


'''simple docstring'''



import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__=False					):






			try:
						UpperCAmelCase__  :					Optional[Any]        =     os.environ[key]
			except KeyError:
						# KEY isn't set, default to `default`.
						UpperCAmelCase__  :					List[str]        =     default
			else:
						# KEY is set, convert it to True or False.
						try:
									UpperCAmelCase__  :					Dict        =     strtobool(__SCREAMING_SNAKE_CASE					)
						except ValueError:
									# More values are supported, but let's keep the message simple.
									raise ValueError(f'''If set, {key} must be yes or no.'''					)
			return _value


__A	=parse_flag_from_env('RUN_SLOW', default=False)







def     _UpperCamelCase      (		UpperCamelCase__					):
			return unittest.skip("""Test was skipped"""					)(__SCREAMING_SNAKE_CASE					)







def     _UpperCamelCase      (		UpperCamelCase__					):
			return unittest.skipUnless(_run_slow_tests ,      """test is slow"""					)(__SCREAMING_SNAKE_CASE					)







def     _UpperCamelCase      (		UpperCamelCase__					):
			return unittest.skipUnless(not torch.cuda.is_available() ,      """test requires only a CPU"""					)(__SCREAMING_SNAKE_CASE					)







def     _UpperCamelCase      (		UpperCamelCase__					):
			return unittest.skipUnless(torch.cuda.is_available() ,      """test requires a GPU"""					)(__SCREAMING_SNAKE_CASE					)







def     _UpperCamelCase      (		UpperCamelCase__					):
			return unittest.skipUnless(is_xpu_available() ,      """test requires a XPU"""					)(__SCREAMING_SNAKE_CASE					)







def     _UpperCamelCase      (		UpperCamelCase__					):
			return unittest.skipUnless(is_mps_available() ,      """test requires a `mps` backend support in `torch`"""					)(__SCREAMING_SNAKE_CASE					)







def     _UpperCamelCase      (		UpperCamelCase__					):
			return unittest.skipUnless(
			    is_transformers_available() and is_datasets_available() ,      """test requires the Hugging Face suite"""					)(__SCREAMING_SNAKE_CASE					)







def     _UpperCamelCase      (		UpperCamelCase__					):
			return unittest.skipUnless(is_bnb_available() ,      """test requires the bitsandbytes library"""					)(__SCREAMING_SNAKE_CASE					)







def     _UpperCamelCase      (		UpperCamelCase__					):
			return unittest.skipUnless(is_tpu_available() ,      """test requires TPU"""					)(__SCREAMING_SNAKE_CASE					)







def     _UpperCamelCase      (		UpperCamelCase__					):
			return unittest.skipUnless(torch.cuda.device_count() == 1 ,      """test requires a GPU"""					)(__SCREAMING_SNAKE_CASE					)







def     _UpperCamelCase      (		UpperCamelCase__					):
			return unittest.skipUnless(torch.xpu.device_count() == 1 ,      """test requires a XPU"""					)(__SCREAMING_SNAKE_CASE					)







def     _UpperCamelCase      (		UpperCamelCase__					):
			return unittest.skipUnless(torch.cuda.device_count() > 1 ,      """test requires multiple GPUs"""					)(__SCREAMING_SNAKE_CASE					)







def     _UpperCamelCase      (		UpperCamelCase__					):
			return unittest.skipUnless(torch.xpu.device_count() > 1 ,      """test requires multiple XPUs"""					)(__SCREAMING_SNAKE_CASE					)







def     _UpperCamelCase      (		UpperCamelCase__					):
			return unittest.skipUnless(is_safetensors_available() ,      """test requires safetensors"""					)(__SCREAMING_SNAKE_CASE					)







def     _UpperCamelCase      (		UpperCamelCase__					):
			return unittest.skipUnless(is_deepspeed_available() ,      """test requires DeepSpeed"""					)(__SCREAMING_SNAKE_CASE					)







def     _UpperCamelCase      (		UpperCamelCase__					):
			return unittest.skipUnless(is_torch_version(""">=""" ,      """1.12.0"""					) ,      """test requires torch version >= 1.12.0"""					)(__SCREAMING_SNAKE_CASE					)







def     _UpperCamelCase      (		UpperCamelCase__=None ,      UpperCamelCase__=None					):






			if test_case is None:
						return partial(__SCREAMING_SNAKE_CASE ,      version=__SCREAMING_SNAKE_CASE					)
			return unittest.skipUnless(is_torch_version(""">=""" ,      __SCREAMING_SNAKE_CASE					) ,      f'''test requires torch version >= {version}'''					)(__SCREAMING_SNAKE_CASE					)







def     _UpperCamelCase      (		UpperCamelCase__					):
			return unittest.skipUnless(is_tensorboard_available() ,      """test requires Tensorboard"""					)(__SCREAMING_SNAKE_CASE					)







def     _UpperCamelCase      (		UpperCamelCase__					):
			return unittest.skipUnless(is_wandb_available() ,      """test requires wandb"""					)(__SCREAMING_SNAKE_CASE					)







def     _UpperCamelCase      (		UpperCamelCase__					):
			return unittest.skipUnless(is_comet_ml_available() ,      """test requires comet_ml"""					)(__SCREAMING_SNAKE_CASE					)


__A	=(
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)







def     _UpperCamelCase      (		UpperCamelCase__					):
			return unittest.skipUnless(
			    _atleast_one_tracker_available ,      """test requires at least one tracker to be available and for `comet_ml` to not be installed""" ,      )(__SCREAMING_SNAKE_CASE					)


class 		_snake_case					(     unittest.TestCase	):
					lowerCAmelCase      :str   =					True




					@classmethod
					def 				snake_case__						(      cls):
								UpperCAmelCase__  :					Optional[Any]        =     tempfile.mkdtemp()




					@classmethod
					def 				snake_case__						(      cls):
								if os.path.exists(cls.tmpdir):
											shutil.rmtree(cls.tmpdir)




					def 				snake_case__						(      self):
								if self.clear_on_setup:
											for path in Path(self.tmpdir).glob("""**/*"""):
														if path.is_file():
																	path.unlink()
														elif path.is_dir():
																	shutil.rmtree(lowercase_)


class 		_snake_case					(     unittest.TestCase	):




					def 				snake_case__						(      self):
								super().tearDown()
								# Reset the state of the AcceleratorState singleton.
								AcceleratorState._reset_state()
								PartialState._reset_state()


class 		_snake_case					(     unittest.TestCase	):




					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					Dict        =     mocks if isinstance(lowercase_							,							(tuple, list)) else [mocks]
								for m in self.mocks:
											m.start()
											self.addCleanup(m.stop)







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					Any        =     AcceleratorState()
			UpperCAmelCase__  :					Optional[int]        =     tensor[None].clone().to(state.device					)
			UpperCAmelCase__  :					Any        =     gather(__SCREAMING_SNAKE_CASE					).cpu()
			UpperCAmelCase__  :					List[str]        =     tensor[0].cpu()
			for i in range(tensors.shape[0]					):
						if not torch.equal(tensors[i] ,      __SCREAMING_SNAKE_CASE					):
									return False
			return True


class 		_snake_case					:




					def __init__(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					Optional[Any]        =     returncode
								UpperCAmelCase__  :					List[Any]        =     stdout
								UpperCAmelCase__  :					Tuple        =     stderr







async def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):






			while True:
						UpperCAmelCase__  :					List[str]        =     await stream.readline()
						if line:
									callback(__SCREAMING_SNAKE_CASE					)
						else:
									break







async def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__=None ,      UpperCamelCase__=None ,      UpperCamelCase__=None ,      UpperCamelCase__=False ,      UpperCamelCase__=False					):






			if echo:
						print("""\nRunning: """ ,      """ """.join(__SCREAMING_SNAKE_CASE					)					)

			UpperCAmelCase__  :					List[Any]        =     await asyncio.create_subprocess_exec(
			    cmd[0] ,      *cmd[1:] ,      stdin=__SCREAMING_SNAKE_CASE ,      stdout=asyncio.subprocess.PIPE ,      stderr=asyncio.subprocess.PIPE ,      env=__SCREAMING_SNAKE_CASE ,      )

			# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
			# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
			#
			# If it starts hanging, will need to switch to the following code. The problem is that no data
			# will be seen until it's done and if it hangs for example there will be no debug info.
			# out, err = await p.communicate()
			# return _RunOutput(p.returncode, out, err)

			UpperCAmelCase__  :					Any        =     []
			UpperCAmelCase__  :					Any        =     []

			def tee(UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__=""					):
						UpperCAmelCase__  :					int        =     line.decode("""utf-8"""					).rstrip()
						sink.append(__SCREAMING_SNAKE_CASE					)
						if not quiet:
									print(__SCREAMING_SNAKE_CASE ,      __SCREAMING_SNAKE_CASE ,      file=__SCREAMING_SNAKE_CASE					)

    # XXX: the timeout doesn't seem to make any difference here
			await asyncio.wait(
			    [
			        asyncio.create_task(_read_stream(p.stdout ,      lambda UpperCamelCase__					: tee(__SCREAMING_SNAKE_CASE ,      __SCREAMING_SNAKE_CASE ,      sys.stdout ,      label="""stdout:"""					)					)					),
			        asyncio.create_task(_read_stream(p.stderr ,      lambda UpperCamelCase__					: tee(__SCREAMING_SNAKE_CASE ,      __SCREAMING_SNAKE_CASE ,      sys.stderr ,      label="""stderr:"""					)					)					),
			    ] ,      timeout=__SCREAMING_SNAKE_CASE ,      )
			return _RunOutput(await p.wait() ,      __SCREAMING_SNAKE_CASE ,      __SCREAMING_SNAKE_CASE					)







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__=None ,      UpperCamelCase__=None ,      UpperCamelCase__=1_8_0 ,      UpperCamelCase__=False ,      UpperCamelCase__=True					):
			UpperCAmelCase__  :					Optional[int]        =     asyncio.get_event_loop()
			UpperCAmelCase__  :					Union[str, Any]        =     loop.run_until_complete(
			    _stream_subprocess(__SCREAMING_SNAKE_CASE ,      env=__SCREAMING_SNAKE_CASE ,      stdin=__SCREAMING_SNAKE_CASE ,      timeout=__SCREAMING_SNAKE_CASE ,      quiet=__SCREAMING_SNAKE_CASE ,      echo=__SCREAMING_SNAKE_CASE					)					)

			UpperCAmelCase__  :					Optional[Any]        =     " ".join(__SCREAMING_SNAKE_CASE					)
			if result.returncode > 0:
						UpperCAmelCase__  :					List[Any]        =     "\n".join(result.stderr					)
						raise RuntimeError(
						    f'''\'{cmd_str}\' failed with returncode {result.returncode}\n\n'''
						    f'''The combined stderr from workers follows:\n{stderr}'''					)

			return result


class 		_snake_case					(     snake_case__	):
					pass







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__=False					):






			try:
						UpperCAmelCase__  :					Dict        =     subprocess.check_output(__SCREAMING_SNAKE_CASE ,      stderr=subprocess.STDOUT					)
						if return_stdout:
									if hasattr(__SCREAMING_SNAKE_CASE ,      """decode"""					):
												UpperCAmelCase__  :					int        =     output.decode("""utf-8"""					)
									return output
			except subprocess.CalledProcessError as e:
						raise SubprocessCallException(
						    f'''Command `{' '.join(__SCREAMING_SNAKE_CASE					)}` failed with the following error:\n\n{e.output.decode()}'''					) from e



'''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()
__A	=logging.get_logger(__name__)







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					str        =     DPTConfig(embedding_type="""hybrid"""					)

			if "large" in checkpoint_url:
						UpperCAmelCase__  :					List[str]        =     1_0_2_4
						UpperCAmelCase__  :					List[str]        =     4_0_9_6
						UpperCAmelCase__  :					Optional[int]        =     2_4
						UpperCAmelCase__  :					str        =     1_6
						UpperCAmelCase__  :					Optional[Any]        =     [5, 1_1, 1_7, 2_3]
						UpperCAmelCase__  :					Any        =     [2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
						UpperCAmelCase__  :					Union[str, Any]        =     (1, 3_8_4, 3_8_4)

			if "nyu" or "midas" in checkpoint_url:
						UpperCAmelCase__  :					List[str]        =     7_6_8
						UpperCAmelCase__  :					Union[str, Any]        =     [1, 1, 1, 0.5]
						UpperCAmelCase__  :					Union[str, Any]        =     [2_5_6, 5_1_2, 7_6_8, 7_6_8]
						UpperCAmelCase__  :					int        =     1_5_0
						UpperCAmelCase__  :					Any        =     1_6
						UpperCAmelCase__  :					Optional[Any]        =     (1, 3_8_4, 3_8_4)
						UpperCAmelCase__  :					Optional[int]        =     False
						UpperCAmelCase__  :					int        =     """project"""

			if "ade" in checkpoint_url:
						UpperCAmelCase__  :					Any        =     True
						UpperCAmelCase__  :					str        =     7_6_8
						UpperCAmelCase__  :					Optional[int]        =     [1, 1, 1, 0.5]
						UpperCAmelCase__  :					str        =     1_5_0
						UpperCAmelCase__  :					str        =     1_6
						UpperCAmelCase__  :					List[Any]        =     """huggingface/label-files"""
						UpperCAmelCase__  :					Optional[int]        =     """ade20k-id2label.json"""
						UpperCAmelCase__  :					Optional[int]        =     json.load(open(cached_download(hf_hub_url(UpperCamelCase__ ,      UpperCamelCase__ ,      repo_type="""dataset"""					)					) ,      """r"""					)					)
						UpperCAmelCase__  :					List[str]        =     {int(UpperCamelCase__					): v for k, v in idalabel.items()}
						UpperCAmelCase__  :					List[str]        =     idalabel
						UpperCAmelCase__  :					List[Any]        =     {v: k for k, v in idalabel.items()}
						UpperCAmelCase__  :					Optional[Any]        =     [1, 1_5_0, 4_8_0, 4_8_0]

			return config, expected_shape







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					Optional[int]        =     ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
			for k in ignore_keys:
						state_dict.pop(UpperCamelCase__ ,      UpperCamelCase__					)







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

			if ".." in name:
						UpperCAmelCase__  :					Dict        =     name.replace("""..""" ,      """."""					)

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







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







def     _UpperCamelCase      (		):
			UpperCAmelCase__  :					str        =     """http://images.cocodataset.org/val2017/000000039769.jpg"""
			UpperCAmelCase__  :					int        =     Image.open(requests.get(UpperCamelCase__ ,      stream=UpperCamelCase__					).raw					)
			return im







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

			# load HuggingFace model
			UpperCAmelCase__  :					Tuple        =     DPTForSemanticSegmentation(UpperCamelCase__					) if """ade""" in checkpoint_url else DPTForDepthEstimation(UpperCamelCase__					)
			model.load_state_dict(UpperCamelCase__					)
			model.eval()

			# Check outputs on an image
			UpperCAmelCase__  :					Tuple        =     4_8_0 if """ade""" in checkpoint_url else 3_8_4
			UpperCAmelCase__  :					Tuple        =     DPTImageProcessor(size=UpperCamelCase__					)

			UpperCAmelCase__  :					Union[str, Any]        =     prepare_img()
			UpperCAmelCase__  :					Any        =     image_processor(UpperCamelCase__ ,      return_tensors="""pt"""					)

			# forward pass
			UpperCAmelCase__  :					Dict        =     model(**UpperCamelCase__					).logits if """ade""" in checkpoint_url else model(**UpperCamelCase__					).predicted_depth

			if show_prediction:
						UpperCAmelCase__  :					str        =     (
						    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_5_5					).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	=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	=parser.parse_args()
		convert_dpt_checkpoint(
		    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
		)