





'''simple docstring'''






import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import ImageGPTImageProcessor







class 		UpperCAmelCase   (				unittest.TestCase):


  """simple docstring"""







  def __init__(				self    :					int							,	UpperCamelCase__    :					Tuple							,	UpperCamelCase__    :					str=7							,	UpperCamelCase__    :					Tuple=3							,	UpperCamelCase__    :					List[Any]=18							,	UpperCamelCase__    :					List[str]=30							,	UpperCamelCase__    :					Optional[Any]=400							,	UpperCamelCase__    :					Any=True							,	UpperCamelCase__    :					Optional[Any]=None							,	UpperCamelCase__    :					List[str]=True							,	)     ->						str:
    _UpperCamelCase       			=size if size is not None else {'''height''': 18, '''width''': 18}
    _UpperCamelCase       			=parent
    _UpperCamelCase       			=batch_size
    _UpperCamelCase       			=num_channels
    _UpperCamelCase       			=image_size
    _UpperCamelCase       			=min_resolution
    _UpperCamelCase       			=max_resolution
    _UpperCamelCase       			=do_resize
    _UpperCamelCase       			=size
    _UpperCamelCase       			=do_normalize




  def   UpperCamelCase__      (				self    :					List[str]  )     ->						List[Any]:
    return {
        # here we create 2 clusters for the sake of simplicity
        "clusters": np.asarray(
            [
                [0.8866443634033203, 0.6618829369544983, 0.3891746401786804],
                [-0.6042559146881104, -0.02295008860528469, 0.5423797369003296],
            ]  ),
        "do_resize": self.do_resize,
        "size": self.size,
        "do_normalize": self.do_normalize,
    }







@require_torch
@require_vision
class 		UpperCAmelCase   (				_lowercase   ,     unittest.TestCase):


  """simple docstring"""







  lowerCAmelCase_					=				ImageGPTImageProcessor if is_vision_available() else None


  def   UpperCamelCase__      (				self    :					List[Any]  )     ->						Any:
    _UpperCamelCase       			=ImageGPTImageProcessingTester(self  )


  @property
  def   UpperCamelCase__      (				self    :					List[str]  )     ->						Union[str, Any]:
    return self.image_processor_tester.prepare_image_processor_dict()


  def   UpperCamelCase__      (				self    :					Any  )     ->						List[Any]:
    _UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )
    self.assertTrue(hasattr(A_							,	'''clusters'''  )  )
    self.assertTrue(hasattr(A_							,	'''do_resize'''  )  )
    self.assertTrue(hasattr(A_							,	'''size'''  )  )
    self.assertTrue(hasattr(A_							,	'''do_normalize'''  )  )


  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						Tuple:
    _UpperCamelCase       			=self.image_processing_class.from_dict(self.image_processor_dict  )
    self.assertEqual(image_processor.size							,	{'''height''': 18, '''width''': 18}  )

    _UpperCamelCase       			=self.image_processing_class.from_dict(self.image_processor_dict							,	size=42  )
    self.assertEqual(image_processor.size							,	{'''height''': 42, '''width''': 42}  )


  def   UpperCamelCase__      (				self    :					List[str]  )     ->						str:
    _UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )
    _UpperCamelCase       			=json.loads(image_processor.to_json_string()  )
    for key, value in self.image_processor_dict.items():
      if key == "clusters":
        self.assertTrue(np.array_equal(A_							,	obj[key]  )  )
      else:
        self.assertEqual(obj[key]							,	A_  )


  def   UpperCamelCase__      (				self    :					List[Any]  )     ->						Union[str, Any]:
    _UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )

    with tempfile.TemporaryDirectory() as tmpdirname:
      _UpperCamelCase       			=os.path.join(A_							,	'''image_processor.json'''  )
      image_processor_first.to_json_file(A_  )
      _UpperCamelCase       			=self.image_processing_class.from_json_file(A_  ).to_dict()

    _UpperCamelCase       			=image_processor_first.to_dict()
    for key, value in image_processor_first.items():
      if key == "clusters":
        self.assertTrue(np.array_equal(A_							,	image_processor_second[key]  )  )
      else:
        self.assertEqual(image_processor_first[key]							,	A_  )


  def   UpperCamelCase__      (				self    :					List[str]  )     ->						int:
    _UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )

    with tempfile.TemporaryDirectory() as tmpdirname:
      image_processor_first.save_pretrained(A_  )
      _UpperCamelCase       			=self.image_processing_class.from_pretrained(A_  ).to_dict()

    _UpperCamelCase       			=image_processor_first.to_dict()
    for key, value in image_processor_first.items():
      if key == "clusters":
        self.assertTrue(np.array_equal(A_							,	image_processor_second[key]  )  )
      else:
        self.assertEqual(image_processor_first[key]							,	A_  )




  @unittest.skip('''ImageGPT requires clusters at initialization'''  )
  def   UpperCamelCase__      (				self    :					Optional[Any]  )     ->						Union[str, Any]:
    pass




def 					_a   ():


  """simple docstring"""

  _UpperCamelCase       			=load_dataset('''hf-internal-testing/fixtures_image_utils'''  ,      split='''test'''   )

  _UpperCamelCase       			=Image.open(dataset[4]['''file''']   )
  _UpperCamelCase       			=Image.open(dataset[5]['''file''']   )

  _UpperCamelCase       			=[imagea, imagea]

  return images







@require_vision
@require_torch
class 		UpperCAmelCase   (				unittest.TestCase):


  """simple docstring"""







  @slow
  def   UpperCamelCase__      (				self    :					str  )     ->						int:
    _UpperCamelCase       			=ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small'''  )

    _UpperCamelCase       			=prepare_images()

    # test non-batched
    _UpperCamelCase       			=image_processing(images[0]							,	return_tensors='''pt'''  )

    self.assertIsInstance(encoding.input_ids							,	torch.LongTensor  )
    self.assertEqual(encoding.input_ids.shape							,	(1, 1024)  )

    _UpperCamelCase       			=[306, 191, 191]
    self.assertEqual(encoding.input_ids[0, :3].tolist()							,	A_  )

    # test batched
    _UpperCamelCase       			=image_processing(A_							,	return_tensors='''pt'''  )

    self.assertIsInstance(encoding.input_ids							,	torch.LongTensor  )
    self.assertEqual(encoding.input_ids.shape							,	(2, 1024)  )

    _UpperCamelCase       			=[303, 13, 13]
    self.assertEqual(encoding.input_ids[1, -3:].tolist()							,	A_  )













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


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=DPTConfig(embedding_type='''hybrid'''   )

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

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

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

  return config, expected_shape


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
  for k in ignore_keys:
    state_dict.pop(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """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'''  ,      ''''''   )
  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 and "backbone" not in name:
    _UpperCamelCase       			=name.replace('''norm1'''  ,      '''layernorm_before'''   )
  if "norm2" in name and "backbone" not 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'''   )
  if "backbone" in name:
    _UpperCamelCase       			=name.replace('''backbone'''  ,      '''backbone.bit.encoder'''   )

  if ".." in name:
    _UpperCamelCase       			=name.replace('''..'''  ,      '''.'''   )

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


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """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(__SCREAMING_SNAKE_CASE  ,      stream=__SCREAMING_SNAKE_CASE   ).raw   )
  return im


@torch.no_grad()
def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

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

  # load HuggingFace model
  _UpperCamelCase       			=DPTForSemanticSegmentation(__SCREAMING_SNAKE_CASE   ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(__SCREAMING_SNAKE_CASE   )
  model.load_state_dict(__SCREAMING_SNAKE_CASE   )
  model.eval()

  # Check outputs on an image
  _UpperCamelCase       			=480 if '''ade''' in checkpoint_url else 384
  _UpperCamelCase       			=DPTImageProcessor(size=__SCREAMING_SNAKE_CASE   )

  _UpperCamelCase       			=prepare_img()
  _UpperCamelCase       			=image_processor(__SCREAMING_SNAKE_CASE  ,      return_tensors='''pt'''   )

  # forward pass
  _UpperCamelCase       			=model(**__SCREAMING_SNAKE_CASE   ).logits if '''ade''' in checkpoint_url else model(**__SCREAMING_SNAKE_CASE   ).predicted_depth

  if show_prediction:
    _UpperCamelCase       			=(
        torch.nn.functional.interpolate(
            outputs.unsqueeze(1   )  ,      size=(image.size[1], image.size[0])  ,      mode='''bicubic'''  ,      align_corners=__SCREAMING_SNAKE_CASE  ,      )
        .squeeze()
        .cpu()
        .numpy()
    )

    Image.fromarray((prediction / prediction.max()) * 255   ).show()

  if pytorch_dump_folder_path is not None:
    Path(__SCREAMING_SNAKE_CASE   ).mkdir(exist_ok=__SCREAMING_SNAKE_CASE   )
    print(f'''Saving model to {pytorch_dump_folder_path}'''   )
    model.save_pretrained(__SCREAMING_SNAKE_CASE   )
    print(f'''Saving image processor to {pytorch_dump_folder_path}'''   )
    image_processor.save_pretrained(__SCREAMING_SNAKE_CASE   )

  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						: List[str]		=   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						: Dict		=   parser.parse_args()
      convert_dpt_checkpoint(
          args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
      )






