
'''simple docstring'''

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

import os
from pathlib import Path


def 		_UpperCAmelCase			(					_lowerCamelCase  :    str					, _lowerCamelCase  :    Optional[int]					, _lowerCamelCase  :    Dict					, _lowerCamelCase  :    Union[str, Any]						)      ->  Dict:
  _lowerCAmelCase    :					str							=   {
      """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]
  _lowerCAmelCase    :					str							=   {
      """wmt19-de-en-6-6-base""": [0, 38.37],
      """wmt19-de-en-6-6-big""": [0, 39.90],
  }
  _lowerCAmelCase    :					List[str]							=   f'{src_lang}-{tgt_lang}'

  _lowerCAmelCase    :					List[str]							=   f'\n---\n\nlanguage:\n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- allenai\nlicense: apache-2.0\ndatasets:\n- wmt19\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of fairseq-based [wmt19 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.\n\nFor more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).\n\n2 models are available:\n\n* [wmt19-de-en-6-6-big](https://huggingface.co/allenai/wmt19-de-en-6-6-big)\n* [wmt19-de-en-6-6-base](https://huggingface.co/allenai/wmt19-de-en-6-6-base)\n\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = "allenai/{model_name}"\ntokenizer = FSMTTokenizer.from_pretrained(mname)\nmodel = FSMTForConditionalGeneration.from_pretrained(mname)\n\ninput = "{texts[src_lang]}"\ninput_ids = tokenizer.encode(input, return_tensors="pt")\noutputs = model.generate(input_ids)\ndecoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\nprint(decoded) # {texts[tgt_lang]}\n\n```\n\n#### Limitations and bias\n\n\n## Training data\n\nPretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).\n\n## Eval results\n\nHere are the BLEU scores:\n\nmodel   |  transformers\n-------|---------\n{model_name}  |  {scores[model_name][1]}\n\nThe score was calculated using this code:\n\n```bash\ngit clone https://github.com/huggingface/transformers\ncd transformers\nexport PAIR={pair}\nexport DATA_DIR=data/$PAIR\nexport SAVE_DIR=data/$PAIR\nexport BS=8\nexport NUM_BEAMS=5\nmkdir -p $DATA_DIR\nsacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target\necho $PAIR\nPYTHONPATH="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\n```\n\n## Data Sources\n\n- [training, etc.](http://www.statmt.org/wmt19/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)\n\n\n### BibTeX entry and citation info\n\n```\n@misc{{kasai2020deep,\n    title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},\n    author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},\n    year={{2020}},\n    eprint={{2006.10369}},\n    archivePrefix={{arXiv}},\n    primaryClass={{cs.CL}}\n}}\n```\n\n'
  model_card_dir.mkdir(parents=_lowerCamelCase					, exist_ok=_lowerCamelCase						)
  _lowerCAmelCase    :					Dict							=   os.path.join(_lowerCamelCase					, """README.md"""						)
  print(f'Generating {path}'						)
  with open(_lowerCamelCase					, """w"""					, encoding="""utf-8"""						) as f:
    f.write(_lowerCamelCase						)


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

for model_name in ["wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big"]:
     UpperCamelCase_            =    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 torch
from clip import load

from transformers import CLIPConfig, CLIPModel


def 		_UpperCAmelCase			(					_lowerCamelCase  :    str					, _lowerCamelCase  :    Dict						)      ->  List[Any]:
  _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase    :					List[Any]							=   pt_attn_layer.in_proj_weight.chunk(3					, dim=0						)
  _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase    :					List[str]							=   pt_attn_layer.in_proj_bias.chunk(3					, dim=0						)

  _lowerCAmelCase    :					Optional[Any]							=   pt_attn_layer.out_proj.weight
  _lowerCAmelCase    :					List[Any]							=   pt_attn_layer.out_proj.bias

  _lowerCAmelCase    :					Union[str, Any]							=   q_proj
  _lowerCAmelCase    :					Optional[Any]							=   q_proj_bias

  _lowerCAmelCase    :					List[Any]							=   k_proj
  _lowerCAmelCase    :					Optional[int]							=   k_proj_bias

  _lowerCAmelCase    :					int							=   v_proj
  _lowerCAmelCase    :					Dict							=   v_proj_bias

  _lowerCAmelCase    :					str							=   out_proj_weights
  _lowerCAmelCase    :					Union[str, Any]							=   out_proj_bias


def 		_UpperCAmelCase			(					_lowerCamelCase  :    List[Any]					, _lowerCamelCase  :    List[Any]						)      ->  Dict:
  copy_linear(hf_mlp.fca					, pt_mlp.c_fc						)
  copy_linear(hf_mlp.fca					, pt_mlp.c_proj						)


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Union[str, Any]					, _lowerCamelCase  :    List[str]						)      ->  Optional[Any]:
  _lowerCAmelCase    :					List[str]							=   pt_linear.weight
  _lowerCAmelCase    :					Union[str, Any]							=   pt_linear.bias


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Optional[Any]					, _lowerCamelCase  :    int						)      ->  Optional[Any]:
  # copy layer norms
  copy_linear(hf_layer.layer_norma					, pt_layer.ln_a						)
  copy_linear(hf_layer.layer_norma					, pt_layer.ln_a						)

  # copy MLP
  copy_mlp(hf_layer.mlp					, pt_layer.mlp						)

  # copy attn
  copy_attn_layer(hf_layer.self_attn					, pt_layer.attn						)


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Optional[int]					, _lowerCamelCase  :    Union[str, Any]						)      ->  Optional[Any]:
  for hf_layer, pt_layer in zip(_lowerCamelCase					, _lowerCamelCase						):
    copy_layer(_lowerCamelCase					, _lowerCamelCase						)


def 		_UpperCAmelCase			(					_lowerCamelCase  :    List[Any]					, _lowerCamelCase  :    Tuple						)      ->  Optional[int]:
  # copy  embeds
  _lowerCAmelCase    :					Optional[int]							=   pt_model.token_embedding.weight
  _lowerCAmelCase    :					List[str]							=   pt_model.positional_embedding

  # copy layer norm
  copy_linear(hf_encoder.final_layer_norm					, pt_model.ln_final						)

  # copy hidden layers
  copy_layers(hf_encoder.encoder.layers					, pt_model.transformer.resblocks						)


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Dict					, _lowerCamelCase  :    Tuple						)      ->  Tuple:
  # copy projection
  _lowerCAmelCase    :					Any							=   pt_model.text_projection.data.T

  # copy text encoder
  copy_encoder(hf_model.text_model					, _lowerCamelCase						)


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Dict					, _lowerCamelCase  :    Union[str, Any]						)      ->  List[Any]:
  # copy projection
  _lowerCAmelCase    :					List[str]							=   pt_model.visual.proj.data.T

  # copy layer norms
  copy_linear(hf_model.vision_model.pre_layrnorm					, pt_model.visual.ln_pre						)
  copy_linear(hf_model.vision_model.post_layernorm					, pt_model.visual.ln_post						)

  # copy embeds
  _lowerCAmelCase    :					List[Any]							=   pt_model.visual.conva.weight.data
  _lowerCAmelCase    :					List[str]							=   pt_model.visual.class_embedding
  _lowerCAmelCase    :					int							=   pt_model.visual.positional_embedding.data

  # copy encoder
  copy_layers(hf_model.vision_model.encoder.layers					, pt_model.visual.transformer.resblocks						)


@torch.no_grad()
def 		_UpperCAmelCase			(					_lowerCamelCase  :    Union[str, Any]					, _lowerCamelCase  :    List[str]					, _lowerCamelCase  :    Any=None						)      ->  int:
  if config_path is not None:
    _lowerCAmelCase    :					Optional[int]							=   CLIPConfig.from_pretrained(_lowerCamelCase						)
  else:
    _lowerCAmelCase    :					int							=   CLIPConfig(projection_dim=5_12					, text_config={}					, vision_config={}						)

  _lowerCAmelCase    :					Optional[int]							=   CLIPModel(_lowerCamelCase						).eval()

  _lowerCAmelCase			,   _lowerCAmelCase    :					List[str]							=   load(_lowerCamelCase					, device="""cpu"""					, jit=_lowerCamelCase						)
  _lowerCAmelCase    :					List[Any]							=   pt_model.eval()

  copy_text_model_and_projection(_lowerCamelCase					, _lowerCamelCase						)
  copy_vison_model_and_projection(_lowerCamelCase					, _lowerCamelCase						)
  _lowerCAmelCase    :					Tuple							=   pt_model.logit_scale

  _lowerCAmelCase    :					List[str]							=   torch.arange(0					, 77						).unsqueeze(0						)
  _lowerCAmelCase    :					int							=   torch.randn(1					, 3					, 2_24					, 2_24						)

  _lowerCAmelCase    :					List[str]							=   hf_model(input_ids=_lowerCamelCase					, pixel_values=_lowerCamelCase					, return_dict=_lowerCamelCase						)
  _lowerCAmelCase    :					Union[str, Any]							=   hf_outputs.logits_per_image
  _lowerCAmelCase    :					List[str]							=   hf_outputs.logits_per_text
  _lowerCAmelCase			,   _lowerCAmelCase    :					Tuple							=   pt_model(_lowerCamelCase					, _lowerCamelCase						)

  assert torch.allclose(_lowerCamelCase					, _lowerCamelCase					, atol=1e-3						)
  assert torch.allclose(_lowerCamelCase					, _lowerCamelCase					, atol=1e-3						)

  hf_model.save_pretrained(_lowerCamelCase						)


if __name__ == "__main__":
     UpperCamelCase_            =    argparse.ArgumentParser()
     parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
     parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
     parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
     UpperCamelCase_            =    parser.parse_args()

     convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)


