






from __future__ import annotations





def 		__UpperCAmelCase     (	a_				,	a_				,	a_				,	a_				,	a_				,	):
							snake_case_           =				len(a_)

							# If row is equal to the size of the board it means there are a queen in each row in
							# the current board (possible_board)
							if row == n:
														# We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
														# this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
														boards.append(['. ' * i + 'Q ' + '. ' * (n - 1 - i) for i in possible_board])
														return

							# We iterate each column in the row to find all possible results in each row
							for col in range(a_):
														# We apply that we learned previously. First we check that in the current board
														# (possible_board) there are not other same value because if there is it means
														# that there are a collision in vertical. Then we apply the two formulas we
														# learned before:
														#
														# 45º: y - x = b or 45: row - col = b
														# 135º: y + x = b or row + col = b.
														#
														# And we verify if the results of this two formulas not exist in their variables
														# respectively.  (diagonal_right_collisions, diagonal_left_collisions)
														#
														# If any or these are True it means there is a collision so we continue to the
														# next value in the for loop.
														if (
														    col in possible_board
														    or row - col in diagonal_right_collisions
														    or row + col in diagonal_left_collisions
														):
																					continue

														# If it is False we call dfs function again and we update the inputs
														depth_first_search(
														    [*possible_board, col]				,	[*diagonal_right_collisions, row - col]				,	[*diagonal_left_collisions, row + col]				,	a_				,	a_				,	)





def 		__UpperCAmelCase     (	a_):
							snake_case_           =				[]
							depth_first_search([]				,	[]				,	[]				,	a_				,	a_)

							# Print all the boards
							for board in boards:
														for column in board:
																					print(a_)
														print('')

							print(len(a_)				,	'solutions were found.')


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	n_queens_solution(4)













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_):
							snake_case_           =				DPTConfig()

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

							if "ade" in checkpoint_url:
														snake_case_           =				True

														snake_case_           =				1_50
														snake_case_           =				'huggingface/label-files'
														snake_case_           =				'ade20k-id2label.json'
														snake_case_           =				json.load(open(cached_download(hf_hub_url(a_				,	a_				,	repo_type='dataset'))				,	'r'))
														snake_case_           =				{int(a_): v for k, v in idalabel.items()}
														snake_case_           =				idalabel
														snake_case_           =				{v: k for k, v in idalabel.items()}
														snake_case_           =				[1, 1_50, 4_80, 4_80]

							return config, expected_shape





def 		__UpperCAmelCase     (	a_):
							snake_case_           =				['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
							):
														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 		__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)
														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 		__UpperCAmelCase     (	):
							snake_case_           =				'http://images.cocodataset.org/val2017/000000039769.jpg'
							snake_case_           =				Image.open(requests.get(a_				,	stream=a_).raw)
							return im





@torch.no_grad()
def 		__UpperCAmelCase     (	a_				,	a_				,	a_				,	a_):
							snake_case_     ,						snake_case_           =				get_dpt_config(a_)
							# load original state_dict from URL
							snake_case_           =				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():
														snake_case_           =				state_dict.pop(a_)
														snake_case_           =				val
							# read in qkv matrices
							read_in_q_k_v(a_				,	a_)

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

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

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

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

							# Assert logits
							snake_case_           =				torch.tensor([[6.31_99, 6.36_29, 6.41_48], [6.38_50, 6.36_15, 6.41_66], [6.35_19, 6.31_76, 6.35_75]])
							if "ade" in checkpoint_url:
														snake_case_           =				torch.tensor([[4.04_80, 4.24_20, 4.43_60], [4.31_24, 4.56_93, 4.82_61], [4.57_68, 4.89_65, 5.21_63]])
							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)





