





"""simple docstring"""





import logging
import os
import threading
import time


try:
							import warnings
except ImportError:
							_UpperCamelCase							=      None

try:
							import msvcrt
except ImportError:
							_UpperCamelCase							=      None

try:
							import fcntl
except ImportError:
							_UpperCamelCase							=      None


# Backward compatibility
# ------------------------------------------------
try:
							TimeoutError
except NameError:
							_UpperCamelCase							=      OSError


# Data
# ------------------------------------------------
_UpperCamelCase							=      [
    'Timeout',
    'BaseFileLock',
    'WindowsFileLock',
    'UnixFileLock',
    'SoftFileLock',
    'FileLock',
]

_UpperCamelCase							=      '3.0.12'


_UpperCamelCase							=      None



def 				lowerCAmelCase_			(      ):


							'''simple docstring'''







							global _logger
							__lowerCamelCase      :      Tuple         =_logger or logging.getLogger(__name__   )
							return _logger
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""




				def __init__(     self  :List[str]						,    __lowercase  :str			):
											__lowerCamelCase      :      List[Any]         =lock_file
											return None




				def __str__(     self  :Dict			):
											__lowerCamelCase      :      str         =f'The file lock \'{self.lock_file}\' could not be acquired.'
											return temp

class 		SCREAMING_SNAKE_CASE_							:
				"""simple docstring"""




				def __init__(     self  :str						,    __lowercase  :List[str]			):
											__lowerCamelCase      :      Dict         =lock
											return None




				def __enter__(     self  :Optional[int]			):
											return self.lock




				def __exit__(     self  :str						,    __lowercase  :Any						,    __lowercase  :Any						,    __lowercase  :Dict			):
											self.lock.release()
											return None

class 		SCREAMING_SNAKE_CASE_							:
				"""simple docstring"""




				def __init__(     self  :Tuple						,    __lowercase  :List[str]						,    __lowercase  :Optional[int]=-1						,    __lowercase  :Union[str, Any]=None			):
											__lowerCamelCase      :      List[Any]         =max_filename_length if max_filename_length is not None else 255
											# Hash the filename if it's too long
											__lowerCamelCase      :      Any         =self.hash_filename_if_too_long(__lowercase						,    __lowercase			)
											# The path to the lock file.
											__lowerCamelCase      :      Dict         =lock_file

											# The file descriptor for the *_lock_file* as it is returned by the
											# os.open() function.
											# This file lock is only NOT None, if the object currently holds the
											# lock.
											__lowerCamelCase      :      Optional[Any]         =None

											# The default timeout value.
											__lowerCamelCase      :      str         =timeout

											# We use this lock primarily for the lock counter.
											__lowerCamelCase      :      Any         =threading.Lock()

											# The lock counter is used for implementing the nested locking
											# mechanism. Whenever the lock is acquired, the counter is increased and
											# the lock is only released, when this value is 0 again.
											__lowerCamelCase      :      int         =0
											return None




				@property
				def 					__lowercase  (     self  :Optional[int]			):
											return self._lock_file




				@property
				def 					__lowercase  (     self  :List[str]			):
											return self._timeout




				@timeout.setter
				def 					__lowercase  (     self  :Tuple						,    __lowercase  :Dict			):
											__lowerCamelCase      :      Optional[int]         =float(__lowercase			)
											return None




				def 					__lowercase  (     self  :Union[str, Any]			):
											raise NotImplementedError()




				def 					__lowercase  (     self  :Any			):
											raise NotImplementedError()




				@property
				def 					__lowercase  (     self  :Any			):
											return self._lock_file_fd is not None




				def 					__lowercase  (     self  :str						,    __lowercase  :Tuple=None						,    __lowercase  :List[Any]=0.05			):
											# Use the default timeout, if no timeout is provided.
											if timeout is None:
																		__lowerCamelCase      :      Optional[int]         =self.timeout

											# Increment the number right at the beginning.
											# We can still undo it, if something fails.
											with self._thread_lock:
																		self._lock_counter += 1

											__lowerCamelCase      :      Dict         =id(self			)
											__lowerCamelCase      :      Optional[Any]         =self._lock_file
											__lowerCamelCase      :      Optional[int]         =time.time()
											try:
																		while True:
																									with self._thread_lock:
																																if not self.is_locked:
																																							logger().debug(f'Attempting to acquire lock {lock_id} on {lock_filename}'			)
																																							self._acquire()

																									if self.is_locked:
																																logger().debug(f'Lock {lock_id} acquired on {lock_filename}'			)
																																break
																									elif timeout >= 0 and time.time() - start_time > timeout:
																																logger().debug(f'Timeout on acquiring lock {lock_id} on {lock_filename}'			)
																																raise Timeout(self._lock_file			)
																									else:
																																logger().debug(
																																    f'Lock {lock_id} not acquired on {lock_filename}, waiting {poll_intervall} seconds ...'			)
																																time.sleep(__lowercase			)
											except:  # noqa
																		# Something did go wrong, so decrement the counter.
																		with self._thread_lock:
																									__lowerCamelCase      :      Union[str, Any]         =max(0						,    self._lock_counter - 1			)

																		raise
											return _Acquire_ReturnProxy(lock=self			)




				def 					__lowercase  (     self  :int						,    __lowercase  :Union[str, Any]=False			):
											with self._thread_lock:
																		if self.is_locked:
																									self._lock_counter -= 1

																									if self._lock_counter == 0 or force:
																																__lowerCamelCase      :      str         =id(self			)
																																__lowerCamelCase      :      Union[str, Any]         =self._lock_file

																																logger().debug(f'Attempting to release lock {lock_id} on {lock_filename}'			)
																																self._release()
																																__lowerCamelCase      :      Dict         =0
																																logger().debug(f'Lock {lock_id} released on {lock_filename}'			)

											return None




				def __enter__(     self  :Union[str, Any]			):
											self.acquire()
											return self




				def __exit__(     self  :List[str]						,    __lowercase  :Dict						,    __lowercase  :Tuple						,    __lowercase  :Optional[int]			):
											self.release()
											return None




				def __del__(     self  :Optional[int]			):
											self.release(force=__lowercase			)
											return None




				def 					__lowercase  (     self  :Union[str, Any]						,    __lowercase  :str						,    __lowercase  :int			):
											__lowerCamelCase      :      Any         =os.path.basename(__lowercase			)
											if len(__lowercase			) > max_length and max_length > 0:
																		__lowerCamelCase      :      Tuple         =os.path.dirname(__lowercase			)
																		__lowerCamelCase      :      Optional[int]         =str(hash(__lowercase			)			)
																		__lowerCamelCase      :      List[Any]         =filename[: max_length - len(__lowercase			) - 8] + '''...''' + hashed_filename + '''.lock'''
																		return os.path.join(__lowercase						,    __lowercase			)
											else:
																		return path

class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""




				def __init__(     self  :int						,    __lowercase  :Optional[int]						,    __lowercase  :str=-1						,    __lowercase  :Optional[int]=None			):
											from .file_utils import relative_to_absolute_path

											super().__init__(__lowercase						,    timeout=__lowercase						,    max_filename_length=__lowercase			)
											__lowerCamelCase      :      str         ='''\\\\?\\''' + relative_to_absolute_path(self.lock_file			)




				def 					__lowercase  (     self  :str			):
											__lowerCamelCase      :      List[str]         =os.O_RDWR | os.O_CREAT | os.O_TRUNC

											try:
																		__lowerCamelCase      :      Any         =os.open(self._lock_file						,    __lowercase			)
											except OSError:
																		pass
											else:
																		try:
																									msvcrt.locking(__lowercase						,    msvcrt.LK_NBLCK						,    1			)
																		except OSError:
																									os.close(__lowercase			)
																		else:
																									__lowerCamelCase      :      str         =fd
											return None




				def 					__lowercase  (     self  :Union[str, Any]			):
											__lowerCamelCase      :      Union[str, Any]         =self._lock_file_fd
											__lowerCamelCase      :      Tuple         =None
											msvcrt.locking(__lowercase						,    msvcrt.LK_UNLCK						,    1			)
											os.close(__lowercase			)

											try:
																		os.remove(self._lock_file			)
											# Probably another instance of the application
											# that acquired the file lock.
											except OSError:
																		pass
											return None

class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""




				def __init__(     self  :List[Any]						,    __lowercase  :Tuple						,    __lowercase  :Union[str, Any]=-1						,    __lowercase  :str=None			):
											__lowerCamelCase      :      Dict         =os.statvfs(os.path.dirname(__lowercase			)			).f_namemax
											super().__init__(__lowercase						,    timeout=__lowercase						,    max_filename_length=__lowercase			)




				def 					__lowercase  (     self  :str			):
											__lowerCamelCase      :      List[Any]         =os.O_RDWR | os.O_CREAT | os.O_TRUNC
											__lowerCamelCase      :      Dict         =os.open(self._lock_file						,    __lowercase			)

											try:
																		fcntl.flock(__lowercase						,    fcntl.LOCK_EX | fcntl.LOCK_NB			)
											except OSError:
																		os.close(__lowercase			)
											else:
																		__lowerCamelCase      :      Dict         =fd
											return None




				def 					__lowercase  (     self  :Tuple			):
											# Do not remove the lockfile:
											#
											#   https://github.com/benediktschmitt/py-filelock/issues/31
											#   https://stackoverflow.com/questions/17708885/flock-removing-locked-file-without-race-condition
											__lowerCamelCase      :      Dict         =self._lock_file_fd
											__lowerCamelCase      :      int         =None
											fcntl.flock(__lowercase						,    fcntl.LOCK_UN			)
											os.close(__lowercase			)
											return None

class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""




				def 					__lowercase  (     self  :List[str]			):
											__lowerCamelCase      :      List[Any]         =os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
											try:
																		__lowerCamelCase      :      int         =os.open(self._lock_file						,    __lowercase			)
											except OSError:
																		pass
											else:
																		__lowerCamelCase      :      List[Any]         =fd
											return None




				def 					__lowercase  (     self  :int			):
											os.close(self._lock_file_fd			)
											__lowerCamelCase      :      List[Any]         =None

											try:
																		os.remove(self._lock_file			)
											# The file is already deleted and that's what we want.
											except OSError:
																		pass
											return None

_UpperCamelCase							=      None

if msvcrt:
							_UpperCamelCase							=      WindowsFileLock
elif fcntl:
							_UpperCamelCase							=      UnixFileLock
else:
							_UpperCamelCase							=      SoftFileLock

							if warnings is not None:
														warnings.warn('only soft file lock is available')











"""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 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Any   ):


							'''simple docstring'''







							__lowerCamelCase      :      Tuple         =DPTConfig()

							if "large" in checkpoint_url:
														__lowerCamelCase      :      List[str]         =1024
														__lowerCamelCase      :      Dict         =4096
														__lowerCamelCase      :      Tuple         =24
														__lowerCamelCase      :      str         =16
														__lowerCamelCase      :      Optional[Any]         =[5, 11, 17, 23]
														__lowerCamelCase      :      List[Any]         =[256, 512, 1024, 1024]
														__lowerCamelCase      :      List[str]         =(1, 384, 384)

							if "ade" in checkpoint_url:
														__lowerCamelCase      :      List[Any]         =True

														__lowerCamelCase      :      Union[str, Any]         =150
														__lowerCamelCase      :      Any         ='''huggingface/label-files'''
														__lowerCamelCase      :      List[str]         ='''ade20k-id2label.json'''
														__lowerCamelCase      :      Optional[Any]         =json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						repo_type='''dataset'''   )   )    ,						'''r'''   )   )
														__lowerCamelCase      :      Union[str, Any]         ={int(SCREAMING_SNAKE_CASE   ): v for k, v in idalabel.items()}
														__lowerCamelCase      :      List[Any]         =idalabel
														__lowerCamelCase      :      Dict         ={v: k for k, v in idalabel.items()}
														__lowerCamelCase      :      str         =[1, 150, 480, 480]

							return config, expected_shape



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							str   ):


							'''simple docstring'''







							__lowerCamelCase      :      List[Any]         =['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
							for k in ignore_keys:
														state_dict.pop(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







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

							return name



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							List[str]    ,						SCREAMING_SNAKE_CASE       :							Union[str, Any]   ):


							'''simple docstring'''







							for i in range(config.num_hidden_layers   ):
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														__lowerCamelCase      :      int         =state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.weight'   )
														__lowerCamelCase      :      Union[str, 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
														__lowerCamelCase      :      Tuple         =in_proj_weight[: config.hidden_size, :]
														__lowerCamelCase      :      List[str]         =in_proj_bias[: config.hidden_size]
														__lowerCamelCase      :      Optional[Any]         =in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														__lowerCamelCase      :      Dict         =in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														__lowerCamelCase      :      List[Any]         =in_proj_weight[
														    -config.hidden_size :, :
														]
														__lowerCamelCase      :      Union[str, Any]         =in_proj_bias[-config.hidden_size :]



def 				lowerCAmelCase_			(      ):


							'''simple docstring'''







							__lowerCamelCase      :      List[Any]         ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
							__lowerCamelCase      :      Optional[Any]         =Image.open(requests.get(SCREAMING_SNAKE_CASE    ,						stream=SCREAMING_SNAKE_CASE   ).raw   )
							return im



@torch.no_grad()
def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Any    ,						SCREAMING_SNAKE_CASE       :							Any    ,						SCREAMING_SNAKE_CASE       :							Union[str, Any]    ,						SCREAMING_SNAKE_CASE       :							Union[str, Any]   ):


							'''simple docstring'''







							__lowerCamelCase    ,						__lowerCamelCase      :      Tuple         =get_dpt_config(SCREAMING_SNAKE_CASE   )
							# load original state_dict from URL
							__lowerCamelCase      :      List[str]         =torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE    ,						map_location='''cpu'''   )
							# remove certain keys
							remove_ignore_keys_(SCREAMING_SNAKE_CASE   )
							# rename keys
							for key in state_dict.copy().keys():
														__lowerCamelCase      :      str         =state_dict.pop(SCREAMING_SNAKE_CASE   )
														__lowerCamelCase      :      Any         =val
							# read in qkv matrices
							read_in_q_k_v(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

							# load HuggingFace model
							__lowerCamelCase      :      Dict         =DPTForSemanticSegmentation(SCREAMING_SNAKE_CASE   ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(SCREAMING_SNAKE_CASE   )
							model.load_state_dict(SCREAMING_SNAKE_CASE   )
							model.eval()

							# Check outputs on an image
							__lowerCamelCase      :      Union[str, Any]         =480 if '''ade''' in checkpoint_url else 384
							__lowerCamelCase      :      Dict         =DPTImageProcessor(size=SCREAMING_SNAKE_CASE   )

							__lowerCamelCase      :      Optional[int]         =prepare_img()
							__lowerCamelCase      :      int         =image_processor(SCREAMING_SNAKE_CASE    ,						return_tensors='''pt'''   )

							# forward pass
							__lowerCamelCase      :      Tuple         =model(**SCREAMING_SNAKE_CASE   ).logits if '''ade''' in checkpoint_url else model(**SCREAMING_SNAKE_CASE   ).predicted_depth

							# Assert logits
							__lowerCamelCase      :      Optional[Any]         =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:
														__lowerCamelCase      :      List[str]         =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(SCREAMING_SNAKE_CASE   )
							assert (
							    torch.allclose(outputs[0, 0, :3, :3]    ,						SCREAMING_SNAKE_CASE    ,						atol=1E-4   )
							    if "ade" in checkpoint_url
							    else torch.allclose(outputs[0, :3, :3]    ,						SCREAMING_SNAKE_CASE   )
							)

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

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


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)




