

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

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	lowerCAmelCase__	:						List[str]  				=	DPTConfig()

	if "large" in checkpoint_url:
		lowerCAmelCase__	:						Tuple  				=	10_24
		lowerCAmelCase__	:						int  				=	40_96
		lowerCAmelCase__	:						Optional[int]  				=	24
		lowerCAmelCase__	:						Optional[Any]  				=	16
		lowerCAmelCase__	:						Any  				=	[5, 11, 17, 23]
		lowerCAmelCase__	:						List[Any]  				=	[2_56, 5_12, 10_24, 10_24]
		lowerCAmelCase__	:						Dict  				=	(1, 3_84, 3_84)

	if "ade" in checkpoint_url:
		lowerCAmelCase__	:						Dict  				=	True

		lowerCAmelCase__	:						Tuple  				=	1_50
		lowerCAmelCase__	:						Union[str, Any]  				=	'''huggingface/label-files'''
		lowerCAmelCase__	:						Dict  				=	'''ade20k-id2label.json'''
		lowerCAmelCase__	:						int  				=	json.load(open(cached_download(hf_hub_url(A_						,     A_						,     repo_type='''dataset''' ) )						,     '''r''' ) )
		lowerCAmelCase__	:						Optional[int]  				=	{int(A_ ): v for k, v in idalabel.items()}
		lowerCAmelCase__	:						Tuple  				=	idalabel
		lowerCAmelCase__	:						Tuple  				=	{v: k for k, v in idalabel.items()}
		lowerCAmelCase__	:						str  				=	[1, 1_50, 4_80, 4_80]

	return config, expected_shape

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	lowerCAmelCase__	:						Union[str, Any]  				=	['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
	for k in ignore_keys:
		state_dict.pop(A_						,     A_ )

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	if (
	    "pretrained.model" in name
	    and "cls_token" not in name
	    and "pos_embed" not in name
	    and "patch_embed" not in name
	):
		lowerCAmelCase__	:						Dict  				=	name.replace('''pretrained.model'''						,     '''dpt.encoder''' )
	if "pretrained.model" in name:
		lowerCAmelCase__	:						str  				=	name.replace('''pretrained.model'''						,     '''dpt.embeddings''' )
	if "patch_embed" in name:
		lowerCAmelCase__	:						int  				=	name.replace('''patch_embed'''						,     '''patch_embeddings''' )
	if "pos_embed" in name:
		lowerCAmelCase__	:						int  				=	name.replace('''pos_embed'''						,     '''position_embeddings''' )
	if "attn.proj" in name:
		lowerCAmelCase__	:						Dict  				=	name.replace('''attn.proj'''						,     '''attention.output.dense''' )
	if "proj" in name and "project" not in name:
		lowerCAmelCase__	:						List[Any]  				=	name.replace('''proj'''						,     '''projection''' )
	if "blocks" in name:
		lowerCAmelCase__	:						Any  				=	name.replace('''blocks'''						,     '''layer''' )
	if "mlp.fc1" in name:
		lowerCAmelCase__	:						List[str]  				=	name.replace('''mlp.fc1'''						,     '''intermediate.dense''' )
	if "mlp.fc2" in name:
		lowerCAmelCase__	:						int  				=	name.replace('''mlp.fc2'''						,     '''output.dense''' )
	if "norm1" in name:
		lowerCAmelCase__	:						Optional[Any]  				=	name.replace('''norm1'''						,     '''layernorm_before''' )
	if "norm2" in name:
		lowerCAmelCase__	:						Tuple  				=	name.replace('''norm2'''						,     '''layernorm_after''' )
	if "scratch.output_conv" in name:
		lowerCAmelCase__	:						int  				=	name.replace('''scratch.output_conv'''						,     '''head''' )
	if "scratch" in name:
		lowerCAmelCase__	:						Tuple  				=	name.replace('''scratch'''						,     '''neck''' )
	if "layer1_rn" in name:
		lowerCAmelCase__	:						int  				=	name.replace('''layer1_rn'''						,     '''convs.0''' )
	if "layer2_rn" in name:
		lowerCAmelCase__	:						Optional[int]  				=	name.replace('''layer2_rn'''						,     '''convs.1''' )
	if "layer3_rn" in name:
		lowerCAmelCase__	:						Tuple  				=	name.replace('''layer3_rn'''						,     '''convs.2''' )
	if "layer4_rn" in name:
		lowerCAmelCase__	:						Union[str, Any]  				=	name.replace('''layer4_rn'''						,     '''convs.3''' )
	if "refinenet" in name:
		lowerCAmelCase__	:						Tuple  				=	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[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__	:						Union[str, Any]  				=	name.replace('''resConfUnit1'''						,     '''residual_layer1''' )
	if "resConfUnit2" in name:
		lowerCAmelCase__	:						Tuple  				=	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__	:						Optional[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__	:						Optional[int]  				=	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__	:						Dict  				=	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__	:						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__	:						Optional[int]  				=	name.replace('''pretrained.act_postprocess1.4'''						,     '''neck.reassemble_stage.layers.0.resize''' )
	if "pretrained.act_postprocess2.3" in name:
		lowerCAmelCase__	:						Optional[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__	:						List[Any]  				=	name.replace('''pretrained.act_postprocess3.3'''						,     '''neck.reassemble_stage.layers.2.projection''' )
	if "pretrained.act_postprocess4.3" in name:
		lowerCAmelCase__	:						Dict  				=	name.replace('''pretrained.act_postprocess4.3'''						,     '''neck.reassemble_stage.layers.3.projection''' )
	if "pretrained.act_postprocess4.4" in name:
		lowerCAmelCase__	:						str  				=	name.replace('''pretrained.act_postprocess4.4'''						,     '''neck.reassemble_stage.layers.3.resize''' )
	if "pretrained" in name:
		lowerCAmelCase__	:						Optional[int]  				=	name.replace('''pretrained'''						,     '''dpt''' )
	if "bn" in name:
		lowerCAmelCase__	:						List[Any]  				=	name.replace('''bn'''						,     '''batch_norm''' )
	if "head" in name:
		lowerCAmelCase__	:						List[Any]  				=	name.replace('''head'''						,     '''head.head''' )
	if "encoder.norm" in name:
		lowerCAmelCase__	:						List[str]  				=	name.replace('''encoder.norm'''						,     '''layernorm''' )
	if "auxlayer" in name:
		lowerCAmelCase__	:						Dict  				=	name.replace('''auxlayer'''						,     '''auxiliary_head.head''' )

	return name

def   __SCREAMING_SNAKE_CASE  (      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)
		lowerCAmelCase__	:						str  				=	state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.weight' )
		lowerCAmelCase__	:						Union[str, 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__	:						Tuple  				=	in_proj_weight[: config.hidden_size, :]
		lowerCAmelCase__	:						Optional[int]  				=	in_proj_bias[: config.hidden_size]
		lowerCAmelCase__	:						Dict  				=	in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		lowerCAmelCase__	:						List[str]  				=	in_proj_bias[
		    config.hidden_size : config.hidden_size * 2
		]
		lowerCAmelCase__	:						Optional[Any]  				=	in_proj_weight[
		    -config.hidden_size :, :
		]
		lowerCAmelCase__	:						str  				=	in_proj_bias[-config.hidden_size :]

def   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						Any  				=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
	lowerCAmelCase__	:						int  				=	Image.open(requests.get(A_						,     stream=A_ ).raw )
	return im

@torch.no_grad()
def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_						,     A_ ):
	lowerCAmelCase__				,lowerCAmelCase__	:						List[Any]  				=	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__	:						int  				=	state_dict.pop(A_ )
		lowerCAmelCase__	:						List[str]  				=	val
	# read in qkv matrices
	read_in_q_k_v(A_						,     A_ )

	# load HuggingFace model
	lowerCAmelCase__	:						Tuple  				=	DPTForSemanticSegmentation(A_ ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(A_ )
	model.load_state_dict(A_ )
	model.eval()

	# Check outputs on an image
	lowerCAmelCase__	:						Union[str, Any]  				=	4_80 if '''ade''' in checkpoint_url else 3_84
	lowerCAmelCase__	:						Tuple  				=	DPTImageProcessor(size=A_ )

	lowerCAmelCase__	:						Any  				=	prepare_img()
	lowerCAmelCase__	:						Tuple  				=	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__	:						List[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__	:						Dict  				=	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__":
							__UpperCamelCase  :      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.''',
							)

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







"""simple docstring"""





# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async

def   __SCREAMING_SNAKE_CASE  (      A_=None ):
	if subparsers is not None:
		lowerCAmelCase__	:						Optional[Any]  				=	subparsers.add_parser('''test''' )
	else:
		lowerCAmelCase__	:						List[str]  				=	argparse.ArgumentParser('''Accelerate test command''' )

	parser.add_argument(
	    '''--config_file'''						,     default=A_						,     help=(
	        '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
	        '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
	        '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
	        '''with \'huggingface\'.'''
	    )						,     )

	if subparsers is not None:
		parser.set_defaults(func=A_ )
	return parser

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	lowerCAmelCase__	:						Optional[int]  				=	os.path.sep.join(__file__.split(os.path.sep )[:-2] + ['''test_utils''', '''scripts''', '''test_script.py'''] )

	if args.config_file is None:
		lowerCAmelCase__	:						Optional[Any]  				=	script_name
	else:
		lowerCAmelCase__	:						Any  				=	f'--config_file={args.config_file} {script_name}'

	lowerCAmelCase__	:						List[Any]  				=	['''accelerate-launch'''] + test_args.split()
	lowerCAmelCase__	:						int  				=	execute_subprocess_async(A_						,     env=os.environ.copy() )
	if result.returncode == 0:
		print('''Test is a success! You are ready for your distributed training!''' )

def   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						Any  				=	test_command_parser()
	lowerCAmelCase__	:						List[Any]  				=	parser.parse_args()
	test_command(A_ )


if __name__ == "__main__":
							main()




