
"""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						=			logging.get_logger(__name__)
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		lowerCamelCase					:      List[Any]          =   DPTConfig()

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

		if "ade" in checkpoint_url:
				lowerCamelCase					:      int          =   True

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

		return config, expected_shape
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		lowerCamelCase					:      Dict          =   ['pretrained.model.head.weight', 'pretrained.model.head.bias']
		for k in ignore_keys:
				state_dict.pop(a_,						a_      )
def 		UpperCAmelCase (		a_      ):





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

		return name
def 		UpperCAmelCase (		a_,						a_      ):





		'''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)
				lowerCamelCase					:      Optional[Any]          =   state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.weight"""      )
				lowerCamelCase					:      Optional[int]          =   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					:      Dict          =   in_proj_weight[: config.hidden_size, :]
				lowerCamelCase					:      Tuple          =   in_proj_bias[: config.hidden_size]
				lowerCamelCase					:      Optional[int]          =   in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				lowerCamelCase					:      List[Any]          =   in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				lowerCamelCase					:      int          =   in_proj_weight[
				    -config.hidden_size :, :
				]
				lowerCamelCase					:      Any          =   in_proj_bias[-config.hidden_size :]
def 		UpperCAmelCase (		):





		'''simple docstring'''


		lowerCamelCase					:      Tuple          =   'http://images.cocodataset.org/val2017/000000039769.jpg'
		lowerCamelCase					:      Any          =   Image.open(requests.get(a_,						stream=a_      ).raw      )
		return im
@torch.no_grad()
def 		UpperCAmelCase (		a_,						a_,						a_,						a_      ):





		'''simple docstring'''


		lowerCamelCase     ,   lowerCamelCase					:      Optional[int]          =   get_dpt_config(a_      )
		# load original state_dict from URL
		lowerCamelCase					:      Optional[int]          =   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					:      Any          =   state_dict.pop(a_      )
				lowerCamelCase					:      List[str]          =   val
		# read in qkv matrices
		read_in_q_k_v(a_,						a_      )

		# load HuggingFace model
		lowerCamelCase					:      Optional[int]          =   DPTForSemanticSegmentation(a_      ) if 'ade' in checkpoint_url else DPTForDepthEstimation(a_      )
		model.load_state_dict(a_      )
		model.eval()

		# Check outputs on an image
		lowerCamelCase					:      int          =   480 if 'ade' in checkpoint_url else 384
		lowerCamelCase					:      Union[str, Any]          =   DPTImageProcessor(size=a_      )

		lowerCamelCase					:      int          =   prepare_img()
		lowerCamelCase					:      Union[str, Any]          =   image_processor(a_,						return_tensors='pt'      )

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

		# Assert logits
		lowerCamelCase					:      Optional[Any]          =   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:
				lowerCamelCase					:      List[Any]          =   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(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						=			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						=			parser.parse_args()
					convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)








"""simple docstring"""

import os

try:
					from .build_directory_md import good_file_paths
except ImportError:
					from build_directory_md import good_file_paths  # type: ignore

_A						=			list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

_A						=			[file for file in filepaths if file != file.lower()]
if upper_files:
					print(F"""{len(upper_files)} files contain uppercase characters:""")
					print('\n'.join(upper_files) + '\n')

_A						=			[file for file in filepaths if ' ' in file]
if space_files:
					print(F"""{len(space_files)} files contain space characters:""")
					print('\n'.join(space_files) + '\n')

_A						=			[file for file in filepaths if '-' in file]
if hyphen_files:
					print(F"""{len(hyphen_files)} files contain hyphen characters:""")
					print('\n'.join(hyphen_files) + '\n')

_A						=			[file for file in filepaths if os.sep not in file]
if nodir_files:
					print(F"""{len(nodir_files)} files are not in a directory:""")
					print('\n'.join(nodir_files) + '\n')

_A						=			len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
					import sys

					sys.exit(bad_files)






