
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()
lowerCamelCase__     :		Dict        = logging.get_logger(__name__)

def 		UpperCamelCase						(   lowercase_ )		->						int:


  '''simple docstring'''






  lowercase__	: List[Any]     		=   DPTConfig(embedding_type="""hybrid""" )

  if "large" in checkpoint_url:
    lowercase__	: Union[str, Any]     		=   10_24
    lowercase__	: Dict     		=   40_96
    lowercase__	: Optional[Any]     		=   24
    lowercase__	: List[Any]     		=   16
    lowercase__	: str     		=   [5, 11, 17, 23]
    lowercase__	: Union[str, Any]     		=   [2_56, 5_12, 10_24, 10_24]
    lowercase__	: Optional[Any]     		=   (1, 3_84, 3_84)

  if "nyu" or "midas" in checkpoint_url:
    lowercase__	: Any     		=   7_68
    lowercase__	: Tuple     		=   [1, 1, 1, 0.5]
    lowercase__	: Union[str, Any]     		=   [2_56, 5_12, 7_68, 7_68]
    lowercase__	: List[Any]     		=   1_50
    lowercase__	: str     		=   16
    lowercase__	: Optional[Any]     		=   (1, 3_84, 3_84)
    lowercase__	: Union[str, Any]     		=   False
    lowercase__	: List[str]     		=   """project"""

  if "ade" in checkpoint_url:
    lowercase__	: Union[str, Any]     		=   True
    lowercase__	: Tuple     		=   7_68
    lowercase__	: Union[str, Any]     		=   [1, 1, 1, 0.5]
    lowercase__	: Optional[Any]     		=   1_50
    lowercase__	: Optional[int]     		=   16
    lowercase__	: Optional[int]     		=   """huggingface/label-files"""
    lowercase__	: List[str]     		=   """ade20k-id2label.json"""
    lowercase__	: List[str]     		=   json.load(open(cached_download(hf_hub_url(lowercase_			,			lowercase_			,			repo_type="""dataset""" ) )			,			"""r""" ) )
    lowercase__	: Optional[Any]     		=   {int(lowercase_ ): v for k, v in idalabel.items()}
    lowercase__	: List[str]     		=   idalabel
    lowercase__	: List[Any]     		=   {v: k for k, v in idalabel.items()}
    lowercase__	: Any     		=   [1, 1_50, 4_80, 4_80]

  return config, expected_shape

def 		UpperCamelCase						(   lowercase_ )		->						Any:


  '''simple docstring'''






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

def 		UpperCamelCase						(   lowercase_ )		->						Any:


  '''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
  ):
    lowercase__	: List[str]     		=   name.replace("""pretrained.model"""			,			"""dpt.encoder""" )
  if "pretrained.model" in name:
    lowercase__	: Tuple     		=   name.replace("""pretrained.model"""			,			"""dpt.embeddings""" )
  if "patch_embed" in name:
    lowercase__	: Any     		=   name.replace("""patch_embed"""			,			"""""" )
  if "pos_embed" in name:
    lowercase__	: Union[str, Any]     		=   name.replace("""pos_embed"""			,			"""position_embeddings""" )
  if "attn.proj" in name:
    lowercase__	: Optional[int]     		=   name.replace("""attn.proj"""			,			"""attention.output.dense""" )
  if "proj" in name and "project" not in name:
    lowercase__	: Any     		=   name.replace("""proj"""			,			"""projection""" )
  if "blocks" in name:
    lowercase__	: Any     		=   name.replace("""blocks"""			,			"""layer""" )
  if "mlp.fc1" in name:
    lowercase__	: Optional[Any]     		=   name.replace("""mlp.fc1"""			,			"""intermediate.dense""" )
  if "mlp.fc2" in name:
    lowercase__	: Any     		=   name.replace("""mlp.fc2"""			,			"""output.dense""" )
  if "norm1" in name and "backbone" not in name:
    lowercase__	: Dict     		=   name.replace("""norm1"""			,			"""layernorm_before""" )
  if "norm2" in name and "backbone" not in name:
    lowercase__	: Tuple     		=   name.replace("""norm2"""			,			"""layernorm_after""" )
  if "scratch.output_conv" in name:
    lowercase__	: Any     		=   name.replace("""scratch.output_conv"""			,			"""head""" )
  if "scratch" in name:
    lowercase__	: int     		=   name.replace("""scratch"""			,			"""neck""" )
  if "layer1_rn" in name:
    lowercase__	: Tuple     		=   name.replace("""layer1_rn"""			,			"""convs.0""" )
  if "layer2_rn" in name:
    lowercase__	: List[str]     		=   name.replace("""layer2_rn"""			,			"""convs.1""" )
  if "layer3_rn" in name:
    lowercase__	: Dict     		=   name.replace("""layer3_rn"""			,			"""convs.2""" )
  if "layer4_rn" in name:
    lowercase__	: List[str]     		=   name.replace("""layer4_rn"""			,			"""convs.3""" )
  if "refinenet" in name:
    lowercase__	: int     		=   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
    lowercase__	: List[str]     		=   name.replace(F'refinenet{layer_idx}'			,			F'fusion_stage.layers.{abs(layer_idx-4 )}' )
  if "out_conv" in name:
    lowercase__	: List[Any]     		=   name.replace("""out_conv"""			,			"""projection""" )
  if "resConfUnit1" in name:
    lowercase__	: Union[str, Any]     		=   name.replace("""resConfUnit1"""			,			"""residual_layer1""" )
  if "resConfUnit2" in name:
    lowercase__	: Optional[Any]     		=   name.replace("""resConfUnit2"""			,			"""residual_layer2""" )
  if "conv1" in name:
    lowercase__	: Optional[Any]     		=   name.replace("""conv1"""			,			"""convolution1""" )
  if "conv2" in name:
    lowercase__	: Optional[Any]     		=   name.replace("""conv2"""			,			"""convolution2""" )
  # readout blocks
  if "pretrained.act_postprocess1.0.project.0" in name:
    lowercase__	: Union[str, 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:
    lowercase__	: 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:
    lowercase__	: Any     		=   name.replace("""pretrained.act_postprocess3.0.project.0"""			,			"""neck.reassemble_stage.readout_projects.2.0""" )
  if "pretrained.act_postprocess4.0.project.0" in name:
    lowercase__	: int     		=   name.replace("""pretrained.act_postprocess4.0.project.0"""			,			"""neck.reassemble_stage.readout_projects.3.0""" )

  # resize blocks
  if "pretrained.act_postprocess1.3" in name:
    lowercase__	: str     		=   name.replace("""pretrained.act_postprocess1.3"""			,			"""neck.reassemble_stage.layers.0.projection""" )
  if "pretrained.act_postprocess1.4" in name:
    lowercase__	: Tuple     		=   name.replace("""pretrained.act_postprocess1.4"""			,			"""neck.reassemble_stage.layers.0.resize""" )
  if "pretrained.act_postprocess2.3" in name:
    lowercase__	: Union[str, Any]     		=   name.replace("""pretrained.act_postprocess2.3"""			,			"""neck.reassemble_stage.layers.1.projection""" )
  if "pretrained.act_postprocess2.4" in name:
    lowercase__	: Tuple     		=   name.replace("""pretrained.act_postprocess2.4"""			,			"""neck.reassemble_stage.layers.1.resize""" )
  if "pretrained.act_postprocess3.3" in name:
    lowercase__	: Union[str, Any]     		=   name.replace("""pretrained.act_postprocess3.3"""			,			"""neck.reassemble_stage.layers.2.projection""" )
  if "pretrained.act_postprocess4.3" in name:
    lowercase__	: Dict     		=   name.replace("""pretrained.act_postprocess4.3"""			,			"""neck.reassemble_stage.layers.3.projection""" )
  if "pretrained.act_postprocess4.4" in name:
    lowercase__	: int     		=   name.replace("""pretrained.act_postprocess4.4"""			,			"""neck.reassemble_stage.layers.3.resize""" )
  if "pretrained" in name:
    lowercase__	: str     		=   name.replace("""pretrained"""			,			"""dpt""" )
  if "bn" in name:
    lowercase__	: Any     		=   name.replace("""bn"""			,			"""batch_norm""" )
  if "head" in name:
    lowercase__	: Tuple     		=   name.replace("""head"""			,			"""head.head""" )
  if "encoder.norm" in name:
    lowercase__	: List[Any]     		=   name.replace("""encoder.norm"""			,			"""layernorm""" )
  if "auxlayer" in name:
    lowercase__	: List[Any]     		=   name.replace("""auxlayer"""			,			"""auxiliary_head.head""" )
  if "backbone" in name:
    lowercase__	: List[Any]     		=   name.replace("""backbone"""			,			"""backbone.bit.encoder""" )

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

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

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						List[Any]:


  '''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)
    lowercase__	: str     		=   state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.weight' )
    lowercase__	: Tuple     		=   state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.bias' )
    # next, add query, keys and values (in that order) to the state dict
    lowercase__	: Tuple     		=   in_proj_weight[: config.hidden_size, :]
    lowercase__	: Union[str, Any]     		=   in_proj_bias[: config.hidden_size]
    lowercase__	: int     		=   in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    lowercase__	: int     		=   in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    lowercase__	: Any     		=   in_proj_weight[
        -config.hidden_size :, :
    ]
    lowercase__	: Optional[Any]     		=   in_proj_bias[-config.hidden_size :]

def 		UpperCamelCase						(   )		->						Tuple:


  '''simple docstring'''






  lowercase__	: Optional[int]     		=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
  lowercase__	: Union[str, Any]     		=   Image.open(requests.get(lowercase_			,			stream=lowercase_ ).raw )
  return im

@torch.no_grad()
def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )		->						Tuple:


  '''simple docstring'''






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

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

  # Check outputs on an image
  lowercase__	: List[Any]     		=   4_80 if """ade""" in checkpoint_url else 3_84
  lowercase__	: Dict     		=   DPTImageProcessor(size=lowercase_ )

  lowercase__	: Optional[int]     		=   prepare_img()
  lowercase__	: Dict     		=   image_processor(lowercase_			,			return_tensors="""pt""" )

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

  if show_prediction:
    lowercase__	: Union[str, 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()) * 2_55 ).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__":
     lowerCamelCase__     :		Optional[int]        = 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""",
     )

     lowerCamelCase__     :		Any        = parser.parse_args()
     convert_dpt_checkpoint(
         args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
     )








from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_torch_available():
     import torch

     from transformers.modeling_outputs import BaseModelOutput

     from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

lowerCamelCase__     :		Optional[Any]        = logging.get_logger(__name__)



@add_end_docstrings(UpperCAmelCase_				)
class    _snake_case (						UpperCAmelCase_				):


 def __init__(       self       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   super().__init__(**SCREAMING_SNAKE_CASE_)

   if self.framework == "tf":
     raise ValueError(f'The {self.__class__} is only available in PyTorch.')

   requires_backends(self       ,	"""vision""")
   self.check_model_type(SCREAMING_SNAKE_CASE_)


 def __call__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   if "text_queries" in kwargs:
     lowercase__	: Any     		=   kwargs.pop("""text_queries""")

   if isinstance(SCREAMING_SNAKE_CASE_       ,	(str, Image.Image)):
     lowercase__	: Optional[Any]     		=   {"""image""": image, """candidate_labels""": candidate_labels}
   else:
     lowercase__	: int     		=   image
   lowercase__	: List[str]     		=   super().__call__(SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)
   return results


 def   lowercase__  (       self       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Tuple     		=   {}
   if "threshold" in kwargs:
     lowercase__	: List[Any]     		=   kwargs["""threshold"""]
   if "top_k" in kwargs:
     lowercase__	: int     		=   kwargs["""top_k"""]
   return {}, {}, postprocess_params


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: str     		=   load_image(inputs["""image"""])
   lowercase__	: Any     		=   inputs["""candidate_labels"""]
   if isinstance(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):
     lowercase__	: List[str]     		=   candidate_labels.split(""",""")

   lowercase__	: Tuple     		=   torch.tensor([[image.height, image.width]]       ,	dtype=torch.intaa)
   for i, candidate_label in enumerate(SCREAMING_SNAKE_CASE_):
     lowercase__	: Optional[Any]     		=   self.tokenizer(SCREAMING_SNAKE_CASE_       ,	return_tensors=self.framework)
     lowercase__	: Union[str, Any]     		=   self.image_processor(SCREAMING_SNAKE_CASE_       ,	return_tensors=self.framework)
     yield {
         "is_last": i == len(SCREAMING_SNAKE_CASE_) - 1,
         "target_size": target_size,
         "candidate_label": candidate_label,
         **text_inputs,
         **image_features,
     }


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: str     		=   model_inputs.pop("""target_size""")
   lowercase__	: Optional[int]     		=   model_inputs.pop("""candidate_label""")
   lowercase__	: Dict     		=   model_inputs.pop("""is_last""")

   lowercase__	: Union[str, Any]     		=   self.model(**SCREAMING_SNAKE_CASE_)

   lowercase__	: Union[str, Any]     		=   {"""target_size""": target_size, """candidate_label""": candidate_label, """is_last""": is_last, **outputs}
   return model_outputs


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=None):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   []
   for model_output in model_outputs:
     lowercase__	: Optional[int]     		=   model_output["""candidate_label"""]
     lowercase__	: Tuple     		=   BaseModelOutput(SCREAMING_SNAKE_CASE_)
     lowercase__	: List[str]     		=   self.image_processor.post_process_object_detection(
         outputs=SCREAMING_SNAKE_CASE_       ,	threshold=SCREAMING_SNAKE_CASE_       ,	target_sizes=model_output["""target_size"""])[0]

     for index in outputs["scores"].nonzero():
       lowercase__	: Optional[Any]     		=   outputs["""scores"""][index].item()
       lowercase__	: Optional[Any]     		=   self._get_bounding_box(outputs["""boxes"""][index][0])

       lowercase__	: Tuple     		=   {"""score""": score, """label""": label, """box""": box}
       results.append(SCREAMING_SNAKE_CASE_)

   lowercase__	: int     		=   sorted(SCREAMING_SNAKE_CASE_       ,	key=lambda SCREAMING_SNAKE_CASE_: x["score"]       ,	reverse=SCREAMING_SNAKE_CASE_)
   if top_k:
     lowercase__	: Any     		=   results[:top_k]

   return results



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   if self.framework != "pt":
     raise ValueError("""The ZeroShotObjectDetectionPipeline is only available in PyTorch.""")
   lowercase__		,     lowercase__		,     lowercase__		,     lowercase__	: List[Any]     		=   box.int().tolist()
   lowercase__	: Optional[int]     		=   {
       """xmin""": xmin,
       """ymin""": ymin,
       """xmax""": xmax,
       """ymax""": ymax,
   }
   return bbox






