



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

import os
from pathlib import Path






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= {
     '''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]
 a__												= {
     '''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'''],
 }
 a__												= f"{src_lang}-{tgt_lang}"

 a__												= f"\n---\nlanguage: \n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- facebook\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 wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.\n\nFor more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).\n\nThe abbreviation FSMT stands for FairSeqMachineTranslation\n\nAll four models are available:\n\n* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)\n* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)\n* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)\n* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = \"facebook/wmt19-{src_lang}-{tgt_lang}\"\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- 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)\n\n## Training data\n\nPretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).\n\n## Eval results\n\npair   | fairseq | transformers\n-------|---------|----------\n{pair}  | {scores[pair][0]} | {scores[pair][1]}\n\nThe score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support:\n- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).\n- re-ranking\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=15\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 $PAIR\nPYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py facebook/wmt19-$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\n```\nnote: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.\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```bibtex\n@inproceedings{{...,\n  year={{2020}},\n  title={{Facebook FAIR's WMT19 News Translation Task Submission}},\n  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},\n  booktitle={{Proc. of WMT}},\n}}\n```\n\n\n## TODO\n\n- port model ensemble (fairseq uses 4 model checkpoints)\n\n"
 os.makedirs(__UpperCAmelCase							,  exist_ok=__UpperCAmelCase       )
 a__												= 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_      :      str    =     Path(__file__).resolve().parent.parent.parent
a_      :      List[Any]    =     repo_dir / 'model_cards'

for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
     a_ ,     a_ ,     a_      :      Any    =     model_name.split('-')
     a_      :      List[str]    =     model_cards_dir / 'facebook' / model_name
     write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)










from __future__ import annotations

from typing import Any
class    __UpperCamelCase			:

 """simple docstring"""



 def __init__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = 0      ) ->			None:
  a__							,			a__												= row, column
  a__												= [[default_value for c in range(SCREAMING_SNAKE_CASE      )] for r in range(SCREAMING_SNAKE_CASE      )]

 def __str__(	self      ) ->			str:
  a__												= f"Matrix consist of {self.row} rows and {self.column} columns\n"

  # Make string identifier
  a__												= 0
  for row_vector in self.array:
   for obj in row_vector:
    a__												= max(SCREAMING_SNAKE_CASE		,				len(str(SCREAMING_SNAKE_CASE      )      )      )
  a__												= f"%{max_element_length}s"

  # Make string and return
  def single_line(SCREAMING_SNAKE_CASE      ) -> str:
   nonlocal string_format_identifier
   a__												= '''['''
   line += ", ".join(string_format_identifier % (obj,) for obj in row_vector      )
   line += "]"
   return line

  s += "\n".join(single_line(SCREAMING_SNAKE_CASE      ) for row_vector in self.array      )
  return s

 def __repr__(	self      ) ->			str:
  return str(self      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			bool:
  if not (isinstance(SCREAMING_SNAKE_CASE		,				(list, tuple)      ) and len(SCREAMING_SNAKE_CASE      ) == 2):
   return False
  elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
   return False
  else:
   return True

 def __getitem__(	self		,				SCREAMING_SNAKE_CASE      ) ->			Any:
  assert self.validate_indicies(SCREAMING_SNAKE_CASE      )
  return self.array[loc[0]][loc[1]]

 def __setitem__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			None:
  assert self.validate_indicies(SCREAMING_SNAKE_CASE      )
  a__												= value

 def __add__(	self		,				SCREAMING_SNAKE_CASE      ) ->			Matrix:
  assert isinstance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
  assert self.row == another.row and self.column == another.column

  # Add
  a__												= Matrix(self.row		,				self.column      )
  for r in range(self.row      ):
   for c in range(self.column      ):
    a__												= self[r, c] + another[r, c]
  return result

 def __neg__(	self      ) ->			Matrix:
  a__												= Matrix(self.row		,				self.column      )
  for r in range(self.row      ):
   for c in range(self.column      ):
    a__												= -self[r, c]
  return result

 def __sub__(	self		,				SCREAMING_SNAKE_CASE      ) ->			Matrix:
  return self + (-another)

 def __mul__(	self		,				SCREAMING_SNAKE_CASE      ) ->			Matrix:

  if isinstance(SCREAMING_SNAKE_CASE		,				(int, float)      ):  # Scalar multiplication
   a__												= Matrix(self.row		,				self.column      )
   for r in range(self.row      ):
    for c in range(self.column      ):
     a__												= self[r, c] * another
   return result
  elif isinstance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ):  # Matrix multiplication
   assert self.column == another.row
   a__												= Matrix(self.row		,				another.column      )
   for r in range(self.row      ):
    for c in range(another.column      ):
     for i in range(self.column      ):
      result[r, c] += self[r, i] * another[i, c]
   return result
  else:
   a__												= f"Unsupported type given for another ({type(SCREAMING_SNAKE_CASE      )})"
   raise TypeError(SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self      ) ->			Matrix:
  a__												= Matrix(self.column		,				self.row      )
  for r in range(self.row      ):
   for c in range(self.column      ):
    a__												= self[r, c]
  return result



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			Any:
  assert isinstance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) and isinstance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
  assert self.row == self.column == u.row == v.row  # u, v should be column vector
  assert u.column == v.column == 1  # u, v should be column vector

  # Calculate
  a__												= v.transpose()
  a__												= (v_t * self * u)[0, 0] + 1
  if numerator_factor == 0:
   return None  # It's not invertable
  return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":






     def     __a					(		):
      # a^(-1)
      a__												= Matrix(3							,  3							,  0       )
      for i in range(3       ):
       a__												= 1
      print(f"a^(-1) is {ainv}"       )
      # u, v
      a__												= Matrix(3							,  1							,  0       )
      a__							,			a__							,			a__												= 1, 2, -3
      a__												= Matrix(3							,  1							,  0       )
      a__							,			a__							,			a__												= 4, -2, 5
      print(f"u is {u}"       )
      print(f"v is {v}"       )
      print(f"uv^T is {u * v.transpose()}"       )
      # Sherman Morrison
      print(f"(a + uv^T)^(-1) is {ainv.sherman_morrison(__UpperCAmelCase							,  __UpperCAmelCase       )}"       )






     def     __a					(		):
      import doctest

      doctest.testmod()

     testa()





