



"""simple docstring"""
import argparse
import json
from pathlib import Path

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

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase       :List[Any]	=   logging.get_logger(__name__)





def 					lowerCamelCase_		(UpperCamelCase__						:						int	,			UpperCamelCase__						:						str=False				):
    _UpperCAmelCase				:    Optional[Any]											=       []
    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'deit.encoder.layer.{i}.layernorm_before.weight')				)
        rename_keys.append((F'blocks.{i}.norm1.bias', F'deit.encoder.layer.{i}.layernorm_before.bias')				)
        rename_keys.append((F'blocks.{i}.attn.proj.weight', F'deit.encoder.layer.{i}.attention.output.dense.weight')				)
        rename_keys.append((F'blocks.{i}.attn.proj.bias', F'deit.encoder.layer.{i}.attention.output.dense.bias')				)
        rename_keys.append((F'blocks.{i}.norm2.weight', F'deit.encoder.layer.{i}.layernorm_after.weight')				)
        rename_keys.append((F'blocks.{i}.norm2.bias', F'deit.encoder.layer.{i}.layernorm_after.bias')				)
        rename_keys.append((F'blocks.{i}.mlp.fc1.weight', F'deit.encoder.layer.{i}.intermediate.dense.weight')				)
        rename_keys.append((F'blocks.{i}.mlp.fc1.bias', F'deit.encoder.layer.{i}.intermediate.dense.bias')				)
        rename_keys.append((F'blocks.{i}.mlp.fc2.weight', F'deit.encoder.layer.{i}.output.dense.weight')				)
        rename_keys.append((F'blocks.{i}.mlp.fc2.bias', F'deit.encoder.layer.{i}.output.dense.bias')				)

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

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

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

    return rename_keys





def 					lowerCamelCase_		(UpperCamelCase__						:						Optional[Any]	,			UpperCamelCase__						:						str	,			UpperCamelCase__						:						Tuple=False				):
    for i in range(config.num_hidden_layers				):
        if base_model:
            _UpperCAmelCase				:    List[str]											=       ""
        else:
            _UpperCAmelCase				:    Any											=       "deit."
        # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
        _UpperCAmelCase				:    List[str]											=       state_dict.pop(F'blocks.{i}.attn.qkv.weight'				)
        _UpperCAmelCase				:    Dict											=       state_dict.pop(F'blocks.{i}.attn.qkv.bias'				)
        # next, add query, keys and values (in that order) to the state dict
        _UpperCAmelCase				:    Optional[int]											=       in_proj_weight[
            : config.hidden_size, :
        ]
        _UpperCAmelCase				:    Optional[Any]											=       in_proj_bias[: config.hidden_size]
        _UpperCAmelCase				:    Any											=       in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        _UpperCAmelCase				:    Optional[Any]											=       in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        _UpperCAmelCase				:    Tuple											=       in_proj_weight[
            -config.hidden_size :, :
        ]
        _UpperCAmelCase				:    Dict											=       in_proj_bias[-config.hidden_size :]





def 					lowerCamelCase_		(UpperCamelCase__						:						Dict	,			UpperCamelCase__						:						Tuple	,			UpperCamelCase__						:						Tuple				):
    _UpperCAmelCase				:    Tuple											=       dct.pop(UpperCamelCase__				)
    _UpperCAmelCase				:    Tuple											=       val





def 					lowerCamelCase_		():
    _UpperCAmelCase				:    Any											=       "http://images.cocodataset.org/val2017/000000039769.jpg"
    _UpperCAmelCase				:    List[Any]											=       Image.open(requests.get(UpperCamelCase__	,			stream=UpperCamelCase__				).raw				)
    return im





@torch.no_grad()
def 					lowerCamelCase_		(UpperCamelCase__						:						List[str]	,			UpperCamelCase__						:						Union[str, Any]				):
    _UpperCAmelCase				:    Tuple											=       DeiTConfig()
    # all deit models have fine-tuned heads
    _UpperCAmelCase				:    List[str]											=       False
    # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
    _UpperCAmelCase				:    List[str]											=       1000
    _UpperCAmelCase				:    List[Any]											=       "huggingface/label-files"
    _UpperCAmelCase				:    Optional[Any]											=       "imagenet-1k-id2label.json"
    _UpperCAmelCase				:    Any											=       json.load(open(hf_hub_download(UpperCamelCase__	,			UpperCamelCase__	,			repo_type='''dataset'''				)	,			'''r'''				)				)
    _UpperCAmelCase				:    Optional[Any]											=       {int(UpperCamelCase__				): v for k, v in idalabel.items()}
    _UpperCAmelCase				:    Optional[int]											=       idalabel
    _UpperCAmelCase				:    Optional[Any]											=       {v: k for k, v in idalabel.items()}
    _UpperCAmelCase				:    Optional[Any]											=       int(deit_name[-6:-4]				)
    _UpperCAmelCase				:    Any											=       int(deit_name[-3:]				)
    # size of the architecture
    if deit_name[9:].startswith('''tiny'''				):
        _UpperCAmelCase				:    List[str]											=       192
        _UpperCAmelCase				:    Optional[int]											=       768
        _UpperCAmelCase				:    Optional[Any]											=       12
        _UpperCAmelCase				:    Optional[int]											=       3
    elif deit_name[9:].startswith('''small'''				):
        _UpperCAmelCase				:    Optional[Any]											=       384
        _UpperCAmelCase				:    List[Any]											=       1536
        _UpperCAmelCase				:    int											=       12
        _UpperCAmelCase				:    List[str]											=       6
    if deit_name[9:].startswith('''base'''				):
        pass
    elif deit_name[4:].startswith('''large'''				):
        _UpperCAmelCase				:    Any											=       1024
        _UpperCAmelCase				:    List[Any]											=       4096
        _UpperCAmelCase				:    Optional[int]											=       24
        _UpperCAmelCase				:    Optional[int]											=       16

    # load original model from timm
    _UpperCAmelCase				:    Tuple											=       timm.create_model(UpperCamelCase__	,			pretrained=UpperCamelCase__				)
    timm_model.eval()

    # load state_dict of original model, remove and rename some keys
    _UpperCAmelCase				:    Union[str, Any]											=       timm_model.state_dict()
    _UpperCAmelCase				:    List[str]											=       create_rename_keys(UpperCamelCase__	,			UpperCamelCase__				)
    for src, dest in rename_keys:
        rename_key(UpperCamelCase__	,			UpperCamelCase__	,			UpperCamelCase__				)
    read_in_q_k_v(UpperCamelCase__	,			UpperCamelCase__	,			UpperCamelCase__				)

    # load HuggingFace model
    _UpperCAmelCase				:    Optional[Any]											=       DeiTForImageClassificationWithTeacher(UpperCamelCase__				).eval()
    model.load_state_dict(UpperCamelCase__				)

    # Check outputs on an image, prepared by DeiTImageProcessor
    _UpperCAmelCase				:    Union[str, Any]											=       int(
        (256 / 224) * config.image_size				)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
    _UpperCAmelCase				:    int											=       DeiTImageProcessor(size=UpperCamelCase__	,			crop_size=config.image_size				)
    _UpperCAmelCase				:    Union[str, Any]											=       image_processor(images=prepare_img()	,			return_tensors='''pt'''				)
    _UpperCAmelCase				:    Optional[Any]											=       encoding["pixel_values"]
    _UpperCAmelCase				:    Tuple											=       model(UpperCamelCase__				)

    _UpperCAmelCase				:    Dict											=       timm_model(UpperCamelCase__				)
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(UpperCamelCase__	,			outputs.logits	,			atol=1E-3				)

    Path(UpperCamelCase__				).mkdir(exist_ok=UpperCamelCase__				)
    print(F'Saving model {deit_name} to {pytorch_dump_folder_path}'				)
    model.save_pretrained(UpperCamelCase__				)
    print(F'Saving image processor to {pytorch_dump_folder_path}'				)
    image_processor.save_pretrained(UpperCamelCase__				)


if __name__ == "__main__":
  _lowerCAmelCase       :str	=   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--deit_name',
      default='vit_deit_base_distilled_patch16_224',
      type=str,
      help='Name of the DeiT timm model you\'d like to convert.',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
  )

  _lowerCAmelCase       :List[str]	=   parser.parse_args()
  convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)






'''simple docstring'''
# Usage:
# ./gen-card-allenai-wmt16.py

import os
from pathlib import Path




def 	_A  (       A							,A							,A							,A							)  ->				List[Any]:
	lowercase						:		Optional[int]   					=		{
	    "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]
	lowercase						:		Optional[int]   					=		{
	    "wmt16-en-de-dist-12-1": [28.3, 27.52],
	    "wmt16-en-de-dist-6-1": [27.4, 27.11],
	    "wmt16-en-de-12-1": [26.9, 25.75],
	}
	lowercase						:		Dict   					=		F'''{src_lang}-{tgt_lang}'''

	lowercase						:		Union[str, Any]   					=		F'''
---
language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt16
- allenai
license: apache-2.0
datasets:
- wmt16
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of fairseq-based [wmt16 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).

All 3 models are available:

* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)
* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)
* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)


## 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   | fairseq | transformers
-------|---------|----------
{model_name}  | {scores[model_name][0]} | {scores[model_name][1]}

The score is slightly below the score reported in the paper, as the researchers don\'t use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs.

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 wmt16 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt16 -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/wmt16/)
- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)


### 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=A							,exist_ok=A							)
	lowercase						:		str   					=		os.path.join(A							,"README.md"							)
	print(F'''Generating {path}'''							)
	with open(A							,"w"							,encoding="utf-8"							) as f:
		f.write(A							)


# make sure we are under the root of the project
lowerCAmelCase   : Union[str, Any]             =				Path(__file__).resolve().parent.parent.parent
lowerCAmelCase   : Union[str, Any]             =				repo_dir / """model_cards"""

for model_name in ["wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1"]:
					lowerCAmelCase   : Optional[Any]             =				model_cards_dir / """allenai""" / model_name
					write_model_card(model_card_dir, src_lang="""en""", tgt_lang="""de""", model_name=model_name)


