
'''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 A (__lowerCamelCase :List[Any]						):
						_lowerCAmelCase    		=					DPTConfig(embedding_type="""hybrid"""						)

						if "large" in checkpoint_url:
												_lowerCAmelCase    		=					1024
												_lowerCAmelCase    		=					4096
												_lowerCAmelCase    		=					24
												_lowerCAmelCase    		=					16
												_lowerCAmelCase    		=					[5, 11, 17, 23]
												_lowerCAmelCase    		=					[256, 512, 1024, 1024]
												_lowerCAmelCase    		=					(1, 384, 384)

						if "nyu" or "midas" in checkpoint_url:
												_lowerCAmelCase    		=					768
												_lowerCAmelCase    		=					[1, 1, 1, 0.5]
												_lowerCAmelCase    		=					[256, 512, 768, 768]
												_lowerCAmelCase    		=					150
												_lowerCAmelCase    		=					16
												_lowerCAmelCase    		=					(1, 384, 384)
												_lowerCAmelCase    		=					False
												_lowerCAmelCase    		=					"""project"""

						if "ade" in checkpoint_url:
												_lowerCAmelCase    		=					True
												_lowerCAmelCase    		=					768
												_lowerCAmelCase    		=					[1, 1, 1, 0.5]
												_lowerCAmelCase    		=					150
												_lowerCAmelCase    		=					16
												_lowerCAmelCase    		=					"""huggingface/label-files"""
												_lowerCAmelCase    		=					"""ade20k-id2label.json"""
												_lowerCAmelCase    		=					json.load(open(cached_download(hf_hub_url(__lowerCamelCase					,      __lowerCamelCase					,      repo_type="""dataset"""						)						)					,      """r"""						)						)
												_lowerCAmelCase    		=					{int(__lowerCamelCase						): v for k, v in idalabel.items()}
												_lowerCAmelCase    		=					idalabel
												_lowerCAmelCase    		=					{v: k for k, v in idalabel.items()}
												_lowerCAmelCase    		=					[1, 150, 480, 480]

						return config, expected_shape


def A (__lowerCamelCase :str						):
						_lowerCAmelCase    		=					["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
						for k in ignore_keys:
												state_dict.pop(__lowerCamelCase					,      __lowerCamelCase						)


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

						if ".." in name:
												_lowerCAmelCase    		=					name.replace(""".."""					,      """."""						)

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


def A (__lowerCamelCase :str					,      __lowerCamelCase :Any						):
						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    		=					state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.weight'						)
												_lowerCAmelCase    		=					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    		=					in_proj_weight[: config.hidden_size, :]
												_lowerCAmelCase    		=					in_proj_bias[: config.hidden_size]
												_lowerCAmelCase    		=					in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												_lowerCAmelCase    		=					in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												_lowerCAmelCase    		=					in_proj_weight[
												    -config.hidden_size :, :
												]
												_lowerCAmelCase    		=					in_proj_bias[-config.hidden_size :]


def A ():
						_lowerCAmelCase    		=					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
						_lowerCAmelCase    		=					Image.open(requests.get(__lowerCamelCase					,      stream=__lowerCamelCase						).raw						)
						return im


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

						# load HuggingFace model
						_lowerCAmelCase    		=					DPTForSemanticSegmentation(__lowerCamelCase						) if """ade""" in checkpoint_url else DPTForDepthEstimation(__lowerCamelCase						)
						model.load_state_dict(__lowerCamelCase						)
						model.eval()

						# Check outputs on an image
						_lowerCAmelCase    		=					480 if """ade""" in checkpoint_url else 384
						_lowerCAmelCase    		=					DPTImageProcessor(size=__lowerCamelCase						)

						_lowerCAmelCase    		=					prepare_img()
						_lowerCAmelCase    		=					image_processor(__lowerCamelCase					,      return_tensors="""pt"""						)

						# forward pass
						_lowerCAmelCase    		=					model(**__lowerCamelCase						).logits if """ade""" in checkpoint_url else model(**__lowerCamelCase						).predicted_depth

						if show_prediction:
												_lowerCAmelCase    		=					(
												    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()) * 255						).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__":
						_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 torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: Tuple										=  (DDPMScheduler,)




						def    _lowercase      (   self       ,  **_lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					{
												    """num_train_timesteps""": 1_000,
												    """beta_start""": 0.0001,
												    """beta_end""": 0.02,
												    """beta_schedule""": """linear""",
												    """variance_type""": """fixed_small""",
												    """clip_sample""": True,
												}

												config.update(**_lowercase )
												return config




						def    _lowercase      (   self ):

												"""simple docstring"""



												for timesteps in [1, 5, 100, 1_000]:
																		self.check_over_configs(num_train_timesteps=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]       ,  [0.002, 0.02, 0.2, 2] ):
																		self.check_over_configs(beta_start=_lowercase       ,  beta_end=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												for schedule in ["linear", "squaredcos_cap_v2"]:
																		self.check_over_configs(beta_schedule=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												for variance in ["fixed_small", "fixed_large", "other"]:
																		self.check_over_configs(variance_type=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												for clip_sample in [True, False]:
																		self.check_over_configs(clip_sample=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												self.check_over_configs(thresholding=_lowercase )
												for threshold in [0.5, 1.0, 2.0]:
																		for prediction_type in ["epsilon", "sample", "v_prediction"]:
																								self.check_over_configs(
																								    thresholding=_lowercase       ,  prediction_type=_lowercase       ,  sample_max_value=_lowercase       ,  )




						def    _lowercase      (   self ):

												"""simple docstring"""



												for prediction_type in ["epsilon", "sample", "v_prediction"]:
																		self.check_over_configs(prediction_type=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												for t in [0, 500, 999]:
																		self.check_over_forward(time_step=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.scheduler_classes[0]
												_lowerCAmelCase    		=					self.get_scheduler_config()
												_lowerCAmelCase    		=					scheduler_class(**_lowercase )

												assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0979 ) ) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1e-5




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.scheduler_classes[0]
												_lowerCAmelCase    		=					self.get_scheduler_config()
												_lowerCAmelCase    		=					scheduler_class(**_lowercase )

												_lowerCAmelCase    		=					len(_lowercase )

												_lowerCAmelCase    		=					self.dummy_model()
												_lowerCAmelCase    		=					self.dummy_sample_deter
												_lowerCAmelCase    		=					torch.manual_seed(0 )

												for t in reversed(range(_lowercase ) ):
																		# 1. predict noise residual
																		_lowerCAmelCase    		=					model(_lowercase       ,  _lowercase )

																		# 2. predict previous mean of sample x_t-1
																		_lowerCAmelCase    		=					scheduler.step(_lowercase       ,  _lowercase       ,  _lowercase       ,  generator=_lowercase ).prev_sample

																		# if t > 0:
																		#     noise = self.dummy_sample_deter
																		#     variance = scheduler.get_variance(t) ** (0.5) * noise
																		#
																		# sample = pred_prev_sample + variance
																		_lowerCAmelCase    		=					pred_prev_sample

												_lowerCAmelCase    		=					torch.sum(torch.abs(_lowercase ) )
												_lowerCAmelCase    		=					torch.mean(torch.abs(_lowercase ) )

												assert abs(result_sum.item() - 258.9606 ) < 1e-2
												assert abs(result_mean.item() - 0.3372 ) < 1e-3




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.scheduler_classes[0]
												_lowerCAmelCase    		=					self.get_scheduler_config(prediction_type="""v_prediction""" )
												_lowerCAmelCase    		=					scheduler_class(**_lowercase )

												_lowerCAmelCase    		=					len(_lowercase )

												_lowerCAmelCase    		=					self.dummy_model()
												_lowerCAmelCase    		=					self.dummy_sample_deter
												_lowerCAmelCase    		=					torch.manual_seed(0 )

												for t in reversed(range(_lowercase ) ):
																		# 1. predict noise residual
																		_lowerCAmelCase    		=					model(_lowercase       ,  _lowercase )

																		# 2. predict previous mean of sample x_t-1
																		_lowerCAmelCase    		=					scheduler.step(_lowercase       ,  _lowercase       ,  _lowercase       ,  generator=_lowercase ).prev_sample

																		# if t > 0:
																		#     noise = self.dummy_sample_deter
																		#     variance = scheduler.get_variance(t) ** (0.5) * noise
																		#
																		# sample = pred_prev_sample + variance
																		_lowerCAmelCase    		=					pred_prev_sample

												_lowerCAmelCase    		=					torch.sum(torch.abs(_lowercase ) )
												_lowerCAmelCase    		=					torch.mean(torch.abs(_lowercase ) )

												assert abs(result_sum.item() - 202.0296 ) < 1e-2
												assert abs(result_mean.item() - 0.2631 ) < 1e-3




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.scheduler_classes[0]
												_lowerCAmelCase    		=					self.get_scheduler_config()
												_lowerCAmelCase    		=					scheduler_class(**_lowercase )

												_lowerCAmelCase    		=					[100, 87, 50, 1, 0]

												scheduler.set_timesteps(timesteps=_lowercase )

												_lowerCAmelCase    		=					scheduler.timesteps

												for i, timestep in enumerate(_lowercase ):
																		if i == len(_lowercase ) - 1:
																								_lowerCAmelCase    		=					-1
																		else:
																								_lowerCAmelCase    		=					timesteps[i + 1]

																		_lowerCAmelCase    		=					scheduler.previous_timestep(_lowercase )
																		_lowerCAmelCase    		=					prev_t.item()

																		self.assertEqual(_lowercase       ,  _lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.scheduler_classes[0]
												_lowerCAmelCase    		=					self.get_scheduler_config()
												_lowerCAmelCase    		=					scheduler_class(**_lowercase )

												_lowerCAmelCase    		=					[100, 87, 50, 51, 0]

												with self.assertRaises(_lowercase       ,  msg="""`custom_timesteps` must be in descending order.""" ):
																		scheduler.set_timesteps(timesteps=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.scheduler_classes[0]
												_lowerCAmelCase    		=					self.get_scheduler_config()
												_lowerCAmelCase    		=					scheduler_class(**_lowercase )

												_lowerCAmelCase    		=					[100, 87, 50, 1, 0]
												_lowerCAmelCase    		=					len(_lowercase )

												with self.assertRaises(_lowercase       ,  msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ):
																		scheduler.set_timesteps(num_inference_steps=_lowercase       ,  timesteps=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.scheduler_classes[0]
												_lowerCAmelCase    		=					self.get_scheduler_config()
												_lowerCAmelCase    		=					scheduler_class(**_lowercase )

												_lowerCAmelCase    		=					[scheduler.config.num_train_timesteps]

												with self.assertRaises(
												    _lowercase       ,  msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""       ,  ):
																		scheduler.set_timesteps(timesteps=_lowercase )
