
"""simple docstring"""




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
A__ :       Optional[int]				= logging.get_logger(__name__)




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      List[str]   			=  SwinConfig(
      embed_dim=192			,					depths=(2, 2, 18, 2)			,					num_heads=(6, 12, 24, 48)			,					window_size=12			,					out_features=['''stage2''', '''stage3''', '''stage4''']			,					)

  _lowercase:      int   			=  DetaConfig(
      backbone_config=_UpperCamelCase			,					num_queries=900			,					encoder_ffn_dim=2_048			,					decoder_ffn_dim=2_048			,					num_feature_levels=5			,					assign_first_stage=_UpperCamelCase			,					with_box_refine=_UpperCamelCase			,					two_stage=_UpperCamelCase			,					)

  # set labels
  _lowercase:      Dict   			=  '''huggingface/label-files'''
  if "o365" in model_name:
    _lowercase:      Optional[Any]   			=  366
    _lowercase:      Tuple   			=  '''object365-id2label.json'''
  else:
    _lowercase:      int   			=  91
    _lowercase:      List[Any]   			=  '''coco-detection-id2label.json'''

  _lowercase:      List[str]   			=  num_labels
  _lowercase:      Tuple   			=  json.load(open(cached_download(hf_hub_url(_UpperCamelCase			,					_UpperCamelCase			,					repo_type='''dataset'''    )    )			,					'''r'''    )    )
  _lowercase:      Optional[int]   			=  {int(_UpperCamelCase    ): v for k, v in idalabel.items()}
  _lowercase:      List[Any]   			=  idalabel
  _lowercase:      Union[str, Any]   			=  {v: k for k, v in idalabel.items()}

  return config




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      List[str]   			=  []

  # stem
  # fmt: off
  rename_keys.append(('''backbone.0.body.patch_embed.proj.weight''', '''model.backbone.model.embeddings.patch_embeddings.projection.weight''')    )
  rename_keys.append(('''backbone.0.body.patch_embed.proj.bias''', '''model.backbone.model.embeddings.patch_embeddings.projection.bias''')    )
  rename_keys.append(('''backbone.0.body.patch_embed.norm.weight''', '''model.backbone.model.embeddings.norm.weight''')    )
  rename_keys.append(('''backbone.0.body.patch_embed.norm.bias''', '''model.backbone.model.embeddings.norm.bias''')    )
  # stages
  for i in range(len(config.backbone_config.depths    )    ):
    for j in range(config.backbone_config.depths[i]    ):
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.norm1.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')    )
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.norm1.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')    )
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')    )
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')    )
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')    )
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')    )
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.norm2.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')    )
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.norm2.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')    )
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')    )
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')    )
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''')    )
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''')    )

    if i < 3:
      rename_keys.append((f'''backbone.0.body.layers.{i}.downsample.reduction.weight''', f'''model.backbone.model.encoder.layers.{i}.downsample.reduction.weight''')    )
      rename_keys.append((f'''backbone.0.body.layers.{i}.downsample.norm.weight''', f'''model.backbone.model.encoder.layers.{i}.downsample.norm.weight''')    )
      rename_keys.append((f'''backbone.0.body.layers.{i}.downsample.norm.bias''', f'''model.backbone.model.encoder.layers.{i}.downsample.norm.bias''')    )

  rename_keys.append(('''backbone.0.body.norm1.weight''', '''model.backbone.model.hidden_states_norms.stage2.weight''')    )
  rename_keys.append(('''backbone.0.body.norm1.bias''', '''model.backbone.model.hidden_states_norms.stage2.bias''')    )
  rename_keys.append(('''backbone.0.body.norm2.weight''', '''model.backbone.model.hidden_states_norms.stage3.weight''')    )
  rename_keys.append(('''backbone.0.body.norm2.bias''', '''model.backbone.model.hidden_states_norms.stage3.bias''')    )
  rename_keys.append(('''backbone.0.body.norm3.weight''', '''model.backbone.model.hidden_states_norms.stage4.weight''')    )
  rename_keys.append(('''backbone.0.body.norm3.bias''', '''model.backbone.model.hidden_states_norms.stage4.bias''')    )

  # transformer encoder
  for i in range(config.encoder_layers    ):
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight''', f'''model.encoder.layers.{i}.self_attn.sampling_offsets.weight''')    )
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias''', f'''model.encoder.layers.{i}.self_attn.sampling_offsets.bias''')    )
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.attention_weights.weight''', f'''model.encoder.layers.{i}.self_attn.attention_weights.weight''')    )
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.attention_weights.bias''', f'''model.encoder.layers.{i}.self_attn.attention_weights.bias''')    )
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.value_proj.weight''', f'''model.encoder.layers.{i}.self_attn.value_proj.weight''')    )
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.value_proj.bias''', f'''model.encoder.layers.{i}.self_attn.value_proj.bias''')    )
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.output_proj.weight''', f'''model.encoder.layers.{i}.self_attn.output_proj.weight''')    )
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.output_proj.bias''', f'''model.encoder.layers.{i}.self_attn.output_proj.bias''')    )
    rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.weight''', f'''model.encoder.layers.{i}.self_attn_layer_norm.weight''')    )
    rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''model.encoder.layers.{i}.self_attn_layer_norm.bias''')    )
    rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''model.encoder.layers.{i}.fc1.weight''')    )
    rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''model.encoder.layers.{i}.fc1.bias''')    )
    rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''model.encoder.layers.{i}.fc2.weight''')    )
    rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''model.encoder.layers.{i}.fc2.bias''')    )
    rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''model.encoder.layers.{i}.final_layer_norm.weight''')    )
    rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''model.encoder.layers.{i}.final_layer_norm.bias''')    )

  # transformer decoder
  for i in range(config.decoder_layers    ):
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight''', f'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight''')    )
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias''', f'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias''')    )
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.attention_weights.weight''', f'''model.decoder.layers.{i}.encoder_attn.attention_weights.weight''')    )
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.attention_weights.bias''', f'''model.decoder.layers.{i}.encoder_attn.attention_weights.bias''')    )
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.value_proj.weight''', f'''model.decoder.layers.{i}.encoder_attn.value_proj.weight''')    )
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.value_proj.bias''', f'''model.decoder.layers.{i}.encoder_attn.value_proj.bias''')    )
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.output_proj.weight''', f'''model.decoder.layers.{i}.encoder_attn.output_proj.weight''')    )
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.output_proj.bias''', f'''model.decoder.layers.{i}.encoder_attn.output_proj.bias''')    )
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.weight''', f'''model.decoder.layers.{i}.encoder_attn_layer_norm.weight''')    )
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''model.decoder.layers.{i}.encoder_attn_layer_norm.bias''')    )
    rename_keys.append((f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''model.decoder.layers.{i}.self_attn.out_proj.weight''')    )
    rename_keys.append((f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''model.decoder.layers.{i}.self_attn.out_proj.bias''')    )
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm2.weight''', f'''model.decoder.layers.{i}.self_attn_layer_norm.weight''')    )
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm2.bias''', f'''model.decoder.layers.{i}.self_attn_layer_norm.bias''')    )
    rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''model.decoder.layers.{i}.fc1.weight''')    )
    rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''model.decoder.layers.{i}.fc1.bias''')    )
    rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''model.decoder.layers.{i}.fc2.weight''')    )
    rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''model.decoder.layers.{i}.fc2.bias''')    )
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''model.decoder.layers.{i}.final_layer_norm.weight''')    )
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''model.decoder.layers.{i}.final_layer_norm.bias''')    )

  # fmt: on

  return rename_keys




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Any   			=  dct.pop(_UpperCamelCase    )
  _lowercase:      Any   			=  val




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Optional[int]   			=  [int(backbone_config.embed_dim * 2**i    ) for i in range(len(backbone_config.depths    )    )]
  for i in range(len(backbone_config.depths    )    ):
    _lowercase:      Any   			=  num_features[i]
    for j in range(backbone_config.depths[i]    ):
      # fmt: off
      # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
      _lowercase:      int   			=  state_dict.pop(f'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'''    )
      _lowercase:      str   			=  state_dict.pop(f'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias'''    )
      # next, add query, keys and values (in that order) to the state dict
      _lowercase:      List[str]   			=  in_proj_weight[:dim, :]
      _lowercase:      List[Any]   			=  in_proj_bias[: dim]
      _lowercase:      Optional[int]   			=  in_proj_weight[
          dim : dim * 2, :
      ]
      _lowercase:      Optional[Any]   			=  in_proj_bias[
          dim : dim * 2
      ]
      _lowercase:      List[Any]   			=  in_proj_weight[
          -dim :, :
      ]
      _lowercase:      List[Any]   			=  in_proj_bias[-dim :]
      # fmt: on




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Dict   			=  config.d_model
  for i in range(config.decoder_layers    ):
    # read in weights + bias of input projection layer of self-attention
    _lowercase:      Union[str, Any]   			=  state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''    )
    _lowercase:      List[Any]   			=  state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_bias'''    )
    # next, add query, keys and values (in that order) to the state dict
    _lowercase:      Optional[Any]   			=  in_proj_weight[:hidden_size, :]
    _lowercase:      List[str]   			=  in_proj_bias[:hidden_size]
    _lowercase:      List[Any]   			=  in_proj_weight[
        hidden_size : hidden_size * 2, :
    ]
    _lowercase:      Tuple   			=  in_proj_bias[hidden_size : hidden_size * 2]
    _lowercase:      Any   			=  in_proj_weight[-hidden_size:, :]
    _lowercase:      Union[str, Any]   			=  in_proj_bias[-hidden_size:]




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Union[str, Any]   			=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
  _lowercase:      str   			=  Image.open(requests.get(_UpperCamelCase			,					stream=_UpperCamelCase    ).raw    )

  return im




@torch.no_grad()
def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      str   			=  get_deta_config(_UpperCamelCase    )

  # load original state dict
  if model_name == "deta-swin-large":
    _lowercase:      Optional[Any]   			=  hf_hub_download(repo_id='''nielsr/deta-checkpoints'''			,					filename='''adet_swin_ft.pth'''    )
  elif model_name == "deta-swin-large-o365":
    _lowercase:      Optional[Any]   			=  hf_hub_download(repo_id='''jozhang97/deta-swin-l-o365'''			,					filename='''deta_swin_pt_o365.pth'''    )
  else:
    raise ValueError(f'''Model name {model_name} not supported'''    )

  _lowercase:      Dict   			=  torch.load(_UpperCamelCase			,					map_location='''cpu'''    )['''model''']

  # original state dict
  for name, param in state_dict.items():
    print(_UpperCamelCase			,					param.shape    )

  # rename keys
  _lowercase:      Optional[Any]   			=  create_rename_keys(_UpperCamelCase    )
  for src, dest in rename_keys:
    rename_key(_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    )
  read_in_swin_q_k_v(_UpperCamelCase			,					config.backbone_config    )
  read_in_decoder_q_k_v(_UpperCamelCase			,					_UpperCamelCase    )

  # fix some prefixes
  for key in state_dict.copy().keys():
    if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
      _lowercase:      List[str]   			=  state_dict.pop(_UpperCamelCase    )
      _lowercase:      Dict   			=  val
    if "input_proj" in key:
      _lowercase:      Any   			=  state_dict.pop(_UpperCamelCase    )
      _lowercase:      Tuple   			=  val
    if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
      _lowercase:      Dict   			=  state_dict.pop(_UpperCamelCase    )
      _lowercase:      Any   			=  val

    # finally, create HuggingFace model and load state dict
  _lowercase:      Any   			=  DetaForObjectDetection(_UpperCamelCase    )
  model.load_state_dict(_UpperCamelCase    )
  model.eval()

  _lowercase:      Tuple   			=  '''cuda''' if torch.cuda.is_available() else '''cpu'''
  model.to(_UpperCamelCase    )

  # load image processor
  _lowercase:      List[str]   			=  DetaImageProcessor(format='''coco_detection'''    )

  # verify our conversion on image
  _lowercase:      Union[str, Any]   			=  prepare_img()
  _lowercase:      Dict   			=  processor(images=_UpperCamelCase			,					return_tensors='''pt'''    )
  _lowercase:      Any   			=  encoding['''pixel_values''']
  _lowercase:      int   			=  model(pixel_values.to(_UpperCamelCase    )    )

  # verify logits
  print('''Logits:'''			,					outputs.logits[0, :3, :3]    )
  print('''Boxes:'''			,					outputs.pred_boxes[0, :3, :3]    )
  if model_name == "deta-swin-large":
    _lowercase:      Optional[int]   			=  torch.tensor(
        [[-7.6_308, -2.8_485, -5.3_737], [-7.2_037, -4.5_505, -4.8_027], [-7.2_943, -4.2_611, -4.6_617]]    )
    _lowercase:      Optional[int]   			=  torch.tensor([[0.4_987, 0.4_969, 0.9_999], [0.2_549, 0.5_498, 0.4_805], [0.5_498, 0.2_757, 0.0_569]]    )
  elif model_name == "deta-swin-large-o365":
    _lowercase:      List[str]   			=  torch.tensor(
        [[-8.0_122, -3.5_720, -4.9_717], [-8.1_547, -3.6_886, -4.6_389], [-7.6_610, -3.6_194, -5.0_134]]    )
    _lowercase:      Dict   			=  torch.tensor([[0.2_523, 0.5_549, 0.4_881], [0.7_715, 0.4_149, 0.4_601], [0.5_503, 0.2_753, 0.0_575]]    )
  assert torch.allclose(outputs.logits[0, :3, :3]			,					expected_logits.to(_UpperCamelCase    )			,					atol=1e-4    )
  assert torch.allclose(outputs.pred_boxes[0, :3, :3]			,					expected_boxes.to(_UpperCamelCase    )			,					atol=1e-4    )
  print('''Everything ok!'''    )

  if pytorch_dump_folder_path:
    # Save model and processor
    logger.info(f'''Saving PyTorch model and processor to {pytorch_dump_folder_path}...'''    )
    Path(_UpperCamelCase    ).mkdir(exist_ok=_UpperCamelCase    )
    model.save_pretrained(_UpperCamelCase    )
    processor.save_pretrained(_UpperCamelCase    )

  # Push to hub
  if push_to_hub:
    print('''Pushing model and processor to hub...'''    )
    model.push_to_hub(f'''jozhang97/{model_name}'''    )
    processor.push_to_hub(f'''jozhang97/{model_name}'''    )


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

  parser.add_argument(
      '--model_name',
      type=str,
      default='deta-swin-large',
      choices=['deta-swin-large', 'deta-swin-large-o365'],
      help='Name of the model you\'d like to convert.',
  )
  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 or not to push the converted model to the 🤗 hub.'
  )
  A__ :       List[str]				= parser.parse_args()
  convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


"""simple docstring"""




# Usage:
# ./gen-card-facebook-wmt19.py

import os
from pathlib import Path




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      int   			=  {
      '''en''': '''Machine learning is great, isn\'t it?''',
      '''ru''': '''Машинное обучение - это здорово, не так ли?''',
      '''de''': '''Maschinelles Lernen ist großartig, oder?''',
  }

  # BLUE scores as follows:
  # "pair": [fairseq, transformers]
  _lowercase:      Dict   			=  {
      '''ru-en''': ['''[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)''', '''39.20'''],
      '''en-ru''': ['''[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)''', '''33.47'''],
      '''en-de''': ['''[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)''', '''42.83'''],
      '''de-en''': ['''[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)''', '''41.35'''],
  }
  _lowercase:      Optional[Any]   			=  f'''{src_lang}-{tgt_lang}'''

  _lowercase:      Tuple   			=  f'''
---
language: 
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- facebook
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.

For more details, please see, [Facebook FAIR\'s WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).

The abbreviation FSMT stands for FairSeqMachineTranslation

All four models are available:

* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)
* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)
* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)
* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)

## Intended uses & limitations

#### How to use

```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = "facebook/wmt19-{src_lang}-{tgt_lang}"
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

- The original (and this ported model) doesn\'t seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)

## Training data

Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).

## Eval results

pair   | fairseq | transformers
-------|---------|----------
{pair}  | {scores[pair][0]} | {scores[pair][1]}

The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn\'t support:
- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).
- re-ranking

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=15
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 $PAIR
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $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
```
note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.

## 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

```bibtex
@inproceedings{{...,
  year={{2020}},
  title={{Facebook FAIR\'s WMT19 News Translation Task Submission}},
  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},
  booktitle={{Proc. of WMT}},
}}
```


## TODO

- port model ensemble (fairseq uses 4 model checkpoints)

'''
  os.makedirs(_UpperCamelCase			,					exist_ok=_UpperCamelCase    )
  _lowercase:      List[str]   			=  os.path.join(_UpperCamelCase			,					'''README.md'''    )
  print(f'''Generating {path}'''    )
  with open(_UpperCamelCase			,					'''w'''			,					encoding='''utf-8'''    ) as f:
    f.write(_UpperCamelCase    )


# make sure we are under the root of the project
A__ :       List[str]				= Path(__file__).resolve().parent.parent.parent
A__ :       Optional[int]				= repo_dir / 'model_cards'

for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
  A__				,			A__				,			A__ :       int				= model_name.split('-')
  A__ :       Optional[int]				= model_cards_dir / 'facebook' / model_name
  write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)
