






'''simple docstring'''

import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A:      Dict  				=				"▁"

A:      Any  				=				{"vocab_file": "spiece.model"}

A:      List[Any]  				=				{
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

A:      Tuple  				=				{
    "google/pegasus-xsum": 5_1_2,
}


A:      Optional[int]  				=				logging.get_logger(__name__)






class 							__magic_name__     (	UpperCamelCase__				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      Optional[Any]        =  VOCAB_FILES_NAMES

	SCREAMING_SNAKE_CASE_					:      str        =  VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE_					:      Union[str, Any]        =  PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE_					:      str        =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE_					:      List[Any]        =  ['input_ids', 'attention_mask']
	def __init__(							self  ,      _lowercase  ,      _lowercase="<pad>"  ,      _lowercase="</s>"  ,      _lowercase="<unk>"  ,      _lowercase="<mask_2>"  ,      _lowercase="<mask_1>"  ,      _lowercase=None  ,      _lowercase=103  ,      _lowercase = None  ,      **_lowercase  ,      )						->  None:
							lowercase_      :   Any        =			offset
							if additional_special_tokens is not None:
													if not isinstance(__A  ,      __A       ):
																			raise TypeError(
																			    f"additional_special_tokens should be of type {type(__A       )}, but is"
																			    f" {type(__A       )}"       )

													lowercase_      :   List[str]        =			(
													    ([mask_token_sent] + additional_special_tokens)
													    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
													    else additional_special_tokens
													)
													# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
													additional_special_tokens_extended += [
													    f"<unk_{i}>" for i in range(len(__A       )  ,      self.offset - 1       )
													]

													if len(set(__A       )       ) != len(__A       ):
																			raise ValueError(
																			    'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
																			    f" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."       )
													lowercase_      :   Optional[int]        =			additional_special_tokens_extended
							else:
													lowercase_      :   Optional[int]        =			[mask_token_sent] if mask_token_sent is not None else []
													additional_special_tokens += [f"<unk_{i}>" for i in range(2  ,      self.offset       )]

							lowercase_      :   List[str]        =			{} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    eos_token=__A  ,      unk_token=__A  ,      mask_token=__A  ,      pad_token=__A  ,      mask_token_sent=__A  ,      offset=__A  ,      additional_special_tokens=__A  ,      sp_model_kwargs=self.sp_model_kwargs  ,      **__A  ,      )
							lowercase_      :   Optional[Any]        =			mask_token_sent
							lowercase_      :   str        =			vocab_file
							lowercase_      :   List[Any]        =			spm.SentencePieceProcessor(**self.sp_model_kwargs       )
							self.sp_model.Load(__A       )

							# add special tokens to encoder dict
							lowercase_      :   List[Any]        =			{
							    0: self.pad_token,
							    1: self.eos_token,
							}

							if self.mask_token_sent is not None:
													self.encoder.update(
													    {
													        2: self.mask_token_sent,
													        3: self.mask_token,
													    }       )

							if self.offset > 0:
													# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
													# mask_token_sent is already added to list -> so start at 1
													self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1  ,      self.offset - 1       )}       )

							lowercase_      :   Any        =			{v: k for k, v in self.encoder.items()}
	@property
	def        lowerCamelCase__		(							self       )						->  int:
							return len(self.sp_model       ) + self.offset
	def        lowerCamelCase__		(							self       )						->  Dict[str, int]:
							lowercase_      :   Tuple        =			{self.convert_ids_to_tokens(__A       ): i for i in range(self.vocab_size       )}
							vocab.update(self.added_tokens_encoder       )
							return vocab
	def __getstate__(							self       )						->  Union[str, Any]:
							lowercase_      :   str        =			self.__dict__.copy()
							lowercase_      :   int        =			None
							return state
	def __setstate__(							self  ,      _lowercase       )						->  str:
							lowercase_      :   Union[str, Any]        =			d

							# for backward compatibility
							if not hasattr(self  ,      'sp_model_kwargs'       ):
													lowercase_      :   Any        =			{}

							lowercase_      :   Dict        =			spm.SentencePieceProcessor(**self.sp_model_kwargs       )
							self.sp_model.Load(self.vocab_file       )
	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  List[str]:
							return self.sp_model.encode(__A  ,      out_type=__A       )
	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  int:



							if token in self.decoder:
													return self.decoder[token]
							elif token in self.added_tokens_decoder:
													return self.added_tokens_decoder[token]
							lowercase_      :   str        =			self.sp_model.piece_to_id(__A       )
							return sp_id + self.offset
	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  str:



							if index in self.encoder:
													return self.encoder[index]
							elif index in self.added_tokens_encoder:
													return self.added_tokens_encoder[index]
							else:
													lowercase_      :   str        =			self.sp_model.IdToPiece(index - self.offset       )
							return token
	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  Optional[Any]:
							lowercase_      :   List[str]        =			[]
							lowercase_      :   str        =			''
							for token in tokens:
													# make sure that special tokens are not decoded using sentencepiece model
													if token in self.all_special_tokens:
																			out_string += self.sp_model.decode(__A       ) + token
																			lowercase_      :   int        =			[]
													else:
																			current_sub_tokens.append(__A       )
							out_string += self.sp_model.decode(__A       )
							return out_string.strip()
	def        lowerCamelCase__		(							self  ,      _lowercase=False       )						->  Any:
							return 1
	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  List[Any]:
							lowercase_      :   Optional[Any]        =			set(self.all_special_ids       )  # call it once instead of inside list comp
							all_special_ids.remove(self.unk_token_id       )  # <unk> is only sometimes special

							return [1 if x in all_special_ids else 0 for x in seq]
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase = None  ,      _lowercase = False       )						->  List[int]:



							if already_has_special_tokens:
													return self._special_token_mask(__A       )
							elif token_ids_a is None:
													return self._special_token_mask(__A       ) + [1]
							else:
													return self._special_token_mask(token_ids_a + token_ids_a       ) + [1]
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase=None       )						->  List[int]:



							if token_ids_a is None:
													return token_ids_a + [self.eos_token_id]
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return token_ids_a + token_ids_a + [self.eos_token_id]







	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase = None       )						->  Tuple[str]:



							if not os.path.isdir(__A       ):
													logger.error(f"Vocabulary path ({save_directory}) should be a directory"       )
													return
							lowercase_      :   Tuple        =			os.path.join(
							    __A  ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )

							if os.path.abspath(self.vocab_file       ) != os.path.abspath(__A       ) and os.path.isfile(self.vocab_file       ):
													copyfile(self.vocab_file  ,      __A       )
							elif not os.path.isfile(self.vocab_file       ):
													with open(__A  ,      'wb'       ) as fi:
																			lowercase_      :   Any        =			self.sp_model.serialized_model_proto()
																			fi.write(__A       )

							return (out_vocab_file,)







'''simple docstring'''

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

import os
from pathlib import Path






def 	_UpperCAmelCase    (  a		:	Dict						,			a		:	Optional[int]						,			a		:	Tuple							)			->			Optional[int]:





						"""simple docstring"""
						lowercase_      :   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]
						lowercase_      :   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'],
						}
						lowercase_      :   Optional[Any]        =			f"{src_lang}-{tgt_lang}"

						lowercase_      :   Optional[Any]        =			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(a						,			exist_ok=a							)
						lowercase_      :   int        =			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
A:      List[str]  				=				Path(__file__).resolve().parent.parent.parent
A:      List[str]  				=				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:      int  				=				model_cards_dir / "facebook" / model_name
						write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)
