


'''simple docstring'''



import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


__A	=datasets.utils.logging.get_logger(__name__)


@dataclass
class 		_snake_case					(     datasets.BuilderConfig	):
					lowerCAmelCase      :Optional[datasets.Features]   =					None
					lowerCAmelCase      :str   =					"utf-8"
					lowerCAmelCase      :Optional[str]   =					None
					lowerCAmelCase      :Optional[str]   =					None
					lowerCAmelCase      :bool   =					True  # deprecated
					lowerCAmelCase      :Optional[int]   =					None  # deprecated
					lowerCAmelCase      :int   =					10 << 20  # 10MB
					lowerCAmelCase      :Optional[bool]   =					None


class 		_snake_case					(     datasets.ArrowBasedBuilder	):
					lowerCAmelCase      :List[Any]   =					JsonConfig




					def 				snake_case__						(      self):
								if self.config.block_size is not None:
											logger.warning("""The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead""")
											UpperCAmelCase__  :					Tuple        =     self.config.block_size
								if self.config.use_threads is not True:
											logger.warning(
											    """The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore.""")
								if self.config.newlines_in_values is not None:
											raise ValueError("""The JSON loader parameter `newlines_in_values` is no longer supported""")
								return datasets.DatasetInfo(features=self.config.features)




					def 				snake_case__						(      self							,							_lowerCamelCase):
								if not self.config.data_files:
											raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}''')
								UpperCAmelCase__  :					Tuple        =     dl_manager.download_and_extract(self.config.data_files)
								if isinstance(_lowerCamelCase							,							(str, list, tuple)):
											UpperCAmelCase__  :					Tuple        =     data_files
											if isinstance(_lowerCamelCase							,							_lowerCamelCase):
														UpperCAmelCase__  :					Optional[Any]        =     [files]
											UpperCAmelCase__  :					List[str]        =     [dl_manager.iter_files(_lowerCamelCase) for file in files]
											return [datasets.SplitGenerator(name=datasets.Split.TRAIN							,							gen_kwargs={"""files""": files})]
								UpperCAmelCase__  :					List[Any]        =     []
								for split_name, files in data_files.items():
											if isinstance(_lowerCamelCase							,							_lowerCamelCase):
														UpperCAmelCase__  :					Any        =     [files]
											UpperCAmelCase__  :					Dict        =     [dl_manager.iter_files(_lowerCamelCase) for file in files]
											splits.append(datasets.SplitGenerator(name=_lowerCamelCase							,							gen_kwargs={"""files""": files}))
								return splits




					def 				snake_case__						(      self							,							_lowerCamelCase):
								if self.config.features is not None:
											# adding missing columns
											for column_name in set(self.config.features) - set(pa_table.column_names):
														UpperCAmelCase__  :					Dict        =     self.config.features.arrow_schema.field(_lowerCamelCase).type
														UpperCAmelCase__  :					Union[str, Any]        =     pa_table.append_column(_lowerCamelCase							,							pa.array([None] * len(_lowerCamelCase)							,							type=_lowerCamelCase))
											# more expensive cast to support nested structures with keys in a different order
											# allows str <-> int/float or str to Audio for example
											UpperCAmelCase__  :					Tuple        =     table_cast(_lowerCamelCase							,							self.config.features.arrow_schema)
								return pa_table




					def 				snake_case__						(      self							,							_lowerCamelCase):
								for file_idx, file in enumerate(itertools.chain.from_iterable(_lowerCamelCase)):
											# If the file is one json object and if we need to look at the list of items in one specific field
											if self.config.field is not None:
														with open(_lowerCamelCase							,							encoding=self.config.encoding							,							errors=self.config.encoding_errors) as f:
																	UpperCAmelCase__  :					Optional[Any]        =     json.load(_lowerCamelCase)

														# We keep only the field we are interested in
														UpperCAmelCase__  :					Any        =     dataset[self.config.field]

														# We accept two format: a list of dicts or a dict of lists
														if isinstance(_lowerCamelCase							,							(list, tuple)):
																	UpperCAmelCase__  :					Union[str, Any]        =     set().union(*[row.keys() for row in dataset])
																	UpperCAmelCase__  :					Any        =     {col: [row.get(_lowerCamelCase) for row in dataset] for col in keys}
														else:
																	UpperCAmelCase__  :					Dict        =     dataset
														UpperCAmelCase__  :					Tuple        =     pa.Table.from_pydict(_lowerCamelCase)
														yield file_idx, self._cast_table(_lowerCamelCase)

											# If the file has one json object per line
											else:
														with open(_lowerCamelCase							,							"""rb""") as f:
																	UpperCAmelCase__  :					Dict        =     0
																	# Use block_size equal to the chunk size divided by 32 to leverage multithreading
																	# Set a default minimum value of 16kB if the chunk size is really small
																	UpperCAmelCase__  :					Optional[Any]        =     max(self.config.chunksize // 32							,							16 << 10)
																	UpperCAmelCase__  :					Optional[Any]        =     (
																	    self.config.encoding_errors if self.config.encoding_errors is not None else """strict"""
																	)
																	while True:
																				UpperCAmelCase__  :					Optional[Any]        =     f.read(self.config.chunksize)
																				if not batch:
																							break
																				# Finish current line
																				try:
																							batch += f.readline()
																				except (AttributeError, io.UnsupportedOperation):
																							batch += readline(_lowerCamelCase)
																				# PyArrow only accepts utf-8 encoded bytes
																				if self.config.encoding != "utf-8":
																							UpperCAmelCase__  :					List[Any]        =     batch.decode(self.config.encoding							,							errors=_lowerCamelCase).encode("""utf-8""")
																				try:
																							while True:
																										try:
																													UpperCAmelCase__  :					Optional[Any]        =     paj.read_json(
																													    io.BytesIO(_lowerCamelCase)							,							read_options=paj.ReadOptions(block_size=_lowerCamelCase))
																													break
																										except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
																													if (
																													    isinstance(_lowerCamelCase							,							pa.ArrowInvalid)
																													    and "straddling" not in str(_lowerCamelCase)
																													    or block_size > len(_lowerCamelCase)
																													):
																																raise
																													else:
																																# Increase the block size in case it was too small.
																																# The block size will be reset for the next file.
																																logger.debug(
																																    f'''Batch of {len(_lowerCamelCase)} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.''')
																																block_size *= 2
																				except pa.ArrowInvalid as e:
																							try:
																										with open(
																										    _lowerCamelCase							,							encoding=self.config.encoding							,							errors=self.config.encoding_errors) as f:
																													UpperCAmelCase__  :					str        =     json.load(_lowerCamelCase)
																							except json.JSONDecodeError:
																										logger.error(f'''Failed to read file \'{file}\' with error {type(_lowerCamelCase)}: {e}''')
																										raise e
																							# If possible, parse the file as a list of json objects and exit the loop
																							if isinstance(_lowerCamelCase							,							_lowerCamelCase):  # list is the only sequence type supported in JSON
																										try:
																													UpperCAmelCase__  :					Tuple        =     set().union(*[row.keys() for row in dataset])
																													UpperCAmelCase__  :					int        =     {col: [row.get(_lowerCamelCase) for row in dataset] for col in keys}
																													UpperCAmelCase__  :					Union[str, Any]        =     pa.Table.from_pydict(_lowerCamelCase)
																										except (pa.ArrowInvalid, AttributeError) as e:
																													logger.error(f'''Failed to read file \'{file}\' with error {type(_lowerCamelCase)}: {e}''')
																													raise ValueError(f'''Not able to read records in the JSON file at {file}.''') from None
																										yield file_idx, self._cast_table(_lowerCamelCase)
																										break
																							else:
																										logger.error(f'''Failed to read file \'{file}\' with error {type(_lowerCamelCase)}: {e}''')
																										raise ValueError(
																										    f'''Not able to read records in the JSON file at {file}. '''
																										    f'''You should probably indicate the field of the JSON file containing your records. '''
																										    f'''This JSON file contain the following fields: {str(list(dataset.keys()))}. '''
																										    f'''Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. ''') from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																				yield (file_idx, batch_idx), self._cast_table(_lowerCamelCase)
																				batch_idx += 1



'''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
		)