



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()
SCREAMING_SNAKE_CASE_				= logging.get_logger(__name__)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Any:
		a_     :      List[str] 						=     DPTConfig()

		if "large" in checkpoint_url:
				a_     :      Dict 						=     1_024
				a_     :      Optional[Any] 						=     4_096
				a_     :      Dict 						=     24
				a_     :      Dict 						=     16
				a_     :      List[str] 						=     [5, 11, 17, 23]
				a_     :      Optional[Any] 						=     [256, 512, 1_024, 1_024]
				a_     :      List[Any] 						=     (1, 384, 384)

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

				a_     :      List[Any] 						=     150
				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, 150, 480, 480]

		return config, expected_shape



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      List[Any]:
		a_     :      Union[str, Any] 						=     ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
		for k in ignore_keys:
				state_dict.pop(_lowerCamelCase, _lowerCamelCase	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Any:
		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        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Dict:
		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        lowerCAmelCase_						(							)			->      Union[str, Any]:
		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        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Any:
		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 						=     480 if """ade""" in checkpoint_url else 384
		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__":
					SCREAMING_SNAKE_CASE_				= 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.""",
					)

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




"""simple docstring"""
import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class 			snake_case_						(						unittest.TestCase				):






							@property
							def snake_case_  (   self      ):
									torch.manual_seed(0      )
									a_     :      Any 						=     UNetaDModel(
									    block_out_channels=(3_2, 6_4)			,     layers_per_block=2			,     sample_size=3_2			,     in_channels=3			,     out_channels=3			,     down_block_types=("DownBlock2D", "AttnDownBlock2D")			,     up_block_types=("AttnUpBlock2D", "UpBlock2D")			,     )
									return model






							def snake_case_  (   self      ):
									a_     :      Any 						=     self.dummy_uncond_unet
									a_     :      Any 						=     PNDMScheduler()

									a_     :      List[Any] 						=     PNDMPipeline(unet=a_			,     scheduler=a_      )
									pndm.to(a_      )
									pndm.set_progress_bar_config(disable=a_      )

									a_     :      Tuple 						=     torch.manual_seed(0      )
									a_     :      Union[str, Any] 						=     pndm(generator=a_			,     num_inference_steps=2_0			,     output_type="numpy"      ).images

									a_     :      int 						=     torch.manual_seed(0      )
									a_     :      Optional[int] 						=     pndm(generator=a_			,     num_inference_steps=2_0			,     output_type="numpy"			,     return_dict=a_      )[0]

									a_     :      List[Any] 						=     image[0, -3:, -3:, -1]
									a_     :      Union[str, Any] 						=     image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 3_2, 3_2, 3)
									a_     :      List[Any] 						=     np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2




@slow
@require_torch
class 			snake_case_						(						unittest.TestCase				):






							def snake_case_  (   self      ):
									a_     :      Optional[Any] 						=     "google/ddpm-cifar10-32"

									a_     :      Optional[int] 						=     UNetaDModel.from_pretrained(a_      )
									a_     :      Union[str, Any] 						=     PNDMScheduler()

									a_     :      List[Any] 						=     PNDMPipeline(unet=a_			,     scheduler=a_      )
									pndm.to(a_      )
									pndm.set_progress_bar_config(disable=a_      )
									a_     :      Optional[int] 						=     torch.manual_seed(0      )
									a_     :      List[Any] 						=     pndm(generator=a_			,     output_type="numpy"      ).images

									a_     :      str 						=     image[0, -3:, -3:, -1]

									assert image.shape == (1, 3_2, 3_2, 3)
									a_     :      Optional[Any] 						=     np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2