


"""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     :   str			    =			logging.get_logger(__name__)


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			DPTConfig(embedding_type="hybrid"      )

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

			if "nyu" or "midas" in checkpoint_url:
						__lowerCAmelCase				    =			768
						__lowerCAmelCase				    =			[1, 1, 1, 0.5]
						__lowerCAmelCase				    =			[256, 512, 768, 768]
						__lowerCAmelCase				    =			150
						__lowerCAmelCase				    =			16
						__lowerCAmelCase				    =			(1, 384, 384)
						__lowerCAmelCase				    =			False
						__lowerCAmelCase				    =			"project"

			if "ade" in checkpoint_url:
						__lowerCAmelCase				    =			True
						__lowerCAmelCase				    =			768
						__lowerCAmelCase				    =			[1, 1, 1, 0.5]
						__lowerCAmelCase				    =			150
						__lowerCAmelCase				    =			16
						__lowerCAmelCase				    =			"huggingface/label-files"
						__lowerCAmelCase				    =			"ade20k-id2label.json"
						__lowerCAmelCase				    =			json.load(open(cached_download(hf_hub_url(_UpperCamelCase    , _UpperCamelCase    , repo_type="dataset"      )      )    , "r"      )      )
						__lowerCAmelCase				    =			{int(_UpperCamelCase      ): v for k, v in idalabel.items()}
						__lowerCAmelCase				    =			idalabel
						__lowerCAmelCase				    =			{v: k for k, v in idalabel.items()}
						__lowerCAmelCase				    =			[1, 150, 480, 480]

			return config, expected_shape


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			["pretrained.model.head.weight", "pretrained.model.head.bias"]
			for k in ignore_keys:
						state_dict.pop(_UpperCamelCase    , _UpperCamelCase      )


def _lowerCamelCase	(		_UpperCamelCase      ):

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

			if ".." in name:
						__lowerCAmelCase				    =			name.replace(".."    , "."      )

			if "stem.conv" in name:
						__lowerCAmelCase				    =			name.replace("stem.conv"    , "bit.embedder.convolution"      )
			if "blocks" in name:
						__lowerCAmelCase				    =			name.replace("blocks"    , "layers"      )
			if "convolution" in name and "backbone" in name:
						__lowerCAmelCase				    =			name.replace("convolution"    , "conv"      )
			if "layer" in name and "backbone" in name:
						__lowerCAmelCase				    =			name.replace("layer"    , "layers"      )
			if "backbone.bit.encoder.bit" in name:
						__lowerCAmelCase				    =			name.replace("backbone.bit.encoder.bit"    , "backbone.bit"      )
			if "embedder.conv" in name:
						__lowerCAmelCase				    =			name.replace("embedder.conv"    , "embedder.convolution"      )
			if "backbone.bit.encoder.stem.norm" in name:
						__lowerCAmelCase				    =			name.replace("backbone.bit.encoder.stem.norm"    , "backbone.bit.embedder.norm"      )
			return name


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''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				    =			state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.weight"      )
						__lowerCAmelCase				    =			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				    =			in_proj_weight[: config.hidden_size, :]
						__lowerCAmelCase				    =			in_proj_bias[: config.hidden_size]
						__lowerCAmelCase				    =			in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						__lowerCAmelCase				    =			in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						__lowerCAmelCase				    =			in_proj_weight[
						    -config.hidden_size :, :
						]
						__lowerCAmelCase				    =			in_proj_bias[-config.hidden_size :]


def _lowerCamelCase	(		):

			'''simple docstring'''




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


@torch.no_grad()
def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase    ,	__lowerCAmelCase				    =			get_dpt_config(_UpperCamelCase      )
			# load original state_dict from URL
			# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
			__lowerCAmelCase				    =			torch.load(_UpperCamelCase    , map_location="cpu"      )
			# remove certain keys
			remove_ignore_keys_(_UpperCamelCase      )
			# rename keys
			for key in state_dict.copy().keys():
						__lowerCAmelCase				    =			state_dict.pop(_UpperCamelCase      )
						__lowerCAmelCase				    =			val
			# read in qkv matrices
			read_in_q_k_v(_UpperCamelCase    , _UpperCamelCase      )

			# load HuggingFace model
			__lowerCAmelCase				    =			DPTForSemanticSegmentation(_UpperCamelCase      ) if "ade" in checkpoint_url else DPTForDepthEstimation(_UpperCamelCase      )
			model.load_state_dict(_UpperCamelCase      )
			model.eval()

			# Check outputs on an image
			__lowerCAmelCase				    =			480 if "ade" in checkpoint_url else 384
			__lowerCAmelCase				    =			DPTImageProcessor(size=_UpperCamelCase      )

			__lowerCAmelCase				    =			prepare_img()
			__lowerCAmelCase				    =			image_processor(_UpperCamelCase    , return_tensors="pt"      )

			# forward pass
			__lowerCAmelCase				    =			model(**_UpperCamelCase      ).logits if "ade" in checkpoint_url else model(**_UpperCamelCase      ).predicted_depth

			if show_prediction:
						__lowerCAmelCase				    =			(
						    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()) * 255      ).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     :   Any			    =			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     :   str			    =			parser.parse_args()
	convert_dpt_checkpoint(
	    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
	)





'''simple docstring'''




import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler





class     __magic_name__						:







			def __init__(	self				:  str				,			lowercase_				:  Dict    ):
										if isinstance(lowercase_				,			lowercase_    ):
																	# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
																	# modified it, it will not be accepted here again, since `auto` values would have been overridden
																	lowercase_					:			List[Any]										=       deepcopy(lowercase_    )
										elif os.path.exists(lowercase_    ):
																	with io.open(lowercase_				,			"""r"""				,			encoding="""utf-8"""    ) as f:
																								lowercase_					:			Union[str, Any]										=       json.load(lowercase_    )
										else:
																	try:
																								lowercase_					:			int										=       baseaa.urlsafe_baadecode(lowercase_    ).decode("""utf-8"""    )
																								lowercase_					:			str										=       json.loads(lowercase_    )
																	except (UnicodeDecodeError, AttributeError, ValueError):
																								raise ValueError(
																								    f'''Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}'''    )

										lowercase_					:			Any										=       config

										self.set_stage_and_offload()







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										# zero stage - this is done as early as possible, before model is created, to allow
										# ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
										# during ``zero.Init()`` which needs to know the dtype, and some other hparams.
										lowercase_					:			Tuple										=       self.get_value("""zero_optimization.stage"""				,			-1    )

										# offload
										lowercase_					:			str										=       False
										if self.is_zeroa() or self.is_zeroa():
																	lowercase_					:			Dict										=       set(["""cpu""", """nvme"""]    )
																	lowercase_					:			List[Any]										=       set(
																	    [
																	        self.get_value("""zero_optimization.offload_optimizer.device"""    ),
																	        self.get_value("""zero_optimization.offload_param.device"""    ),
																	    ]    )
																	if len(offload_devices & offload_devices_valid    ) > 0:
																								lowercase_					:			Tuple										=       True







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]				,			lowercase_				:  Any    ):
										lowercase_					:			Optional[Any]										=       self.config

										# find the config node of interest if it exists
										lowercase_					:			Tuple										=       ds_key_long.split("""."""    )
										lowercase_					:			Union[str, Any]										=       nodes.pop()
										for node in nodes:
																	lowercase_					:			List[str]										=       config.get(lowercase_    )
																	if config is None:
																								return None, ds_key

										return config, ds_key







			def 	SCREAMING_SNAKE_CASE_     (	self				:  str				,			lowercase_				:  List[str]				,			lowercase_				:  List[str]=None    ):
										lowercase_						,						lowercase_					:			List[Any]										=       self.find_config_node(lowercase_    )
										if config is None:
																	return default
										return config.get(lowercase_				,			lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]				,			lowercase_				:  Optional[int]				,			lowercase_				:  int=False    ):
										lowercase_					:			int										=       self.config

										# find the config node of interest if it exists
										lowercase_					:			Dict										=       ds_key_long.split("""."""    )
										for node in nodes:
																	lowercase_					:			List[Any]										=       config
																	lowercase_					:			Dict										=       config.get(lowercase_    )
																	if config is None:
																								if must_exist:
																															raise ValueError(f'''Can\'t find {ds_key_long} entry in the config: {self.config}'''    )
																								else:
																															return

        # if found remove it
										if parent_config is not None:
																	parent_config.pop(lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]				,			lowercase_				:  Tuple    ):
										lowercase_					:			str										=       self.get_value(lowercase_    )
										return False if value is None else bool(lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]				,			lowercase_				:  Union[str, Any]    ):
										lowercase_					:			Union[str, Any]										=       self.get_value(lowercase_    )
										return False if value is None else not bool(lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Dict    ):
										return self._stage == 2







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any    ):
										return self._stage == 3







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										return self._offload





class     __magic_name__						:







			def __init__(	self				:  Any				,			lowercase_				:  Union[str, Any]    ):
										lowercase_					:			Any										=       engine







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple				,			lowercase_				:  int				,			**lowercase_				:  str    ):
										# runs backpropagation and handles mixed precision
										self.engine.backward(lowercase_				,			**lowercase_    )

										# Deepspeed's `engine.step` performs the following operations:
										# - gradient accumulation check
										# - gradient clipping
										# - optimizer step
										# - zero grad
										# - checking overflow
										# - lr_scheduler step (only if engine.lr_scheduler is not None)
										self.engine.step()
										# and this plugin overrides the above calls with no-ops when Accelerate runs under
										# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
										# training loop that works transparently under many training regimes.





class     __magic_name__						(    _UpperCAmelCase):







			def __init__(	self				:  Optional[Any]				,			lowercase_				:  Tuple    ):
										super().__init__(lowercase_				,			device_placement=lowercase_				,			scaler=lowercase_    )
										lowercase_					:			Any										=       hasattr(self.optimizer				,			"""overflow"""    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  int				,			lowercase_				:  Tuple=None    ):
										pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										if self.__has_overflow__:
																	return self.optimizer.overflow
										return False





class     __magic_name__						(    _UpperCAmelCase):







			def __init__(	self				:  Dict				,			lowercase_				:  Optional[Any]				,			lowercase_				:  Tuple    ):
										super().__init__(lowercase_				,			lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]    ):
										pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed





class     __magic_name__						:







			def __init__(	self				:  Any				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  List[str]=0.0_01				,			lowercase_				:  List[str]=0				,			**lowercase_				:  List[Any]    ):
										lowercase_					:			str										=       params
										lowercase_					:			List[Any]										=       lr
										lowercase_					:			int										=       weight_decay
										lowercase_					:			Union[str, Any]										=       kwargs





class     __magic_name__						:







			def __init__(	self				:  Tuple				,			lowercase_				:  Optional[Any]				,			lowercase_				:  List[str]=None				,			lowercase_				:  int=0				,			**lowercase_				:  int    ):
										lowercase_					:			Union[str, Any]										=       optimizer
										lowercase_					:			List[str]										=       total_num_steps
										lowercase_					:			Dict										=       warmup_num_steps
										lowercase_					:			Dict										=       kwargs





