





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  :   int						      =logging.get_logger(__name__)




def     a__ (					lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=DPTConfig()

   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 "ade" in checkpoint_url:
      UpperCAmelCase_											=True

      UpperCAmelCase_											=1_5_0
      UpperCAmelCase_											="huggingface/label-files"
      UpperCAmelCase_											="ade20k-id2label.json"
      UpperCAmelCase_											=json.load(open(cached_download(hf_hub_url(lowercase__  ,    lowercase__  ,    repo_type="dataset"  )  )  ,    "r"  )  )
      UpperCAmelCase_											={int(lowercase__  ): 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__ (					lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=["pretrained.model.head.weight", "pretrained.model.head.bias"]
   for k in ignore_keys:
      state_dict.pop(lowercase__  ,    lowercase__  )




def     a__ (					lowercase__  ):






   '''simple docstring'''






   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"  ,    "patch_embeddings"  )
   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:
      UpperCAmelCase_											=name.replace("norm1"  ,    "layernorm_before"  )
   if "norm2" 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"  )

   return name




def     a__ (					lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   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__ (					):






   '''simple docstring'''






   UpperCAmelCase_											="http://images.cocodataset.org/val2017/000000039769.jpg"
   UpperCAmelCase_											=Image.open(requests.get(lowercase__  ,    stream=lowercase__  ).raw  )
   return im




@torch.no_grad()
def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_						,       UpperCAmelCase_											=get_dpt_config(lowercase__  )
   # load original state_dict from URL
   UpperCAmelCase_											=torch.hub.load_state_dict_from_url(lowercase__  ,    map_location="cpu"  )
   # remove certain keys
   remove_ignore_keys_(lowercase__  )
   # rename keys
   for key in state_dict.copy().keys():
      UpperCAmelCase_											=state_dict.pop(lowercase__  )
      UpperCAmelCase_											=val
   # read in qkv matrices
   read_in_q_k_v(lowercase__  ,    lowercase__  )

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

   # Check outputs on an image
   UpperCAmelCase_											=4_8_0 if "ade" in checkpoint_url else 3_8_4
   UpperCAmelCase_											=DPTImageProcessor(size=lowercase__  )

   UpperCAmelCase_											=prepare_img()
   UpperCAmelCase_											=image_processor(lowercase__  ,    return_tensors="pt"  )

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

   # Assert logits
   UpperCAmelCase_											=torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]  )
   if "ade" in checkpoint_url:
      UpperCAmelCase_											=torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]  )
   assert outputs.shape == torch.Size(lowercase__  )
   assert (
       torch.allclose(outputs[0, 0, :3, :3]  ,    lowercase__  ,    atol=1E-4  )
       if "ade" in checkpoint_url
       else torch.allclose(outputs[0, :3, :3]  ,    lowercase__  )
   )

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


if __name__ == "__main__":
     __lowercase  :   List[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=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  :   int						      =parser.parse_args()
     convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)











import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class      A       (	__lowercase						):
      _snake_case							     =(DDIMParallelScheduler,)
      _snake_case							     =(('''eta''', 0.0), ('''num_inference_steps''', 50))



      def 	lowerCAmelCase__    (	self:						str		,  **_lowerCAmelCase:						str       )						->    str:
         '''simple docstring'''

         UpperCAmelCase_											={
             "num_train_timesteps": 1000,
             "beta_start": 0.00_01,
             "beta_end": 0.02,
             "beta_schedule": "linear",
             "clip_sample": True,
         }

         config.update(**_lowerCAmelCase       )
         return config



      def 	lowerCAmelCase__    (	self:						int		,  **_lowerCAmelCase:						Tuple       )						->    str:
         '''simple docstring'''

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

         UpperCAmelCase_						,       UpperCAmelCase_											=10, 0.0

         UpperCAmelCase_											=self.dummy_model()
         UpperCAmelCase_											=self.dummy_sample_deter

         scheduler.set_timesteps(_lowerCAmelCase       )

         for t in scheduler.timesteps:
            UpperCAmelCase_											=model(_lowerCAmelCase		,  _lowerCAmelCase       )
            UpperCAmelCase_											=scheduler.step(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase       ).prev_sample

         return sample



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Optional[int]:
         '''simple docstring'''

         for timesteps in [100, 500, 1000]:
            self.check_over_configs(num_train_timesteps=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						int       )						->    List[str]:
         '''simple docstring'''

         for steps_offset in [0, 1]:
            self.check_over_configs(steps_offset=_lowerCAmelCase       )

         UpperCAmelCase_											=self.scheduler_classes[0]
         UpperCAmelCase_											=self.get_scheduler_config(steps_offset=1       )
         UpperCAmelCase_											=scheduler_class(**_lowerCAmelCase       )
         scheduler.set_timesteps(5       )
         assert torch.equal(scheduler.timesteps		,  torch.LongTensor([801, 601, 401, 201, 1]       )       )



      def 	lowerCAmelCase__    (	self:						int       )						->    Dict:
         '''simple docstring'''

         for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]		,  [0.0_02, 0.02, 0.2, 2]       ):
            self.check_over_configs(beta_start=_lowerCAmelCase		,  beta_end=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						int       )						->    Any:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    Optional[Any]:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Tuple:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    List[Any]:
         '''simple docstring'''

         for timestep_spacing in ["trailing", "leading"]:
            self.check_over_configs(timestep_spacing=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    Any:
         '''simple docstring'''

         for rescale_betas_zero_snr in [True, False]:
            self.check_over_configs(rescale_betas_zero_snr=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    Optional[Any]:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    List[str]:
         '''simple docstring'''

         for t in [1, 10, 49]:
            self.check_over_forward(time_step=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Any       )						->    List[str]:
         '''simple docstring'''

         for t, num_inference_steps in zip([1, 10, 50]		,  [10, 50, 500]       ):
            self.check_over_forward(time_step=_lowerCAmelCase		,  num_inference_steps=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						int       )						->    Tuple:
         '''simple docstring'''

         for t, eta in zip([1, 10, 49]		,  [0.0, 0.5, 1.0]       ):
            self.check_over_forward(time_step=_lowerCAmelCase		,  eta=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    Optional[Any]:
         '''simple docstring'''

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

         assert torch.sum(torch.abs(scheduler._get_variance(0		,  0       ) - 0.0       )       ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(420		,  400       ) - 0.1_47_71       )       ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(980		,  960       ) - 0.3_24_60       )       ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(0		,  0       ) - 0.0       )       ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(487		,  486       ) - 0.0_09_79       )       ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(999		,  998       ) - 0.02       )       ) < 1e-5



      def 	lowerCAmelCase__    (	self:						int       )						->    Optional[Any]:
         '''simple docstring'''

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

         UpperCAmelCase_						,       UpperCAmelCase_											=10, 0.0
         scheduler.set_timesteps(_lowerCAmelCase       )

         UpperCAmelCase_											=self.dummy_model()
         UpperCAmelCase_											=self.dummy_sample_deter
         UpperCAmelCase_											=self.dummy_sample_deter + 0.1
         UpperCAmelCase_											=self.dummy_sample_deter - 0.1

         UpperCAmelCase_											=samplea.shape[0]
         UpperCAmelCase_											=torch.stack([samplea, samplea, samplea]		,  dim=0       )
         UpperCAmelCase_											=torch.arange(_lowerCAmelCase       )[0:3, None].repeat(1		,  _lowerCAmelCase       )

         UpperCAmelCase_											=model(samples.flatten(0		,  1       )		,  timesteps.flatten(0		,  1       )       )
         UpperCAmelCase_											=scheduler.batch_step_no_noise(_lowerCAmelCase		,  timesteps.flatten(0		,  1       )		,  samples.flatten(0		,  1       )		,  _lowerCAmelCase       )

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

         assert abs(result_sum.item() - 11_47.79_04       ) < 1e-2
         assert abs(result_mean.item() - 0.49_82       ) < 1e-3



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.full_loop()

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

         assert abs(result_sum.item() - 1_72.00_67       ) < 1e-2
         assert abs(result_mean.item() - 0.22_39_67       ) < 1e-3



      def 	lowerCAmelCase__    (	self:						int       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.full_loop(prediction_type="v_prediction"       )

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

         assert abs(result_sum.item() - 52.53_02       ) < 1e-2
         assert abs(result_mean.item() - 0.06_84       ) < 1e-3



      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    Tuple:
         '''simple docstring'''

         UpperCAmelCase_											=self.full_loop(set_alpha_to_one=_lowerCAmelCase		,  beta_start=0.01       )
         UpperCAmelCase_											=torch.sum(torch.abs(_lowerCAmelCase       )       )
         UpperCAmelCase_											=torch.mean(torch.abs(_lowerCAmelCase       )       )

         assert abs(result_sum.item() - 1_49.82_95       ) < 1e-2
         assert abs(result_mean.item() - 0.19_51       ) < 1e-3






      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    str:
         '''simple docstring'''

         UpperCAmelCase_											=self.full_loop(set_alpha_to_one=_lowerCAmelCase		,  beta_start=0.01       )
         UpperCAmelCase_											=torch.sum(torch.abs(_lowerCAmelCase       )       )
         UpperCAmelCase_											=torch.mean(torch.abs(_lowerCAmelCase       )       )

         assert abs(result_sum.item() - 1_49.07_84       ) < 1e-2
         assert abs(result_mean.item() - 0.19_41       ) < 1e-3




