





import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class    A__  (			__snake_case			,  unittest.TestCase     ):
      _UpperCAmelCase      :Optional[Any]		       =  CodeGenTokenizer
      _UpperCAmelCase      :Optional[Any]		       =  CodeGenTokenizerFast
      _UpperCAmelCase      :Tuple		       =  True
      _UpperCAmelCase      :List[Any]		       =  {'add_prefix_space': True}
      _UpperCAmelCase      :Any		       =  False





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       UpperCamelCase      :   List[str]			      =  [
           "l",
           "o",
           "w",
           "e",
           "r",
           "s",
           "t",
           "i",
           "d",
           "n",
           "\u0120",
           "\u0120l",
           "\u0120n",
           "\u0120lo",
           "\u0120low",
           "er",
           "\u0120lowest",
           "\u0120newer",
           "\u0120wider",
           "<unk>",
           "<|endoftext|>",
       ]
       UpperCamelCase      :   Tuple			      =  dict(zip(A_			,				range(len(A_  )  )  )  )
       UpperCamelCase      :   List[Any]			      =  ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
       UpperCamelCase      :   Optional[Any]			      =  {"unk_token": "<unk>"}

       UpperCamelCase      :   Any			      =  os.path.join(self.tmpdirname			,				VOCAB_FILES_NAMES["vocab_file"]  )
       UpperCamelCase      :   Dict			      =  os.path.join(self.tmpdirname			,				VOCAB_FILES_NAMES["merges_file"]  )
       with open(self.vocab_file			,				"w"			,				encoding="utf-8"  ) as fp:
        fp.write(json.dumps(A_  ) + "\n"  )
       with open(self.merges_file			,				"w"			,				encoding="utf-8"  ) as fp:
        fp.write("\n".join(A_  )  )





      def 			__UpperCamelCase(					self			,				**A_  ):
       '''simple docstring'''




       kwargs.update(self.special_tokens_map  )
       return CodeGenTokenizer.from_pretrained(self.tmpdirname			,				**A_  )





      def 			__UpperCamelCase(					self			,				**A_  ):
       '''simple docstring'''




       kwargs.update(self.special_tokens_map  )
       return CodeGenTokenizerFast.from_pretrained(self.tmpdirname			,				**A_  )





      def 			__UpperCamelCase(					self			,				A_  ):
       '''simple docstring'''




       UpperCamelCase      :   Optional[int]			      =  "lower newer"
       UpperCamelCase      :   Optional[int]			      =  "lower newer"
       return input_text, output_text





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Optional[int]			      =  CodeGenTokenizer(self.vocab_file			,				self.merges_file			,				**self.special_tokens_map  )
       UpperCamelCase      :   Optional[Any]			      =  "lower newer"
       UpperCamelCase      :   Dict			      =  ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
       UpperCamelCase      :   List[Any]			      =  tokenizer.tokenize(A_			,				add_prefix_space=A_  )
       self.assertListEqual(A_			,				A_  )

       UpperCamelCase      :   Tuple			      =  tokens + [tokenizer.unk_token]
       UpperCamelCase      :   Optional[int]			      =  [14, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(A_  )			,				A_  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       if not self.test_rust_tokenizer:
        return

       UpperCamelCase      :   str			      =  self.get_tokenizer()
       UpperCamelCase      :   int			      =  self.get_rust_tokenizer(add_prefix_space=A_  )

       UpperCamelCase      :   str			      =  "lower newer"

       # Testing tokenization
       UpperCamelCase      :   int			      =  tokenizer.tokenize(A_			,				add_prefix_space=A_  )
       UpperCamelCase      :   int			      =  rust_tokenizer.tokenize(A_  )
       self.assertListEqual(A_			,				A_  )

       # Testing conversion to ids without special tokens
       UpperCamelCase      :   Optional[int]			      =  tokenizer.encode(A_			,				add_special_tokens=A_			,				add_prefix_space=A_  )
       UpperCamelCase      :   List[str]			      =  rust_tokenizer.encode(A_			,				add_special_tokens=A_  )
       self.assertListEqual(A_			,				A_  )

       # Testing conversion to ids with special tokens
       UpperCamelCase      :   Union[str, Any]			      =  self.get_rust_tokenizer(add_prefix_space=A_  )
       UpperCamelCase      :   List[Any]			      =  tokenizer.encode(A_			,				add_prefix_space=A_  )
       UpperCamelCase      :   Dict			      =  rust_tokenizer.encode(A_  )
       self.assertListEqual(A_			,				A_  )

       # Testing the unknown token
       UpperCamelCase      :   Dict			      =  tokens + [rust_tokenizer.unk_token]
       UpperCamelCase      :   List[Any]			      =  [14, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(A_  )			,				A_  )





      def 			__UpperCamelCase(					self			,				*A_			,				**A_  ):
       '''simple docstring'''




       pass





      def 			__UpperCamelCase(					self			,				A_=15  ):
       '''simple docstring'''




       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
         UpperCamelCase      :   Union[str, Any]			      =  self.rust_tokenizer_class.from_pretrained(A_			,				**A_  )

         # Simple input
         UpperCamelCase      :   Tuple			      =  "This is a simple input"
         UpperCamelCase      :   Optional[Any]			      =  ["This is a simple input 1", "This is a simple input 2"]
         UpperCamelCase      :   List[str]			      =  ("This is a simple input", "This is a pair")
         UpperCamelCase      :   Optional[Any]			      =  [
             ("This is a simple input 1", "This is a simple input 2"),
             ("This is a simple pair 1", "This is a simple pair 2"),
         ]

         # Simple input tests
         self.assertRaises(A_			,				tokenizer_r.encode			,				A_			,				max_length=A_			,				padding="max_length"  )

         # Simple input
         self.assertRaises(A_			,				tokenizer_r.encode_plus			,				A_			,				max_length=A_			,				padding="max_length"  )

         # Simple input
         self.assertRaises(
             A_			,				tokenizer_r.batch_encode_plus			,				A_			,				max_length=A_			,				padding="max_length"			,				)

         # Pair input
         self.assertRaises(A_			,				tokenizer_r.encode			,				A_			,				max_length=A_			,				padding="max_length"  )

         # Pair input
         self.assertRaises(A_			,				tokenizer_r.encode_plus			,				A_			,				max_length=A_			,				padding="max_length"  )

         # Pair input
         self.assertRaises(
             A_			,				tokenizer_r.batch_encode_plus			,				A_			,				max_length=A_			,				padding="max_length"			,				)





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   List[Any]			      =  CodeGenTokenizer.from_pretrained(self.tmpdirname			,				pad_token="<pad>"  )

       # Simple input
       UpperCamelCase      :   Dict			      =  "This is a simple input"
       UpperCamelCase      :   Dict			      =  ["This is a simple input looooooooong", "This is a simple input"]
       UpperCamelCase      :   str			      =  ("This is a simple input", "This is a pair")
       UpperCamelCase      :   List[Any]			      =  [
           ("This is a simple input loooooong", "This is a simple input"),
           ("This is a simple pair loooooong", "This is a simple pair"),
       ]

       UpperCamelCase      :   List[Any]			      =  tokenizer.pad_token_id

       UpperCamelCase      :   int			      =  tokenizer(A_			,				padding="max_length"			,				max_length=30			,				return_tensors="np"  )
       UpperCamelCase      :   Optional[int]			      =  tokenizer(A_			,				padding=A_			,				truncate=A_			,				return_tensors="np"  )
       UpperCamelCase      :   Tuple			      =  tokenizer(*A_			,				padding="max_length"			,				max_length=60			,				return_tensors="np"  )
       UpperCamelCase      :   Dict			      =  tokenizer(A_			,				padding=A_			,				truncate=A_			,				return_tensors="np"  )

       # s
       # test single string max_length padding
       self.assertEqual(out_s["input_ids"].shape[-1]			,				30  )
       self.assertTrue(pad_token_id in out_s["input_ids"]  )
       self.assertTrue(0 in out_s["attention_mask"]  )

       # s2
       # test automatic padding
       self.assertEqual(out_sa["input_ids"].shape[-1]			,				33  )
       # long slice doesn't have padding
       self.assertFalse(pad_token_id in out_sa["input_ids"][0]  )
       self.assertFalse(0 in out_sa["attention_mask"][0]  )
       # short slice does have padding
       self.assertTrue(pad_token_id in out_sa["input_ids"][1]  )
       self.assertTrue(0 in out_sa["attention_mask"][1]  )

       # p
       # test single pair max_length padding
       self.assertEqual(out_p["input_ids"].shape[-1]			,				60  )
       self.assertTrue(pad_token_id in out_p["input_ids"]  )
       self.assertTrue(0 in out_p["attention_mask"]  )

       # p2
       # test automatic padding pair
       self.assertEqual(out_pa["input_ids"].shape[-1]			,				52  )
       # long slice pair doesn't have padding
       self.assertFalse(pad_token_id in out_pa["input_ids"][0]  )
       self.assertFalse(0 in out_pa["attention_mask"][0]  )
       # short slice pair does have padding
       self.assertTrue(pad_token_id in out_pa["input_ids"][1]  )
       self.assertTrue(0 in out_pa["attention_mask"][1]  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   List[str]			      =  "$$$"
       UpperCamelCase      :   Tuple			      =  CodeGenTokenizer.from_pretrained(self.tmpdirname			,				bos_token=A_			,				add_bos_token=A_  )

       UpperCamelCase      :   Optional[int]			      =  "This is a simple input"
       UpperCamelCase      :   Union[str, Any]			      =  ["This is a simple input 1", "This is a simple input 2"]

       UpperCamelCase      :   List[str]			      =  tokenizer.bos_token_id

       UpperCamelCase      :   Dict			      =  tokenizer(A_  )
       UpperCamelCase      :   Union[str, Any]			      =  tokenizer(A_  )

       self.assertEqual(out_s.input_ids[0]			,				A_  )
       self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids  )  )

       UpperCamelCase      :   Optional[int]			      =  tokenizer.decode(out_s.input_ids  )
       UpperCamelCase      :   Optional[Any]			      =  tokenizer.batch_decode(out_sa.input_ids  )

       self.assertEqual(decode_s.split()[0]			,				A_  )
       self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa  )  )





      @slow
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Union[str, Any]			      =  CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono"  )

       UpperCamelCase      :   Optional[int]			      =  "\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#"
       UpperCamelCase      :   List[str]			      =  "\nif len_a > len_b:      result = a\nelse:      result = b"

       UpperCamelCase      :   Optional[int]			      =  tokenizer.encode(A_  )
       UpperCamelCase      :   Union[str, Any]			      =  ["^#", re.escape("<|endoftext|>"  ), "^'''", "^\"\"\"", "\n\n\n"]
       UpperCamelCase      :   int			      =  tokenizer.decode(A_			,				truncate_before_pattern=A_  )
       self.assertEqual(A_			,				A_  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       pass







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

import os
from pathlib import Path



def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase	)		->					str:
 UpperCamelCase      :   Union[str, Any]			      =  {
     "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]
 UpperCamelCase      :   List[str]			      =  {
     "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"],
 }
 UpperCamelCase      :   str			      =  F"""{src_lang}-{tgt_lang}"""

 UpperCamelCase      :   Dict			      =  F"""
---
language: 
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- facebook
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.

For more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).

The abbreviation FSMT stands for FairSeqMachineTranslation

All four models are available:

* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)
* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)
* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)
* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)

## Intended uses & limitations

#### How to use

```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = \"facebook/wmt19-{src_lang}-{tgt_lang}\"
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

- 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)

## Training data

Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).

## Eval results

pair   | fairseq | transformers
-------|---------|----------
{pair}  | {scores[pair][0]} | {scores[pair][1]}

The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support:
- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).
- re-ranking

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=15
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py facebook/wmt19-$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
```
note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.

## Data Sources

- [training, etc.](http://www.statmt.org/wmt19/)
- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)


### BibTeX entry and citation info

```bibtex
@inproceedings{{...,
  year={{2020}},
  title={{Facebook FAIR's WMT19 News Translation Task Submission}},
  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},
  booktitle={{Proc. of WMT}},
}}
```


## TODO

- port model ensemble (fairseq uses 4 model checkpoints)

"""
 os.makedirs(_lowerCAmelCase				,  exist_ok=_lowerCAmelCase	)
 UpperCamelCase      :   Union[str, Any]			      =  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
__lowerCamelCase			:	str					    =	Path(__file__).resolve().parent.parent.parent
__lowerCamelCase			:	Optional[Any]					    =	repo_dir / """model_cards"""

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