
"""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()
_lowerCamelCase	:    List[str]          =      logging.get_logger(__name__)






def 	a_		(	__lowercase       :     List[Any]			)			->  List[Any]:
			_snake_case          =      DPTConfig()

			if "large" in checkpoint_url:
						_snake_case          =      1_024
						_snake_case          =      4_096
						_snake_case          =      24
						_snake_case          =      16
						_snake_case          =      [5, 11, 17, 23]
						_snake_case          =      [256, 512, 1_024, 1_024]
						_snake_case          =      (1, 384, 384)

			if "ade" in checkpoint_url:
						_snake_case          =      True

						_snake_case          =      150
						_snake_case          =      'huggingface/label-files'
						_snake_case          =      'ade20k-id2label.json'
						_snake_case          =      json.load(open(cached_download(hf_hub_url(snake_case_	, snake_case_	, repo_type='dataset'			)			)	, 'r'			)			)
						_snake_case          =      {int(snake_case_			): v for k, v in idalabel.items()}
						_snake_case          =      idalabel
						_snake_case          =      {v: k for k, v in idalabel.items()}
						_snake_case          =      [1, 150, 480, 480]

			return config, expected_shape






def 	a_		(	__lowercase       :     Any			)			->  Dict:
			_snake_case          =      ['pretrained.model.head.weight', 'pretrained.model.head.bias']
			for k in ignore_keys:
						state_dict.pop(snake_case_	, snake_case_			)






def 	a_		(	__lowercase       :     List[Any]			)			->  Optional[int]:




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

			return name






def 	a_		(	__lowercase       :     Any	, __lowercase       :     str			)			->  List[str]:




			for i in range(config.num_hidden_layers			):
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						_snake_case          =      state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight'''			)
						_snake_case          =      state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.bias'''			)
						# next, add query, keys and values (in that order) to the state dict
						_snake_case          =      in_proj_weight[: config.hidden_size, :]
						_snake_case          =      in_proj_bias[: config.hidden_size]
						_snake_case          =      in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						_snake_case          =      in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						_snake_case          =      in_proj_weight[
						    -config.hidden_size :, :
						]
						_snake_case          =      in_proj_bias[-config.hidden_size :]






def 	a_		(	)			->  Dict:
			_snake_case          =      'http://images.cocodataset.org/val2017/000000039769.jpg'
			_snake_case          =      Image.open(requests.get(snake_case_	, stream=snake_case_			).raw			)
			return im






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

			# load HuggingFace model
			_snake_case          =      DPTForSemanticSegmentation(snake_case_			) if 'ade' in checkpoint_url else DPTForDepthEstimation(snake_case_			)
			model.load_state_dict(snake_case_			)
			model.eval()

			# Check outputs on an image
			_snake_case          =      480 if 'ade' in checkpoint_url else 384
			_snake_case          =      DPTImageProcessor(size=snake_case_			)

			_snake_case          =      prepare_img()
			_snake_case          =      image_processor(snake_case_	, return_tensors='pt'			)

			# forward pass
			_snake_case          =      model(**snake_case_			).logits if 'ade' in checkpoint_url else model(**snake_case_			).predicted_depth

			# Assert logits
			_snake_case          =      torch.tensor([[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]]			)
			if "ade" in checkpoint_url:
						_snake_case          =      torch.tensor([[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]]			)
			assert outputs.shape == torch.Size(snake_case_			)
			assert (
			    torch.allclose(outputs[0, 0, :3, :3]	, snake_case_	, atol=1E-4			)
			    if "ade" in checkpoint_url
			    else torch.allclose(outputs[0, :3, :3]	, snake_case_			)
			)

			Path(snake_case_			).mkdir(exist_ok=snake_case_			)
			print(f'''Saving model to {pytorch_dump_folder_path}'''			)
			model.save_pretrained(snake_case_			)
			print(f'''Saving image processor to {pytorch_dump_folder_path}'''			)
			image_processor.save_pretrained(snake_case_			)

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


if __name__ == "__main__":
			_lowerCamelCase	:    List[str]          =      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.''',
			)

			_lowerCamelCase	:    Union[str, Any]          =      parser.parse_args()
			convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)

def 	a_		(	__lowercase       :     list[int]	, __lowercase       :     list[int]			)			->  tuple[float, float]:

			# Check if the input is valid
			if not len(__lowercase			) == len(__lowercase			) == 3:
						raise ValueError('Please enter a valid equation.'			)
			if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
						raise ValueError('Both a & b of two equations can\'t be zero.'			)

			# Extract the coefficients
			_snake_case     , _snake_case     , _snake_case          =      equationa
			_snake_case     , _snake_case     , _snake_case          =      equationa

			# Calculate the determinants of the matrices
			_snake_case          =      aa * ba - aa * ba
			_snake_case          =      ca * ba - ca * ba
			_snake_case          =      aa * ca - aa * ca

			# Check if the system of linear equations has a solution (using Cramer's rule)
			if determinant == 0:
						if determinant_x == determinant_y == 0:
									raise ValueError('Infinite solutions. (Consistent system)'			)
						else:
									raise ValueError('No solution. (Inconsistent system)'			)
			else:
						if determinant_x == determinant_y == 0:
									# Trivial solution (Inconsistent system)
									return (0.0, 0.0)
						else:
									_snake_case          =      determinant_x / determinant
									_snake_case          =      determinant_y / determinant
									# Non-Trivial Solution (Consistent system)
									return (x, y)