



"""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       __magic_name__    (   _lowerCamelCase				: int					):
							__a		:		List[str]									=       DPTConfig()

							if "large" in checkpoint_url:
														__a		:		Dict									=       1_0_2_4
														__a		:		Optional[Any]									=       4_0_9_6
														__a		:		Dict									=       2_4
														__a		:		Dict									=       1_6
														__a		:		List[str]									=       [5, 1_1, 1_7, 2_3]
														__a		:		Optional[Any]									=       [2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
														__a		:		List[Any]									=       (1, 3_8_4, 3_8_4)

							if "ade" in checkpoint_url:
														__a		:		Tuple									=       True

														__a		:		List[Any]									=       1_5_0
														__a		:		Optional[int]									=       """huggingface/label-files"""
														__a		:		Union[str, Any]									=       """ade20k-id2label.json"""
														__a		:		Dict									=       json.load(open(cached_download(hf_hub_url(_lowerCamelCase  ,       _lowerCamelCase  ,       repo_type="""dataset"""					)					)  ,       """r"""					)					)
														__a		:		Optional[int]									=       {int(_lowerCamelCase					): v for k, v in idalabel.items()}
														__a		:		str									=       idalabel
														__a		:		Dict									=       {v: k for k, v in idalabel.items()}
														__a		:		Union[str, Any]									=       [1, 1_5_0, 4_8_0, 4_8_0]

							return config, expected_shape





def       __magic_name__    (   _lowerCamelCase				: Tuple					):
							__a		:		Union[str, Any]									=       ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
							for k in ignore_keys:
														state_dict.pop(_lowerCamelCase  ,       _lowerCamelCase					)





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

							return name





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





def       __magic_name__    (   ):
							__a		:		Union[str, Any]									=       """http://images.cocodataset.org/val2017/000000039769.jpg"""
							__a		:		Tuple									=       Image.open(requests.get(_lowerCamelCase  ,       stream=_lowerCamelCase					).raw					)
							return im





@torch.no_grad()
def       __magic_name__    (   _lowerCamelCase				: Optional[Any]  ,       _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: Dict  ,       _lowerCamelCase				: List[Any]					):
							__a							,     __a		:		Optional[int]									=       get_dpt_config(_lowerCamelCase					)
							# load original state_dict from URL
							__a		:		Optional[Any]									=       torch.hub.load_state_dict_from_url(_lowerCamelCase  ,       map_location="""cpu"""					)
							# remove certain keys
							remove_ignore_keys_(_lowerCamelCase					)
							# rename keys
							for key in state_dict.copy().keys():
														__a		:		Dict									=       state_dict.pop(_lowerCamelCase					)
														__a		:		Dict									=       val
							# read in qkv matrices
							read_in_q_k_v(_lowerCamelCase  ,       _lowerCamelCase					)

							# load HuggingFace model
							__a		:		int									=       DPTForSemanticSegmentation(_lowerCamelCase					) if """ade""" in checkpoint_url else DPTForDepthEstimation(_lowerCamelCase					)
							model.load_state_dict(_lowerCamelCase					)
							model.eval()

							# Check outputs on an image
							__a		:		int									=       4_8_0 if """ade""" in checkpoint_url else 3_8_4
							__a		:		str									=       DPTImageProcessor(size=_lowerCamelCase					)

							__a		:		Dict									=       prepare_img()
							__a		:		List[Any]									=       image_processor(_lowerCamelCase  ,       return_tensors="""pt"""					)

							# forward pass
							__a		:		Union[str, Any]									=       model(**_lowerCamelCase					).logits if """ade""" in checkpoint_url else model(**_lowerCamelCase					).predicted_depth

							# Assert logits
							__a		:		Optional[Any]									=       torch.tensor([[6.31_99, 6.36_29, 6.41_48], [6.38_50, 6.36_15, 6.41_66], [6.35_19, 6.31_76, 6.35_75]]					)
							if "ade" in checkpoint_url:
														__a		:		Optional[Any]									=       torch.tensor([[4.04_80, 4.24_20, 4.43_60], [4.31_24, 4.56_93, 4.82_61], [4.57_68, 4.89_65, 5.21_63]]					)
							assert outputs.shape == torch.Size(_lowerCamelCase					)
							assert (
							    torch.allclose(outputs[0, 0, :3, :3]  ,       _lowerCamelCase  ,       atol=1E-4					)
							    if "ade" in checkpoint_url
							    else torch.allclose(outputs[0, :3, :3]  ,       _lowerCamelCase					)
							)

							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:
														print("""Pushing model to hub..."""					)
														model.push_to_hub(
														    repo_path_or_name=Path(_lowerCamelCase  ,       _lowerCamelCase					)  ,       organization="""nielsr"""  ,       commit_message="""Add model"""  ,       use_temp_dir=_lowerCamelCase  ,       )
														image_processor.push_to_hub(
														    repo_path_or_name=Path(_lowerCamelCase  ,       _lowerCamelCase					)  ,       organization="""nielsr"""  ,       commit_message="""Add image processor"""  ,       use_temp_dir=_lowerCamelCase  ,       )


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=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.",
	)

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






"""simple docstring"""

from manim import *




class SCREAMING_SNAKE_CASE__					(		__snake_case      ):



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		List[str]									=       Rectangle(height=0.5						,     width=0.5							)
													__a		:		Union[str, Any]									=       Rectangle(height=0.25						,     width=0.25							)
													__a		:		Dict									=       Rectangle(height=0.46						,     width=0.46							).set_stroke(width=0							)

													__a		:		Dict									=       [mem.copy() for i in range(6							)]
													__a		:		str									=       [mem.copy() for i in range(6							)]
													__a		:		Tuple									=       VGroup(*_lowercase							).arrange(_lowercase						,     buff=0							)
													__a		:		List[Any]									=       VGroup(*_lowercase							).arrange(_lowercase						,     buff=0							)
													__a		:		List[Any]									=       VGroup(_lowercase						,     _lowercase							).arrange(_lowercase						,     buff=0							)
													__a		:		Union[str, Any]									=       Text("""CPU"""						,     font_size=24							)
													__a		:		Dict									=       Group(_lowercase						,     _lowercase							).arrange(_lowercase						,     buff=0.5						,     aligned_edge=_lowercase							)
													cpu.move_to([-2.5, -0.5, 0]							)
													self.add(_lowercase							)

													__a		:		Optional[Any]									=       [mem.copy() for i in range(4							)]
													__a		:		Dict									=       VGroup(*_lowercase							).arrange(_lowercase						,     buff=0							)
													__a		:		List[str]									=       Text("""GPU"""						,     font_size=24							)
													__a		:		Any									=       Group(_lowercase						,     _lowercase							).arrange(_lowercase						,     buff=0.5						,     aligned_edge=_lowercase							)
													gpu.move_to([-1, -1, 0]							)
													self.add(_lowercase							)

													__a		:		List[Any]									=       [mem.copy() for i in range(6							)]
													__a		:		Any									=       VGroup(*_lowercase							).arrange(_lowercase						,     buff=0							)

													__a		:		Optional[Any]									=       Text("""Model"""						,     font_size=24							)
													__a		:		Any									=       Group(_lowercase						,     _lowercase							).arrange(_lowercase						,     buff=0.5						,     aligned_edge=_lowercase							)
													model.move_to([3, -1.0, 0]							)
													self.add(_lowercase							)

													__a		:		Tuple									=       []
													__a		:		Tuple									=       []
													__a		:		Optional[int]									=       []

													for i, rect in enumerate(_lowercase							):
																				rect.set_stroke(_lowercase							)

																				__a		:		str									=       Rectangle(height=0.46 / 4						,     width=0.46 / 3							).set_stroke(width=0.0							).set_fill(_lowercase						,     opacity=0.7							)

																				if i == 0:
																											cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT							)						,     buff=0.02						,     direction=_lowercase							)
																											cpu_target.set_x(cpu_target.get_x() + 0.1							)
																				elif i == 3:
																											cpu_target.next_to(model_cpu_arr[0]						,     direction=_lowercase						,     buff=0.0							)
																				else:
																											cpu_target.next_to(model_cpu_arr[i - 1]						,     direction=_lowercase						,     buff=0.0							)
																				self.add(_lowercase							)
																				model_cpu_arr.append(_lowercase							)

													self.add(*_lowercase						,     *_lowercase						,     *_lowercase							)

													__a		:		Optional[Any]									=       [mem.copy() for i in range(6							)]
													__a		:		Union[str, Any]									=       VGroup(*_lowercase							).arrange(_lowercase						,     buff=0							)

													__a		:		Any									=       Text("""Loaded Checkpoint"""						,     font_size=24							)
													__a		:		str									=       Group(_lowercase						,     _lowercase							).arrange(_lowercase						,     buff=0.5						,     aligned_edge=_lowercase							)
													checkpoint.move_to([3, 0.5, 0]							)

													self.add(_lowercase							)

													__a		:		Dict									=       []
													__a		:		int									=       []

													for i, rect in enumerate(_lowercase							):
																				__a		:		List[str]									=       fill.copy().set_fill(_lowercase						,     opacity=0.7							)
																				target.move_to(_lowercase							)
																				ckpt_arr.append(_lowercase							)

																				__a		:		Union[str, Any]									=       target.copy()
																				if i < 5:
																											cpu_target.move_to(cpu_left_col_base[i + 1]							)
																				else:
																											cpu_target.move_to(cpu_right_col_base[i - 5]							)
																				ckpt_cpu_arr.append(_lowercase							)
													self.add(*_lowercase						,     *_lowercase							)

													__a		:		List[str]									=       Square(side_length=2.2							)
													key.move_to([-5, 2, 0]							)

													__a		:		List[Any]									=       MarkupText(
													    F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'''						,     font_size=18						,     )

													key_text.move_to([-5, 2.4, 0]							)

													self.add(_lowercase						,     _lowercase							)

													__a		:		str									=       MarkupText(
													    F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'''						,     font_size=18						,     )

													blue_text.next_to(_lowercase						,     DOWN * 2.4						,     aligned_edge=key_text.get_left()							)
													self.add(_lowercase							)

													__a		:		Optional[int]									=       MarkupText(
													    F'''Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.'''						,     font_size=24						,     )
													step_a.move_to([2, 2, 0]							)

													__a		:		List[Any]									=       [meta_mem.copy() for i in range(6							)]
													__a		:		Optional[int]									=       [meta_mem.copy() for i in range(6							)]
													__a		:		List[Any]									=       VGroup(*_lowercase							).arrange(_lowercase						,     buff=0							)
													__a		:		List[str]									=       VGroup(*_lowercase							).arrange(_lowercase						,     buff=0							)
													__a		:		Tuple									=       VGroup(_lowercase						,     _lowercase							).arrange(_lowercase						,     buff=0							)
													__a		:		Dict									=       Text("""Disk"""						,     font_size=24							)
													__a		:		Dict									=       Group(_lowercase						,     _lowercase							).arrange(_lowercase						,     buff=0.5						,     aligned_edge=_lowercase							)
													disk.move_to([-4.0, -1.25, 0]							)
													self.play(Write(_lowercase						,     run_time=3							)						,     Write(_lowercase						,     run_time=1							)						,     Create(_lowercase						,     run_time=1							)							)

													__a		:		Optional[Any]									=       []
													for i, rect in enumerate(_lowercase							):
																				__a		:		List[str]									=       rect.copy()
																				target.generate_target()
																				target.target.move_to(disk_left_col_base[i]							).scale(0.5							)
																				animations.append(MoveToTarget(_lowercase						,     run_time=1.5							)							)
													self.play(*_lowercase							)

													self.play(FadeOut(_lowercase							)							)

													__a		:		List[str]									=       MarkupText(F'''Then, the checkpoint is removed from memory\nthrough garbage collection.'''						,     font_size=24							)
													step_a.move_to([2, 2, 0]							)

													self.play(Write(_lowercase						,     run_time=3							)							)

													self.play(
													    FadeOut(_lowercase						,     _lowercase						,     *_lowercase						,     *_lowercase							)						,     )

													self.wait()

