
"""simple docstring"""







import os
from pathlib import Path




def 			A_			(					lowercase						, lowercase						, lowercase						, lowercase      )     ->							int:


     """simple docstring"""







     UpperCAmelCase_						:    Union[str, Any]    					=  {
         """en""": """Machine learning is great, isn't it?""",
         """ru""": """Машинное обучение - это здорово, не так ли?""",
         """de""": """Maschinelles Lernen ist großartig, nicht wahr?""",
     }

     # BLUE scores as follows:
     # "pair": [fairseq, transformers]
     UpperCAmelCase_						:    Dict    					=  {
         """wmt19-de-en-6-6-base""": [0, 38.37],
         """wmt19-de-en-6-6-big""": [0, 39.90],
     }
     UpperCAmelCase_						:    Optional[int]    					=  f'''{src_lang}-{tgt_lang}'''

     UpperCAmelCase_						:    int    					=  f'''
---

language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- allenai
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of fairseq-based [wmt19 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.

For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).

2 models are available:

* [wmt19-de-en-6-6-big](https://huggingface.co/allenai/wmt19-de-en-6-6-big)
* [wmt19-de-en-6-6-base](https://huggingface.co/allenai/wmt19-de-en-6-6-base)


## Intended uses & limitations

#### How to use

```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = "allenai/{model_name}"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)

input = "{texts[src_lang]}"
input_ids = tokenizer.encode(input, return_tensors="pt")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # {texts[tgt_lang]}

```

#### Limitations and bias


## Training data

Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).

## Eval results

Here are the BLEU scores:

model   |  transformers
-------|---------
{model_name}  |  {scores[model_name][1]}

The score was calculated using this code:

```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR={pair}
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=5
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py allenai/{model_name} $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
```

## Data Sources

- [training, etc.](http://www.statmt.org/wmt19/)
- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)


### BibTeX entry and citation info

```
@misc{{kasai2020deep,
    title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},
    author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},
    year={{2020}},
    eprint={{2006.10369}},
    archivePrefix={{arXiv}},
    primaryClass={{cs.CL}}
}}
```

'''
     model_card_dir.mkdir(parents=lowercase						, exist_ok=lowercase      )
     UpperCAmelCase_						:    Optional[int]    					=  os.path.join(lowercase						, """README.md"""      )
     print(f'''Generating {path}'''      )
     with open(lowercase						, """w"""						, encoding="""utf-8"""      ) as f:
          f.write(lowercase      )


# make sure we are under the root of the project
lowercase_						    =					Path(__file__).resolve().parent.parent.parent
lowercase_						    =					repo_dir / "model_cards"

for model_name in ["wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big"]:
       lowercase_						    =					model_cards_dir / "allenai" / model_name
       write_model_card(model_card_dir, src_lang="de", tgt_lang="en", model_name=model_name)



"""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 DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_						    =					logging.get_logger(__name__)




def 			A_			(					lowercase      )     ->							List[Any]:


     """simple docstring"""







     if "backbone.0.body" in orig_key:
          UpperCAmelCase_						:    Dict    					=  orig_key.replace("""backbone.0.body"""						, """backbone.conv_encoder.model"""      )
     if "transformer" in orig_key:
          UpperCAmelCase_						:    List[Any]    					=  orig_key.replace("""transformer."""						, """"""      )
     if "norm1" in orig_key:
          if "encoder" in orig_key:
               UpperCAmelCase_						:    List[str]    					=  orig_key.replace("""norm1"""						, """self_attn_layer_norm"""      )
          else:
               UpperCAmelCase_						:    Any    					=  orig_key.replace("""norm1"""						, """encoder_attn_layer_norm"""      )
     if "norm2" in orig_key:
          if "encoder" in orig_key:
               UpperCAmelCase_						:    Any    					=  orig_key.replace("""norm2"""						, """final_layer_norm"""      )
          else:
               UpperCAmelCase_						:    Union[str, Any]    					=  orig_key.replace("""norm2"""						, """self_attn_layer_norm"""      )
     if "norm3" in orig_key:
          UpperCAmelCase_						:    Optional[Any]    					=  orig_key.replace("""norm3"""						, """final_layer_norm"""      )
     if "linear1" in orig_key:
          UpperCAmelCase_						:    Union[str, Any]    					=  orig_key.replace("""linear1"""						, """fc1"""      )
     if "linear2" in orig_key:
          UpperCAmelCase_						:    List[str]    					=  orig_key.replace("""linear2"""						, """fc2"""      )
     if "query_embed" in orig_key:
          UpperCAmelCase_						:    str    					=  orig_key.replace("""query_embed"""						, """query_position_embeddings"""      )
     if "cross_attn" in orig_key:
          UpperCAmelCase_						:    Optional[int]    					=  orig_key.replace("""cross_attn"""						, """encoder_attn"""      )

     return orig_key




def 			A_			(					lowercase      )     ->							Union[str, Any]:


     """simple docstring"""







     for i in range(6      ):
          # read in weights + bias of input projection layer of self-attention
          UpperCAmelCase_						:    int    					=  state_dict.pop(f'''decoder.layers.{i}.self_attn.in_proj_weight'''      )
          UpperCAmelCase_						:    List[Any]    					=  state_dict.pop(f'''decoder.layers.{i}.self_attn.in_proj_bias'''      )
          # next, add query, keys and values (in that order) to the state dict
          UpperCAmelCase_						:    List[Any]    					=  in_proj_weight[:256, :]
          UpperCAmelCase_						:    Union[str, Any]    					=  in_proj_bias[:256]
          UpperCAmelCase_						:    int    					=  in_proj_weight[256:512, :]
          UpperCAmelCase_						:    List[Any]    					=  in_proj_bias[256:512]
          UpperCAmelCase_						:    List[Any]    					=  in_proj_weight[-256:, :]
          UpperCAmelCase_						:    List[str]    					=  in_proj_bias[-256:]




def 			A_			(					)     ->							Any:


     """simple docstring"""







     UpperCAmelCase_						:    List[str]    					=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
     UpperCAmelCase_						:    Union[str, Any]    					=  Image.open(requests.get(lowercase						, stream=lowercase      ).raw      )

     return im




@torch.no_grad()
def 			A_			(					lowercase						, lowercase						, lowercase						, lowercase						, lowercase						, lowercase						, lowercase						, )     ->							List[Any]:


     """simple docstring"""







     UpperCAmelCase_						:    Dict    					=  DeformableDetrConfig()
     # set config attributes
     if single_scale:
          UpperCAmelCase_						:    Optional[Any]    					=  1
     UpperCAmelCase_						:    Dict    					=  dilation
     UpperCAmelCase_						:    Optional[int]    					=  with_box_refine
     UpperCAmelCase_						:    str    					=  two_stage
     # set labels
     UpperCAmelCase_						:    Optional[int]    					=  91
     UpperCAmelCase_						:    Dict    					=  """huggingface/label-files"""
     UpperCAmelCase_						:    int    					=  """coco-detection-id2label.json"""
     UpperCAmelCase_						:    List[str]    					=  json.load(open(cached_download(hf_hub_url(lowercase						, lowercase						, repo_type="""dataset"""      )      )						, """r"""      )      )
     UpperCAmelCase_						:    List[Any]    					=  {int(lowercase      ): v for k, v in idalabel.items()}
     UpperCAmelCase_						:    List[Any]    					=  idalabel
     UpperCAmelCase_						:    int    					=  {v: k for k, v in idalabel.items()}

     # load image processor
     UpperCAmelCase_						:    Union[str, Any]    					=  DeformableDetrImageProcessor(format="""coco_detection"""      )

     # prepare image
     UpperCAmelCase_						:    Tuple    					=  prepare_img()
     UpperCAmelCase_						:    Optional[int]    					=  image_processor(images=lowercase						, return_tensors="""pt"""      )
     UpperCAmelCase_						:    List[str]    					=  encoding["""pixel_values"""]

     logger.info("""Converting model..."""      )

     # load original state dict
     UpperCAmelCase_						:    Union[str, Any]    					=  torch.load(lowercase						, map_location="""cpu"""      )["""model"""]
     # rename keys
     for key in state_dict.copy().keys():
          UpperCAmelCase_						:    List[str]    					=  state_dict.pop(lowercase      )
          UpperCAmelCase_						:    Optional[int]    					=  val
     # query, key and value matrices need special treatment
     read_in_q_k_v(lowercase      )
     # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
     UpperCAmelCase_						:    Optional[int]    					=  """model."""
     for key in state_dict.copy().keys():
          if not key.startswith("""class_embed"""      ) and not key.startswith("""bbox_embed"""      ):
               UpperCAmelCase_						:    Dict    					=  state_dict.pop(lowercase      )
               UpperCAmelCase_						:    Tuple    					=  val
    # finally, create HuggingFace model and load state dict
     UpperCAmelCase_						:    List[Any]    					=  DeformableDetrForObjectDetection(lowercase      )
     model.load_state_dict(lowercase      )
     model.eval()

     UpperCAmelCase_						:    Optional[Any]    					=  """cuda""" if torch.cuda.is_available() else """cpu"""
     model.to(lowercase      )
     # verify our conversion
     UpperCAmelCase_						:    Dict    					=  model(pixel_values.to(lowercase      )      )

     UpperCAmelCase_						:    Optional[int]    					=  torch.tensor(
         [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]      )
     UpperCAmelCase_						:    Dict    					=  torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]      )

     if single_scale:
          UpperCAmelCase_						:    Optional[Any]    					=  torch.tensor(
              [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]      )
          UpperCAmelCase_						:    Optional[int]    					=  torch.tensor([[0.7292, 0.4991, 0.5532], [0.7959, 0.2426, 0.4236], [0.7582, 0.3518, 0.4451]]      )

     if single_scale and dilation:
          UpperCAmelCase_						:    Any    					=  torch.tensor(
              [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]      )
          UpperCAmelCase_						:    List[str]    					=  torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]      )

     if with_box_refine:
          UpperCAmelCase_						:    Optional[int]    					=  torch.tensor(
              [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]      )
          UpperCAmelCase_						:    List[str]    					=  torch.tensor([[0.7828, 0.2208, 0.4323], [0.0892, 0.5996, 0.1319], [0.5524, 0.6389, 0.8914]]      )

     if with_box_refine and two_stage:
          UpperCAmelCase_						:    str    					=  torch.tensor(
              [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]      )
          UpperCAmelCase_						:    Optional[Any]    					=  torch.tensor([[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]]      )

     print("""Logits:"""						, outputs.logits[0, :3, :3]      )

     assert torch.allclose(outputs.logits[0, :3, :3]						, expected_logits.to(lowercase      )						, atol=1E-4      )
     assert torch.allclose(outputs.pred_boxes[0, :3, :3]						, expected_boxes.to(lowercase      )						, atol=1E-4      )

     print("""Everything ok!"""      )

     # Save model and image processor
     logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''      )
     Path(lowercase      ).mkdir(exist_ok=lowercase      )
     model.save_pretrained(lowercase      )
     image_processor.save_pretrained(lowercase      )

     # Push to hub
     if push_to_hub:
          UpperCAmelCase_						:    Any    					=  """deformable-detr"""
          model_name += "-single-scale" if single_scale else ""
          model_name += "-dc5" if dilation else ""
          model_name += "-with-box-refine" if with_box_refine else ""
          model_name += "-two-stage" if two_stage else ""
          print("""Pushing model to hub..."""      )
          model.push_to_hub(repo_path_or_name=lowercase						, organization="""nielsr"""						, commit_message="""Add model"""      )


if __name__ == "__main__":
       lowercase_						    =					argparse.ArgumentParser()

       parser.add_argument(
           "--checkpoint_path",
           type=str,
           default="/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth",
           help="Path to Pytorch checkpoint (.pth file) you'd like to convert.",
       )
       parser.add_argument("--single_scale", action="store_true", help="Whether to set config.num_features_levels = 1.")
       parser.add_argument("--dilation", action="store_true", help="Whether to set config.dilation=True.")
       parser.add_argument("--with_box_refine", action="store_true", help="Whether to set config.with_box_refine=True.")
       parser.add_argument("--two_stage", action="store_true", help="Whether to set config.two_stage=True.")
       parser.add_argument(
           "--pytorch_dump_folder_path",
           default=None,
           type=str,
           required=True,
           help="Path to the folder to output PyTorch model.",
       )
       parser.add_argument(
           "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
       )
       lowercase_						    =					parser.parse_args()
       convert_deformable_detr_checkpoint(
           args.checkpoint_path,
           args.single_scale,
           args.dilation,
           args.with_box_refine,
           args.two_stage,
           args.pytorch_dump_folder_path,
           args.push_to_hub,
       )

