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



def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							DPTConfig(embedding_type='hybrid'       )

				if "large" in checkpoint_url:
								UpperCAmelCase__             =							1_0_2_4
								UpperCAmelCase__             =							4_0_9_6
								UpperCAmelCase__             =							2_4
								UpperCAmelCase__             =							1_6
								UpperCAmelCase__             =							[5, 1_1, 1_7, 2_3]
								UpperCAmelCase__             =							[2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
								UpperCAmelCase__             =							(1, 3_8_4, 3_8_4)

				if "nyu" or "midas" in checkpoint_url:
								UpperCAmelCase__             =							7_6_8
								UpperCAmelCase__             =							[1, 1, 1, 0.5]
								UpperCAmelCase__             =							[2_5_6, 5_1_2, 7_6_8, 7_6_8]
								UpperCAmelCase__             =							1_5_0
								UpperCAmelCase__             =							1_6
								UpperCAmelCase__             =							(1, 3_8_4, 3_8_4)
								UpperCAmelCase__             =							False
								UpperCAmelCase__             =							'project'

				if "ade" in checkpoint_url:
								UpperCAmelCase__             =							True
								UpperCAmelCase__             =							7_6_8
								UpperCAmelCase__             =							[1, 1, 1, 0.5]
								UpperCAmelCase__             =							1_5_0
								UpperCAmelCase__             =							1_6
								UpperCAmelCase__             =							'huggingface/label-files'
								UpperCAmelCase__             =							'ade20k-id2label.json'
								UpperCAmelCase__             =							json.load(open(cached_download(hf_hub_url(lowerCamelCase					,			lowerCamelCase					,			repo_type='dataset'       )       )					,			'r'       )       )
								UpperCAmelCase__             =							{int(lowerCamelCase       ): v for k, v in idalabel.items()}
								UpperCAmelCase__             =							idalabel
								UpperCAmelCase__             =							{v: k for k, v in idalabel.items()}
								UpperCAmelCase__             =							[1, 1_5_0, 4_8_0, 4_8_0]

				return config, expected_shape



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



def   a_						(					lowerCamelCase       ):
				if (
				    "pretrained.model" in name
				    and "cls_token" not in name
				    and "pos_embed" not in name
				    and "patch_embed" not in name
				):
								UpperCAmelCase__             =							name.replace('pretrained.model'					,			'dpt.encoder'       )
				if "pretrained.model" in name:
								UpperCAmelCase__             =							name.replace('pretrained.model'					,			'dpt.embeddings'       )
				if "patch_embed" in name:
								UpperCAmelCase__             =							name.replace('patch_embed'					,			''       )
				if "pos_embed" in name:
								UpperCAmelCase__             =							name.replace('pos_embed'					,			'position_embeddings'       )
				if "attn.proj" in name:
								UpperCAmelCase__             =							name.replace('attn.proj'					,			'attention.output.dense'       )
				if "proj" in name and "project" not in name:
								UpperCAmelCase__             =							name.replace('proj'					,			'projection'       )
				if "blocks" in name:
								UpperCAmelCase__             =							name.replace('blocks'					,			'layer'       )
				if "mlp.fc1" in name:
								UpperCAmelCase__             =							name.replace('mlp.fc1'					,			'intermediate.dense'       )
				if "mlp.fc2" in name:
								UpperCAmelCase__             =							name.replace('mlp.fc2'					,			'output.dense'       )
				if "norm1" in name and "backbone" not in name:
								UpperCAmelCase__             =							name.replace('norm1'					,			'layernorm_before'       )
				if "norm2" in name and "backbone" not in name:
								UpperCAmelCase__             =							name.replace('norm2'					,			'layernorm_after'       )
				if "scratch.output_conv" in name:
								UpperCAmelCase__             =							name.replace('scratch.output_conv'					,			'head'       )
				if "scratch" in name:
								UpperCAmelCase__             =							name.replace('scratch'					,			'neck'       )
				if "layer1_rn" in name:
								UpperCAmelCase__             =							name.replace('layer1_rn'					,			'convs.0'       )
				if "layer2_rn" in name:
								UpperCAmelCase__             =							name.replace('layer2_rn'					,			'convs.1'       )
				if "layer3_rn" in name:
								UpperCAmelCase__             =							name.replace('layer3_rn'					,			'convs.2'       )
				if "layer4_rn" in name:
								UpperCAmelCase__             =							name.replace('layer4_rn'					,			'convs.3'       )
				if "refinenet" in name:
								UpperCAmelCase__             =							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
								UpperCAmelCase__             =							name.replace(f'''refinenet{layer_idx}'''					,			f'''fusion_stage.layers.{abs(layer_idx-4       )}'''       )
				if "out_conv" in name:
								UpperCAmelCase__             =							name.replace('out_conv'					,			'projection'       )
				if "resConfUnit1" in name:
								UpperCAmelCase__             =							name.replace('resConfUnit1'					,			'residual_layer1'       )
				if "resConfUnit2" in name:
								UpperCAmelCase__             =							name.replace('resConfUnit2'					,			'residual_layer2'       )
				if "conv1" in name:
								UpperCAmelCase__             =							name.replace('conv1'					,			'convolution1'       )
				if "conv2" in name:
								UpperCAmelCase__             =							name.replace('conv2'					,			'convolution2'       )
				# readout blocks
				if "pretrained.act_postprocess1.0.project.0" in name:
								UpperCAmelCase__             =							name.replace('pretrained.act_postprocess1.0.project.0'					,			'neck.reassemble_stage.readout_projects.0.0'       )
				if "pretrained.act_postprocess2.0.project.0" in name:
								UpperCAmelCase__             =							name.replace('pretrained.act_postprocess2.0.project.0'					,			'neck.reassemble_stage.readout_projects.1.0'       )
				if "pretrained.act_postprocess3.0.project.0" in name:
								UpperCAmelCase__             =							name.replace('pretrained.act_postprocess3.0.project.0'					,			'neck.reassemble_stage.readout_projects.2.0'       )
				if "pretrained.act_postprocess4.0.project.0" in name:
								UpperCAmelCase__             =							name.replace('pretrained.act_postprocess4.0.project.0'					,			'neck.reassemble_stage.readout_projects.3.0'       )

				# resize blocks
				if "pretrained.act_postprocess1.3" in name:
								UpperCAmelCase__             =							name.replace('pretrained.act_postprocess1.3'					,			'neck.reassemble_stage.layers.0.projection'       )
				if "pretrained.act_postprocess1.4" in name:
								UpperCAmelCase__             =							name.replace('pretrained.act_postprocess1.4'					,			'neck.reassemble_stage.layers.0.resize'       )
				if "pretrained.act_postprocess2.3" in name:
								UpperCAmelCase__             =							name.replace('pretrained.act_postprocess2.3'					,			'neck.reassemble_stage.layers.1.projection'       )
				if "pretrained.act_postprocess2.4" in name:
								UpperCAmelCase__             =							name.replace('pretrained.act_postprocess2.4'					,			'neck.reassemble_stage.layers.1.resize'       )
				if "pretrained.act_postprocess3.3" in name:
								UpperCAmelCase__             =							name.replace('pretrained.act_postprocess3.3'					,			'neck.reassemble_stage.layers.2.projection'       )
				if "pretrained.act_postprocess4.3" in name:
								UpperCAmelCase__             =							name.replace('pretrained.act_postprocess4.3'					,			'neck.reassemble_stage.layers.3.projection'       )
				if "pretrained.act_postprocess4.4" in name:
								UpperCAmelCase__             =							name.replace('pretrained.act_postprocess4.4'					,			'neck.reassemble_stage.layers.3.resize'       )
				if "pretrained" in name:
								UpperCAmelCase__             =							name.replace('pretrained'					,			'dpt'       )
				if "bn" in name:
								UpperCAmelCase__             =							name.replace('bn'					,			'batch_norm'       )
				if "head" in name:
								UpperCAmelCase__             =							name.replace('head'					,			'head.head'       )
				if "encoder.norm" in name:
								UpperCAmelCase__             =							name.replace('encoder.norm'					,			'layernorm'       )
				if "auxlayer" in name:
								UpperCAmelCase__             =							name.replace('auxlayer'					,			'auxiliary_head.head'       )
				if "backbone" in name:
								UpperCAmelCase__             =							name.replace('backbone'					,			'backbone.bit.encoder'       )

				if ".." in name:
								UpperCAmelCase__             =							name.replace('..'					,			'.'       )

				if "stem.conv" in name:
								UpperCAmelCase__             =							name.replace('stem.conv'					,			'bit.embedder.convolution'       )
				if "blocks" in name:
								UpperCAmelCase__             =							name.replace('blocks'					,			'layers'       )
				if "convolution" in name and "backbone" in name:
								UpperCAmelCase__             =							name.replace('convolution'					,			'conv'       )
				if "layer" in name and "backbone" in name:
								UpperCAmelCase__             =							name.replace('layer'					,			'layers'       )
				if "backbone.bit.encoder.bit" in name:
								UpperCAmelCase__             =							name.replace('backbone.bit.encoder.bit'					,			'backbone.bit'       )
				if "embedder.conv" in name:
								UpperCAmelCase__             =							name.replace('embedder.conv'					,			'embedder.convolution'       )
				if "backbone.bit.encoder.stem.norm" in name:
								UpperCAmelCase__             =							name.replace('backbone.bit.encoder.stem.norm'					,			'backbone.bit.embedder.norm'       )
				return name



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



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



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

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

				# Check outputs on an image
				UpperCAmelCase__             =							4_8_0 if 'ade' in checkpoint_url else 3_8_4
				UpperCAmelCase__             =							DPTImageProcessor(size=lowerCamelCase       )

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

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

				if show_prediction:
								UpperCAmelCase__             =							(
								    torch.nn.functional.interpolate(
								        outputs.unsqueeze(1       )					,			size=(image.size[1], image.size[0])					,			mode='bicubic'					,			align_corners=lowerCamelCase					,			)
								    .squeeze()
								    .cpu()
								    .numpy()
								)

								Image.fromarray((prediction / prediction.max()) * 2_5_5       ).show()

				if pytorch_dump_folder_path is not None:
								Path(lowerCamelCase       ).mkdir(exist_ok=lowerCamelCase       )
								print(f'''Saving model to {pytorch_dump_folder_path}'''       )
								model.save_pretrained(lowerCamelCase       )
								print(f'''Saving image processor to {pytorch_dump_folder_path}'''       )
								image_processor.save_pretrained(lowerCamelCase       )

				if push_to_hub:
								model.push_to_hub('ybelkada/dpt-hybrid-midas'       )
								image_processor.push_to_hub('ybelkada/dpt-hybrid-midas'       )


if __name__ == "__main__":
						lowerCAmelCase__  :  Optional[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=False,
						    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.',
						)
						parser.add_argument(
						    '--show_prediction',
						    action='store_true',
						)

						lowerCAmelCase__  :  List[str]						     =	parser.parse_args()
						convert_dpt_checkpoint(
						    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
						)










import os
from datetime import datetime as dt

from github import Github


snake_case__					:							Union[str, Any]													=     [
    'good first issue',
    'feature request',
    'wip',
]



def _a				(	)	->						List[Any]:







      '''simple docstring'''






      __A          =				Github(os.environ['''GITHUB_TOKEN'''] )
      __A          =				g.get_repo('''huggingface/accelerate''' )
      __A          =				repo.get_issues(state='''open''' )

      for issue in open_issues:
            __A          =				sorted([comment for comment in issue.get_comments()]					,				key=lambda lowerCamelCase : i.created_at					,				reverse=lowerCamelCase )
            __A          =				comments[0] if len(lowerCamelCase ) > 0 else None
            __A          =				dt.utcnow()
            __A          =				(current_time - issue.updated_at).days
            __A          =				(current_time - issue.created_at).days
            if (
                last_comment is not None
                and last_comment.user.login == "github-actions[bot]"
                and days_since_updated > 7
                and days_since_creation >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
            ):
                  # Close issue since it has been 7 days of inactivity since bot mention.
                  issue.edit(state='''closed''' )
            elif (
                days_since_updated > 23
                and days_since_creation >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
            ):
                  # Add stale comment
                  issue.create_comment(
                      '''This issue has been automatically marked as stale because it has not had '''
                      '''recent activity. If you think this still needs to be addressed '''
                      '''please comment on this thread.\n\nPlease note that issues that do not follow the '''
                      '''[contributing guidelines](https://github.com/huggingface/accelerate/blob/main/CONTRIBUTING.md) '''
                      '''are likely to be ignored.''' )


if __name__ == "__main__":
       main()





