"""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()
__SCREAMING_SNAKE_CASE    	=logging.get_logger(__name__)



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     str      ):
     lowercase_	:				Dict													=	DPTConfig(embedding_type='hybrid'      )

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

     if "nyu" or "midas" in checkpoint_url:
          lowercase_	:				str													=	7_68
          lowercase_	:				Optional[Any]													=	[1, 1, 1, 0.5]
          lowercase_	:				Optional[int]													=	[2_56, 5_12, 7_68, 7_68]
          lowercase_	:				List[Any]													=	1_50
          lowercase_	:				Dict													=	16
          lowercase_	:				List[str]													=	(1, 3_84, 3_84)
          lowercase_	:				Tuple													=	False
          lowercase_	:				Any													=	'''project'''

     if "ade" in checkpoint_url:
          lowercase_	:				Any													=	True
          lowercase_	:				Optional[Any]													=	7_68
          lowercase_	:				List[str]													=	[1, 1, 1, 0.5]
          lowercase_	:				Dict													=	1_50
          lowercase_	:				Optional[int]													=	16
          lowercase_	:				int													=	'''huggingface/label-files'''
          lowercase_	:				Dict													=	'''ade20k-id2label.json'''
          lowercase_	:				Any													=	json.load(open(cached_download(hf_hub_url(_UpperCamelCase     ,			_UpperCamelCase     ,			repo_type='dataset'      )      )     ,			'r'      )      )
          lowercase_	:				List[str]													=	{int(_UpperCamelCase      ): v for k, v in idalabel.items()}
          lowercase_	:				List[str]													=	idalabel
          lowercase_	:				List[Any]													=	{v: k for k, v in idalabel.items()}
          lowercase_	:				int													=	[1, 1_50, 4_80, 4_80]

     return config, expected_shape



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     List[Any]      ):
     lowercase_	:				Tuple													=	['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
     for k in ignore_keys:
          state_dict.pop(_UpperCamelCase     ,			_UpperCamelCase      )



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     int      ):


     if (
         "pretrained.model" in name
         and "cls_token" not in name
         and "pos_embed" not in name
         and "patch_embed" not in name
     ):
          lowercase_	:				Union[str, Any]													=	name.replace('pretrained.model'     ,			'dpt.encoder'      )
     if "pretrained.model" in name:
          lowercase_	:				Any													=	name.replace('pretrained.model'     ,			'dpt.embeddings'      )
     if "patch_embed" in name:
          lowercase_	:				str													=	name.replace('patch_embed'     ,			''      )
     if "pos_embed" in name:
          lowercase_	:				Dict													=	name.replace('pos_embed'     ,			'position_embeddings'      )
     if "attn.proj" in name:
          lowercase_	:				Dict													=	name.replace('attn.proj'     ,			'attention.output.dense'      )
     if "proj" in name and "project" not in name:
          lowercase_	:				Tuple													=	name.replace('proj'     ,			'projection'      )
     if "blocks" in name:
          lowercase_	:				Any													=	name.replace('blocks'     ,			'layer'      )
     if "mlp.fc1" in name:
          lowercase_	:				Any													=	name.replace('mlp.fc1'     ,			'intermediate.dense'      )
     if "mlp.fc2" in name:
          lowercase_	:				Dict													=	name.replace('mlp.fc2'     ,			'output.dense'      )
     if "norm1" in name and "backbone" not in name:
          lowercase_	:				List[Any]													=	name.replace('norm1'     ,			'layernorm_before'      )
     if "norm2" in name and "backbone" not in name:
          lowercase_	:				Optional[int]													=	name.replace('norm2'     ,			'layernorm_after'      )
     if "scratch.output_conv" in name:
          lowercase_	:				str													=	name.replace('scratch.output_conv'     ,			'head'      )
     if "scratch" in name:
          lowercase_	:				List[Any]													=	name.replace('scratch'     ,			'neck'      )
     if "layer1_rn" in name:
          lowercase_	:				List[Any]													=	name.replace('layer1_rn'     ,			'convs.0'      )
     if "layer2_rn" in name:
          lowercase_	:				List[str]													=	name.replace('layer2_rn'     ,			'convs.1'      )
     if "layer3_rn" in name:
          lowercase_	:				Dict													=	name.replace('layer3_rn'     ,			'convs.2'      )
     if "layer4_rn" in name:
          lowercase_	:				List[Any]													=	name.replace('layer4_rn'     ,			'convs.3'      )
     if "refinenet" in name:
          lowercase_	:				Dict													=	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
          lowercase_	:				str													=	name.replace(F'''refinenet{layer_idx}'''     ,			F'''fusion_stage.layers.{abs(layer_idx-4      )}'''      )
     if "out_conv" in name:
          lowercase_	:				List[str]													=	name.replace('out_conv'     ,			'projection'      )
     if "resConfUnit1" in name:
          lowercase_	:				int													=	name.replace('resConfUnit1'     ,			'residual_layer1'      )
     if "resConfUnit2" in name:
          lowercase_	:				Optional[int]													=	name.replace('resConfUnit2'     ,			'residual_layer2'      )
     if "conv1" in name:
          lowercase_	:				Any													=	name.replace('conv1'     ,			'convolution1'      )
     if "conv2" in name:
          lowercase_	:				Optional[Any]													=	name.replace('conv2'     ,			'convolution2'      )
     # readout blocks
     if "pretrained.act_postprocess1.0.project.0" in name:
          lowercase_	:				Union[str, Any]													=	name.replace('pretrained.act_postprocess1.0.project.0'     ,			'neck.reassemble_stage.readout_projects.0.0'      )
     if "pretrained.act_postprocess2.0.project.0" in name:
          lowercase_	:				Union[str, Any]													=	name.replace('pretrained.act_postprocess2.0.project.0'     ,			'neck.reassemble_stage.readout_projects.1.0'      )
     if "pretrained.act_postprocess3.0.project.0" in name:
          lowercase_	:				Any													=	name.replace('pretrained.act_postprocess3.0.project.0'     ,			'neck.reassemble_stage.readout_projects.2.0'      )
     if "pretrained.act_postprocess4.0.project.0" in name:
          lowercase_	:				str													=	name.replace('pretrained.act_postprocess4.0.project.0'     ,			'neck.reassemble_stage.readout_projects.3.0'      )

     # resize blocks
     if "pretrained.act_postprocess1.3" in name:
          lowercase_	:				Dict													=	name.replace('pretrained.act_postprocess1.3'     ,			'neck.reassemble_stage.layers.0.projection'      )
     if "pretrained.act_postprocess1.4" in name:
          lowercase_	:				Dict													=	name.replace('pretrained.act_postprocess1.4'     ,			'neck.reassemble_stage.layers.0.resize'      )
     if "pretrained.act_postprocess2.3" in name:
          lowercase_	:				Optional[Any]													=	name.replace('pretrained.act_postprocess2.3'     ,			'neck.reassemble_stage.layers.1.projection'      )
     if "pretrained.act_postprocess2.4" in name:
          lowercase_	:				Optional[Any]													=	name.replace('pretrained.act_postprocess2.4'     ,			'neck.reassemble_stage.layers.1.resize'      )
     if "pretrained.act_postprocess3.3" in name:
          lowercase_	:				Optional[int]													=	name.replace('pretrained.act_postprocess3.3'     ,			'neck.reassemble_stage.layers.2.projection'      )
     if "pretrained.act_postprocess4.3" in name:
          lowercase_	:				Any													=	name.replace('pretrained.act_postprocess4.3'     ,			'neck.reassemble_stage.layers.3.projection'      )
     if "pretrained.act_postprocess4.4" in name:
          lowercase_	:				str													=	name.replace('pretrained.act_postprocess4.4'     ,			'neck.reassemble_stage.layers.3.resize'      )
     if "pretrained" in name:
          lowercase_	:				List[Any]													=	name.replace('pretrained'     ,			'dpt'      )
     if "bn" in name:
          lowercase_	:				int													=	name.replace('bn'     ,			'batch_norm'      )
     if "head" in name:
          lowercase_	:				str													=	name.replace('head'     ,			'head.head'      )
     if "encoder.norm" in name:
          lowercase_	:				List[Any]													=	name.replace('encoder.norm'     ,			'layernorm'      )
     if "auxlayer" in name:
          lowercase_	:				Any													=	name.replace('auxlayer'     ,			'auxiliary_head.head'      )
     if "backbone" in name:
          lowercase_	:				List[str]													=	name.replace('backbone'     ,			'backbone.bit.encoder'      )

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

     if "stem.conv" in name:
          lowercase_	:				int													=	name.replace('stem.conv'     ,			'bit.embedder.convolution'      )
     if "blocks" in name:
          lowercase_	:				List[str]													=	name.replace('blocks'     ,			'layers'      )
     if "convolution" in name and "backbone" in name:
          lowercase_	:				str													=	name.replace('convolution'     ,			'conv'      )
     if "layer" in name and "backbone" in name:
          lowercase_	:				List[Any]													=	name.replace('layer'     ,			'layers'      )
     if "backbone.bit.encoder.bit" in name:
          lowercase_	:				Union[str, Any]													=	name.replace('backbone.bit.encoder.bit'     ,			'backbone.bit'      )
     if "embedder.conv" in name:
          lowercase_	:				Optional[Any]													=	name.replace('embedder.conv'     ,			'embedder.convolution'      )
     if "backbone.bit.encoder.stem.norm" in name:
          lowercase_	:				List[Any]													=	name.replace('backbone.bit.encoder.stem.norm'     ,			'backbone.bit.embedder.norm'      )
     return name



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Dict     ,			__SCREAMING_SNAKE_CASE							:     Optional[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)
          lowercase_	:				Dict													=	state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight'''      )
          lowercase_	:				Any													=	state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.bias'''      )
          # next, add query, keys and values (in that order) to the state dict
          lowercase_	:				List[Any]													=	in_proj_weight[: config.hidden_size, :]
          lowercase_	:				int													=	in_proj_bias[: config.hidden_size]
          lowercase_	:				Dict													=	in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          lowercase_	:				str													=	in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          lowercase_	:				Tuple													=	in_proj_weight[
              -config.hidden_size :, :
          ]
          lowercase_	:				Dict													=	in_proj_bias[-config.hidden_size :]



def 			lowercase__(       ):
     lowercase_	:				int													=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     lowercase_	:				Dict													=	Image.open(requests.get(_UpperCamelCase     ,			stream=_UpperCamelCase      ).raw      )
     return im



@torch.no_grad()
def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,			__SCREAMING_SNAKE_CASE							:     List[Any]     ,			__SCREAMING_SNAKE_CASE							:     Dict      ):
     lowercase_	:				Dict													=	get_dpt_config(_UpperCamelCase      )
     # load original state_dict from URL
     # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
     lowercase_	:				Union[str, Any]													=	torch.load(_UpperCamelCase     ,			map_location='cpu'      )
     # remove certain keys
     remove_ignore_keys_(_UpperCamelCase      )
     # rename keys
     for key in state_dict.copy().keys():
          lowercase_	:				Dict													=	state_dict.pop(_UpperCamelCase      )
          lowercase_	:				Any													=	val
     # read in qkv matrices
     read_in_q_k_v(_UpperCamelCase     ,			_UpperCamelCase      )

     # load HuggingFace model
     lowercase_	:				str													=	DPTForSemanticSegmentation(_UpperCamelCase      ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(_UpperCamelCase      )
     model.load_state_dict(_UpperCamelCase      )
     model.eval()

     # Check outputs on an image
     lowercase_	:				Tuple													=	4_80 if '''ade''' in checkpoint_url else 3_84
     lowercase_	:				List[Any]													=	DPTImageProcessor(size=_UpperCamelCase      )

     lowercase_	:				Any													=	prepare_img()
     lowercase_	:				Optional[int]													=	image_processor(_UpperCamelCase     ,			return_tensors='pt'      )

     # forward pass
     lowercase_	:				Optional[int]													=	model(**_UpperCamelCase      ).logits if '''ade''' in checkpoint_url else model(**_UpperCamelCase      ).predicted_depth

     if show_prediction:
          lowercase_	:				Dict													=	(
              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__":
       __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=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",
       )

       __SCREAMING_SNAKE_CASE    	=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 os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE



class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     int       			=      ''''''
			lowerCamelCase_     :     str       			=      (
			    None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
			)
			lowerCamelCase_     :     str       			=      None  # compression type in fsspec. ex: "gzip"
			lowerCamelCase_     :     str       			=      None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz






			def __init__(self					,							__magic_name__ = ""					,							__magic_name__ = None					,							__magic_name__ = None					,							**__magic_name__    )     ->   Any:


							'''simple docstring'''



							super().__init__(self					,							**__magic_name__    )
							# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
							snake_case_						:    Union[str, Any]				=  fsspec.open(
							    __magic_name__					,							mode='''rb'''					,							protocol=__magic_name__					,							compression=self.compression					,							client_kwargs={
							        '''requote_redirect_url''': False,  # see https://github.com/huggingface/datasets/pull/5459
							        '''trust_env''': True,  # Enable reading proxy env variables.
							        **(target_options or {}).pop('''client_kwargs'''					,							{}    ),  # To avoid issues if it was already passed.
							    }					,							**(target_options or {})					,							)
							snake_case_						:    Tuple				=  os.path.basename(self.file.path.split('''::'''    )[0]    )
							snake_case_						:    Optional[Any]				=  (
							    self.compressed_name[: self.compressed_name.rindex('''.'''    )]
							    if '''.''' in self.compressed_name
							    else self.compressed_name
							)
							snake_case_						:    Dict				=  None






			@classmethod
			def 			lowerCamelCase  (cls					,							__magic_name__    )     ->   Optional[int]:


							'''simple docstring'''



							return super()._strip_protocol(__magic_name__    ).lstrip('''/'''    )






			def 			lowerCamelCase  (self    )     ->   Union[str, Any]:


							'''simple docstring'''



							if self.dir_cache is None:
											snake_case_						:    Optional[int]				=  {**self.file.fs.info(self.file.path    ), '''name''': self.uncompressed_name}
											snake_case_						:    List[str]				=  {f['''name''']: f}






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   Optional[Any]:


							'''simple docstring'''



							return self.file.open().read()






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__ = "rb"					,							__magic_name__=None					,							__magic_name__=True					,							__magic_name__=None					,							**__magic_name__					,							)     ->   int:


							'''simple docstring'''



							snake_case_						:    Union[str, Any]				=  self._strip_protocol(__magic_name__    )
							if mode != "rb":
											raise ValueError(F'''Tried to read with mode {mode} on file {self.file.path} opened with mode \'rb\''''    )
							return self.file.open()




class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     Union[str, Any]       			=      '''bz2'''
			lowerCamelCase_     :     Any       			=      '''bz2'''
			lowerCamelCase_     :     int       			=      '''.bz2'''




class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     Union[str, Any]       			=      '''gzip'''
			lowerCamelCase_     :     Dict       			=      '''gzip'''
			lowerCamelCase_     :     int       			=      '''.gz'''




class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     Any       			=      '''lz4'''
			lowerCamelCase_     :     Any       			=      '''lz4'''
			lowerCamelCase_     :     Optional[Any]       			=      '''.lz4'''




class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     Tuple       			=      '''xz'''
			lowerCamelCase_     :     Any       			=      '''xz'''
			lowerCamelCase_     :     int       			=      '''.xz'''




class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     Union[str, Any]       			=      '''zstd'''
			lowerCamelCase_     :     Tuple       			=      '''zstd'''
			lowerCamelCase_     :     Any       			=      '''.zst'''






			def __init__(self					,							__magic_name__					,							__magic_name__ = "rb"					,							__magic_name__ = None					,							__magic_name__ = None					,							__magic_name__ = DEFAULT_BLOCK_SIZE					,							**__magic_name__					,							)     ->   Tuple:


							'''simple docstring'''



							super().__init__(
							    fo=__magic_name__					,							mode=__magic_name__					,							target_protocol=__magic_name__					,							target_options=__magic_name__					,							block_size=__magic_name__					,							**__magic_name__					,							)
							# We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
							#
							# File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
							#     out.close = close
							# AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
							#
							# see https://github.com/intake/filesystem_spec/issues/725
							snake_case_						:    Dict				=  self.file.__enter__



							class 					__lowerCAmelCase       :






										def __init__(self					,							__magic_name__    )     ->   List[Any]:


														'''simple docstring'''



														snake_case_						:    str				=  file_






										def __enter__(self    )     ->   List[Any]:


														'''simple docstring'''



														self._file.__enter__()
														return self






										def __exit__(self					,							*__magic_name__					,							**__magic_name__    )     ->   int:


														'''simple docstring'''



														self._file.__exit__(*__magic_name__					,							**__magic_name__    )






										def __iter__(self    )     ->   Optional[int]:


														'''simple docstring'''



														return iter(self._file    )






										def 			lowerCamelCase  (self    )     ->   Union[str, Any]:


														'''simple docstring'''



														return next(self._file    )






										def __getattr__(self					,							__magic_name__    )     ->   str:


														'''simple docstring'''



														return getattr(self._file					,							__magic_name__    )

							def fixed_enter(*__magic_name__					,							**__magic_name__    ):
											return WrappedFile(_enter(*__magic_name__					,							**__magic_name__    )    )

							snake_case_						:    Tuple				=  fixed_enter
