






import comet  # From: unbabel-comet
import torch

import datasets


UpperCAmelCase__							  =						datasets.logging.get_logger(__name__)

UpperCAmelCase__							  =						"\\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"

UpperCAmelCase__							  =						"\\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"

UpperCAmelCase__							  =						"\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 						lowercase_   (      datasets.Metric  ):






      '''simple docstring'''




      def  __lowerCAmelCase   ( self :		Optional[Any]    )       ->Any:




       """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  __lowerCAmelCase   ( self :		List[str]					,   __UpperCAmelCase :		Union[str, Any]    )       ->Any:




       """simple docstring"""


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







      def  __lowerCAmelCase   ( self :		List[str]					,   __UpperCAmelCase :		Any					,   __UpperCAmelCase :		Optional[Any]					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		int=None					,   __UpperCAmelCase :		List[Any]=False    )       ->List[str]:




       """simple docstring"""


       if gpus is None:
        a						    = 1 if torch.cuda.is_available() else 0
       a						    = {'''src''': sources, '''mt''': predictions, '''ref''': references}
       a						    = [dict(zip(__UpperCAmelCase					,   __UpperCAmelCase    )    ) for t in zip(*data.values()    )]
       a    ,	a						    = self.scorer.predict(__UpperCAmelCase					,   gpus=__UpperCAmelCase					,   progress_bar=__UpperCAmelCase    )
       return {"mean_score": mean_score, "scores": scores}










import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__							  =						logging.get_logger(__name__)
def 		_a       ( a					:List[str]      ,				a					:Tuple=False					)				->     List[Any]:
 a						    = []
 for i in range(config.num_hidden_layers					):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append((F"""blocks.{i}.norm1.weight""", F"""vit.encoder.layer.{i}.layernorm_before.weight""")					)
  rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""vit.encoder.layer.{i}.layernorm_before.bias""")					)
  rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""vit.encoder.layer.{i}.attention.output.dense.weight""")					)
  rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""vit.encoder.layer.{i}.attention.output.dense.bias""")					)
  rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""vit.encoder.layer.{i}.layernorm_after.weight""")					)
  rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""vit.encoder.layer.{i}.layernorm_after.bias""")					)
  rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""vit.encoder.layer.{i}.intermediate.dense.weight""")					)
  rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""vit.encoder.layer.{i}.intermediate.dense.bias""")					)
  rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""vit.encoder.layer.{i}.output.dense.weight""")					)
  rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""vit.encoder.layer.{i}.output.dense.bias""")					)

 # projection layer + position embeddings
 rename_keys.extend(
     [
         ('''cls_token''', '''vit.embeddings.cls_token'''),
         ('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
         ('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
         ('''pos_embed''', '''vit.embeddings.position_embeddings'''),
     ]					)

 if base_model:
  # layernorm + pooler
  rename_keys.extend(
      [
          ('''norm.weight''', '''layernorm.weight'''),
          ('''norm.bias''', '''layernorm.bias'''),
      ]					)

  # if just the base model, we should remove "vit" from all keys that start with "vit"
  a						    = [(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''					) else pair for pair in rename_keys]
 else:
  # layernorm + classification head
  rename_keys.extend(
      [
          ('''norm.weight''', '''vit.layernorm.weight'''),
          ('''norm.bias''', '''vit.layernorm.bias'''),
          ('''head.weight''', '''classifier.weight'''),
          ('''head.bias''', '''classifier.bias'''),
      ]					)

 return rename_keys
def 		_a       ( a					:List[str]      ,				a					:int      ,				a					:Tuple=False					)				->     Any:
 for i in range(config.num_hidden_layers					):
  if base_model:
   a						    = ''''''
  else:
   a						    = '''vit.'''
  # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
  a						    = state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""					)
  a						    = state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""					)
  # next, add query, keys and values (in that order) to the state dict
  a						    = in_proj_weight[
      : config.hidden_size, :
  ]
  a						    = in_proj_bias[: config.hidden_size]
  a						    = in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  a						    = in_proj_bias[
      config.hidden_size : config.hidden_size * 2
  ]
  a						    = in_proj_weight[
      -config.hidden_size :, :
  ]
  a						    = in_proj_bias[-config.hidden_size :]
def 		_a       ( a					:List[Any]					)				->     Dict:
 a						    = ['''head.weight''', '''head.bias''']
 for k in ignore_keys:
  state_dict.pop(a      ,				a					)
def 		_a       ( a					:Union[str, Any]      ,				a					:str      ,				a					:List[Any]					)				->     str:
 a						    = dct.pop(a					)
 a						    = val
def 		_a       ( )				->     Optional[Any]:
 a						    = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 a						    = Image.open(requests.get(a      ,				stream=a					).raw					)
 return im
@torch.no_grad()
def 		_a       ( a					:Tuple      ,				a					:str      ,				a					:Optional[int]=True					)				->     Dict:
 a						    = ViTConfig()
 # patch_size
 if model_name[-1] == "8":
  a						    = 8
 # set labels if required
 if not base_model:
  a						    = 1_000
  a						    = '''huggingface/label-files'''
  a						    = '''imagenet-1k-id2label.json'''
  a						    = json.load(open(hf_hub_download(a      ,				a      ,				repo_type='''dataset'''					)      ,				'''r'''					)					)
  a						    = {int(a					): v for k, v in idalabel.items()}
  a						    = idalabel
  a						    = {v: k for k, v in idalabel.items()}
 # size of the architecture
 if model_name in ["dino_vits8", "dino_vits16"]:
  a						    = 384
  a						    = 1_536
  a						    = 12
  a						    = 6

 # load original model from torch hub
 a						    = torch.hub.load('''facebookresearch/dino:main'''      ,				a					)
 original_model.eval()

 # load state_dict of original model, remove and rename some keys
 a						    = original_model.state_dict()
 if base_model:
  remove_classification_head_(a					)
 a						    = create_rename_keys(a      ,				base_model=a					)
 for src, dest in rename_keys:
  rename_key(a      ,				a      ,				a					)
 read_in_q_k_v(a      ,				a      ,				a					)

 # load HuggingFace model
 if base_model:
  a						    = ViTModel(a      ,				add_pooling_layer=a					).eval()
 else:
  a						    = ViTForImageClassification(a					).eval()
 model.load_state_dict(a					)

 # Check outputs on an image, prepared by ViTImageProcessor
 a						    = ViTImageProcessor()
 a						    = image_processor(images=prepare_img()      ,				return_tensors='''pt'''					)
 a						    = encoding['''pixel_values''']
 a						    = model(a					)

 if base_model:
  a						    = original_model(a					)
  assert torch.allclose(a      ,				outputs.last_hidden_state[:, 0, :]      ,				atol=1e-1					)
 else:
  a						    = original_model(a					)
  assert logits.shape == outputs.logits.shape
  assert torch.allclose(a      ,				outputs.logits      ,				atol=1e-3					)

 Path(a					).mkdir(exist_ok=a					)
 print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""					)
 model.save_pretrained(a					)
 print(F"""Saving image processor to {pytorch_dump_folder_path}"""					)
 image_processor.save_pretrained(a					)


if __name__ == "__main__":
     UpperCAmelCase__							  =						argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--model_name",
         default="dino_vitb16",
         type=str,
         help="Name of the model trained with DINO you'd like to convert.",
     )
     parser.add_argument(
         "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
     )
     parser.add_argument(
         "--base_model",
         action="store_true",
         help="Whether to only convert the base model (no projection head weights).",
     )

     parser.set_defaults(base_model=True)
     UpperCAmelCase__							  =						parser.parse_args()
     convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)


