
'''simple docstring'''







import comet  # From: unbabel-comet
import torch

import datasets


_lowercase :  List[Any]    							=       datasets.logging.get_logger(__name__)

_lowercase :  Any    							=       "\\n@inproceedings{rei-EtAl:2020:WMT,\n   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},\n   title     = {Unbabel's Participation in the WMT20 Metrics Shared Task},\n   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},\n   month          = {November},\n   year           = {2020},\n   address        = {Online},\n   publisher      = {Association for Computational Linguistics},\n   pages     = {909--918},\n}\n@inproceedings{rei-etal-2020-comet,\n   title = \"{COMET}: A Neural Framework for {MT} Evaluation\",\n   author = \"Rei, Ricardo  and\n      Stewart, Craig  and\n      Farinha, Ana C  and\n      Lavie, Alon\",\n   booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n   month = nov,\n   year = \"2020\",\n   address = \"Online\",\n   publisher = \"Association for Computational Linguistics\",\n   url = \"https://www.aclweb.org/anthology/2020.emnlp-main.213\",\n   pages = \"2685--2702\",\n}\n"

_lowercase :  Dict    							=       "\\nCrosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA's or MQM).\nWith the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n"

_lowercase :  Tuple    							=       "\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n    `scores`: List of scores.\n\nExamples:\n\n    >>> comet_metric = datasets.load_metric('comet')\n    >>> # comet_metric = load_metric('comet', 'wmt20-comet-da')  # you can also choose which model to use\n    >>> source = [\"Dem Feuer konnte Einhalt geboten werden\", \"Schulen und Kindergärten wurden eröffnet.\"]\n    >>> hypothesis = [\"The fire could be stopped\", \"Schools and kindergartens were open\"]\n    >>> reference = [\"They were able to control the fire.\", \"Schools and kindergartens opened\"]\n    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n    >>> print([round(v, 2) for v in results[\"scores\"]])\n    [0.19, 0.92]\n"






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






					def 	_snake_case    (   self		):


											"""simple docstring"""





											return datasets.MetricInfo(
											    description=_DESCRIPTION				,							citation=_CITATION				,							homepage='''https://unbabel.github.io/COMET/html/index.html'''				,							inputs_description=_KWARGS_DESCRIPTION				,							features=datasets.Features(
											        {
											            '''sources''': datasets.Value('''string'''				,							id='''sequence'''		),
											            '''predictions''': datasets.Value('''string'''				,							id='''sequence'''		),
											            '''references''': datasets.Value('''string'''				,							id='''sequence'''		),
											        }		)				,							codebase_urls=['''https://github.com/Unbabel/COMET''']				,							reference_urls=[
											        '''https://github.com/Unbabel/COMET''',
											        '''https://www.aclweb.org/anthology/2020.emnlp-main.213/''',
											        '''http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6''',
											    ]				,							)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if self.config_name == "default":
																	lowercase_	:     Any													=       comet.load_from_checkpoint(comet.download_model('''wmt20-comet-da'''		)		)
											else:
																	lowercase_	:     str													=       comet.load_from_checkpoint(comet.download_model(self.config_name		)		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE=False		):


											"""simple docstring"""





											if gpus is None:
																	lowercase_	:     Any													=       1 if torch.cuda.is_available() else 0
											lowercase_	:     Tuple													=       {"src": sources, "mt": predictions, "ref": references}
											lowercase_	:     Optional[Any]													=       [dict(zip(_lowerCAmelCase				,							_lowerCAmelCase		)		) for t in zip(*data.values()		)]
											lowercase_	:     List[str]													=       self.scorer.predict(_lowerCAmelCase				,							gpus=_lowerCAmelCase				,							progress_bar=_lowerCAmelCase		)
											return {"mean_score": mean_score, "scores": scores}









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()
_UpperCAmelCase		:  Union[str, Any]	=   logging.get_logger(__name__)



def 					UpperCAmelCase__     (   lowerCamelCase      ):
  lowercase			:int							=  DPTConfig(embedding_type="hybrid"      )

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

  if "nyu" or "midas" in checkpoint_url:
    lowercase			:Any							=  768
    lowercase			:Optional[int]							=  [1, 1, 1, 0.5]
    lowercase			:int							=  [256, 512, 768, 768]
    lowercase			:str							=  150
    lowercase			:Tuple							=  16
    lowercase			:List[str]							=  (1, 384, 384)
    lowercase			:Any							=  False
    lowercase			:Optional[int]							=  "project"

  if "ade" in checkpoint_url:
    lowercase			:Dict							=  True
    lowercase			:str							=  768
    lowercase			:Tuple							=  [1, 1, 1, 0.5]
    lowercase			:Dict							=  150
    lowercase			:Any							=  16
    lowercase			:List[Any]							=  "huggingface/label-files"
    lowercase			:Union[str, Any]							=  "ade20k-id2label.json"
    lowercase			:List[str]							=  json.load(open(cached_download(hf_hub_url(lowerCamelCase,	lowerCamelCase,	repo_type="dataset"      )      ),	"r"      )      )
    lowercase			:Optional[Any]							=  {int(lowerCamelCase      ): v for k, v in idalabel.items()}
    lowercase			:Union[str, Any]							=  idalabel
    lowercase			:Optional[Any]							=  {v: k for k, v in idalabel.items()}
    lowercase			:List[str]							=  [1, 150, 480, 480]

  return config, expected_shape



def 					UpperCAmelCase__     (   lowerCamelCase      ):
  lowercase			:List[str]							=  ["pretrained.model.head.weight", "pretrained.model.head.bias"]
  for k in ignore_keys:
    state_dict.pop(lowerCamelCase,	lowerCamelCase      )



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

  if ".." in name:
    lowercase			:Optional[int]							=  name.replace("..",	"."      )

  if "stem.conv" in name:
    lowercase			:Union[str, Any]							=  name.replace("stem.conv",	"bit.embedder.convolution"      )
  if "blocks" in name:
    lowercase			:List[str]							=  name.replace("blocks",	"layers"      )
  if "convolution" in name and "backbone" in name:
    lowercase			:int							=  name.replace("convolution",	"conv"      )
  if "layer" in name and "backbone" in name:
    lowercase			:List[str]							=  name.replace("layer",	"layers"      )
  if "backbone.bit.encoder.bit" in name:
    lowercase			:Dict							=  name.replace("backbone.bit.encoder.bit",	"backbone.bit"      )
  if "embedder.conv" in name:
    lowercase			:Dict							=  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__     (   lowerCamelCase,	lowerCamelCase      ):
  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			:str							=  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			:List[str]							=  in_proj_weight[: config.hidden_size, :]
    lowercase			:int							=  in_proj_bias[: config.hidden_size]
    lowercase			:Union[str, Any]							=  in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    lowercase			:Union[str, Any]							=  in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    lowercase			:Dict							=  in_proj_weight[
        -config.hidden_size :, :
    ]
    lowercase			:List[Any]							=  in_proj_bias[-config.hidden_size :]



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



@torch.no_grad()
def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase      ):
  lowercase		,			lowercase			:Optional[int]							=  get_dpt_config(lowerCamelCase      )
  # load original state_dict from URL
  # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
  lowercase			:List[str]							=  torch.load(lowerCamelCase,	map_location="cpu"      )
  # remove certain keys
  remove_ignore_keys_(lowerCamelCase      )
  # rename keys
  for key in state_dict.copy().keys():
    lowercase			:int							=  state_dict.pop(lowerCamelCase      )
    lowercase			:Dict							=  val
  # read in qkv matrices
  read_in_q_k_v(lowerCamelCase,	lowerCamelCase      )

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

  # Check outputs on an image
  lowercase			:Union[str, Any]							=  480 if "ade" in checkpoint_url else 384
  lowercase			:Optional[int]							=  DPTImageProcessor(size=lowerCamelCase      )

  lowercase			:Dict							=  prepare_img()
  lowercase			:Optional[Any]							=  image_processor(lowerCamelCase,	return_tensors="pt"      )

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

  if show_prediction:
    lowercase			:List[Any]							=  (
        torch.nn.functional.interpolate(
            outputs.unsqueeze(1      ),	size=(image.size[1], image.size[0]),	mode="bicubic",	align_corners=lowerCamelCase,	)
        .squeeze()
        .cpu()
        .numpy()
    )

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

  if pytorch_dump_folder_path is not None:
    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:
    model.push_to_hub("ybelkada/dpt-hybrid-midas"      )
    image_processor.push_to_hub("ybelkada/dpt-hybrid-midas"      )


if __name__ == "__main__":
     _UpperCAmelCase		:  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",
     )

     _UpperCAmelCase		:  List[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
     )



