" in model_name:
    # TODO
    UpperCamelCase_:   List[str]        =							1_3_3
    UpperCamelCase_:   Optional[Any]        =							'coco-panoptic-id2label.json'
  elif "cityscapes" in model_name:
    # this should be ok
    UpperCamelCase_:   Any        =							1_9
    UpperCamelCase_:   int        =							'cityscapes-id2label.json'
  elif "vistas" in model_name:
    # this should be ok
    UpperCamelCase_:   Optional[int]        =							6_5
    UpperCamelCase_:   Dict        =							'mapillary-vistas-id2label.json'

  UpperCamelCase_:   str        =							json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_      ,       SCREAMING_SNAKE_CASE_      ,       repo_type='dataset'    )      ,       'r'    )    )
  UpperCamelCase_:   Union[str, Any]        =							{int(SCREAMING_SNAKE_CASE_    ): v for k, v in idalabel.items()}

  return config

def     snake_case   (UpperCAmelCase__    )			->							List[str]:
  UpperCamelCase_:   Tuple        =							[]
  # stem
  # fmt: off
  rename_keys.append(('backbone.patch_embed.proj.weight', 'model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight')    )
  rename_keys.append(('backbone.patch_embed.proj.bias', 'model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias')    )
  rename_keys.append(('backbone.patch_embed.norm.weight', 'model.pixel_level_module.encoder.model.embeddings.norm.weight')    )
  rename_keys.append(('backbone.patch_embed.norm.bias', 'model.pixel_level_module.encoder.model.embeddings.norm.bias')    )
  # stages
  for i in range(len(config.backbone_config.depths    )    ):
    for j in range(config.backbone_config.depths[i]    ):
      rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')    )
      rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')    )
      rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')    )
      rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_index''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')    )
      rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')    )
      rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')    )
      rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')    )
      rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')    )
      rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')    )
      rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')    )
      rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''')    )
      rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''')    )

    if i < 3:
      rename_keys.append((F'''backbone.layers.{i}.downsample.reduction.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight''')    )
      rename_keys.append((F'''backbone.layers.{i}.downsample.norm.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight''')    )
      rename_keys.append((F'''backbone.layers.{i}.downsample.norm.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias''')    )
    rename_keys.append((F'''backbone.norm{i}.weight''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.weight''')    )
    rename_keys.append((F'''backbone.norm{i}.bias''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.bias''')    )

  # FPN
  rename_keys.append(('sem_seg_head.layer_4.weight', 'model.pixel_level_module.decoder.fpn.stem.0.weight')    )
  rename_keys.append(('sem_seg_head.layer_4.norm.weight', 'model.pixel_level_module.decoder.fpn.stem.1.weight')    )
  rename_keys.append(('sem_seg_head.layer_4.norm.bias', 'model.pixel_level_module.decoder.fpn.stem.1.bias')    )
  for source_index, target_index in zip(range(3      ,       0      ,       -1    )      ,       range(0      ,       3    )    ):
    rename_keys.append((F'''sem_seg_head.adapter_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight''')    )
    rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight''')    )
    rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias''')    )
    rename_keys.append((F'''sem_seg_head.layer_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight''')    )
    rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight''')    )
    rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias''')    )
  rename_keys.append(('sem_seg_head.mask_features.weight', 'model.pixel_level_module.decoder.mask_projection.weight')    )
  rename_keys.append(('sem_seg_head.mask_features.bias', 'model.pixel_level_module.decoder.mask_projection.bias')    )

  # Transformer decoder
  for idx in range(config.decoder_config.decoder_layers    ):
    # self-attention out projection
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight''')    )
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias''')    )
    # cross-attention out projection
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight''')    )
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias''')    )
    # MLP 1
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc1.weight''')    )
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc1.bias''')    )
    # MLP 2
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc2.weight''')    )
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc2.bias''')    )
    # layernorm 1 (self-attention layernorm)
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight''')    )
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias''')    )
    # layernorm 2 (cross-attention layernorm)
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight''')    )
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias''')    )
    # layernorm 3 (final layernorm)
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight''')    )
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias''')    )

  rename_keys.append(('sem_seg_head.predictor.transformer.decoder.norm.weight', 'model.transformer_module.decoder.layernorm.weight')    )
  rename_keys.append(('sem_seg_head.predictor.transformer.decoder.norm.bias', 'model.transformer_module.decoder.layernorm.bias')    )

  # heads on top
  rename_keys.append(('sem_seg_head.predictor.query_embed.weight', 'model.transformer_module.queries_embedder.weight')    )

  rename_keys.append(('sem_seg_head.predictor.input_proj.weight', 'model.transformer_module.input_projection.weight')    )
  rename_keys.append(('sem_seg_head.predictor.input_proj.bias', 'model.transformer_module.input_projection.bias')    )

  rename_keys.append(('sem_seg_head.predictor.class_embed.weight', 'class_predictor.weight')    )
  rename_keys.append(('sem_seg_head.predictor.class_embed.bias', 'class_predictor.bias')    )

  for i in range(3    ):
    rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.weight''', F'''mask_embedder.{i}.0.weight''')    )
    rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.bias''', F'''mask_embedder.{i}.0.bias''')    )
  # fmt: on

  return rename_keys

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )			->							Any:
  UpperCamelCase_:   Union[str, Any]        =							dct.pop(SCREAMING_SNAKE_CASE_    )
  UpperCamelCase_:   List[Any]        =							val

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__    )			->							str:
  UpperCamelCase_:   str        =							[int(backbone_config.embed_dim * 2**i    ) for i in range(len(backbone_config.depths    )    )]
  for i in range(len(backbone_config.depths    )    ):
    UpperCamelCase_:   List[Any]        =							num_features[i]
    for j in range(backbone_config.depths[i]    ):
      # fmt: off
      # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
      UpperCamelCase_:   Optional[int]        =							state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''    )
      UpperCamelCase_:   Dict        =							state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.bias'''    )
      # next, add query, keys and values (in that order) to the state dict
      UpperCamelCase_:   str        =							in_proj_weight[:dim, :]
      UpperCamelCase_:   List[str]        =							in_proj_bias[: dim]
      UpperCamelCase_:   int        =							in_proj_weight[
          dim : dim * 2, :
      ]
      UpperCamelCase_:   Dict        =							in_proj_bias[
          dim : dim * 2
      ]
      UpperCamelCase_:   List[str]        =							in_proj_weight[
          -dim :, :
      ]
      UpperCamelCase_:   Optional[Any]        =							in_proj_bias[-dim :]
      # fmt: on

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__    )			->							Dict:
  UpperCamelCase_:   List[str]        =							config.decoder_config.hidden_size
  for idx in range(config.decoder_config.decoder_layers    ):
    # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
    UpperCamelCase_:   Dict        =							state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''    )
    UpperCamelCase_:   Tuple        =							state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias'''    )
    # next, add query, keys and values (in that order) to the state dict
    UpperCamelCase_:   str        =							in_proj_weight[: hidden_size, :]
    UpperCamelCase_:   int        =							in_proj_bias[:config.hidden_size]
    UpperCamelCase_:   List[Any]        =							in_proj_weight[hidden_size : hidden_size * 2, :]
    UpperCamelCase_:   List[str]        =							in_proj_bias[hidden_size : hidden_size * 2]
    UpperCamelCase_:   List[Any]        =							in_proj_weight[-hidden_size :, :]
    UpperCamelCase_:   Dict        =							in_proj_bias[-hidden_size :]
    # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
    UpperCamelCase_:   Tuple        =							state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''    )
    UpperCamelCase_:   int        =							state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias'''    )
    # next, add query, keys and values (in that order) to the state dict
    UpperCamelCase_:   str        =							in_proj_weight[: hidden_size, :]
    UpperCamelCase_:   Tuple        =							in_proj_bias[:config.hidden_size]
    UpperCamelCase_:   Tuple        =							in_proj_weight[hidden_size : hidden_size * 2, :]
    UpperCamelCase_:   str        =							in_proj_bias[hidden_size : hidden_size * 2]
    UpperCamelCase_:   Any        =							in_proj_weight[-hidden_size :, :]
    UpperCamelCase_:   str        =							in_proj_bias[-hidden_size :]
  # fmt: on

def     snake_case   ()			->							torch.Tensor:
  UpperCamelCase_:   int        =							'http://images.cocodataset.org/val2017/000000039769.jpg'
  UpperCamelCase_:   str        =							Image.open(requests.get(SCREAMING_SNAKE_CASE_      ,       stream=SCREAMING_SNAKE_CASE_    ).raw    )
  return im

@torch.no_grad()
def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__ = False    )			->							List[Any]:
  UpperCamelCase_:   Union[str, Any]        =							get_maskformer_config(SCREAMING_SNAKE_CASE_    )

  # load original state_dict
  with open(SCREAMING_SNAKE_CASE_      ,       'rb'    ) as f:
    UpperCamelCase_:   str        =							pickle.load(SCREAMING_SNAKE_CASE_    )
  UpperCamelCase_:   Tuple        =							data['model']

  # for name, param in state_dict.items():
  #     print(name, param.shape)

  # rename keys
  UpperCamelCase_:   Dict        =							create_rename_keys(SCREAMING_SNAKE_CASE_    )
  for src, dest in rename_keys:
    rename_key(SCREAMING_SNAKE_CASE_      ,       SCREAMING_SNAKE_CASE_      ,       SCREAMING_SNAKE_CASE_    )
  read_in_swin_q_k_v(SCREAMING_SNAKE_CASE_      ,       config.backbone_config    )
  read_in_decoder_q_k_v(SCREAMING_SNAKE_CASE_      ,       SCREAMING_SNAKE_CASE_    )

  # update to torch tensors
  for key, value in state_dict.items():
    UpperCamelCase_:   Dict        =							torch.from_numpy(SCREAMING_SNAKE_CASE_    )

  # load 🤗 model
  UpperCamelCase_:   List[str]        =							MaskFormerForInstanceSegmentation(SCREAMING_SNAKE_CASE_    )
  model.eval()

  for name, param in model.named_parameters():
    print(SCREAMING_SNAKE_CASE_      ,       param.shape    )

  UpperCamelCase_ ,UpperCamelCase_:   Optional[Any]        =							model.load_state_dict(SCREAMING_SNAKE_CASE_      ,       strict=SCREAMING_SNAKE_CASE_    )
  assert missing_keys == [
      "model.pixel_level_module.encoder.model.layernorm.weight",
      "model.pixel_level_module.encoder.model.layernorm.bias",
  ]
  assert len(SCREAMING_SNAKE_CASE_    ) == 0, F'''Unexpected keys: {unexpected_keys}'''

  # verify results
  UpperCamelCase_:   Tuple        =							prepare_img()
  if "vistas" in model_name:
    UpperCamelCase_:   Optional[Any]        =							6_5
  elif "cityscapes" in model_name:
    UpperCamelCase_:   str        =							6_5_5_3_5
  else:
    UpperCamelCase_:   str        =							2_5_5
  UpperCamelCase_:   Union[str, Any]        =							True if 'ade' in model_name else False
  UpperCamelCase_:   Union[str, Any]        =							MaskFormerImageProcessor(ignore_index=SCREAMING_SNAKE_CASE_      ,       reduce_labels=SCREAMING_SNAKE_CASE_    )

  UpperCamelCase_:   Tuple        =							image_processor(SCREAMING_SNAKE_CASE_      ,       return_tensors='pt'    )

  UpperCamelCase_:   Any        =							model(**SCREAMING_SNAKE_CASE_    )

  print('Logits:'      ,       outputs.class_queries_logits[0, :3, :3]    )

  if model_name == "maskformer-swin-tiny-ade":
    UpperCamelCase_:   Any        =							torch.tensor(
        [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]    )
  assert torch.allclose(outputs.class_queries_logits[0, :3, :3]      ,       SCREAMING_SNAKE_CASE_      ,       atol=1E-4    )
  print('Looks ok!'    )

  if pytorch_dump_folder_path is not None:
    print(F'''Saving 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 push_to_hub:
    print('Pushing model and image processor to the hub...'    )
    model.push_to_hub(F'''nielsr/{model_name}'''    )
    image_processor.push_to_hub(F'''nielsr/{model_name}'''    )


if __name__ == "__main__":
   A_						:						List[str]						      =					argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--model_name',
       default='maskformer-swin-tiny-ade',
       type=str,
       help=('Name of the MaskFormer model you\'d like to convert',),
   )
   parser.add_argument(
       '--checkpoint_path',
       default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
       type=str,
       help='Path to the original state dict (.pth file).',
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
   )
   parser.add_argument(
       '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
   )

   A_						:						Dict						      =					parser.parse_args()
   convert_maskformer_checkpoint(
       args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
   )


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}},
             ]		,						)
