"""simple docstring"""


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()
__UpperCamelCase									=			logging.get_logger(__name__)







def 					UpperCAmelCase    (		UpperCAmelCase					)				->      Any:
     snake_case_					       =					DPTConfig()

     if "large" in checkpoint_url:
          snake_case_					       =					1024
          snake_case_					       =					4096
          snake_case_					       =					24
          snake_case_					       =					16
          snake_case_					       =					[5, 11, 17, 23]
          snake_case_					       =					[256, 512, 1024, 1024]
          snake_case_					       =					(1, 384, 384)

     if "ade" in checkpoint_url:
          snake_case_					       =					True

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

     return config, expected_shape







def 					UpperCAmelCase    (		UpperCAmelCase					)				->      Optional[int]:
     snake_case_					       =					['pretrained.model.head.weight', 'pretrained.model.head.bias']
     for k in ignore_keys:
          state_dict.pop(UpperCAmelCase						,					UpperCAmelCase					)







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

     return name







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







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







@torch.no_grad()
def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					)				->      Tuple:
     snake_case_       ,	snake_case_					       =					get_dpt_config(UpperCAmelCase					)
     # load original state_dict from URL
     snake_case_					       =					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():
          snake_case_					       =					state_dict.pop(UpperCAmelCase					)
          snake_case_					       =					val
     # read in qkv matrices
     read_in_q_k_v(UpperCAmelCase						,					UpperCAmelCase					)

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

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

     snake_case_					       =					prepare_img()
     snake_case_					       =					image_processor(UpperCAmelCase						,					return_tensors='pt'					)

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

     # Assert logits
     snake_case_					       =					torch.tensor([[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]]					)
     if "ade" in checkpoint_url:
          snake_case_					       =					torch.tensor([[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]]					)
     assert outputs.shape == torch.Size(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__":
    __UpperCamelCase									=			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.''',
    )

    __UpperCamelCase									=			parser.parse_args()
    convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__		:				Tuple          =	logging.get_logger(__name__)

lowercase__		:				Any          =	{
    """facebook/dpr-ctx_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-ctx_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-multiset-base""": (
        """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
    ),
}







class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     """dpr"""



      def __init__(      self				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      Tuple=3_0_5_2_2    ,			SCREAMING_SNAKE_CASE_				:      Any=7_6_8    ,			SCREAMING_SNAKE_CASE_				:      Dict=1_2    ,			SCREAMING_SNAKE_CASE_				:      List[str]=1_2    ,			SCREAMING_SNAKE_CASE_				:      int=3_0_7_2    ,			SCREAMING_SNAKE_CASE_				:      List[Any]="gelu"    ,			SCREAMING_SNAKE_CASE_				:      int=0.1    ,			SCREAMING_SNAKE_CASE_				:      int=0.1    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=5_1_2    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=2    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=0.02    ,			SCREAMING_SNAKE_CASE_				:      List[str]=1E-12    ,			SCREAMING_SNAKE_CASE_				:      List[Any]=0    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]="absolute"    ,			SCREAMING_SNAKE_CASE_				:      int = 0    ,			**SCREAMING_SNAKE_CASE_				:      Optional[int]    ,			):
        super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				Optional[int]								= vocab_size
        lowerCAmelCase_   :				Tuple								= hidden_size
        lowerCAmelCase_   :				int								= num_hidden_layers
        lowerCAmelCase_   :				str								= num_attention_heads
        lowerCAmelCase_   :				Any								= hidden_act
        lowerCAmelCase_   :				List[str]								= intermediate_size
        lowerCAmelCase_   :				int								= hidden_dropout_prob
        lowerCAmelCase_   :				Union[str, Any]								= attention_probs_dropout_prob
        lowerCAmelCase_   :				Tuple								= max_position_embeddings
        lowerCAmelCase_   :				List[str]								= type_vocab_size
        lowerCAmelCase_   :				str								= initializer_range
        lowerCAmelCase_   :				str								= layer_norm_eps
        lowerCAmelCase_   :				List[str]								= projection_dim
        lowerCAmelCase_   :				List[str]								= position_embedding_type





