



"""simple docstring"""


import comet  # From: unbabel-comet
import torch

import datasets


snake_case_				     =       datasets.logging.get_logger(__name__)

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

snake_case_				     =       '''\
Crosslingual 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).
With 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.

See the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.
'''

snake_case_				     =       '''
COMET score.

Args:

`sources` (list of str): Source sentences
`predictions` (list of str): candidate translations
`references` (list of str): reference translations
`cuda` (bool): If set to True, runs COMET using GPU
`show_progress` (bool): Shows progress
`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.

Returns:
    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.
    `scores`: List of scores.

Examples:

    >>> comet_metric = datasets.load_metric(\'comet\')
    >>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\')  # you can also choose which model to use
    >>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]
    >>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]
    >>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]
    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)
    >>> print([round(v, 2) for v in results["scores"]])
    [0.19, 0.92]
'''







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



						"""simple docstring"""






						def 	UpperCAmelCase__     (					self :List[str]					)				->			List[str]:
												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 	UpperCAmelCase__     (					self :Dict   ,  lowercase_ :Tuple					)				->			Dict:
												if self.config_name == "default":
																		UpperCAmelCase             = comet.load_from_checkpoint(comet.download_model('wmt20-comet-da'					)					)
												else:
																		UpperCAmelCase             = comet.load_from_checkpoint(comet.download_model(self.config_name					)					)






						def 	UpperCAmelCase__     (					self :Optional[Any]   ,  lowercase_ :Optional[int]   ,  lowercase_ :Union[str, Any]   ,  lowercase_ :Optional[int]   ,  lowercase_ :str=None   ,  lowercase_ :Tuple=False					)				->			Dict:
												if gpus is None:
																		UpperCAmelCase             = 1 if torch.cuda.is_available() else 0
												UpperCAmelCase             = {'''src''': sources, '''mt''': predictions, '''ref''': references}
												UpperCAmelCase             = [dict(zip(UpperCamelCase_   ,  UpperCamelCase_					)					) for t in zip(*data.values()					)]
												UpperCAmelCase             = self.scorer.predict(UpperCamelCase_   ,  gpus=UpperCamelCase_   ,  progress_bar=UpperCamelCase_					)
												return {"mean_score": mean_score, "scores": scores}









"""simple docstring"""





import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
snake_case__						:   int			 = logging.get_logger(__name__)


def    _snake_case  (  _snake_case :	Union[str, Any]	):
      lowerCAmelCase    :		Dict				=      OrderedDict()
      for key, value in state_dict.items():
            if key.startswith('''module.encoder'''	):
                  lowerCAmelCase    :		Union[str, Any]				=      key.replace('''module.encoder'''       ,      '''glpn.encoder'''	)
            if key.startswith('''module.decoder'''	):
                  lowerCAmelCase    :		str				=      key.replace('''module.decoder'''       ,      '''decoder.stages'''	)
            if "patch_embed" in key:
                  # replace for example patch_embed1 by patch_embeddings.0
                  lowerCAmelCase    :		Union[str, Any]				=      key[key.find('''patch_embed'''	) + len('''patch_embed'''	)]
                  lowerCAmelCase    :		str				=      key.replace(f'''patch_embed{idx}'''       ,      f'''patch_embeddings.{int(_snake_case	)-1}'''	)
            if "norm" in key:
                  lowerCAmelCase    :		str				=      key.replace('''norm'''       ,      '''layer_norm'''	)
            if "glpn.encoder.layer_norm" in key:
                  # replace for example layer_norm1 by layer_norm.0
                  lowerCAmelCase    :		Optional[int]				=      key[key.find('''glpn.encoder.layer_norm'''	) + len('''glpn.encoder.layer_norm'''	)]
                  lowerCAmelCase    :		List[str]				=      key.replace(f'''layer_norm{idx}'''       ,      f'''layer_norm.{int(_snake_case	)-1}'''	)
            if "layer_norm1" in key:
                  lowerCAmelCase    :		Union[str, Any]				=      key.replace('''layer_norm1'''       ,      '''layer_norm_1'''	)
            if "layer_norm2" in key:
                  lowerCAmelCase    :		Any				=      key.replace('''layer_norm2'''       ,      '''layer_norm_2'''	)
            if "block" in key:
                  # replace for example block1 by block.0
                  lowerCAmelCase    :		Tuple				=      key[key.find('''block'''	) + len('''block'''	)]
                  lowerCAmelCase    :		Tuple				=      key.replace(f'''block{idx}'''       ,      f'''block.{int(_snake_case	)-1}'''	)
            if "attn.q" in key:
                  lowerCAmelCase    :		Optional[Any]				=      key.replace('''attn.q'''       ,      '''attention.self.query'''	)
            if "attn.proj" in key:
                  lowerCAmelCase    :		Dict				=      key.replace('''attn.proj'''       ,      '''attention.output.dense'''	)
            if "attn" in key:
                  lowerCAmelCase    :		List[str]				=      key.replace('''attn'''       ,      '''attention.self'''	)
            if "fc1" in key:
                  lowerCAmelCase    :		List[Any]				=      key.replace('''fc1'''       ,      '''dense1'''	)
            if "fc2" in key:
                  lowerCAmelCase    :		Optional[Any]				=      key.replace('''fc2'''       ,      '''dense2'''	)
            if "linear_pred" in key:
                  lowerCAmelCase    :		List[Any]				=      key.replace('''linear_pred'''       ,      '''classifier'''	)
            if "linear_fuse" in key:
                  lowerCAmelCase    :		Optional[Any]				=      key.replace('''linear_fuse.conv'''       ,      '''linear_fuse'''	)
                  lowerCAmelCase    :		int				=      key.replace('''linear_fuse.bn'''       ,      '''batch_norm'''	)
            if "linear_c" in key:
                  # replace for example linear_c4 by linear_c.3
                  lowerCAmelCase    :		Optional[Any]				=      key[key.find('''linear_c'''	) + len('''linear_c'''	)]
                  lowerCAmelCase    :		int				=      key.replace(f'''linear_c{idx}'''       ,      f'''linear_c.{int(_snake_case	)-1}'''	)
            if "bot_conv" in key:
                  lowerCAmelCase    :		str				=      key.replace('''bot_conv'''       ,      '''0.convolution'''	)
            if "skip_conv1" in key:
                  lowerCAmelCase    :		int				=      key.replace('''skip_conv1'''       ,      '''1.convolution'''	)
            if "skip_conv2" in key:
                  lowerCAmelCase    :		str				=      key.replace('''skip_conv2'''       ,      '''2.convolution'''	)
            if "fusion1" in key:
                  lowerCAmelCase    :		Union[str, Any]				=      key.replace('''fusion1'''       ,      '''1.fusion'''	)
            if "fusion2" in key:
                  lowerCAmelCase    :		Any				=      key.replace('''fusion2'''       ,      '''2.fusion'''	)
            if "fusion3" in key:
                  lowerCAmelCase    :		List[Any]				=      key.replace('''fusion3'''       ,      '''3.fusion'''	)
            if "fusion" in key and "conv" in key:
                  lowerCAmelCase    :		Union[str, Any]				=      key.replace('''conv'''       ,      '''convolutional_layer'''	)
            if key.startswith('''module.last_layer_depth'''	):
                  lowerCAmelCase    :		Optional[Any]				=      key.replace('''module.last_layer_depth'''       ,      '''head.head'''	)
            lowerCAmelCase    :		Union[str, Any]				=      value

      return new_state_dict


def    _snake_case  (  _snake_case :	Optional[Any]       ,      _snake_case :	str	):
      # for each of the encoder blocks:
      for i in range(config.num_encoder_blocks	):
            for j in range(config.depths[i]	):
                  # read in weights + bias of keys and values (which is a single matrix in the original implementation)
                  lowerCAmelCase    :		int				=      state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''	)
                  lowerCAmelCase    :		Optional[int]				=      state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.bias'''	)
                  # next, add keys and values (in that order) to the state dict
                  lowerCAmelCase    :		str				=      kv_weight[
                      : config.hidden_sizes[i], :
                  ]
                  lowerCAmelCase    :		Union[str, Any]				=      kv_bias[: config.hidden_sizes[i]]
                  lowerCAmelCase    :		Dict				=      kv_weight[
                      config.hidden_sizes[i] :, :
                  ]
                  lowerCAmelCase    :		List[str]				=      kv_bias[config.hidden_sizes[i] :]


def    _snake_case  (  ):
      lowerCAmelCase    :		int				=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      lowerCAmelCase    :		str				=      Image.open(requests.get(_snake_case       ,      stream=_snake_case	).raw	)

      return image


@torch.no_grad()
def    _snake_case  (  _snake_case :	Dict       ,      _snake_case :	Dict       ,      _snake_case :	Union[str, Any]=False       ,      _snake_case :	List[str]=None	):
      lowerCAmelCase    :		Optional[int]				=      GLPNConfig(hidden_sizes=[64, 128, 320, 512]       ,      decoder_hidden_size=64       ,      depths=[3, 8, 27, 3]	)

      # load image processor (only resize + rescale)
      lowerCAmelCase    :		Union[str, Any]				=      GLPNImageProcessor()

      # prepare image
      lowerCAmelCase    :		Tuple				=      prepare_img()
      lowerCAmelCase    :		Dict				=      image_processor(images=_snake_case       ,      return_tensors='''pt'''	).pixel_values

      logger.info('''Converting model...'''	)

      # load original state dict
      lowerCAmelCase    :		List[str]				=      torch.load(_snake_case       ,      map_location=torch.device('''cpu'''	)	)

      # rename keys
      lowerCAmelCase    :		Tuple				=      rename_keys(_snake_case	)

      # key and value matrices need special treatment
      read_in_k_v(_snake_case       ,      _snake_case	)

      # create HuggingFace model and load state dict
      lowerCAmelCase    :		str				=      GLPNForDepthEstimation(_snake_case	)
      model.load_state_dict(_snake_case	)
      model.eval()

      # forward pass
      lowerCAmelCase    :		Union[str, Any]				=      model(_snake_case	)
      lowerCAmelCase    :		int				=      outputs.predicted_depth

      # verify output
      if model_name is not None:
            if "nyu" in model_name:
                  lowerCAmelCase    :		str				=      torch.tensor(
                      [[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]]	)
            elif "kitti" in model_name:
                  lowerCAmelCase    :		str				=      torch.tensor(
                      [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]	)
            else:
                  raise ValueError(f'''Unknown model name: {model_name}'''	)

            lowerCAmelCase    :		List[Any]				=      torch.Size([1, 480, 640]	)

            assert predicted_depth.shape == expected_shape
            assert torch.allclose(predicted_depth[0, :3, :3]       ,      _snake_case       ,      atol=1E-4	)
            print('''Looks ok!'''	)

      # finally, push to hub if required
      if push_to_hub:
            logger.info('''Pushing model and image processor to the hub...'''	)
            model.push_to_hub(
                repo_path_or_name=Path(_snake_case       ,      _snake_case	)       ,      organization='''nielsr'''       ,      commit_message='''Add model'''       ,      use_temp_dir=_snake_case       ,      )
            image_processor.push_to_hub(
                repo_path_or_name=Path(_snake_case       ,      _snake_case	)       ,      organization='''nielsr'''       ,      commit_message='''Add image processor'''       ,      use_temp_dir=_snake_case       ,      )


if __name__ == "__main__":
 snake_case__						:   Tuple			 = argparse.ArgumentParser()

 parser.add_argument(
     '''--checkpoint_path''',
     default=None,
     type=str,
     help='''Path to the original PyTorch checkpoint (.pth file).''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
 )
 parser.add_argument(
     '''--push_to_hub''', action='''store_true''', help='''Whether to upload the model to the HuggingFace hub.'''
 )
 parser.add_argument(
     '''--model_name''',
     default='''glpn-kitti''',
     type=str,
     help='''Name of the model in case you\'re pushing to the hub.''',
 )
 snake_case__						:   List[str]			 = parser.parse_args()
 convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)


