




'''simple docstring'''







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

import os
from pathlib import Path





def 						snake_case_							(_a     :							Union[str, Any]   ,       _a     :							List[str]   ,       _a     :							Any   ,       _a     :							Tuple					):
    UpperCAmelCase      	=		{
        '''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      	=		{
        '''wmt19-de-en-6-6-base''': [0, 38.37],
        '''wmt19-de-en-6-6-big''': [0, 39.90],
    }
    UpperCAmelCase      	=		F"{src_lang}-{tgt_lang}"

    UpperCAmelCase      	=		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=_a   ,       exist_ok=_a					)
    UpperCAmelCase      	=		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
A			   =Path(__file__).resolve().parent.parent.parent
A			   =repo_dir / 'model_cards'

for model_name in ["wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big"]:
   A			   =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 collections
from pathlib import Path

import torch
from torch.serialization import default_restore_location

from transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader


A			   =collections.namedtuple(
    'CheckpointState', ['model_dict', 'optimizer_dict', 'scheduler_dict', 'offset', 'epoch', 'encoder_params']
)





def 						snake_case_							(_a     :							str					):
    print(F"Reading saved model from {model_file}"					)
    UpperCAmelCase      	=		torch.load(_a   ,       map_location=lambda _a   ,       _a					: default_restore_location(_a   ,       '''cpu'''					)					)
    return CheckpointState(**_a					)


class       _a  :
       def __init__(     self						:     List[Any]       ,    lowercase						:     Path    ):



           '''simple docstring'''

           UpperCAmelCase      	=		src_file
       def    A				(     self						:     str    ):



           '''simple docstring'''

           raise NotImplementedError







       @staticmethod
       def    A				(     lowercase						:     str       ,    *lowercase						:     Tuple       ,    **lowercase						:     Union[str, Any]    ):



           '''simple docstring'''

           if comp_type.startswith('''c'''    ):
               return DPRContextEncoderState(*lowercase       ,    **lowercase    )
           if comp_type.startswith('''q'''    ):
               return DPRQuestionEncoderState(*lowercase       ,    **lowercase    )
           if comp_type.startswith('''r'''    ):
               return DPRReaderState(*lowercase       ,    **lowercase    )
           else:
               raise ValueError('''Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'.'''    )






class       _a  (     __a			):
       def    A				(     self						:     int    ):



           '''simple docstring'''

           UpperCAmelCase      	=		DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''    )[0]    )    )
           print(f"Loading DPR biencoder from {self.src_file}"    )
           UpperCAmelCase      	=		load_states_from_checkpoint(self.src_file    )
           UpperCAmelCase	,					UpperCAmelCase      	=		model.ctx_encoder, '''ctx_model.'''
           # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
           UpperCAmelCase      	=		{'''bert_model.embeddings.position_ids''': model.ctx_encoder.bert_model.embeddings.position_ids}
           for key, value in saved_state.model_dict.items():
               if key.startswith(lowercase    ):
                   UpperCAmelCase      	=		key[len(lowercase    ) :]
                   if not key.startswith('''encode_proj.'''    ):
                       UpperCAmelCase      	=		'''bert_model.''' + key
                   UpperCAmelCase      	=		value
           encoder.load_state_dict(lowercase    )
           return model






class       _a  (     __a			):
       def    A				(     self						:     int    ):



           '''simple docstring'''

           UpperCAmelCase      	=		DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''    )[0]    )    )
           print(f"Loading DPR biencoder from {self.src_file}"    )
           UpperCAmelCase      	=		load_states_from_checkpoint(self.src_file    )
           UpperCAmelCase	,					UpperCAmelCase      	=		model.question_encoder, '''question_model.'''
           # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
           UpperCAmelCase      	=		{'''bert_model.embeddings.position_ids''': model.question_encoder.bert_model.embeddings.position_ids}
           for key, value in saved_state.model_dict.items():
               if key.startswith(lowercase    ):
                   UpperCAmelCase      	=		key[len(lowercase    ) :]
                   if not key.startswith('''encode_proj.'''    ):
                       UpperCAmelCase      	=		'''bert_model.''' + key
                   UpperCAmelCase      	=		value
           encoder.load_state_dict(lowercase    )
           return model






class       _a  (     __a			):
       def    A				(     self						:     int    ):



           '''simple docstring'''

           UpperCAmelCase      	=		DPRReader(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''    )[0]    )    )
           print(f"Loading DPR reader from {self.src_file}"    )
           UpperCAmelCase      	=		load_states_from_checkpoint(self.src_file    )
           # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
           UpperCAmelCase      	=		{
               '''encoder.bert_model.embeddings.position_ids''': model.span_predictor.encoder.bert_model.embeddings.position_ids
           }
           for key, value in saved_state.model_dict.items():
               if key.startswith('''encoder.'''    ) and not key.startswith('''encoder.encode_proj'''    ):
                   UpperCAmelCase      	=		'''encoder.bert_model.''' + key[len('''encoder.'''    ) :]
               UpperCAmelCase      	=		value
           model.span_predictor.load_state_dict(lowercase    )
           return model






def 						snake_case_							(_a     :							str   ,       _a     :							Path   ,       _a     :							Path					):
    UpperCAmelCase      	=		Path(_a					)
    dest_dir.mkdir(exist_ok=_a					)

    UpperCAmelCase      	=		DPRState.from_type(_a   ,       src_file=_a					)
    UpperCAmelCase      	=		dpr_state.load_dpr_model()
    model.save_pretrained(_a					)
    model.from_pretrained(_a					)  # sanity check


if __name__ == "__main__":
   A			   =argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--type', type=str, help='Type of the component to convert: \'ctx_encoder\', \'question_encoder\' or \'reader\'.'
   )
   parser.add_argument(
       '--src',
       type=str,
       help=(
           'Path to the dpr checkpoint file. They can be downloaded from the official DPR repo'
           ' https://github.com/facebookresearch/DPR. Note that in the official repo, both encoders are stored in the'
           ' \'retriever\' checkpoints.'
       ),
   )
   parser.add_argument('--dest', type=str, default=None, help='Path to the output PyTorch model directory.')
   A			   =parser.parse_args()

   A			   =Path(args.src)
   A			   =f"""converted-{src_file.name}""" if args.dest is None else args.dest
   A			   =Path(dest_dir)
   assert src_file.exists()
   assert (
       args.type is not None
   ), "Please specify the component type of the DPR model to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
   convert(args.type, src_file, dest_dir)




