





'''simple docstring'''






import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class 		UpperCAmelCase   (				lowercase_   ,     unittest.TestCase):


  """simple docstring"""







  lowerCAmelCase_					=				KandinskyVaaControlnetPipeline
  lowerCAmelCase_					=				["""image_embeds""", """negative_image_embeds""", """hint"""]
  lowerCAmelCase_					=				["""image_embeds""", """negative_image_embeds""", """hint"""]
  lowerCAmelCase_					=				[
      """generator""",
      """height""",
      """width""",
      """latents""",
      """guidance_scale""",
      """num_inference_steps""",
      """return_dict""",
      """guidance_scale""",
      """num_images_per_prompt""",
      """output_type""",
      """return_dict""",
  ]
  lowerCAmelCase_					=				False


  @property
  def   UpperCamelCase__      (				self    :					List[Any]  )     ->						List[str]:
    return 32


  @property
  def   UpperCamelCase__      (				self    :					Dict  )     ->						List[str]:
    return 32


  @property
  def   UpperCamelCase__      (				self    :					int  )     ->						str:
    return self.time_input_dim


  @property
  def   UpperCamelCase__      (				self    :					List[str]  )     ->						Union[str, Any]:
    return self.time_input_dim * 4


  @property
  def   UpperCamelCase__      (				self    :					Dict  )     ->						str:
    return 100


  @property
  def   UpperCamelCase__      (				self    :					Optional[int]  )     ->						str:
    torch.manual_seed(0  )

    _UpperCamelCase       			={
        '''in_channels''': 8,
        # Out channels is double in channels because predicts mean and variance
        '''out_channels''': 8,
        '''addition_embed_type''': '''image_hint''',
        '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
        '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
        '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
        '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
        '''layers_per_block''': 1,
        '''encoder_hid_dim''': self.text_embedder_hidden_size,
        '''encoder_hid_dim_type''': '''image_proj''',
        '''cross_attention_dim''': self.cross_attention_dim,
        '''attention_head_dim''': 4,
        '''resnet_time_scale_shift''': '''scale_shift''',
        '''class_embed_type''': None,
    }

    _UpperCamelCase       			=UNetaDConditionModel(**UpperCamelCase__  )
    return model


  @property
  def   UpperCamelCase__      (				self    :					Optional[int]  )     ->						List[Any]:
    return {
        "block_out_channels": [32, 32, 64, 64],
        "down_block_types": [
            "DownEncoderBlock2D",
            "DownEncoderBlock2D",
            "DownEncoderBlock2D",
            "AttnDownEncoderBlock2D",
        ],
        "in_channels": 3,
        "latent_channels": 4,
        "layers_per_block": 1,
        "norm_num_groups": 8,
        "norm_type": "spatial",
        "num_vq_embeddings": 12,
        "out_channels": 3,
        "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
        "vq_embed_dim": 4,
    }


  @property
  def   UpperCamelCase__      (				self    :					Dict  )     ->						Optional[Any]:
    torch.manual_seed(0  )
    _UpperCamelCase       			=VQModel(**self.dummy_movq_kwargs  )
    return model


  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						Dict:
    _UpperCamelCase       			=self.dummy_unet
    _UpperCamelCase       			=self.dummy_movq

    _UpperCamelCase       			=DDIMScheduler(
        num_train_timesteps=1000							,	beta_schedule='''linear'''							,	beta_start=0.00085							,	beta_end=0.012							,	clip_sample=UpperCamelCase__							,	set_alpha_to_one=UpperCamelCase__							,	steps_offset=1							,	prediction_type='''epsilon'''							,	thresholding=UpperCamelCase__							,	)

    _UpperCamelCase       			={
        '''unet''': unet,
        '''scheduler''': scheduler,
        '''movq''': movq,
    }
    return components


  def   UpperCamelCase__      (				self    :					Any							,	UpperCamelCase__    :					Dict							,	UpperCamelCase__    :					List[Any]=0  )     ->						Dict:
    _UpperCamelCase       			=floats_tensor((1, self.text_embedder_hidden_size)							,	rng=random.Random(UpperCamelCase__  )  ).to(UpperCamelCase__  )
    _UpperCamelCase       			=floats_tensor((1, self.text_embedder_hidden_size)							,	rng=random.Random(seed + 1  )  ).to(
        UpperCamelCase__  )

    # create hint
    _UpperCamelCase       			=floats_tensor((1, 3, 64, 64)							,	rng=random.Random(UpperCamelCase__  )  ).to(UpperCamelCase__  )

    if str(UpperCamelCase__  ).startswith('''mps'''  ):
      _UpperCamelCase       			=torch.manual_seed(UpperCamelCase__  )
    else:
      _UpperCamelCase       			=torch.Generator(device=UpperCamelCase__  ).manual_seed(UpperCamelCase__  )
    _UpperCamelCase       			={
        '''image_embeds''': image_embeds,
        '''negative_image_embeds''': negative_image_embeds,
        '''hint''': hint,
        '''generator''': generator,
        '''height''': 64,
        '''width''': 64,
        '''guidance_scale''': 4.0,
        '''num_inference_steps''': 2,
        '''output_type''': '''np''',
    }
    return inputs




  def   UpperCamelCase__      (				self    :					List[str]  )     ->						List[str]:
    _UpperCamelCase       			='''cpu'''

    _UpperCamelCase       			=self.get_dummy_components()

    _UpperCamelCase       			=self.pipeline_class(**UpperCamelCase__  )
    _UpperCamelCase       			=pipe.to(UpperCamelCase__  )

    pipe.set_progress_bar_config(disable=UpperCamelCase__  )

    _UpperCamelCase       			=pipe(**self.get_dummy_inputs(UpperCamelCase__  )  )
    _UpperCamelCase       			=output.images

    _UpperCamelCase       			=pipe(
        **self.get_dummy_inputs(UpperCamelCase__  )							,	return_dict=UpperCamelCase__							,	)[0]

    _UpperCamelCase       			=image[0, -3:, -3:, -1]
    _UpperCamelCase       			=image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 64, 64, 3)

    _UpperCamelCase       			=np.array(
        [0.6959826, 0.868279, 0.7558092, 0.68769467, 0.85805804, 0.65977496, 0.44885302, 0.5959111, 0.4251595]  )

    assert (
        np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2
    ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''

    assert (
        np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1E-2
    ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''







@slow
@require_torch_gpu
class 		UpperCAmelCase   (				unittest.TestCase):


  """simple docstring"""







  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						Dict:
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()




  def   UpperCamelCase__      (				self    :					Tuple  )     ->						Optional[int]:
    _UpperCamelCase       			=load_numpy(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy'''  )

    _UpperCamelCase       			=load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/kandinskyv22/hint_image_cat.png'''  )
    _UpperCamelCase       			=torch.from_numpy(np.array(UpperCamelCase__  )  ).float() / 255.0
    _UpperCamelCase       			=hint.permute(2							,	0							,	1  ).unsqueeze(0  )

    _UpperCamelCase       			=KandinskyVaaPriorPipeline.from_pretrained(
        '''kandinsky-community/kandinsky-2-2-prior'''							,	torch_dtype=torch.floataa  )
    pipe_prior.to(UpperCamelCase__  )

    _UpperCamelCase       			=KandinskyVaaControlnetPipeline.from_pretrained(
        '''kandinsky-community/kandinsky-2-2-controlnet-depth'''							,	torch_dtype=torch.floataa  )
    _UpperCamelCase       			=pipeline.to(UpperCamelCase__  )
    pipeline.set_progress_bar_config(disable=UpperCamelCase__  )

    _UpperCamelCase       			='''A robot, 4k photo'''

    _UpperCamelCase       			=torch.Generator(device='''cuda'''  ).manual_seed(0  )
    _UpperCamelCase			,     _UpperCamelCase       			=pipe_prior(
        UpperCamelCase__							,	generator=UpperCamelCase__							,	num_inference_steps=5							,	negative_prompt=''''''							,	).to_tuple()

    _UpperCamelCase       			=torch.Generator(device='''cuda'''  ).manual_seed(0  )
    _UpperCamelCase       			=pipeline(
        image_embeds=UpperCamelCase__							,	negative_image_embeds=UpperCamelCase__							,	hint=UpperCamelCase__							,	generator=UpperCamelCase__							,	num_inference_steps=100							,	output_type='''np'''							,	)

    _UpperCamelCase       			=output.images[0]

    assert image.shape == (512, 512, 3)

    assert_mean_pixel_difference(UpperCamelCase__							,	UpperCamelCase__  )













'''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
      )






