
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


lowercase  :					Optional[Any]													=       '▁'

lowercase  :					Any													=       {'vocab_file': 'spiece.model'}

lowercase  :					Dict													=       {
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'}
}

lowercase  :					List[Any]													=       {
    'google/pegasus-xsum': 512,
}


lowercase  :					int													=       logging.get_logger(__name__)




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     VOCAB_FILES_NAMES

	_A								=     VOCAB_FILES_NAMES
	_A								=     PRETRAINED_VOCAB_FILES_MAP
	_A								=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A								=     ['input_ids', 'attention_mask']







	def __init__(					self		:Dict     ,   a		:int     ,   a		:Any="<pad>"     ,   a		:List[str]="</s>"     ,   a		:int="<unk>"     ,   a		:Tuple="<mask_2>"     ,   a		:str="<mask_1>"     ,   a		:str=None     ,   a		:int=1_0_3     ,   a		:Optional[Dict[str, Any]] = None     ,   **a		:Dict     ,   )		-> None:
								__UpperCamelCase	:   int			   =					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						)}'						)

															__UpperCamelCase	:   Union[str, Any]			   =					(
															    ([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}.'						)
															__UpperCamelCase	:   List[Any]			   =					additional_special_tokens_extended
								else:
															__UpperCamelCase	:   Union[str, Any]			   =					[mask_token_sent] if mask_token_sent is not None else []
															additional_special_tokens += [f'<unk_{i}>' for i in range(2     ,   self.offset						)]

								__UpperCamelCase	:   Union[str, Any]			   =					{} 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     ,   )
								__UpperCamelCase	:   Optional[Any]			   =					mask_token_sent
								__UpperCamelCase	:   List[str]			   =					vocab_file
								__UpperCamelCase	:   List[str]			   =					spm.SentencePieceProcessor(**self.sp_model_kwargs						)
								self.sp_model.Load(a						)

								# add special tokens to encoder dict
								__UpperCamelCase	:   Dict[int, str]			   =					{
								    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						)}						)

								__UpperCamelCase	:   Dict[str, int]			   =					{v: k for k, v in self.encoder.items()}







	@property
	def    _lowerCamelCase				(					self		:Tuple						)		-> int:
								return len(self.sp_model						) + self.offset







	def    _lowerCamelCase				(					self		:Tuple						)		-> Dict[str, int]:
								__UpperCamelCase	:   Any			   =					{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		:Optional[int]						)		-> Optional[Any]:
								__UpperCamelCase	:   List[str]			   =					self.__dict__.copy()
								__UpperCamelCase	:   Dict			   =					None
								return state







	def __setstate__(					self		:str     ,   a		:str						)		-> Optional[int]:
								__UpperCamelCase	:   Union[str, Any]			   =					d

								# for backward compatibility
								if not hasattr(self     ,   "sp_model_kwargs"						):
															__UpperCamelCase	:   Optional[int]			   =					{}

								__UpperCamelCase	:   Any			   =					spm.SentencePieceProcessor(**self.sp_model_kwargs						)
								self.sp_model.Load(self.vocab_file						)







	def    _lowerCamelCase				(					self		:Tuple     ,   a		:str						)		-> List[str]:
								return self.sp_model.encode(a     ,   out_type=a						)







	def    _lowerCamelCase				(					self		:Union[str, Any]     ,   a		:str						)		-> int:
								if token in self.decoder:
															return self.decoder[token]
								elif token in self.added_tokens_decoder:
															return self.added_tokens_decoder[token]
								__UpperCamelCase	:   Tuple			   =					self.sp_model.piece_to_id(a						)
								return sp_id + self.offset







	def    _lowerCamelCase				(					self		:Any     ,   a		:int						)		-> str:
								if index in self.encoder:
															return self.encoder[index]
								elif index in self.added_tokens_encoder:
															return self.added_tokens_encoder[index]
								else:
															__UpperCamelCase	:   Optional[int]			   =					self.sp_model.IdToPiece(index - self.offset						)
								return token







	def    _lowerCamelCase				(					self		:int     ,   a		:int						)		-> int:
								__UpperCamelCase	:   Tuple			   =					[]
								__UpperCamelCase	:   Optional[int]			   =					""
								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
																						__UpperCamelCase	:   Any			   =					[]
															else:
																						current_sub_tokens.append(a						)
								out_string += self.sp_model.decode(a						)
								return out_string.strip()







	def    _lowerCamelCase				(					self		:Optional[int]     ,   a		:str=False						)		-> List[Any]:
								return 1







	def    _lowerCamelCase				(					self		:int     ,   a		:Any						)		-> List[str]:
								__UpperCamelCase	:   Union[str, 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		:int     ,   a		:List     ,   a		:Optional[List] = None     ,   a		:bool = 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		:Tuple     ,   a		:Tuple     ,   a		:Union[str, Any]=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		:List[str]     ,   a		:str     ,   a		:Optional[str] = None						)		-> Tuple[str]:
								if not os.path.isdir(a						):
															logger.error(f'Vocabulary path ({save_directory}) should be a directory'						)
															return
								__UpperCamelCase	:   Any			   =					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:
																						__UpperCamelCase	:   List[Any]			   =					self.sp_model.serialized_model_proto()
																						fi.write(a						)

								return (out_vocab_file,)

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

import os
from pathlib import Path




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Any						,  _lowerCamelCase :			List[str]						,  _lowerCamelCase :			Any)					->    Dict:






							'''simple docstring'''





							__UpperCamelCase	:   Optional[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	:   Any			   =					{
							    "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	:   Tuple			   =					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(_lowerCamelCase						,  exist_ok=_lowerCamelCase)
							__UpperCamelCase	:   Dict			   =					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
lowercase  :					List[str]													=       Path(__file__).resolve().parent.parent.parent
lowercase  :					Union[str, Any]													=       repo_dir / 'model_cards'

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