


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()
snake_case      :   int							= logging.get_logger(__name__)


def 					SCREAMING_SNAKE_CASE			(  UpperCAmelCase__				):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE   =			DPTConfig()

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

				if "ade" in checkpoint_url:
								_SCREAMING_SNAKE_CASE   =			True

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

				return config, expected_shape


def 					SCREAMING_SNAKE_CASE			(  UpperCAmelCase__				):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE   =			['pretrained.model.head.weight', 'pretrained.model.head.bias']
				for k in ignore_keys:
								state_dict.pop(UpperCAmelCase__ ,UpperCAmelCase__				)


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

				return name


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


def 					SCREAMING_SNAKE_CASE			(  ):

				"""simple docstring"""






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


@torch.no_grad()
def 					SCREAMING_SNAKE_CASE			(  UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__				):

				"""simple docstring"""






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

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

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

				_SCREAMING_SNAKE_CASE   =			prepare_img()
				_SCREAMING_SNAKE_CASE   =			image_processor(UpperCAmelCase__ ,return_tensors='pt'				)

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

				# Assert logits
				_SCREAMING_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:
								_SCREAMING_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(UpperCAmelCase__				)
				assert (
				    torch.allclose(outputs[0, 0, :3, :3] ,UpperCAmelCase__ ,atol=1e-4				)
				    if "ade" in checkpoint_url
				    else torch.allclose(outputs[0, :3, :3] ,UpperCAmelCase__				)
				)

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


if __name__ == "__main__":
							snake_case      :   Tuple							= 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.',
							)

							snake_case      :   Optional[Any]							= parser.parse_args()
							convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)







def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    int:

		def count_of_possible_combinations(snake_case							) -> int:
				if target < 0:
						return 0
				if target == 0:
						return 1
				return sum(count_of_possible_combinations(target - item							) for item in array							)

		return count_of_possible_combinations(snake_case							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    int:

		def count_of_possible_combinations_with_dp_array(
		    snake_case						,   snake_case							) -> int:
				if target < 0:
						return 0
				if target == 0:
						return 1
				if dp_array[target] != -1:
						return dp_array[target]
				__lowercase											=     sum(
				    count_of_possible_combinations_with_dp_array(target - item						,   snake_case							)
				    for item in array							)
				__lowercase											=     answer
				return answer

		__lowercase											=     [-1] * (target + 1)
		return count_of_possible_combinations_with_dp_array(snake_case						,   snake_case							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    int:
		__lowercase											=     [0] * (target + 1)
		__lowercase											=     1

		for i in range(1						,   target + 1							):
				for j in range(snake_case							):
						if i - array[j] >= 0:
								dp_array[i] += dp_array[i - array[j]]

		return dp_array[target]


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		SCREAMING_SNAKE_CASE_   :      Tuple			=   3
		SCREAMING_SNAKE_CASE_   :      Dict			=   5
		SCREAMING_SNAKE_CASE_   :      Any			=   [1, 2, 5]
		print(combination_sum_iv(n, array, target))




