





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()
a__: List[Any]							   =						logging.get_logger(__name__)


def       UpperCamelCase__(			UpperCamelCase__     :			Tuple    )->Tuple:
       A__		   =		DPTConfig(embedding_type='''hybrid'''    )

       if "large" in checkpoint_url:
              A__		   =		10_24
              A__		   =		40_96
              A__		   =		24
              A__		   =		16
              A__		   =		[5, 11, 17, 23]
              A__		   =		[2_56, 5_12, 10_24, 10_24]
              A__		   =		(1, 3_84, 3_84)

       if "nyu" or "midas" in checkpoint_url:
              A__		   =		7_68
              A__		   =		[1, 1, 1, 0.5]
              A__		   =		[2_56, 5_12, 7_68, 7_68]
              A__		   =		1_50
              A__		   =		16
              A__		   =		(1, 3_84, 3_84)
              A__		   =		False
              A__		   =		'''project'''

       if "ade" in checkpoint_url:
              A__		   =		True
              A__		   =		7_68
              A__		   =		[1, 1, 1, 0.5]
              A__		   =		1_50
              A__		   =		16
              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, 1_50, 4_80, 4_80]

       return config, expected_shape


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


def       UpperCamelCase__(			UpperCamelCase__     :			List[Any]    )->Tuple:
       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'''					,	''''''    )
       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 and "backbone" not in name:
              A__		   =		name.replace('''norm1'''					,	'''layernorm_before'''    )
       if "norm2" in name and "backbone" not 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'''    )
       if "backbone" in name:
              A__		   =		name.replace('''backbone'''					,	'''backbone.bit.encoder'''    )

       if ".." in name:
              A__		   =		name.replace('''..'''					,	'''.'''    )

       if "stem.conv" in name:
              A__		   =		name.replace('''stem.conv'''					,	'''bit.embedder.convolution'''    )
       if "blocks" in name:
              A__		   =		name.replace('''blocks'''					,	'''layers'''    )
       if "convolution" in name and "backbone" in name:
              A__		   =		name.replace('''convolution'''					,	'''conv'''    )
       if "layer" in name and "backbone" in name:
              A__		   =		name.replace('''layer'''					,	'''layers'''    )
       if "backbone.bit.encoder.bit" in name:
              A__		   =		name.replace('''backbone.bit.encoder.bit'''					,	'''backbone.bit'''    )
       if "embedder.conv" in name:
              A__		   =		name.replace('''embedder.conv'''					,	'''embedder.convolution'''    )
       if "backbone.bit.encoder.stem.norm" in name:
              A__		   =		name.replace('''backbone.bit.encoder.stem.norm'''					,	'''backbone.bit.embedder.norm'''    )
       return name


def       UpperCamelCase__(			UpperCamelCase__     :			Optional[int]					,	UpperCamelCase__     :			Optional[int]    )->List[str]:
       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       UpperCamelCase__(			)->Dict:
       A__		   =		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
       A__		   =		Image.open(requests.get(UpperCamelCase__					,	stream=UpperCamelCase__    ).raw    )
       return im


@torch.no_grad()
def       UpperCamelCase__(			UpperCamelCase__     :			str					,	UpperCamelCase__     :			str					,	UpperCamelCase__     :			List[Any]					,	UpperCamelCase__     :			Union[str, Any]					,	UpperCamelCase__     :			Any    )->Dict:
       A__   ,      A__		   =		get_dpt_config(UpperCamelCase__    )
       # load original state_dict from URL
       # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
       A__		   =		torch.load(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__		   =		4_80 if '''ade''' in checkpoint_url else 3_84
       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

       if show_prediction:
              A__		   =		(
                  torch.nn.functional.interpolate(
                      outputs.unsqueeze(1    )					,	size=(image.size[1], image.size[0])					,	mode='''bicubic'''					,	align_corners=UpperCamelCase__					,	)
                  .squeeze()
                  .cpu()
                  .numpy()
              )

              Image.fromarray((prediction / prediction.max()) * 2_55    ).show()

       if pytorch_dump_folder_path is not None:
              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:
              model.push_to_hub('''ybelkada/dpt-hybrid-midas'''    )
              image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas'''    )


if __name__ == "__main__":
       a__: 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=False,
           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.',
       )
       parser.add_argument(
           '--show_prediction',
           action='store_true',
       )

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












import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY




@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class        SCREAMING_SNAKE_CASE__					(		unittest.TestCase ):
 __SCREAMING_SNAKE_CASE							=      MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase					):
        A__		   =		hf_hub_download(
            repo_id='''nateraw/video-demo''',filename='''archery.mp4''',repo_type='''dataset'''					)
        A__		   =		VideoClassificationPipeline(model=__lowerCamelCase,image_processor=__lowerCamelCase,top_k=2					)
        A__		   =		[
            example_video_filepath,
            '''https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4''',
        ]
        return video_classifier, examples



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase					):
        for example in examples:
               A__		   =		video_classifier(__lowerCamelCase					)

               self.assertEqual(
                   __lowerCamelCase,[
                       {'''score''': ANY(__lowerCamelCase					), '''label''': ANY(__lowerCamelCase					)},
                       {'''score''': ANY(__lowerCamelCase					), '''label''': ANY(__lowerCamelCase					)},
                   ],)



 @require_torch
 def 							UpperCamelCase     (				self					):
        A__		   =		'''hf-internal-testing/tiny-random-VideoMAEForVideoClassification'''
        A__		   =		VideoMAEFeatureExtractor(
            size={'''shortest_edge''': 10},crop_size={'''height''': 10, '''width''': 10}					)
        A__		   =		pipeline(
            '''video-classification''',model=__lowerCamelCase,feature_extractor=__lowerCamelCase,frame_sampling_rate=4					)

        A__		   =		hf_hub_download(repo_id='''nateraw/video-demo''',filename='''archery.mp4''',repo_type='''dataset'''					)
        A__		   =		video_classifier(__lowerCamelCase,top_k=2					)
        self.assertEqual(
            nested_simplify(__lowerCamelCase,decimals=4					),[{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],)

        A__		   =		video_classifier(
            [
                video_file_path,
                video_file_path,
            ],top_k=2,)
        self.assertEqual(
            nested_simplify(__lowerCamelCase,decimals=4					),[
                [{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],
                [{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],
            ],)




 @require_tf
 def 							UpperCamelCase     (				self					):
        pass





