
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()
lowerCamelCase__     :		Dict        = logging.get_logger(__name__)

def 		UpperCamelCase						(   lowercase_ )		->						int:


  '''simple docstring'''






  lowercase__	: List[Any]     		=   DPTConfig(embedding_type="""hybrid""" )

  if "large" in checkpoint_url:
    lowercase__	: Union[str, Any]     		=   10_24
    lowercase__	: Dict     		=   40_96
    lowercase__	: Optional[Any]     		=   24
    lowercase__	: List[Any]     		=   16
    lowercase__	: str     		=   [5, 11, 17, 23]
    lowercase__	: Union[str, Any]     		=   [2_56, 5_12, 10_24, 10_24]
    lowercase__	: Optional[Any]     		=   (1, 3_84, 3_84)

  if "nyu" or "midas" in checkpoint_url:
    lowercase__	: Any     		=   7_68
    lowercase__	: Tuple     		=   [1, 1, 1, 0.5]
    lowercase__	: Union[str, Any]     		=   [2_56, 5_12, 7_68, 7_68]
    lowercase__	: List[Any]     		=   1_50
    lowercase__	: str     		=   16
    lowercase__	: Optional[Any]     		=   (1, 3_84, 3_84)
    lowercase__	: Union[str, Any]     		=   False
    lowercase__	: List[str]     		=   """project"""

  if "ade" in checkpoint_url:
    lowercase__	: Union[str, Any]     		=   True
    lowercase__	: Tuple     		=   7_68
    lowercase__	: Union[str, Any]     		=   [1, 1, 1, 0.5]
    lowercase__	: Optional[Any]     		=   1_50
    lowercase__	: Optional[int]     		=   16
    lowercase__	: Optional[int]     		=   """huggingface/label-files"""
    lowercase__	: List[str]     		=   """ade20k-id2label.json"""
    lowercase__	: List[str]     		=   json.load(open(cached_download(hf_hub_url(lowercase_			,			lowercase_			,			repo_type="""dataset""" ) )			,			"""r""" ) )
    lowercase__	: Optional[Any]     		=   {int(lowercase_ ): v for k, v in idalabel.items()}
    lowercase__	: List[str]     		=   idalabel
    lowercase__	: List[Any]     		=   {v: k for k, v in idalabel.items()}
    lowercase__	: Any     		=   [1, 1_50, 4_80, 4_80]

  return config, expected_shape

def 		UpperCamelCase						(   lowercase_ )		->						Any:


  '''simple docstring'''






  lowercase__	: Dict     		=   ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
  for k in ignore_keys:
    state_dict.pop(lowercase_			,			lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						Any:


  '''simple docstring'''






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

  if ".." in name:
    lowercase__	: Tuple     		=   name.replace(""".."""			,			""".""" )

  if "stem.conv" in name:
    lowercase__	: List[Any]     		=   name.replace("""stem.conv"""			,			"""bit.embedder.convolution""" )
  if "blocks" in name:
    lowercase__	: Any     		=   name.replace("""blocks"""			,			"""layers""" )
  if "convolution" in name and "backbone" in name:
    lowercase__	: Dict     		=   name.replace("""convolution"""			,			"""conv""" )
  if "layer" in name and "backbone" in name:
    lowercase__	: Dict     		=   name.replace("""layer"""			,			"""layers""" )
  if "backbone.bit.encoder.bit" in name:
    lowercase__	: Optional[int]     		=   name.replace("""backbone.bit.encoder.bit"""			,			"""backbone.bit""" )
  if "embedder.conv" in name:
    lowercase__	: Union[str, Any]     		=   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						(   lowercase_			,			lowercase_ )		->						List[Any]:


  '''simple docstring'''






  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__	: Tuple     		=   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__	: Tuple     		=   in_proj_weight[: config.hidden_size, :]
    lowercase__	: Union[str, Any]     		=   in_proj_bias[: config.hidden_size]
    lowercase__	: int     		=   in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    lowercase__	: int     		=   in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    lowercase__	: Any     		=   in_proj_weight[
        -config.hidden_size :, :
    ]
    lowercase__	: Optional[Any]     		=   in_proj_bias[-config.hidden_size :]

def 		UpperCamelCase						(   )		->						Tuple:


  '''simple docstring'''






  lowercase__	: Optional[int]     		=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
  lowercase__	: Union[str, Any]     		=   Image.open(requests.get(lowercase_			,			stream=lowercase_ ).raw )
  return im

@torch.no_grad()
def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )		->						Tuple:


  '''simple docstring'''






  lowercase__		,     lowercase__	: str     		=   get_dpt_config(lowercase_ )
  # load original state_dict from URL
  # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
  lowercase__	: Any     		=   torch.load(lowercase_			,			map_location="""cpu""" )
  # remove certain keys
  remove_ignore_keys_(lowercase_ )
  # rename keys
  for key in state_dict.copy().keys():
    lowercase__	: str     		=   state_dict.pop(lowercase_ )
    lowercase__	: int     		=   val
  # read in qkv matrices
  read_in_q_k_v(lowercase_			,			lowercase_ )

  # load HuggingFace model
  lowercase__	: Any     		=   DPTForSemanticSegmentation(lowercase_ ) if """ade""" in checkpoint_url else DPTForDepthEstimation(lowercase_ )
  model.load_state_dict(lowercase_ )
  model.eval()

  # Check outputs on an image
  lowercase__	: List[Any]     		=   4_80 if """ade""" in checkpoint_url else 3_84
  lowercase__	: Dict     		=   DPTImageProcessor(size=lowercase_ )

  lowercase__	: Optional[int]     		=   prepare_img()
  lowercase__	: Dict     		=   image_processor(lowercase_			,			return_tensors="""pt""" )

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

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

    Image.fromarray((prediction / prediction.max()) * 2_55 ).show()

  if pytorch_dump_folder_path is not None:
    Path(lowercase_ ).mkdir(exist_ok=lowercase_ )
    print(F'Saving model to {pytorch_dump_folder_path}' )
    model.save_pretrained(lowercase_ )
    print(F'Saving image processor to {pytorch_dump_folder_path}' )
    image_processor.save_pretrained(lowercase_ )

  if push_to_hub:
    model.push_to_hub("""ybelkada/dpt-hybrid-midas""" )
    image_processor.push_to_hub("""ybelkada/dpt-hybrid-midas""" )


if __name__ == "__main__":
     lowerCamelCase__     :		Optional[int]        = 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""",
     )

     lowerCamelCase__     :		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
     )








"""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)
