




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





def 						UpperCAmelCase				(			A__:    int     )  ->     int:
			__lowerCamelCase					:	Any	       =				DPTConfig()

			if "large" in checkpoint_url:
						__lowerCamelCase					:	Optional[Any]	       =				1024
						__lowerCamelCase					:	Tuple	       =				4096
						__lowerCamelCase					:	int	       =				24
						__lowerCamelCase					:	str	       =				16
						__lowerCamelCase					:	Optional[int]	       =				[5, 11, 17, 23]
						__lowerCamelCase					:	List[str]	       =				[256, 512, 1024, 1024]
						__lowerCamelCase					:	Optional[Any]	       =				(1, 384, 384)

			if "ade" in checkpoint_url:
						__lowerCamelCase					:	List[str]	       =				True

						__lowerCamelCase					:	Union[str, Any]	       =				150
						__lowerCamelCase					:	Tuple	       =				'huggingface/label-files'
						__lowerCamelCase					:	Tuple	       =				'ade20k-id2label.json'
						__lowerCamelCase					:	Optional[int]	       =				json.load(open(cached_download(hf_hub_url(A__							,				A__							,				repo_type='dataset'     )     )							,				'r'     )     )
						__lowerCamelCase					:	Tuple	       =				{int(A__     ): v for k, v in idalabel.items()}
						__lowerCamelCase					:	List[Any]	       =				idalabel
						__lowerCamelCase					:	List[str]	       =				{v: k for k, v in idalabel.items()}
						__lowerCamelCase					:	Optional[Any]	       =				[1, 150, 480, 480]

			return config, expected_shape





def 						UpperCAmelCase				(			A__:    str     )  ->     Tuple:
			__lowerCamelCase					:	Union[str, Any]	       =				['pretrained.model.head.weight', 'pretrained.model.head.bias']
			for k in ignore_keys:
						state_dict.pop(A__							,				A__     )





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

			return name





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





def 						UpperCAmelCase				(			)  ->     Union[str, Any]:
			__lowerCamelCase					:	Any	       =				'http://images.cocodataset.org/val2017/000000039769.jpg'
			__lowerCamelCase					:	List[Any]	       =				Image.open(requests.get(A__							,				stream=A__     ).raw     )
			return im





@torch.no_grad()
def 						UpperCAmelCase				(			A__:    Dict							,				A__:    Tuple							,				A__:    Union[str, Any]							,				A__:    List[Any]     )  ->     Any:
			__lowerCamelCase       ,      __lowerCamelCase					:	Union[str, Any]	       =				get_dpt_config(A__     )
			# load original state_dict from URL
			__lowerCamelCase					:	Any	       =				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():
						__lowerCamelCase					:	str	       =				state_dict.pop(A__     )
						__lowerCamelCase					:	Any	       =				val
			# read in qkv matrices
			read_in_q_k_v(A__							,				A__     )

			# load HuggingFace model
			__lowerCamelCase					:	List[Any]	       =				DPTForSemanticSegmentation(A__     ) if 'ade' in checkpoint_url else DPTForDepthEstimation(A__     )
			model.load_state_dict(A__     )
			model.eval()

			# Check outputs on an image
			__lowerCamelCase					:	List[Any]	       =				480 if 'ade' in checkpoint_url else 384
			__lowerCamelCase					:	Optional[int]	       =				DPTImageProcessor(size=A__     )

			__lowerCamelCase					:	List[str]	       =				prepare_img()
			__lowerCamelCase					:	Any	       =				image_processor(A__							,				return_tensors='pt'     )

			# forward pass
			__lowerCamelCase					:	Optional[Any]	       =				model(**A__     ).logits if 'ade' in checkpoint_url else model(**A__     ).predicted_depth

			# Assert logits
			__lowerCamelCase					:	Any	       =				torch.tensor([[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]]     )
			if "ade" in checkpoint_url:
						__lowerCamelCase					:	str	       =				torch.tensor([[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]]     )
			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__":
					a_				:    Any										=     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.''',
					)

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









"""simple docstring"""
import random
from typing import Any





def 						UpperCAmelCase				(			A__:    list     )  ->     list[Any]:
			for _ in range(len(A__     )     ):
						__lowerCamelCase					:	List[Any]	       =				random.randint(0							,				len(A__     ) - 1     )
						__lowerCamelCase					:	Optional[Any]	       =				random.randint(0							,				len(A__     ) - 1     )
						__lowerCamelCase       ,      __lowerCamelCase					:	Any	       =				data[b], data[a]
			return data


if __name__ == "__main__":
					a_				:    Any										=     [0, 1, 2, 3, 4, 5, 6, 7]
					a_				:    int										=     ['''python''', '''says''', '''hello''', '''!''']
					print('''Fisher-Yates Shuffle:''')
					print('''List''', integers, strings)
					print('''FY Shuffle''', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))



