





'''simple docstring'''






from scipy.stats import spearmanr

import datasets


A__							:  Optional[int]    ='''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.

Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.

The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
'''

A__							:  List[str]    ='''
Args:
    predictions (`List[float]`): Predicted labels, as returned by a model.
    references (`List[float]`): Ground truth labels.
    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
            only the spearmanr score. Defaults to `False`.
Returns:
    spearmanr (`float`): Spearman correlation coefficient.
    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
    Example 1:
        >>> spearmanr_metric = datasets.load_metric("spearmanr")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
        >>> print(results)
        {\'spearmanr\': -0.7}

    Example 2:
        >>> spearmanr_metric = datasets.load_metric("spearmanr")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
        ...                                     predictions=[10, 9, 2.5, 6, 4],
        ...                                     return_pvalue=True)
        >>> print(results[\'spearmanr\'])
        -0.7
        >>> print(round(results[\'spearmanr_pvalue\'], 2))
        0.19
'''

A__							:  Optional[int]    =r'''\
@book{kokoska2000crc,
  title={CRC standard probability and statistics tables and formulae},
  author={Kokoska, Stephen and Zwillinger, Daniel},
  year={2000},
  publisher={Crc Press}
}
@article{2020SciPy-NMeth,
  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
            Haberland, Matt and Reddy, Tyler and Cournapeau, David and
            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
            Kern, Robert and Larson, Eric and Carey, C J and
            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
            Harris, Charles R. and Archibald, Anne M. and
            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
            Computing in Python}},
  journal = {Nature Methods},
  year    = {2020},
  volume  = {17},
  pages   = {261--272},
  adsurl  = {https://rdcu.be/b08Wh},
  doi     = {10.1038/s41592-019-0686-2},
}
'''




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,						_KWARGS_DESCRIPTION  )
class 					UpperCAmelCase	(     datasets.Metric  ):




      def 							lowercase__       (     self     :					Union[str, Any]    )							-> List[Any]:
            return datasets.MetricInfo(
                description=_DESCRIPTION     ,     citation=_CITATION     ,     inputs_description=_KWARGS_DESCRIPTION     ,     features=datasets.Features(
                    {
                        """predictions""": datasets.Value("""float"""    ),
                        """references""": datasets.Value("""float"""    ),
                    }    )     ,     reference_urls=["""https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html"""]     ,     )




      def 							lowercase__       (     self     :					str     ,     __snake_case     :					Optional[Any]     ,     __snake_case     :					Any     ,     __snake_case     :					Dict=False    )							-> str:
            _lowerCAmelCase					=				spearmanr(__snake_case     ,     __snake_case    )
            if return_pvalue:
                  return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
            else:
                  return {"spearmanr": results[0]}













'''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()
A__							:  Any    =logging.get_logger(__name__)
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				DPTConfig()

      if "large" in checkpoint_url:
            _lowerCAmelCase					=				10_24
            _lowerCAmelCase					=				40_96
            _lowerCAmelCase					=				24
            _lowerCAmelCase					=				16
            _lowerCAmelCase					=				[5, 11, 17, 23]
            _lowerCAmelCase					=				[2_56, 5_12, 10_24, 10_24]
            _lowerCAmelCase					=				(1, 3_84, 3_84)

      if "ade" in checkpoint_url:
            _lowerCAmelCase					=				True

            _lowerCAmelCase					=				1_50
            _lowerCAmelCase					=				"""huggingface/label-files"""
            _lowerCAmelCase					=				"""ade20k-id2label.json"""
            _lowerCAmelCase					=				json.load(open(cached_download(hf_hub_url(lowerCAmelCase				,      lowerCAmelCase				,      repo_type="""dataset"""    )    )				,      """r"""    )    )
            _lowerCAmelCase					=				{int(lowerCAmelCase    ): v for k, v in idalabel.items()}
            _lowerCAmelCase					=				idalabel
            _lowerCAmelCase					=				{v: k for k, v in idalabel.items()}
            _lowerCAmelCase					=				[1, 1_50, 4_80, 4_80]

      return config, expected_shape
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
      for k in ignore_keys:
            state_dict.pop(lowerCAmelCase				,      lowerCAmelCase    )
def   UpperCamelCase__ (						lowerCAmelCase    ):





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

      return name
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase    ):





      """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)
            _lowerCAmelCase					=				state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.weight"    )
            _lowerCAmelCase					=				state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.bias"    )
            # next, add query, keys and values (in that order) to the state dict
            _lowerCAmelCase					=				in_proj_weight[: config.hidden_size, :]
            _lowerCAmelCase					=				in_proj_bias[: config.hidden_size]
            _lowerCAmelCase					=				in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            _lowerCAmelCase					=				in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            _lowerCAmelCase					=				in_proj_weight[
                -config.hidden_size :, :
            ]
            _lowerCAmelCase					=				in_proj_bias[-config.hidden_size :]
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      _lowerCAmelCase					=				Image.open(requests.get(lowerCAmelCase				,      stream=lowerCAmelCase    ).raw    )
      return im
@torch.no_grad()
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


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

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

      # Check outputs on an image
      _lowerCAmelCase					=				4_80 if """ade""" in checkpoint_url else 3_84
      _lowerCAmelCase					=				DPTImageProcessor(size=lowerCAmelCase    )

      _lowerCAmelCase					=				prepare_img()
      _lowerCAmelCase					=				image_processor(lowerCAmelCase				,      return_tensors="""pt"""    )

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

      # Assert logits
      _lowerCAmelCase					=				torch.tensor([[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]]    )
      if "ade" in checkpoint_url:
            _lowerCAmelCase					=				torch.tensor([[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]]    )
      assert outputs.shape == torch.Size(lowerCAmelCase    )
      assert (
          torch.allclose(outputs[0, 0, :3, :3]				,      lowerCAmelCase				,      atol=1e-4    )
          if "ade" in checkpoint_url
          else torch.allclose(outputs[0, :3, :3]				,      lowerCAmelCase    )
      )

      Path(lowerCAmelCase    ).mkdir(exist_ok=lowerCAmelCase    )
      print(f"Saving model to {pytorch_dump_folder_path}"    )
      model.save_pretrained(lowerCAmelCase    )
      print(f"Saving image processor to {pytorch_dump_folder_path}"    )
      image_processor.save_pretrained(lowerCAmelCase    )

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


if __name__ == "__main__":
      A__							:  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=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.''',
      )

      A__							:  Optional[int]    =parser.parse_args()
      convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






