




import logging
import os
from typing import List, Tuple

import numpy as np
import psutil
import torch
import torch.distributed as dist

from transformers import RagRetriever


UpperCAmelCase_ 		=      logging.getLogger(__name__)





class    __lowercase						(  __magic_name__ ):







   def __init__(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase=None      )				->    Tuple:
     super().__init__(
         UpperCamelCase      ,							question_encoder_tokenizer=UpperCamelCase      ,							generator_tokenizer=UpperCamelCase      ,							index=UpperCamelCase      ,							init_retrieval=UpperCamelCase      ,							)
     __a			=					None







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    Any:
     logger.info('initializing retrieval'      )

     # initializing a separate process group for retrieval as the default
     # nccl backend doesn't support gather/scatter operations while gloo
     # is too slow to replace nccl for the core gpu communication
     if dist.is_initialized():
       logger.info('dist initialized'      )
       # needs to be set manually
       __a			=					self._infer_socket_ifname()
       # avoid clash with the NCCL port
       __a			=					str(distributed_port + 1      )
       __a			=					dist.new_group(ranks=UpperCamelCase      ,							backend='gloo'      )

     # initialize retriever only on the main worker
     if not dist.is_initialized() or self._is_main():
       logger.info('dist not initialized / main'      )
       self.index.init_index()

     # all processes wait untill the retriever is initialized by the main process
     if dist.is_initialized():
       torch.distributed.barrier(group=self.process_group      )







   def 		UpperCamelCase__			(	self      )				->    Tuple:
     return dist.get_rank(group=self.process_group      ) == 0







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase=torch.floataa      )				->    List[str]:
     __a			=					torch.empty(UpperCamelCase      ,							dtype=UpperCamelCase      )
     dist.scatter(UpperCamelCase      ,							src=0      ,							scatter_list=UpperCamelCase      ,							group=self.process_group      )
     return target_tensor







   def 		UpperCamelCase__			(	self      )				->    str:
     __a			=					psutil.net_if_addrs()
     # a hacky way to deal with varying network interface names
     __a			=					next((addr for addr in addrs if addr.startswith('e'      ))      ,							UpperCamelCase      )
     return ifname







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      )				->    Tuple[np.ndarray, List[dict]]:

     # single GPU training
     if not dist.is_initialized():
       __a	,       __a			=					self._main_retrieve(UpperCamelCase      ,							UpperCamelCase      )
       return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(UpperCamelCase      )

     # distributed training
     __a			=					dist.get_world_size(group=self.process_group      )

     # gather logic
     __a			=					None
     if self._is_main():
       __a			=					[torch.empty(question_hidden_states.shape      ,							dtype=torch.floataa      ) for _ in range(UpperCamelCase      )]
     dist.gather(torch.tensor(UpperCamelCase      )      ,							dst=0      ,							gather_list=UpperCamelCase      ,							group=self.process_group      )

     # scatter logic
     __a			=					question_hidden_states.shape[0]
     __a			=					[]
     __a			=					[]
     if self._is_main():
       assert len(UpperCamelCase      ) == world_size
       __a	,       __a			=					self._main_retrieve(torch.cat(UpperCamelCase      ).numpy()      ,							UpperCamelCase      )
       __a	,       __a			=					torch.tensor(UpperCamelCase      ), torch.tensor(UpperCamelCase      )
       __a			=					self._chunk_tensor(UpperCamelCase      ,							UpperCamelCase      )
       __a			=					self._chunk_tensor(UpperCamelCase      ,							UpperCamelCase      )
     __a			=					self._scattered(UpperCamelCase      ,							[n_queries, n_docs]      ,							target_type=torch.intaa      )
     __a			=					self._scattered(UpperCamelCase      ,							[n_queries, n_docs, question_hidden_states.shape[1]]      )

     return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(UpperCamelCase      )









'''simple docstring'''


# Usage:
# ./gen-card-allenai-wmt16.py

import os
from pathlib import Path


def 						SCREAMING_SNAKE_CASE       (      a_	:							Optional[Any]				,					a_	:							int				,					a_	:							Tuple				,					a_	:							Tuple			):
  __a			=					{
      '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]
  __a			=					{
      '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],
  }
  __a			=					f"{src_lang}-{tgt_lang}"

  __a			=					f"\n---\nlanguage:\n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt16\n- allenai\nlicense: apache-2.0\ndatasets:\n- wmt16\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of fairseq-based [wmt16 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\nAll 3 models are available:\n\n* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)\n* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)\n* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)\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   | fairseq | transformers\n-------|---------|----------\n{model_name}  | {scores[model_name][0]} | {scores[model_name][1]}\n\nThe 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.\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 wmt16 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt16 -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/wmt16/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)\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_			)
  __a			=					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
UpperCAmelCase_ 		=      Path(__file__).resolve().parent.parent.parent
UpperCAmelCase_ 		=      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"]:
  UpperCAmelCase_ 		=      model_cards_dir / "allenai" / model_name
  write_model_card(model_card_dir, src_lang="en", tgt_lang="de", model_name=model_name)



