



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__   			=   logging.get_logger(__name__)


def A    (							__UpperCamelCase       )    ->   List[Any]:
			A__	  =     DPTConfig()

			if "large" in checkpoint_url:
						A__	  =     1_024
						A__	  =     4_096
						A__	  =     24
						A__	  =     16
						A__	  =     [5, 11, 17, 23]
						A__	  =     [256, 512, 1_024, 1_024]
						A__	  =     (1, 384, 384)

			if "ade" in checkpoint_url:
						A__	  =     True

						A__	  =     150
						A__	  =     'huggingface/label-files'
						A__	  =     'ade20k-id2label.json'
						A__	  =     json.load(open(cached_download(hf_hub_url(__UpperCamelCase			,						__UpperCamelCase			,						repo_type='dataset'       )       )			,						'r'       )       )
						A__	  =     {int(__UpperCamelCase       ): v for k, v in idalabel.items()}
						A__	  =     idalabel
						A__	  =     {v: k for k, v in idalabel.items()}
						A__	  =     [1, 150, 480, 480]

			return config, expected_shape


def A    (							__UpperCamelCase       )    ->   List[str]:
			A__	  =     ['pretrained.model.head.weight', 'pretrained.model.head.bias']
			for k in ignore_keys:
						state_dict.pop(__UpperCamelCase			,						__UpperCamelCase       )


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

			return name


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   Any:
			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__	  =     state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight'''       )
						A__	  =     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__	  =     in_proj_weight[: config.hidden_size, :]
						A__	  =     in_proj_bias[: config.hidden_size]
						A__	  =     in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						A__	  =     in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						A__	  =     in_proj_weight[
						    -config.hidden_size :, :
						]
						A__	  =     in_proj_bias[-config.hidden_size :]


def A    (							)    ->   str:
			A__	  =     'http://images.cocodataset.org/val2017/000000039769.jpg'
			A__	  =     Image.open(requests.get(__UpperCamelCase			,						stream=__UpperCamelCase       ).raw       )
			return im


@torch.no_grad()
def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   Any:
			A__  ,			A__	  =     get_dpt_config(__UpperCamelCase       )
			# load original state_dict from URL
			A__	  =     torch.hub.load_state_dict_from_url(__UpperCamelCase			,						map_location='cpu'       )
			# remove certain keys
			remove_ignore_keys_(__UpperCamelCase       )
			# rename keys
			for key in state_dict.copy().keys():
						A__	  =     state_dict.pop(__UpperCamelCase       )
						A__	  =     val
			# read in qkv matrices
			read_in_q_k_v(__UpperCamelCase			,						__UpperCamelCase       )

			# load HuggingFace model
			A__	  =     DPTForSemanticSegmentation(__UpperCamelCase       ) if 'ade' in checkpoint_url else DPTForDepthEstimation(__UpperCamelCase       )
			model.load_state_dict(__UpperCamelCase       )
			model.eval()

			# Check outputs on an image
			A__	  =     480 if 'ade' in checkpoint_url else 384
			A__	  =     DPTImageProcessor(size=__UpperCamelCase       )

			A__	  =     prepare_img()
			A__	  =     image_processor(__UpperCamelCase			,						return_tensors='pt'       )

			# forward pass
			A__	  =     model(**__UpperCamelCase       ).logits if 'ade' in checkpoint_url else model(**__UpperCamelCase       ).predicted_depth

			# Assert logits
			A__	  =     torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]       )
			if "ade" in checkpoint_url:
						A__	  =     torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]       )
			assert outputs.shape == torch.Size(__UpperCamelCase       )
			assert (
			    torch.allclose(outputs[0, 0, :3, :3]			,						__UpperCamelCase			,						atol=1E-4       )
			    if "ade" in checkpoint_url
			    else torch.allclose(outputs[0, :3, :3]			,						__UpperCamelCase       )
			)

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

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


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__   			=   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__   			=   parser.parse_args()
							convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)









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   __lowerCAmelCase  :
												"""simple docstring"""






												@staticmethod
												def      _a				(  *_snake_case       :							int					,       **_snake_case       :							List[str]       ):






															"""simple docstring"""


															pass


@is_pipeline_test
@require_vision
@require_torch
class   __lowerCAmelCase  (		unittest.TestCase   ):
					"""simple docstring"""






					A__					:		List[str]      							=     MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING



					def      _a				(  self       :							Any					,       _snake_case       :							Union[str, Any]					,       _snake_case       :							Tuple					,       _snake_case       :							Optional[Any]       ):






								"""simple docstring"""


								A__	  =     pipeline(
								    'zero-shot-object-detection'					,       model='hf-internal-testing/tiny-random-owlvit-object-detection'       )

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



					def      _a				(  self       :							int					,       _snake_case       :							int					,       _snake_case       :							List[str]       ):






								"""simple docstring"""


								A__	  =     object_detector(examples[0]					,       threshold=0.0       )

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



					@require_tf
					@unittest.skip('Zero Shot Object Detection not implemented in TF'       )
					def      _a				(  self       :							List[str]       ):






								"""simple docstring"""


								pass



					@require_torch
					def      _a				(  self       :							Optional[int]       ):






								"""simple docstring"""


								A__	  =     pipeline(
								    'zero-shot-object-detection'					,       model='hf-internal-testing/tiny-random-owlvit-object-detection'       )

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

								self.assertEqual(
								    nested_simplify(_snake_case					,       decimals=4       )					,       [
								        {'score': 0.7235, 'label': 'cat', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
								        {'score': 0.7218, 'label': 'remote', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
								        {'score': 0.7184, 'label': 'couch', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
								        {'score': 0.6748, 'label': 'remote', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
								        {'score': 0.6656, 'label': 'cat', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
								        {'score': 0.6614, 'label': 'couch', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
								        {'score': 0.6456, 'label': 'remote', 'box': {'xmin': 4_94, 'ymin': 1_05, 'xmax': 5_21, 'ymax': 1_27}},
								        {'score': 0.642, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 2_74, 'xmax': 93, 'ymax': 2_97}},
								        {'score': 0.6419, 'label': 'cat', 'box': {'xmin': 4_94, 'ymin': 1_05, 'xmax': 5_21, 'ymax': 1_27}},
								    ]					,       )

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

								self.assertEqual(
								    nested_simplify(_snake_case					,       decimals=4       )					,       [
								        [
								            {'score': 0.7235, 'label': 'cat', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
								            {'score': 0.7218, 'label': 'remote', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
								            {'score': 0.7184, 'label': 'couch', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
								            {'score': 0.6748, 'label': 'remote', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
								            {'score': 0.6656, 'label': 'cat', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
								            {'score': 0.6614, 'label': 'couch', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
								            {'score': 0.6456, 'label': 'remote', 'box': {'xmin': 4_94, 'ymin': 1_05, 'xmax': 5_21, 'ymax': 1_27}},
								            {'score': 0.642, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 2_74, 'xmax': 93, 'ymax': 2_97}},
								            {'score': 0.6419, 'label': 'cat', 'box': {'xmin': 4_94, 'ymin': 1_05, 'xmax': 5_21, 'ymax': 1_27}},
								        ]
								    ]					,       )



					@require_torch
					@slow
					def      _a				(  self       :							int       ):






								"""simple docstring"""


								A__	  =     pipeline('zero-shot-object-detection'       )

								A__	  =     object_detector(
								    'http://images.cocodataset.org/val2017/000000039769.jpg'					,       candidate_labels=['cat', 'remote', 'couch']					,       )
								self.assertEqual(
								    nested_simplify(_snake_case					,       decimals=4       )					,       [
								        {'score': 0.2868, 'label': 'cat', 'box': {'xmin': 3_24, 'ymin': 20, 'xmax': 6_40, 'ymax': 3_73}},
								        {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 1_77, 'ymax': 1_15}},
								        {'score': 0.2537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 3_15, 'ymax': 4_72}},
								        {'score': 0.1474, 'label': 'remote', 'box': {'xmin': 3_35, 'ymin': 74, 'xmax': 3_71, 'ymax': 1_87}},
								        {'score': 0.1208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 6_42, 'ymax': 4_76}},
								    ]					,       )

								A__	  =     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(_snake_case					,       decimals=4       )					,       [
								        [
								            {'score': 0.2868, 'label': 'cat', 'box': {'xmin': 3_24, 'ymin': 20, 'xmax': 6_40, 'ymax': 3_73}},
								            {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 1_77, 'ymax': 1_15}},
								            {'score': 0.2537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 3_15, 'ymax': 4_72}},
								            {'score': 0.1474, 'label': 'remote', 'box': {'xmin': 3_35, 'ymin': 74, 'xmax': 3_71, 'ymax': 1_87}},
								            {'score': 0.1208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 6_42, 'ymax': 4_76}},
								        ],
								        [
								            {'score': 0.2868, 'label': 'cat', 'box': {'xmin': 3_24, 'ymin': 20, 'xmax': 6_40, 'ymax': 3_73}},
								            {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 1_77, 'ymax': 1_15}},
								            {'score': 0.2537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 3_15, 'ymax': 4_72}},
								            {'score': 0.1474, 'label': 'remote', 'box': {'xmin': 3_35, 'ymin': 74, 'xmax': 3_71, 'ymax': 1_87}},
								            {'score': 0.1208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 6_42, 'ymax': 4_76}},
								        ],
								    ]					,       )



					@require_tf
					@unittest.skip('Zero Shot Object Detection not implemented in TF'       )
					def      _a				(  self       :							int       ):






								"""simple docstring"""


								pass



					@require_torch
					@slow
					def      _a				(  self       :							str       ):






								"""simple docstring"""


								A__	  =     0.2
								A__	  =     pipeline('zero-shot-object-detection'       )

								A__	  =     object_detector(
								    'http://images.cocodataset.org/val2017/000000039769.jpg'					,       candidate_labels=['cat', 'remote', 'couch']					,       threshold=_snake_case					,       )
								self.assertEqual(
								    nested_simplify(_snake_case					,       decimals=4       )					,       [
								        {'score': 0.2868, 'label': 'cat', 'box': {'xmin': 3_24, 'ymin': 20, 'xmax': 6_40, 'ymax': 3_73}},
								        {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 1_77, 'ymax': 1_15}},
								        {'score': 0.2537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 3_15, 'ymax': 4_72}},
								    ]					,       )



					@require_torch
					@slow
					def      _a				(  self       :							Any       ):






								"""simple docstring"""


								A__	  =     2
								A__	  =     pipeline('zero-shot-object-detection'       )

								A__	  =     object_detector(
								    'http://images.cocodataset.org/val2017/000000039769.jpg'					,       candidate_labels=['cat', 'remote', 'couch']					,       top_k=_snake_case					,       )
								self.assertEqual(
								    nested_simplify(_snake_case					,       decimals=4       )					,       [
								        {'score': 0.2868, 'label': 'cat', 'box': {'xmin': 3_24, 'ymin': 20, 'xmax': 6_40, 'ymax': 3_73}},
								        {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 1_77, 'ymax': 1_15}},
								    ]					,       )




