




"""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()
lowercase__  		=		logging.get_logger(__name__)







def   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    List[str]					=					DPTConfig(embedding_type='hybrid'	)

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

				if "nyu" or "midas" in checkpoint_url:
								_lowerCamelCase  :    Optional[int]					=					768
								_lowerCamelCase  :    Optional[int]					=					[1, 1, 1, 0.5]
								_lowerCamelCase  :    List[Any]					=					[256, 512, 768, 768]
								_lowerCamelCase  :    Tuple					=					150
								_lowerCamelCase  :    Optional[Any]					=					16
								_lowerCamelCase  :    Tuple					=					(1, 384, 384)
								_lowerCamelCase  :    Any					=					False
								_lowerCamelCase  :    Tuple					=					'project'

				if "ade" in checkpoint_url:
								_lowerCamelCase  :    Optional[Any]					=					True
								_lowerCamelCase  :    Optional[int]					=					768
								_lowerCamelCase  :    Tuple					=					[1, 1, 1, 0.5]
								_lowerCamelCase  :    List[Any]					=					150
								_lowerCamelCase  :    Union[str, Any]					=					16
								_lowerCamelCase  :    Optional[Any]					=					'huggingface/label-files'
								_lowerCamelCase  :    int					=					'ade20k-id2label.json'
								_lowerCamelCase  :    str					=					json.load(open(cached_download(hf_hub_url(lowercase__							,					lowercase__							,					repo_type='dataset'	)	)							,					'r'	)	)
								_lowerCamelCase  :    Optional[Any]					=					{int(lowercase__	): v for k, v in idalabel.items()}
								_lowerCamelCase  :    List[Any]					=					idalabel
								_lowerCamelCase  :    Union[str, Any]					=					{v: k for k, v in idalabel.items()}
								_lowerCamelCase  :    List[str]					=					[1, 150, 480, 480]

				return config, expected_shape







def   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    Dict					=					['pretrained.model.head.weight', 'pretrained.model.head.bias']
				for k in ignore_keys:
								state_dict.pop(lowercase__							,					lowercase__	)







def   _snake_case    (       lowercase__	):
				if (
				    "pretrained.model" in name
				    and "cls_token" not in name
				    and "pos_embed" not in name
				    and "patch_embed" not in name
				):
								_lowerCamelCase  :    Union[str, Any]					=					name.replace('pretrained.model'							,					'dpt.encoder'	)
				if "pretrained.model" in name:
								_lowerCamelCase  :    Any					=					name.replace('pretrained.model'							,					'dpt.embeddings'	)
				if "patch_embed" in name:
								_lowerCamelCase  :    Tuple					=					name.replace('patch_embed'							,					''	)
				if "pos_embed" in name:
								_lowerCamelCase  :    Tuple					=					name.replace('pos_embed'							,					'position_embeddings'	)
				if "attn.proj" in name:
								_lowerCamelCase  :    int					=					name.replace('attn.proj'							,					'attention.output.dense'	)
				if "proj" in name and "project" not in name:
								_lowerCamelCase  :    Union[str, Any]					=					name.replace('proj'							,					'projection'	)
				if "blocks" in name:
								_lowerCamelCase  :    Union[str, Any]					=					name.replace('blocks'							,					'layer'	)
				if "mlp.fc1" in name:
								_lowerCamelCase  :    Tuple					=					name.replace('mlp.fc1'							,					'intermediate.dense'	)
				if "mlp.fc2" in name:
								_lowerCamelCase  :    Tuple					=					name.replace('mlp.fc2'							,					'output.dense'	)
				if "norm1" in name and "backbone" not in name:
								_lowerCamelCase  :    str					=					name.replace('norm1'							,					'layernorm_before'	)
				if "norm2" in name and "backbone" not in name:
								_lowerCamelCase  :    Any					=					name.replace('norm2'							,					'layernorm_after'	)
				if "scratch.output_conv" in name:
								_lowerCamelCase  :    Dict					=					name.replace('scratch.output_conv'							,					'head'	)
				if "scratch" in name:
								_lowerCamelCase  :    int					=					name.replace('scratch'							,					'neck'	)
				if "layer1_rn" in name:
								_lowerCamelCase  :    Optional[Any]					=					name.replace('layer1_rn'							,					'convs.0'	)
				if "layer2_rn" in name:
								_lowerCamelCase  :    List[str]					=					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  :    Tuple					=					name.replace('layer4_rn'							,					'convs.3'	)
				if "refinenet" in name:
								_lowerCamelCase  :    Union[str, 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  :    int					=					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  :    List[Any]					=					name.replace('resConfUnit1'							,					'residual_layer1'	)
				if "resConfUnit2" in name:
								_lowerCamelCase  :    List[Any]					=					name.replace('resConfUnit2'							,					'residual_layer2'	)
				if "conv1" in name:
								_lowerCamelCase  :    Tuple					=					name.replace('conv1'							,					'convolution1'	)
				if "conv2" in name:
								_lowerCamelCase  :    Tuple					=					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  :    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  :    List[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  :    Union[str, Any]					=					name.replace('pretrained.act_postprocess1.3'							,					'neck.reassemble_stage.layers.0.projection'	)
				if "pretrained.act_postprocess1.4" in name:
								_lowerCamelCase  :    Any					=					name.replace('pretrained.act_postprocess1.4'							,					'neck.reassemble_stage.layers.0.resize'	)
				if "pretrained.act_postprocess2.3" in name:
								_lowerCamelCase  :    int					=					name.replace('pretrained.act_postprocess2.3'							,					'neck.reassemble_stage.layers.1.projection'	)
				if "pretrained.act_postprocess2.4" in name:
								_lowerCamelCase  :    Tuple					=					name.replace('pretrained.act_postprocess2.4'							,					'neck.reassemble_stage.layers.1.resize'	)
				if "pretrained.act_postprocess3.3" in name:
								_lowerCamelCase  :    Optional[Any]					=					name.replace('pretrained.act_postprocess3.3'							,					'neck.reassemble_stage.layers.2.projection'	)
				if "pretrained.act_postprocess4.3" in name:
								_lowerCamelCase  :    int					=					name.replace('pretrained.act_postprocess4.3'							,					'neck.reassemble_stage.layers.3.projection'	)
				if "pretrained.act_postprocess4.4" in name:
								_lowerCamelCase  :    List[Any]					=					name.replace('pretrained.act_postprocess4.4'							,					'neck.reassemble_stage.layers.3.resize'	)
				if "pretrained" in name:
								_lowerCamelCase  :    Union[str, Any]					=					name.replace('pretrained'							,					'dpt'	)
				if "bn" in name:
								_lowerCamelCase  :    List[str]					=					name.replace('bn'							,					'batch_norm'	)
				if "head" in name:
								_lowerCamelCase  :    int					=					name.replace('head'							,					'head.head'	)
				if "encoder.norm" in name:
								_lowerCamelCase  :    str					=					name.replace('encoder.norm'							,					'layernorm'	)
				if "auxlayer" in name:
								_lowerCamelCase  :    Dict					=					name.replace('auxlayer'							,					'auxiliary_head.head'	)
				if "backbone" in name:
								_lowerCamelCase  :    str					=					name.replace('backbone'							,					'backbone.bit.encoder'	)

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

				if "stem.conv" in name:
								_lowerCamelCase  :    Optional[Any]					=					name.replace('stem.conv'							,					'bit.embedder.convolution'	)
				if "blocks" in name:
								_lowerCamelCase  :    str					=					name.replace('blocks'							,					'layers'	)
				if "convolution" in name and "backbone" in name:
								_lowerCamelCase  :    Tuple					=					name.replace('convolution'							,					'conv'	)
				if "layer" in name and "backbone" in name:
								_lowerCamelCase  :    Dict					=					name.replace('layer'							,					'layers'	)
				if "backbone.bit.encoder.bit" in name:
								_lowerCamelCase  :    Union[str, Any]					=					name.replace('backbone.bit.encoder.bit'							,					'backbone.bit'	)
				if "embedder.conv" in name:
								_lowerCamelCase  :    Tuple					=					name.replace('embedder.conv'							,					'embedder.convolution'	)
				if "backbone.bit.encoder.stem.norm" in name:
								_lowerCamelCase  :    List[Any]					=					name.replace('backbone.bit.encoder.stem.norm'							,					'backbone.bit.embedder.norm'	)
				return name







def   _snake_case    (       lowercase__							,					lowercase__	):
				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  :    Optional[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  :    Any					=					in_proj_weight[: config.hidden_size, :]
								_lowerCamelCase  :    Tuple					=					in_proj_bias[: config.hidden_size]
								_lowerCamelCase  :    str					=					in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								_lowerCamelCase  :    Dict					=					in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								_lowerCamelCase  :    Optional[int]					=					in_proj_weight[
								    -config.hidden_size :, :
								]
								_lowerCamelCase  :    Optional[Any]					=					in_proj_bias[-config.hidden_size :]







def   _snake_case    (       ):
				_lowerCamelCase  :    Dict					=					'http://images.cocodataset.org/val2017/000000039769.jpg'
				_lowerCamelCase  :    int					=					Image.open(requests.get(lowercase__							,					stream=lowercase__	).raw	)
				return im







@torch.no_grad()
def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__							,					lowercase__							,					lowercase__	):
				_lowerCamelCase,				_lowerCamelCase  :    int					=					get_dpt_config(lowercase__	)
				# load original state_dict from URL
				# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
				_lowerCamelCase  :    Tuple					=					torch.load(lowercase__							,					map_location='cpu'	)
				# remove certain keys
				remove_ignore_keys_(lowercase__	)
				# rename keys
				for key in state_dict.copy().keys():
								_lowerCamelCase  :    str					=					state_dict.pop(lowercase__	)
								_lowerCamelCase  :    Any					=					val
				# read in qkv matrices
				read_in_q_k_v(lowercase__							,					lowercase__	)

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

				# Check outputs on an image
				_lowerCamelCase  :    Dict					=					480 if 'ade' in checkpoint_url else 384
				_lowerCamelCase  :    Dict					=					DPTImageProcessor(size=lowercase__	)

				_lowerCamelCase  :    Tuple					=					prepare_img()
				_lowerCamelCase  :    int					=					image_processor(lowercase__							,					return_tensors='pt'	)

				# forward pass
				_lowerCamelCase  :    Optional[int]					=					model(**lowercase__	).logits if 'ade' in checkpoint_url else model(**lowercase__	).predicted_depth

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

								Image.fromarray((prediction / prediction.max()) * 255	).show()

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

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


if __name__ == "__main__":
			lowercase__  		=		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""",
			)

			lowercase__  		=		parser.parse_args()
			convert_dpt_checkpoint(
			    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
			)





"""simple docstring"""






import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt






class lowerCAmelCase__			:






			'''simple docstring'''



			def __init__(						self						):
							_lowerCamelCase  :    Tuple					=					''
							_lowerCamelCase  :    Any					=					''
							_lowerCamelCase  :    List[Any]					=					[]
							_lowerCamelCase  :    Tuple					=					0
							_lowerCamelCase  :    Dict					=					256
							_lowerCamelCase  :    Optional[Any]					=					0
							_lowerCamelCase  :    List[Any]					=					0
							_lowerCamelCase  :    int					=					0
							_lowerCamelCase  :    Dict					=					0
			def       A_					(						self						,   lowercase						):
							_lowerCamelCase  :    Any					=					cva.imread(lowercase						,   0						)
							_lowerCamelCase  :    Optional[int]					=					copy.deepcopy(self.img						)
							_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase  :    List[str]					=					plt.hist(self.img.ravel()						,   256						,   [0, 256]						,   label='x'						)
							_lowerCamelCase  :    Optional[int]					=					np.sum(lowercase						)
							for i in range(len(lowercase						)						):
											_lowerCamelCase  :    List[Any]					=					x[i] / self.k
											self.sk += prk
											_lowerCamelCase  :    int					=					(self.L - 1) * self.sk
											if self.rem != 0:
															_lowerCamelCase  :    Union[str, Any]					=					int(last % last						)
											_lowerCamelCase  :    int					=					int(last + 1 if self.rem >= 0.5 else last						)
											self.last_list.append(lowercase						)
											_lowerCamelCase  :    List[str]					=					int(np.ma.count(self.img						) / self.img[1].size						)
											_lowerCamelCase  :    int					=					self.img[1].size
							for i in range(self.number_of_cols						):
											for j in range(self.number_of_rows						):
															_lowerCamelCase  :    Optional[Any]					=					self.img[j][i]
															if num != self.last_list[num]:
																			_lowerCamelCase  :    Any					=					self.last_list[num]
							cva.imwrite('output_data/output.jpg'						,   self.img						)
			def       A_					(						self						):
							plt.hist(self.img.ravel()						,   256						,   [0, 256]						)






			def       A_					(						self						):
							cva.imshow('Output-Image'						,   self.img						)
							cva.imshow('Input-Image'						,   self.original_image						)
							cva.waitKey(5000						)
							cva.destroyAllWindows()


if __name__ == "__main__":
			lowercase__  		=		os.path.join(os.path.basename(__file__), """image_data/input.jpg""")
			lowercase__  		=		ConstantStretch()
			stretcher.stretch(file_path)
			stretcher.plot_histogram()
			stretcher.show_image()