


"""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()
__snake_case             =   logging.get_logger(__name__)







def   __lowerCAmelCase						(						lowercase  :  Optional[int]			)     ->						Optional[int]:


    """simple docstring"""





    snake_case   :				Optional[Any]				 =   DPTConfig()

    if "large" in checkpoint_url:
        snake_case   :				Any				 =   1024
        snake_case   :				Optional[int]				 =   4096
        snake_case   :				List[Any]				 =   24
        snake_case   :				List[Any]				 =   16
        snake_case   :				List[str]				 =   [5, 11, 17, 23]
        snake_case   :				Optional[int]				 =   [256, 512, 1024, 1024]
        snake_case   :				List[str]				 =   (1, 384, 384)

    if "ade" in checkpoint_url:
        snake_case   :				List[Any]				 =   True

        snake_case   :				int				 =   150
        snake_case   :				Union[str, Any]				 =   "huggingface/label-files"
        snake_case   :				Optional[int]				 =   "ade20k-id2label.json"
        snake_case   :				int				 =   json.load(open(cached_download(hf_hub_url(lowercase						,							lowercase						,							repo_type="dataset"			)			)						,							"r"			)			)
        snake_case   :				int				 =   {int(lowercase			): v for k, v in idalabel.items()}
        snake_case   :				int				 =   idalabel
        snake_case   :				Optional[Any]				 =   {v: k for k, v in idalabel.items()}
        snake_case   :				Optional[int]				 =   [1, 150, 480, 480]

    return config, expected_shape







def   __lowerCAmelCase						(						lowercase  :  Dict			)     ->						Optional[int]:


    """simple docstring"""





    snake_case   :				Optional[Any]				 =   ["pretrained.model.head.weight", "pretrained.model.head.bias"]
    for k in ignore_keys:
        state_dict.pop(lowercase						,							lowercase			)







def   __lowerCAmelCase						(						lowercase  :  int			)     ->						Dict:


    """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
    ):
        snake_case   :				Optional[Any]				 =   name.replace("pretrained.model"						,							"dpt.encoder"			)
    if "pretrained.model" in name:
        snake_case   :				List[Any]				 =   name.replace("pretrained.model"						,							"dpt.embeddings"			)
    if "patch_embed" in name:
        snake_case   :				Dict				 =   name.replace("patch_embed"						,							"patch_embeddings"			)
    if "pos_embed" in name:
        snake_case   :				int				 =   name.replace("pos_embed"						,							"position_embeddings"			)
    if "attn.proj" in name:
        snake_case   :				Dict				 =   name.replace("attn.proj"						,							"attention.output.dense"			)
    if "proj" in name and "project" not in name:
        snake_case   :				Tuple				 =   name.replace("proj"						,							"projection"			)
    if "blocks" in name:
        snake_case   :				List[Any]				 =   name.replace("blocks"						,							"layer"			)
    if "mlp.fc1" in name:
        snake_case   :				Optional[Any]				 =   name.replace("mlp.fc1"						,							"intermediate.dense"			)
    if "mlp.fc2" in name:
        snake_case   :				Optional[int]				 =   name.replace("mlp.fc2"						,							"output.dense"			)
    if "norm1" in name:
        snake_case   :				List[Any]				 =   name.replace("norm1"						,							"layernorm_before"			)
    if "norm2" in name:
        snake_case   :				str				 =   name.replace("norm2"						,							"layernorm_after"			)
    if "scratch.output_conv" in name:
        snake_case   :				Optional[Any]				 =   name.replace("scratch.output_conv"						,							"head"			)
    if "scratch" in name:
        snake_case   :				int				 =   name.replace("scratch"						,							"neck"			)
    if "layer1_rn" in name:
        snake_case   :				List[str]				 =   name.replace("layer1_rn"						,							"convs.0"			)
    if "layer2_rn" in name:
        snake_case   :				Optional[Any]				 =   name.replace("layer2_rn"						,							"convs.1"			)
    if "layer3_rn" in name:
        snake_case   :				str				 =   name.replace("layer3_rn"						,							"convs.2"			)
    if "layer4_rn" in name:
        snake_case   :				Union[str, Any]				 =   name.replace("layer4_rn"						,							"convs.3"			)
    if "refinenet" in name:
        snake_case   :				List[str]				 =   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
        snake_case   :				Union[str, Any]				 =   name.replace(F'refinenet{layer_idx}'						,							F'fusion_stage.layers.{abs(layer_idx-4			)}'			)
    if "out_conv" in name:
        snake_case   :				Optional[int]				 =   name.replace("out_conv"						,							"projection"			)
    if "resConfUnit1" in name:
        snake_case   :				Dict				 =   name.replace("resConfUnit1"						,							"residual_layer1"			)
    if "resConfUnit2" in name:
        snake_case   :				Optional[int]				 =   name.replace("resConfUnit2"						,							"residual_layer2"			)
    if "conv1" in name:
        snake_case   :				Optional[Any]				 =   name.replace("conv1"						,							"convolution1"			)
    if "conv2" in name:
        snake_case   :				Optional[int]				 =   name.replace("conv2"						,							"convolution2"			)
    # readout blocks
    if "pretrained.act_postprocess1.0.project.0" in name:
        snake_case   :				List[str]				 =   name.replace("pretrained.act_postprocess1.0.project.0"						,							"neck.reassemble_stage.readout_projects.0.0"			)
    if "pretrained.act_postprocess2.0.project.0" in name:
        snake_case   :				Optional[Any]				 =   name.replace("pretrained.act_postprocess2.0.project.0"						,							"neck.reassemble_stage.readout_projects.1.0"			)
    if "pretrained.act_postprocess3.0.project.0" in name:
        snake_case   :				str				 =   name.replace("pretrained.act_postprocess3.0.project.0"						,							"neck.reassemble_stage.readout_projects.2.0"			)
    if "pretrained.act_postprocess4.0.project.0" in name:
        snake_case   :				Dict				 =   name.replace("pretrained.act_postprocess4.0.project.0"						,							"neck.reassemble_stage.readout_projects.3.0"			)
    # resize blocks
    if "pretrained.act_postprocess1.3" in name:
        snake_case   :				Dict				 =   name.replace("pretrained.act_postprocess1.3"						,							"neck.reassemble_stage.layers.0.projection"			)
    if "pretrained.act_postprocess1.4" in name:
        snake_case   :				Tuple				 =   name.replace("pretrained.act_postprocess1.4"						,							"neck.reassemble_stage.layers.0.resize"			)
    if "pretrained.act_postprocess2.3" in name:
        snake_case   :				List[str]				 =   name.replace("pretrained.act_postprocess2.3"						,							"neck.reassemble_stage.layers.1.projection"			)
    if "pretrained.act_postprocess2.4" in name:
        snake_case   :				Optional[Any]				 =   name.replace("pretrained.act_postprocess2.4"						,							"neck.reassemble_stage.layers.1.resize"			)
    if "pretrained.act_postprocess3.3" in name:
        snake_case   :				Any				 =   name.replace("pretrained.act_postprocess3.3"						,							"neck.reassemble_stage.layers.2.projection"			)
    if "pretrained.act_postprocess4.3" in name:
        snake_case   :				Any				 =   name.replace("pretrained.act_postprocess4.3"						,							"neck.reassemble_stage.layers.3.projection"			)
    if "pretrained.act_postprocess4.4" in name:
        snake_case   :				List[str]				 =   name.replace("pretrained.act_postprocess4.4"						,							"neck.reassemble_stage.layers.3.resize"			)
    if "pretrained" in name:
        snake_case   :				Optional[Any]				 =   name.replace("pretrained"						,							"dpt"			)
    if "bn" in name:
        snake_case   :				Optional[int]				 =   name.replace("bn"						,							"batch_norm"			)
    if "head" in name:
        snake_case   :				Union[str, Any]				 =   name.replace("head"						,							"head.head"			)
    if "encoder.norm" in name:
        snake_case   :				Optional[int]				 =   name.replace("encoder.norm"						,							"layernorm"			)
    if "auxlayer" in name:
        snake_case   :				int				 =   name.replace("auxlayer"						,							"auxiliary_head.head"			)

    return name







def   __lowerCAmelCase						(						lowercase  :  Optional[int]						,							lowercase  :  List[str]			)     ->						Tuple:


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







def   __lowerCAmelCase						(						)     ->						int:


    """simple docstring"""





    snake_case   :				Tuple				 =   "http://images.cocodataset.org/val2017/000000039769.jpg"
    snake_case   :				List[str]				 =   Image.open(requests.get(lowercase						,							stream=lowercase			).raw			)
    return im







@torch.no_grad()
def   __lowerCAmelCase						(						lowercase  :  Optional[int]						,							lowercase  :  Optional[Any]						,							lowercase  :  List[str]						,							lowercase  :  Union[str, Any]			)     ->						Optional[int]:


    """simple docstring"""





    snake_case ,snake_case   :				List[Any]				 =   get_dpt_config(lowercase			)
    # load original state_dict from URL
    snake_case   :				List[str]				 =   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():
        snake_case   :				Optional[int]				 =   state_dict.pop(lowercase			)
        snake_case   :				Any				 =   val
    # read in qkv matrices
    read_in_q_k_v(lowercase						,							lowercase			)

    # load HuggingFace model
    snake_case   :				Dict				 =   DPTForSemanticSegmentation(lowercase			) if "ade" in checkpoint_url else DPTForDepthEstimation(lowercase			)
    model.load_state_dict(lowercase			)
    model.eval()

    # Check outputs on an image
    snake_case   :				List[Any]				 =   480 if "ade" in checkpoint_url else 384
    snake_case   :				List[str]				 =   DPTImageProcessor(size=lowercase			)

    snake_case   :				Optional[Any]				 =   prepare_img()
    snake_case   :				List[Any]				 =   image_processor(lowercase						,							return_tensors="pt"			)

    # forward pass
    snake_case   :				Dict				 =   model(**lowercase			).logits if "ade" in checkpoint_url else model(**lowercase			).predicted_depth

    # Assert logits
    snake_case   :				Optional[Any]				 =   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:
        snake_case   :				List[str]				 =   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__":
  __snake_case             =   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.""",
  )

  __snake_case             =   parser.parse_args()
  convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)









"""simple docstring"""



import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
  import tensorflow as tf
  from tensorflow.python.eager import context
  from tensorflow.python.framework import ops

  from transformers import GradientAccumulator, create_optimizer







@require_tf
class _lowerCAmelCase    (				unittest.TestCase     ):







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Optional[Any]:

       '''simple docstring'''




       self.assertEqual(len(UpperCamelCase__						)						,     len(UpperCamelCase__						)						)
       for a, b in zip(UpperCamelCase__						,     UpperCamelCase__						):
           self.assertAlmostEqual(UpperCamelCase__						,     UpperCamelCase__						,     delta=UpperCamelCase__						)







   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




       snake_case   :				List[Any]				 =   GradientAccumulator()
       accumulator([tf.constant([1.0, 2.0]						)]						)
       accumulator([tf.constant([-2.0, 1.0]						)]						)
       accumulator([tf.constant([-1.0, 2.0]						)]						)
       with self.assertRaises(UpperCamelCase__						):
           accumulator([tf.constant([1.0, 1.0]						), tf.constant([2.0, 2.0]						)]						)
       self.assertEqual(accumulator.step						,     3						)
       self.assertEqual(len(accumulator.gradients						)						,     1						)
       self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()						,     [-2.0, 5.0]						,     tol=1e-2						)
       accumulator.reset()
       self.assertEqual(accumulator.step						,     0						)
       self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()						,     [0.0, 0.0]						,     tol=1e-2						)







   def 			lowerCamelCase		(     self						)     ->		int:

       '''simple docstring'''




       snake_case   :				Optional[Any]				 =   None
       ops.enable_eager_execution_internal()
       snake_case   :				str				 =   tf.config.list_physical_devices("CPU"						)
       if len(UpperCamelCase__						) == 1:
           tf.config.set_logical_device_configuration(
               physical_devices[0]						,     [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]						)
       snake_case   :				Optional[int]				 =   tf.config.list_logical_devices(device_type="CPU"						)
       snake_case   :				List[str]				 =   tf.distribute.MirroredStrategy(devices=devices[:2]						)

       with strategy.scope():
           snake_case   :				int				 =   GradientAccumulator()
           snake_case   :				int				 =   tf.Variable([4.0, 3.0]						)
           snake_case ,snake_case   :				Any				 =   create_optimizer(5e-5						,     10						,     5						)
           snake_case   :				Tuple				 =   tf.Variable([0.0, 0.0]						,     trainable=UpperCamelCase__						)

       def accumulate_on_replica(UpperCamelCase__						):
           accumulator([gradient]						)

       def apply_on_replica():
           optimizer.apply_gradients(list(zip(accumulator.gradients						,     [variable]						)						)						)

       @tf.function
       def accumulate(UpperCamelCase__						,     UpperCamelCase__						):
           with strategy.scope():
               snake_case   :				Union[str, Any]				 =   strategy.experimental_local_results(UpperCamelCase__						)
               local_variables[0].assign(UpperCamelCase__						)
               local_variables[1].assign(UpperCamelCase__						)
               strategy.run(UpperCamelCase__						,     args=(gradient_placeholder,)						)

       @tf.function
       def apply_grad():
           with strategy.scope():
               strategy.run(UpperCamelCase__						)

       def _check_local_values(UpperCamelCase__						,     UpperCamelCase__						):
           snake_case   :				List[Any]				 =   strategy.experimental_local_results(accumulator._gradients[0]						)
           self.assertListAlmostEqual(values[0].value()						,     UpperCamelCase__						,     tol=1e-2						)
           self.assertListAlmostEqual(values[1].value()						,     UpperCamelCase__						,     tol=1e-2						)

       accumulate([1.0, 2.0]						,     [-1.0, 1.0]						)
       accumulate([3.0, -1.0]						,     [-1.0, -1.0]						)
       accumulate([-2.0, 2.0]						,     [3.0, -2.0]						)
       self.assertEqual(accumulator.step						,     3						)
       _check_local_values([2.0, 3.0]						,     [1.0, -2.0]						)
       apply_grad()
       self.assertListAlmostEqual(variable.value()						,     [4.0, 3.0]						,     tol=1e-2						)
       accumulator.reset()
       self.assertEqual(accumulator.step						,     0						)
       _check_local_values([0.0, 0.0]						,     [0.0, 0.0]						)





