






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 		_snake_case	(	A__ ,     unittest.TestCase       ):
			_lowercase		:     Tuple        =				CodeGenTokenizer
			_lowercase		:     Tuple        =				CodeGenTokenizerFast
			_lowercase		:     Optional[int]        =				True
			_lowercase		:     List[str]        =				{'''add_prefix_space''': True}
			_lowercase		:     Optional[int]        =				False






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Optional[Any]:
						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						SCREAMING_SNAKE_CASE							=			[
						    'l',
						    'o',
						    'w',
						    'e',
						    'r',
						    's',
						    't',
						    'i',
						    'd',
						    'n',
						    '\u0120',
						    '\u0120l',
						    '\u0120n',
						    '\u0120lo',
						    '\u0120low',
						    'er',
						    '\u0120lowest',
						    '\u0120newer',
						    '\u0120wider',
						    '<unk>',
						    '<|endoftext|>',
						]
						SCREAMING_SNAKE_CASE							=			dict(zip(a		,       range(len(a))))
						SCREAMING_SNAKE_CASE							=			['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
						SCREAMING_SNAKE_CASE							=			{'unk_token': '<unk>'}

						SCREAMING_SNAKE_CASE							=			os.path.join(self.tmpdirname		,       VOCAB_FILES_NAMES['vocab_file'])
						SCREAMING_SNAKE_CASE							=			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  SCREAMING_SNAKE_CASE__     (							self		,       **a)   ->							Any:
						kwargs.update(self.special_tokens_map)
						return CodeGenTokenizer.from_pretrained(self.tmpdirname		,       **a)






			def  SCREAMING_SNAKE_CASE__     (							self		,       **a)   ->							Optional[Any]:
						kwargs.update(self.special_tokens_map)
						return CodeGenTokenizerFast.from_pretrained(self.tmpdirname		,       **a)






			def  SCREAMING_SNAKE_CASE__     (							self		,       a)   ->							Tuple:
						SCREAMING_SNAKE_CASE							=			'lower newer'
						SCREAMING_SNAKE_CASE							=			'lower newer'
						return input_text, output_text






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							int:
						SCREAMING_SNAKE_CASE							=			CodeGenTokenizer(self.vocab_file		,       self.merges_file		,       **self.special_tokens_map)
						SCREAMING_SNAKE_CASE							=			'lower newer'
						SCREAMING_SNAKE_CASE							=			['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
						SCREAMING_SNAKE_CASE							=			tokenizer.tokenize(a		,       add_prefix_space=a)
						self.assertListEqual(a		,       a)

						SCREAMING_SNAKE_CASE							=			tokens + [tokenizer.unk_token]
						SCREAMING_SNAKE_CASE							=			[14, 15, 10, 9, 3, 2, 15, 19]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(a)		,       a)






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Tuple:
						if not self.test_rust_tokenizer:
									return

						SCREAMING_SNAKE_CASE							=			self.get_tokenizer()
						SCREAMING_SNAKE_CASE							=			self.get_rust_tokenizer(add_prefix_space=a)

						SCREAMING_SNAKE_CASE							=			'lower newer'

						# Testing tokenization
						SCREAMING_SNAKE_CASE							=			tokenizer.tokenize(a		,       add_prefix_space=a)
						SCREAMING_SNAKE_CASE							=			rust_tokenizer.tokenize(a)
						self.assertListEqual(a		,       a)

						# Testing conversion to ids without special tokens
						SCREAMING_SNAKE_CASE							=			tokenizer.encode(a		,       add_special_tokens=a		,       add_prefix_space=a)
						SCREAMING_SNAKE_CASE							=			rust_tokenizer.encode(a		,       add_special_tokens=a)
						self.assertListEqual(a		,       a)

						# Testing conversion to ids with special tokens
						SCREAMING_SNAKE_CASE							=			self.get_rust_tokenizer(add_prefix_space=a)
						SCREAMING_SNAKE_CASE							=			tokenizer.encode(a		,       add_prefix_space=a)
						SCREAMING_SNAKE_CASE							=			rust_tokenizer.encode(a)
						self.assertListEqual(a		,       a)

						# Testing the unknown token
						SCREAMING_SNAKE_CASE							=			tokens + [rust_tokenizer.unk_token]
						SCREAMING_SNAKE_CASE							=			[14, 15, 10, 9, 3, 2, 15, 19]
						self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(a)		,       a)






			def  SCREAMING_SNAKE_CASE__     (							self		,       *a		,       **a)   ->							str:
						# It's very difficult to mix/test pretokenization with byte-level
						# And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
						pass






			def  SCREAMING_SNAKE_CASE__     (							self		,       a=15)   ->							List[str]:
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''):
												SCREAMING_SNAKE_CASE							=			self.rust_tokenizer_class.from_pretrained(a		,       **a)

												# Simple input
												SCREAMING_SNAKE_CASE							=			'This is a simple input'
												SCREAMING_SNAKE_CASE							=			['This is a simple input 1', 'This is a simple input 2']
												SCREAMING_SNAKE_CASE							=			('This is a simple input', 'This is a pair')
												SCREAMING_SNAKE_CASE							=			[
												    ('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  SCREAMING_SNAKE_CASE__     (							self)   ->							Union[str, Any]:
						SCREAMING_SNAKE_CASE							=			CodeGenTokenizer.from_pretrained(self.tmpdirname		,       pad_token='<pad>')

						# Simple input
						SCREAMING_SNAKE_CASE							=			'This is a simple input'
						SCREAMING_SNAKE_CASE							=			['This is a simple input looooooooong', 'This is a simple input']
						SCREAMING_SNAKE_CASE							=			('This is a simple input', 'This is a pair')
						SCREAMING_SNAKE_CASE							=			[
						    ('This is a simple input loooooong', 'This is a simple input'),
						    ('This is a simple pair loooooong', 'This is a simple pair'),
						]

						SCREAMING_SNAKE_CASE							=			tokenizer.pad_token_id

						SCREAMING_SNAKE_CASE							=			tokenizer(a		,       padding='max_length'		,       max_length=30		,       return_tensors='np')
						SCREAMING_SNAKE_CASE							=			tokenizer(a		,       padding=a		,       truncate=a		,       return_tensors='np')
						SCREAMING_SNAKE_CASE							=			tokenizer(*a		,       padding='max_length'		,       max_length=60		,       return_tensors='np')
						SCREAMING_SNAKE_CASE							=			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  SCREAMING_SNAKE_CASE__     (							self)   ->							Any:
						SCREAMING_SNAKE_CASE							=			'$$$'
						SCREAMING_SNAKE_CASE							=			CodeGenTokenizer.from_pretrained(self.tmpdirname		,       bos_token=a		,       add_bos_token=a)

						SCREAMING_SNAKE_CASE							=			'This is a simple input'
						SCREAMING_SNAKE_CASE							=			['This is a simple input 1', 'This is a simple input 2']

						SCREAMING_SNAKE_CASE							=			tokenizer.bos_token_id

						SCREAMING_SNAKE_CASE							=			tokenizer(a)
						SCREAMING_SNAKE_CASE							=			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))

						SCREAMING_SNAKE_CASE							=			tokenizer.decode(out_s.input_ids)
						SCREAMING_SNAKE_CASE							=			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  SCREAMING_SNAKE_CASE__     (							self)   ->							int:
						SCREAMING_SNAKE_CASE							=			CodeGenTokenizer.from_pretrained('Salesforce/codegen-350M-mono')

						SCREAMING_SNAKE_CASE							=			'\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#'
						SCREAMING_SNAKE_CASE							=			'\nif len_a > len_b:      result = a\nelse:      result = b'

						SCREAMING_SNAKE_CASE							=			tokenizer.encode(a)
						SCREAMING_SNAKE_CASE							=			['^#', re.escape('<|endoftext|>'), '^\'\'\'', '^"""', '\n\n\n']
						SCREAMING_SNAKE_CASE							=			tokenizer.decode(a		,       truncate_before_pattern=a)
						self.assertEqual(a		,       a)







			def  SCREAMING_SNAKE_CASE__     (							self)   ->							str:
						pass






"""simple docstring"""
# Usage:
# ./gen-card-allenai-wmt16.py

import os
from pathlib import Path

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						__lowerCAmelCase     =  {
						    """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     =  {
						    """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],
						}
						__lowerCAmelCase     =  f"""{src_lang}-{tgt_lang}"""

						__lowerCAmelCase     =  f"""
---
language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt16
- allenai
license: apache-2.0
datasets:
- wmt16
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.

For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).

All 3 models are available:

* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)
* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)
* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)


## Intended uses & limitations

#### How to use

```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = \"allenai/{model_name}\"
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


## Training data

Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).

## Eval results

Here are the BLEU scores:

model   | fairseq | transformers
-------|---------|----------
{model_name}  | {scores[model_name][0]} | {scores[model_name][1]}

The 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.

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=5
mkdir -p $DATA_DIR
sacrebleu -t wmt16 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt16 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH=\"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
```

## Data Sources

- [training, etc.](http://www.statmt.org/wmt16/)
- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)


### BibTeX entry and citation info

```
@misc{{kasai2020deep,
    title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},
    author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},
    year={{2020}},
    eprint={{2006.10369}},
    archivePrefix={{arXiv}},
    primaryClass={{cs.CL}}
}}
```

"""
						model_card_dir.mkdir(parents=_lowerCAmelCase ,    exist_ok=_lowerCAmelCase						)
						__lowerCAmelCase     =  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
SCREAMING_SNAKE_CASE_					     =	Path(__file__).resolve().parent.parent.parent
SCREAMING_SNAKE_CASE_					     =	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"]:
			SCREAMING_SNAKE_CASE_					     =	model_cards_dir / '''allenai''' / model_name
			write_model_card(model_card_dir, src_lang='''en''', tgt_lang='''de''', model_name=model_name)
