





'''simple docstring'''






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 (					__lowercase						,					unittest.TestCase		):
		lowercase__:   Optional[int]				     =   CodeGenTokenizer
		lowercase__:   str				     =   CodeGenTokenizerFast
		lowercase__:   Dict				     =   True
		lowercase__:   List[str]				     =   {'''add_prefix_space''': True}
		lowercase__:   Optional[int]				     =   False

		def 	lowercase__     (   self					:   Dict			)					->    Any:


					"""simple docstring"""

					super().setUp()

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

					__snake_case					:      Dict							      =     os.path.join(self.tmpdirname  ,      VOCAB_FILES_NAMES["""vocab_file"""]			)
					__snake_case					:      Tuple							      =     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(__magic_name__			) + """\n"""			)
					with open(self.merges_file  ,      """w"""  ,      encoding="""utf-8"""			) as fp:
								fp.write("""\n""".join(__magic_name__			)			)

		def 	lowercase__     (   self					:   Dict  ,      **__magic_name__					:   Tuple			)					->    List[Any]:


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   Any  ,      **__magic_name__					:   Union[str, Any]			)					->    List[Any]:


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   int  ,      __magic_name__					:   Union[str, Any]			)					->    List[Any]:


					"""simple docstring"""

					__snake_case					:      int							      =     """lower newer"""
					__snake_case					:      Any							      =     """lower newer"""
					return input_text, output_text

		def 	lowercase__     (   self					:   List[str]			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     CodeGenTokenizer(self.vocab_file  ,      self.merges_file  ,      **self.special_tokens_map			)
					__snake_case					:      List[Any]							      =     """lower newer"""
					__snake_case					:      int							      =     ["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
					__snake_case					:      List[str]							      =     tokenizer.tokenize(__magic_name__  ,      add_prefix_space=__magic_name__			)
					self.assertListEqual(__magic_name__  ,      __magic_name__			)

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

		def 	lowercase__     (   self					:   Union[str, Any]			)					->    Union[str, Any]:


					"""simple docstring"""

					if not self.test_rust_tokenizer:
								return

					__snake_case					:      str							      =     self.get_tokenizer()
					__snake_case					:      int							      =     self.get_rust_tokenizer(add_prefix_space=__magic_name__			)

					__snake_case					:      List[Any]							      =     """lower newer"""

					# Testing tokenization
					__snake_case					:      int							      =     tokenizer.tokenize(__magic_name__  ,      add_prefix_space=__magic_name__			)
					__snake_case					:      Dict							      =     rust_tokenizer.tokenize(__magic_name__			)
					self.assertListEqual(__magic_name__  ,      __magic_name__			)

					# Testing conversion to ids without special tokens
					__snake_case					:      str							      =     tokenizer.encode(__magic_name__  ,      add_special_tokens=__magic_name__  ,      add_prefix_space=__magic_name__			)
					__snake_case					:      Any							      =     rust_tokenizer.encode(__magic_name__  ,      add_special_tokens=__magic_name__			)
					self.assertListEqual(__magic_name__  ,      __magic_name__			)

					# Testing conversion to ids with special tokens
					__snake_case					:      Optional[int]							      =     self.get_rust_tokenizer(add_prefix_space=__magic_name__			)
					__snake_case					:      List[str]							      =     tokenizer.encode(__magic_name__  ,      add_prefix_space=__magic_name__			)
					__snake_case					:      Union[str, Any]							      =     rust_tokenizer.encode(__magic_name__			)
					self.assertListEqual(__magic_name__  ,      __magic_name__			)

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

		def 	lowercase__     (   self					:   Any  ,      *__magic_name__					:   str  ,      **__magic_name__					:   int			)					->    int:


					"""simple docstring"""

					pass

		def 	lowercase__     (   self					:   Union[str, Any]  ,      __magic_name__					:   Tuple=15			)					->    Any:


					"""simple docstring"""

					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
								with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''			):
											__snake_case					:      Any							      =     self.rust_tokenizer_class.from_pretrained(__magic_name__  ,      **__magic_name__			)

											# Simple input
											__snake_case					:      Any							      =     """This is a simple input"""
											__snake_case					:      int							      =     ["""This is a simple input 1""", """This is a simple input 2"""]
											__snake_case					:      int							      =     ("""This is a simple input""", """This is a pair""")
											__snake_case					:      Optional[int]							      =     [
											    ("""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(__magic_name__  ,      tokenizer_r.encode  ,      __magic_name__  ,      max_length=__magic_name__  ,      padding="""max_length"""			)

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

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

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

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

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

		def 	lowercase__     (   self					:   Optional[Any]			)					->    Union[str, Any]:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     CodeGenTokenizer.from_pretrained(self.tmpdirname  ,      pad_token="""<pad>"""			)

					# Simple input
					__snake_case					:      Tuple							      =     """This is a simple input"""
					__snake_case					:      Union[str, Any]							      =     ["""This is a simple input looooooooong""", """This is a simple input"""]
					__snake_case					:      int							      =     ("""This is a simple input""", """This is a pair""")
					__snake_case					:      str							      =     [
					    ("""This is a simple input loooooong""", """This is a simple input"""),
					    ("""This is a simple pair loooooong""", """This is a simple pair"""),
					]

					__snake_case					:      Any							      =     tokenizer.pad_token_id

					__snake_case					:      List[str]							      =     tokenizer(__magic_name__  ,      padding="""max_length"""  ,      max_length=30  ,      return_tensors="""np"""			)
					__snake_case					:      List[Any]							      =     tokenizer(__magic_name__  ,      padding=__magic_name__  ,      truncate=__magic_name__  ,      return_tensors="""np"""			)
					__snake_case					:      Optional[Any]							      =     tokenizer(*__magic_name__  ,      padding="""max_length"""  ,      max_length=60  ,      return_tensors="""np"""			)
					__snake_case					:      Optional[Any]							      =     tokenizer(__magic_name__  ,      padding=__magic_name__  ,      truncate=__magic_name__  ,      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 	lowercase__     (   self					:   Any			)					->    Dict:


					"""simple docstring"""

					__snake_case					:      Optional[Any]							      =     """$$$"""
					__snake_case					:      Union[str, Any]							      =     CodeGenTokenizer.from_pretrained(self.tmpdirname  ,      bos_token=__magic_name__  ,      add_bos_token=__magic_name__			)

					__snake_case					:      Tuple							      =     """This is a simple input"""
					__snake_case					:      Dict							      =     ["""This is a simple input 1""", """This is a simple input 2"""]

					__snake_case					:      int							      =     tokenizer.bos_token_id

					__snake_case					:      List[Any]							      =     tokenizer(__magic_name__			)
					__snake_case					:      str							      =     tokenizer(__magic_name__			)

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

					__snake_case					:      str							      =     tokenizer.decode(out_s.input_ids			)
					__snake_case					:      Tuple							      =     tokenizer.batch_decode(out_sa.input_ids			)

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

		@slow
		def 	lowercase__     (   self					:   Union[str, Any]			)					->    int:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     CodeGenTokenizer.from_pretrained("""Salesforce/codegen-350M-mono"""			)

					__snake_case					:      Dict							      =     """\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#"""
					__snake_case					:      Any							      =     """\nif len_a > len_b:      result = a\nelse:      result = b"""

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

		def 	lowercase__     (   self					:   Union[str, Any]			)					->    Union[str, Any]:


					"""simple docstring"""

					pass






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

import os
from pathlib import Path




def 	_A  (       A							,A							,A							,A							)  ->				List[Any]:
	lowercase						:		Optional[int]   					=		{
	    "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]
	lowercase						:		Optional[int]   					=		{
	    "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],
	}
	lowercase						:		Dict   					=		F'''{src_lang}-{tgt_lang}'''

	lowercase						:		Union[str, Any]   					=		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=A							,exist_ok=A							)
	lowercase						:		str   					=		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
lowerCAmelCase   : Union[str, Any]             =				Path(__file__).resolve().parent.parent.parent
lowerCAmelCase   : Union[str, Any]             =				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"]:
					lowerCAmelCase   : Optional[Any]             =				model_cards_dir / """allenai""" / model_name
					write_model_card(model_card_dir, src_lang="""en""", tgt_lang="""de""", model_name=model_name)


