






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      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= DPTConfig(embedding_type='''hybrid''' )

					if "large" in checkpoint_url:
										_A								= 10_24
										_A								= 40_96
										_A								= 24
										_A								= 16
										_A								= [5, 11, 17, 23]
										_A								= [2_56, 5_12, 10_24, 10_24]
										_A								= (1, 3_84, 3_84)

					if "nyu" or "midas" in checkpoint_url:
										_A								= 7_68
										_A								= [1, 1, 1, 0.5]
										_A								= [2_56, 5_12, 7_68, 7_68]
										_A								= 1_50
										_A								= 16
										_A								= (1, 3_84, 3_84)
										_A								= False
										_A								= "project"

					if "ade" in checkpoint_url:
										_A								= True
										_A								= 7_68
										_A								= [1, 1, 1, 0.5]
										_A								= 1_50
										_A								= 16
										_A								= "huggingface/label-files"
										_A								= "ade20k-id2label.json"
										_A								= json.load(open(cached_download(hf_hub_url(_A					,			_A					,			repo_type='''dataset''' ) )					,			'''r''' ) )
										_A								= {int(_A ): v for k, v in idalabel.items()}
										_A								= idalabel
										_A								= {v: k for k, v in idalabel.items()}
										_A								= [1, 1_50, 4_80, 4_80]

					return config, expected_shape




def      __A					(     _lowercase ):

					'''simple docstring'''







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




def      __A					(     _lowercase ):

					'''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
					):
										_A								= name.replace('''pretrained.model'''					,			'''dpt.encoder''' )
					if "pretrained.model" in name:
										_A								= name.replace('''pretrained.model'''					,			'''dpt.embeddings''' )
					if "patch_embed" in name:
										_A								= name.replace('''patch_embed'''					,			'''''' )
					if "pos_embed" in name:
										_A								= name.replace('''pos_embed'''					,			'''position_embeddings''' )
					if "attn.proj" in name:
										_A								= name.replace('''attn.proj'''					,			'''attention.output.dense''' )
					if "proj" in name and "project" not in name:
										_A								= name.replace('''proj'''					,			'''projection''' )
					if "blocks" in name:
										_A								= name.replace('''blocks'''					,			'''layer''' )
					if "mlp.fc1" in name:
										_A								= name.replace('''mlp.fc1'''					,			'''intermediate.dense''' )
					if "mlp.fc2" in name:
										_A								= name.replace('''mlp.fc2'''					,			'''output.dense''' )
					if "norm1" in name and "backbone" not in name:
										_A								= name.replace('''norm1'''					,			'''layernorm_before''' )
					if "norm2" in name and "backbone" not in name:
										_A								= name.replace('''norm2'''					,			'''layernorm_after''' )
					if "scratch.output_conv" in name:
										_A								= name.replace('''scratch.output_conv'''					,			'''head''' )
					if "scratch" in name:
										_A								= name.replace('''scratch'''					,			'''neck''' )
					if "layer1_rn" in name:
										_A								= name.replace('''layer1_rn'''					,			'''convs.0''' )
					if "layer2_rn" in name:
										_A								= name.replace('''layer2_rn'''					,			'''convs.1''' )
					if "layer3_rn" in name:
										_A								= name.replace('''layer3_rn'''					,			'''convs.2''' )
					if "layer4_rn" in name:
										_A								= name.replace('''layer4_rn'''					,			'''convs.3''' )
					if "refinenet" in name:
										_A								= 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
										_A								= name.replace(f"""refinenet{layer_idx}"""					,			f"""fusion_stage.layers.{abs(layer_idx-4 )}""" )
					if "out_conv" in name:
										_A								= name.replace('''out_conv'''					,			'''projection''' )
					if "resConfUnit1" in name:
										_A								= name.replace('''resConfUnit1'''					,			'''residual_layer1''' )
					if "resConfUnit2" in name:
										_A								= name.replace('''resConfUnit2'''					,			'''residual_layer2''' )
					if "conv1" in name:
										_A								= name.replace('''conv1'''					,			'''convolution1''' )
					if "conv2" in name:
										_A								= name.replace('''conv2'''					,			'''convolution2''' )
					# readout blocks
					if "pretrained.act_postprocess1.0.project.0" in name:
										_A								= name.replace('''pretrained.act_postprocess1.0.project.0'''					,			'''neck.reassemble_stage.readout_projects.0.0''' )
					if "pretrained.act_postprocess2.0.project.0" in name:
										_A								= name.replace('''pretrained.act_postprocess2.0.project.0'''					,			'''neck.reassemble_stage.readout_projects.1.0''' )
					if "pretrained.act_postprocess3.0.project.0" in name:
										_A								= name.replace('''pretrained.act_postprocess3.0.project.0'''					,			'''neck.reassemble_stage.readout_projects.2.0''' )
					if "pretrained.act_postprocess4.0.project.0" in name:
										_A								= name.replace('''pretrained.act_postprocess4.0.project.0'''					,			'''neck.reassemble_stage.readout_projects.3.0''' )

					# resize blocks
					if "pretrained.act_postprocess1.3" in name:
										_A								= name.replace('''pretrained.act_postprocess1.3'''					,			'''neck.reassemble_stage.layers.0.projection''' )
					if "pretrained.act_postprocess1.4" in name:
										_A								= name.replace('''pretrained.act_postprocess1.4'''					,			'''neck.reassemble_stage.layers.0.resize''' )
					if "pretrained.act_postprocess2.3" in name:
										_A								= name.replace('''pretrained.act_postprocess2.3'''					,			'''neck.reassemble_stage.layers.1.projection''' )
					if "pretrained.act_postprocess2.4" in name:
										_A								= name.replace('''pretrained.act_postprocess2.4'''					,			'''neck.reassemble_stage.layers.1.resize''' )
					if "pretrained.act_postprocess3.3" in name:
										_A								= name.replace('''pretrained.act_postprocess3.3'''					,			'''neck.reassemble_stage.layers.2.projection''' )
					if "pretrained.act_postprocess4.3" in name:
										_A								= name.replace('''pretrained.act_postprocess4.3'''					,			'''neck.reassemble_stage.layers.3.projection''' )
					if "pretrained.act_postprocess4.4" in name:
										_A								= name.replace('''pretrained.act_postprocess4.4'''					,			'''neck.reassemble_stage.layers.3.resize''' )
					if "pretrained" in name:
										_A								= name.replace('''pretrained'''					,			'''dpt''' )
					if "bn" in name:
										_A								= name.replace('''bn'''					,			'''batch_norm''' )
					if "head" in name:
										_A								= name.replace('''head'''					,			'''head.head''' )
					if "encoder.norm" in name:
										_A								= name.replace('''encoder.norm'''					,			'''layernorm''' )
					if "auxlayer" in name:
										_A								= name.replace('''auxlayer'''					,			'''auxiliary_head.head''' )
					if "backbone" in name:
										_A								= name.replace('''backbone'''					,			'''backbone.bit.encoder''' )

					if ".." in name:
										_A								= name.replace('''..'''					,			'''.''' )

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




def      __A					(     _lowercase					,			_lowercase ):

					'''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)
										_A								= state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.weight""" )
										_A								= state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.bias""" )
										# next, add query, keys and values (in that order) to the state dict
										_A								= in_proj_weight[: config.hidden_size, :]
										_A								= in_proj_bias[: config.hidden_size]
										_A								= in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										_A								= in_proj_bias[
										    config.hidden_size : config.hidden_size * 2
										]
										_A								= in_proj_weight[
										    -config.hidden_size :, :
										]
										_A								= in_proj_bias[-config.hidden_size :]




def      __A					(     ):

					'''simple docstring'''







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




@torch.no_grad()
def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







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

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

					# Check outputs on an image
					_A								= 4_80 if "ade" in checkpoint_url else 3_84
					_A								= DPTImageProcessor(size=_A )

					_A								= prepare_img()
					_A								= image_processor(_A					,			return_tensors='''pt''' )

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

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

										Image.fromarray((prediction / prediction.max()) * 2_55 ).show()

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

					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
				)











from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A      =				logging.get_logger(__name__)

__A      =				{
    'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/config.json',
    'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/config.json',
    'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/config.json',
    'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/config.json',
    'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json',
    'roberta-large-openai-detector': 'https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json',
}





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							A_						  =					"roberta"

							def __init__(  self:   Dict		,     __A:   int=5_02_65		,     __A:   Union[str, Any]=7_68		,     __A:   Union[str, Any]=12		,     __A:   str=12		,     __A:   int=30_72		,     __A:   str="gelu"		,     __A:   Union[str, Any]=0.1		,     __A:   int=0.1		,     __A:   Optional[int]=5_12		,     __A:   Union[str, Any]=2		,     __A:   str=0.02		,     __A:   str=1e-12		,     __A:   Any=1		,     __A:   str=0		,     __A:   Any=2		,     __A:   Optional[int]="absolute"		,     __A:   Optional[Any]=True		,     __A:   Union[str, Any]=None		,     **__A:   List[str]		,     )    ->				Dict:
												super().__init__(pad_token_id=__A		,     bos_token_id=__A		,     eos_token_id=__A		,     **__A   )

												_A								= vocab_size
												_A								= hidden_size
												_A								= num_hidden_layers
												_A								= num_attention_heads
												_A								= hidden_act
												_A								= intermediate_size
												_A								= hidden_dropout_prob
												_A								= attention_probs_dropout_prob
												_A								= max_position_embeddings
												_A								= type_vocab_size
												_A								= initializer_range
												_A								= layer_norm_eps
												_A								= position_embedding_type
												_A								= use_cache
												_A								= classifier_dropout





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							@property
							def       __A (  self:   Dict   )    ->				Mapping[str, Mapping[int, str]]:
												if self.task == "multiple-choice":
																	_A								= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
												else:
																	_A								= {0: '''batch''', 1: '''sequence'''}
												return OrderedDict(
												    [
												        ('''input_ids''', dynamic_axis),
												        ('''attention_mask''', dynamic_axis),
												    ]   )



