
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
							from .features import FeatureType







@dataclass
class 				lowercase		:
							lowercase_       :       str       					=42
							lowercase_       :       Tuple       					=None
							# Automatically constructed
							lowercase_       :       Dict       					='''dict'''
							lowercase_       :       Any       					=None
							lowercase_       :       Optional[int]       					=field(default='''Translation'''       ,  init=A__       ,  repr=A__		)




							def __call__(				self):
												return pa.struct({lang: pa.string() for lang in sorted(self.languages)})





							def  A__      (				self):
												from .features import Value

												return {k: Value('''string''') for k in sorted(self.languages)}







@dataclass
class 				lowercase		:
							lowercase_       :       List[Any]       					=None
							lowercase_       :       Union[str, Any]       					=None
							lowercase_       :       Optional[int]       					=None
							# Automatically constructed
							lowercase_       :       Optional[int]       					='''dict'''
							lowercase_       :       Union[str, Any]       					=None
							lowercase_       :       List[str]       					=field(default='''TranslationVariableLanguages'''       ,  init=A__       ,  repr=A__		)




							def  A__      (				self):
												lowercase							=						sorted(set(self.languages)) if self.languages else None
												lowercase							=						len(self.languages) if self.languages else None




							def __call__(				self):
												return pa.struct({'''language''': pa.list_(pa.string()), '''translation''': pa.list_(pa.string())})




							def  A__      (				self   ,A__):
												lowercase							=						set(self.languages)
												if self.languages and set(__snake_case) - lang_set:
																	raise ValueError(
																	    f'Some languages in example ({", ".join(sorted(set(__snake_case) - lang_set))}) are not in valid set ({", ".join(__snake_case)}).')

												# Convert dictionary into tuples, splitting out cases where there are
												# multiple translations for a single language.
												lowercase							=						[]
												for lang, text in translation_dict.items():
																	if isinstance(__snake_case   ,__snake_case):
																						translation_tuples.append((lang, text))
																	else:
																						translation_tuples.extend([(lang, el) for el in text])

        # Ensure translations are in ascending order by language code.
												lowercase							=						zip(*sorted(__snake_case))

												return {"language": languages, "translation": translations}





							def  A__      (				self):
												from .features import Sequence, Value

												return {
												    "language": Sequence(Value('''string''')),
												    "translation": Sequence(Value('''string''')),
												}









'''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__:   Optional[int]								=			logging.get_logger(__name__)


def  snake_case_     (					_lowerCAmelCase  :						Optional[int]  )      ->   Optional[int]:
							UpperCAmelCase :       Tuple 							=		DPTConfig(embedding_type='''hybrid'''  )

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

							if "nyu" or "midas" in checkpoint_url:
														UpperCAmelCase :       Optional[Any] 							=		768
														UpperCAmelCase :       Tuple 							=		[1, 1, 1, 0.5]
														UpperCAmelCase :       int 							=		[256, 512, 768, 768]
														UpperCAmelCase :       Any 							=		150
														UpperCAmelCase :       Tuple 							=		16
														UpperCAmelCase :       Any 							=		(1, 384, 384)
														UpperCAmelCase :       Optional[Any] 							=		False
														UpperCAmelCase :       Tuple 							=		'''project'''

							if "ade" in checkpoint_url:
														UpperCAmelCase :       Any 							=		True
														UpperCAmelCase :       str 							=		768
														UpperCAmelCase :       Optional[int] 							=		[1, 1, 1, 0.5]
														UpperCAmelCase :       List[Any] 							=		150
														UpperCAmelCase :       List[Any] 							=		16
														UpperCAmelCase :       str 							=		'''huggingface/label-files'''
														UpperCAmelCase :       Tuple 							=		'''ade20k-id2label.json'''
														UpperCAmelCase :       Any 							=		json.load(open(cached_download(hf_hub_url(_lowerCAmelCase   ,      _lowerCAmelCase   ,      repo_type='''dataset'''  )  )   ,      '''r'''  )  )
														UpperCAmelCase :       Optional[Any] 							=		{int(_lowerCAmelCase  ): v for k, v in idalabel.items()}
														UpperCAmelCase :       List[Any] 							=		idalabel
														UpperCAmelCase :       Optional[int] 							=		{v: k for k, v in idalabel.items()}
														UpperCAmelCase :       Union[str, Any] 							=		[1, 150, 480, 480]

							return config, expected_shape


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


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

							if ".." in name:
														UpperCAmelCase :       Optional[int] 							=		name.replace('''..'''   ,      '''.'''  )

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


def  snake_case_     (					_lowerCAmelCase  :						List[str]   ,      _lowerCAmelCase  :						List[Any]  )      ->   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)
														UpperCAmelCase :       Optional[int] 							=		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 :       Tuple 							=		in_proj_weight[: config.hidden_size, :]
														UpperCAmelCase :       int 							=		in_proj_bias[: config.hidden_size]
														UpperCAmelCase :       List[str] 							=		in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														UpperCAmelCase :       List[str] 							=		in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														UpperCAmelCase :       str 							=		in_proj_weight[
														    -config.hidden_size :, :
														]
														UpperCAmelCase :       Union[str, Any] 							=		in_proj_bias[-config.hidden_size :]


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


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

							# load HuggingFace model
							UpperCAmelCase :       Optional[Any] 							=		DPTForSemanticSegmentation(_lowerCAmelCase  ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(_lowerCAmelCase  )
							model.load_state_dict(_lowerCAmelCase  )
							model.eval()

							# Check outputs on an image
							UpperCAmelCase :       int 							=		480 if '''ade''' in checkpoint_url else 384
							UpperCAmelCase :       List[Any] 							=		DPTImageProcessor(size=_lowerCAmelCase  )

							UpperCAmelCase :       Dict 							=		prepare_img()
							UpperCAmelCase :       Optional[int] 							=		image_processor(_lowerCAmelCase   ,      return_tensors='''pt'''  )

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

							if show_prediction:
														UpperCAmelCase :       Dict 							=		(
														    torch.nn.functional.interpolate(
														        outputs.unsqueeze(1  )   ,      size=(image.size[1], image.size[0])   ,      mode='''bicubic'''   ,      align_corners=_lowerCAmelCase   ,      )
														    .squeeze()
														    .cpu()
														    .numpy()
														)

														Image.fromarray((prediction / prediction.max()) * 255  ).show()

							if pytorch_dump_folder_path is not None:
														Path(_lowerCAmelCase  ).mkdir(exist_ok=_lowerCAmelCase  )
														print(f"""Saving model to {pytorch_dump_folder_path}"""  )
														model.save_pretrained(_lowerCAmelCase  )
														print(f"""Saving image processor to {pytorch_dump_folder_path}"""  )
														image_processor.save_pretrained(_lowerCAmelCase  )

							if push_to_hub:
														model.push_to_hub('''ybelkada/dpt-hybrid-midas'''  )
														image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas'''  )


if __name__ == "__main__":
						UpperCamelCase__:   Dict								=			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",
						)

						UpperCamelCase__:   Tuple								=			parser.parse_args()
						convert_dpt_checkpoint(
						    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
						)





