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






def      UpperCamelCase_				(      A__						):
	a_      				=						DPTConfig()

	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 "ade" in checkpoint_url:
		a_      				=						True

		a_      				=						1_50
		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      UpperCamelCase_				(      A__						):
	a_      				=						["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
	for k in ignore_keys:
		state_dict.pop(A__  ,      A__						)






def      UpperCamelCase_				(      A__						):
	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"""  ,      """patch_embeddings"""						)
	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:
		a_      				=						name.replace("""norm1"""  ,      """layernorm_before"""						)
	if "norm2" 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"""						)

	return name






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






@torch.no_grad()
def      UpperCamelCase_				(      A__  ,      A__  ,      A__  ,      A__						):
	a_				,  a_      				=						get_dpt_config(A__						)
	# load original state_dict from URL
	a_      				=						torch.hub.load_state_dict_from_url(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

	# Assert logits
	a_      				=						torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]						)
	if "ade" in checkpoint_url:
		a_      				=						torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]						)
	assert outputs.shape == torch.Size(A__						)
	assert (
	    torch.allclose(outputs[0, 0, :3, :3]  ,      A__  ,      atol=1e-4						)
	    if "ade" in checkpoint_url
	    else torch.allclose(outputs[0, :3, :3]  ,      A__						)
	)

	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:
		print("""Pushing model to hub..."""						)
		model.push_to_hub(
		    repo_path_or_name=Path(A__  ,      A__						)  ,      organization="""nielsr"""  ,      commit_message="""Add model"""  ,      use_temp_dir=A__  ,      )
		image_processor.push_to_hub(
		    repo_path_or_name=Path(A__  ,      A__						)  ,      organization="""nielsr"""  ,      commit_message="""Add image processor"""  ,      use_temp_dir=A__  ,      )


if __name__ == "__main__":
		lowercase__											=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=True,
		    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.',
		)

		lowercase__											=parser.parse_args()
		convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)


'''simple docstring'''
import enum
import shutil
import sys


lowercase__				, lowercase__											=shutil.get_terminal_size()

lowercase__											={'UP': 'A', 'DOWN': 'B', 'RIGHT': 'C', 'LEFT': 'D'}







class   a_ (      enum.Enum					):
			lowerCamelCase__	:			int       =    0
			lowerCamelCase__	:			Union[str, Any]       =    1







def      UpperCamelCase_				(      A__  ,      A__=""						):
	sys.stdout.write(str(A__						) + end						)
	sys.stdout.flush()






def      UpperCamelCase_				(      A__  ,      A__  ,      A__=""						):
	forceWrite(F'''\u001b[{color}m{content}\u001b[0m'''  ,      A__						)






def      UpperCamelCase_				(      ):
	forceWrite("""\r"""						)






def      UpperCamelCase_				(      A__  ,      A__						):
	forceWrite(F'''\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}'''						)






def      UpperCamelCase_				(      ):
	forceWrite(""" """ * TERMINAL_WIDTH						)
	reset_cursor()






def      UpperCamelCase_				(      ):
	reset_cursor()
	forceWrite("""-""" * TERMINAL_WIDTH						)
