
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


lowercase  :					Dict													=       logging.get_logger(__name__)


lowercase  :					Any													=       {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all LED models at https://huggingface.co/models?filter=LED
lowercase  :					Any													=       {
    'vocab_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
    },
    'merges_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
    },
}

lowercase  :					str													=       {
    'allenai/led-base-16384': 16384,
}




@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 	_SCREAMING_SNAKE_CASE					(		)					->    Dict:






							'''simple docstring'''





							__UpperCamelCase	:   str			   =					(
							    list(range(ord("!")						,  ord("~") + 1)) + list(range(ord("¡")						,  ord("¬") + 1)) + list(range(ord("®")						,  ord("ÿ") + 1))
							)
							__UpperCamelCase	:   Optional[Any]			   =					bs[:]
							__UpperCamelCase	:   str			   =					0
							for b in range(2**8):
														if b not in bs:
																					bs.append(_lowerCamelCase)
																					cs.append(2**8 + n)
																					n += 1
							__UpperCamelCase	:   Dict			   =					[chr(_lowerCamelCase) for n in cs]
							return dict(zip(_lowerCamelCase						,  _lowerCamelCase))




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Dict)					->    Optional[int]:






							'''simple docstring'''





							__UpperCamelCase	:   Any			   =					set()
							__UpperCamelCase	:   List[str]			   =					word[0]
							for char in word[1:]:
														pairs.add((prev_char, char))
														__UpperCamelCase	:   Optional[int]			   =					char
							return pairs




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




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







	def __init__(					self		:List[Any]     ,   a		:Any     ,   a		:str     ,   a		:Optional[int]="replace"     ,   a		:Optional[Any]="<s>"     ,   a		:Dict="</s>"     ,   a		:str="</s>"     ,   a		:Dict="<s>"     ,   a		:str="<unk>"     ,   a		:Optional[Any]="<pad>"     ,   a		:str="<mask>"     ,   a		:Tuple=False     ,   **a		:Union[str, Any]     ,   )		-> Tuple:
								__UpperCamelCase	:   List[Any]			   =					AddedToken(a     ,   lstrip=a     ,   rstrip=a						) if isinstance(a     ,   a						) else bos_token
								__UpperCamelCase	:   List[Any]			   =					AddedToken(a     ,   lstrip=a     ,   rstrip=a						) if isinstance(a     ,   a						) else eos_token
								__UpperCamelCase	:   int			   =					AddedToken(a     ,   lstrip=a     ,   rstrip=a						) if isinstance(a     ,   a						) else sep_token
								__UpperCamelCase	:   Any			   =					AddedToken(a     ,   lstrip=a     ,   rstrip=a						) if isinstance(a     ,   a						) else cls_token
								__UpperCamelCase	:   Optional[Any]			   =					AddedToken(a     ,   lstrip=a     ,   rstrip=a						) if isinstance(a     ,   a						) else unk_token
								__UpperCamelCase	:   Optional[int]			   =					AddedToken(a     ,   lstrip=a     ,   rstrip=a						) if isinstance(a     ,   a						) else pad_token

								# Mask token behave like a normal word, i.e. include the space before it
								__UpperCamelCase	:   int			   =					AddedToken(a     ,   lstrip=a     ,   rstrip=a						) if isinstance(a     ,   a						) else mask_token

								super().__init__(
								    errors=a     ,   bos_token=a     ,   eos_token=a     ,   unk_token=a     ,   sep_token=a     ,   cls_token=a     ,   pad_token=a     ,   mask_token=a     ,   add_prefix_space=a     ,   **a     ,   )

								with open(a     ,   encoding="utf-8"						) as vocab_handle:
															__UpperCamelCase	:   str			   =					json.load(a						)
								__UpperCamelCase	:   List[str]			   =					{v: k for k, v in self.encoder.items()}
								__UpperCamelCase	:   Optional[int]			   =					errors  # how to handle errors in decoding
								__UpperCamelCase	:   Any			   =					bytes_to_unicode()
								__UpperCamelCase	:   Optional[int]			   =					{v: k for k, v in self.byte_encoder.items()}
								with open(a     ,   encoding="utf-8"						) as merges_handle:
															__UpperCamelCase	:   int			   =					merges_handle.read().split("\n"						)[1:-1]
								__UpperCamelCase	:   Optional[Any]			   =					[tuple(merge.split()						) for merge in bpe_merges]
								__UpperCamelCase	:   Dict			   =					dict(zip(a     ,   range(len(a						)						)						)						)
								__UpperCamelCase	:   str			   =					{}
								__UpperCamelCase	:   Union[str, Any]			   =					add_prefix_space

								# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
								__UpperCamelCase	:   Any			   =					re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"						)







	@property
	# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
	def    _lowerCamelCase				(					self		:int						)		-> List[Any]:
								return len(self.encoder						)







	def    _lowerCamelCase				(					self		:Optional[Any]						)		-> Union[str, Any]:
								return dict(self.encoder     ,   **self.added_tokens_encoder						)







	def    _lowerCamelCase				(					self		:List[Any]     ,   a		:List[str]						)		-> Union[str, Any]:
								if token in self.cache:
															return self.cache[token]
								__UpperCamelCase	:   Union[str, Any]			   =					tuple(a						)
								__UpperCamelCase	:   Optional[Any]			   =					get_pairs(a						)

								if not pairs:
															return token

								while True:
															__UpperCamelCase	:   Optional[Any]			   =					min(a     ,   key=lambda a						: self.bpe_ranks.get(a     ,   float("inf"						)						)						)
															if bigram not in self.bpe_ranks:
																						break
															__UpperCamelCase   ,						__UpperCamelCase	:   str			   =					bigram
															__UpperCamelCase	:   str			   =					[]
															__UpperCamelCase	:   List[str]			   =					0
															while i < len(a						):
																						try:
																													__UpperCamelCase	:   Dict			   =					word.index(a     ,   a						)
																						except ValueError:
																													new_word.extend(word[i:]						)
																													break
																						else:
																													new_word.extend(word[i:j]						)
																													__UpperCamelCase	:   Union[str, Any]			   =					j

																						if word[i] == first and i < len(a						) - 1 and word[i + 1] == second:
																													new_word.append(first + second						)
																													i += 2
																						else:
																													new_word.append(word[i]						)
																													i += 1
															__UpperCamelCase	:   Dict			   =					tuple(a						)
															__UpperCamelCase	:   Tuple			   =					new_word
															if len(a						) == 1:
																						break
															else:
																						__UpperCamelCase	:   Optional[Any]			   =					get_pairs(a						)
								__UpperCamelCase	:   Union[str, Any]			   =					" ".join(a						)
								__UpperCamelCase	:   Union[str, Any]			   =					word
								return word







	def    _lowerCamelCase				(					self		:str     ,   a		:Union[str, Any]						)		-> Tuple:
								__UpperCamelCase	:   str			   =					[]
								for token in re.findall(self.pat     ,   a						):
															__UpperCamelCase	:   Any			   =					"".join(
															    self.byte_encoder[b] for b in token.encode("utf-8"						)						)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
															bpe_tokens.extend(bpe_token for bpe_token in self.bpe(a						).split(" "						)						)
								return bpe_tokens







	def    _lowerCamelCase				(					self		:Tuple     ,   a		:List[Any]						)		-> List[Any]:
								return self.encoder.get(a     ,   self.encoder.get(self.unk_token						)						)







	def    _lowerCamelCase				(					self		:Tuple     ,   a		:List[Any]						)		-> Dict:
								return self.decoder.get(a						)







	def    _lowerCamelCase				(					self		:int     ,   a		:List[str]						)		-> Any:
								__UpperCamelCase	:   str			   =					"".join(a						)
								__UpperCamelCase	:   Any			   =					bytearray([self.byte_decoder[c] for c in text]						).decode("utf-8"     ,   errors=self.errors						)
								return text







	def    _lowerCamelCase				(					self		:List[Any]     ,   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	:   Optional[Any]			   =					os.path.join(
								    a     ,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)
								__UpperCamelCase	:   List[Any]			   =					os.path.join(
								    a     ,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]						)

								with open(a     ,   "w"     ,   encoding="utf-8"						) as f:
															f.write(json.dumps(self.encoder     ,   indent=2     ,   sort_keys=a     ,   ensure_ascii=a						) + "\n"						)

								__UpperCamelCase	:   Any			   =					0
								with open(a     ,   "w"     ,   encoding="utf-8"						) as writer:
															writer.write("#version: 0.2\n"						)
															for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,   key=lambda a						: kv[1]						):
																						if index != token_index:
																													logger.warning(
																													    f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
																													    " Please check that the tokenizer is not corrupted!"						)
																													__UpperCamelCase	:   Tuple			   =					token_index
																						writer.write(" ".join(a						) + "\n"						)
																						index += 1

								return vocab_file, merge_file







	def    _lowerCamelCase				(					self		:List[str]     ,   a		:List[int]     ,   a		:Optional[List[int]] = None						)		-> List[int]:
								if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								__UpperCamelCase	:   Dict			   =					[self.cls_token_id]
								__UpperCamelCase	:   List[str]			   =					[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep







	def    _lowerCamelCase				(					self		:List[str]     ,   a		:List[int]     ,   a		:Optional[List[int]] = None     ,   a		:bool = False						)		-> List[int]:
								if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=a     ,   token_ids_a=a     ,   already_has_special_tokens=a						)

								if token_ids_a is None:
															return [1] + ([0] * len(a						)) + [1]
								return [1] + ([0] * len(a						)) + [1, 1] + ([0] * len(a						)) + [1]







	def    _lowerCamelCase				(					self		:Optional[int]     ,   a		:List[int]     ,   a		:Optional[List[int]] = None						)		-> List[int]:
								__UpperCamelCase	:   List[Any]			   =					[self.sep_token_id]
								__UpperCamelCase	:   List[str]			   =					[self.cls_token_id]

								if token_ids_a is None:
															return len(cls + token_ids_a + sep						) * [0]
								return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]







	def    _lowerCamelCase				(					self		:int     ,   a		:Any     ,   a		:List[str]=False     ,   **a		:str						)		-> List[Any]:
								__UpperCamelCase	:   List[str]			   =					kwargs.pop("add_prefix_space"     ,   self.add_prefix_space						)
								if (is_split_into_words or add_prefix_space) and (len(a						) > 0 and not text[0].isspace()):
															__UpperCamelCase	:   Optional[Any]			   =					" " + text
								return (text, kwargs)







	def    _lowerCamelCase				(					self		:List[str]     ,   a		:Union[Dict[str, EncodedInput], BatchEncoding]     ,   a		:Optional[int] = None     ,   a		:PaddingStrategy = PaddingStrategy.DO_NOT_PAD     ,   a		:Optional[int] = None     ,   a		:Optional[bool] = None     ,   )		-> dict:
								__UpperCamelCase	:   int			   =					super()._pad(
								    encoded_inputs=a     ,   max_length=a     ,   padding_strategy=a     ,   pad_to_multiple_of=a     ,   return_attention_mask=a     ,   )

								# Load from model defaults
								if return_attention_mask is None:
															__UpperCamelCase	:   int			   =					"attention_mask" in self.model_input_names

								if return_attention_mask and "global_attention_mask" in encoded_inputs:
															__UpperCamelCase	:   List[Any]			   =					encoded_inputs[self.model_input_names[0]]
															# `global_attention_mask` need to have the same length as other (sequential) inputs.
															__UpperCamelCase	:   int			   =					len(encoded_inputs["global_attention_mask"]						) != len(a						)

															if needs_to_be_padded:
																						__UpperCamelCase	:   List[str]			   =					len(a						) - len(encoded_inputs["global_attention_mask"]						)

																						if self.padding_side == "right":
																													# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																													__UpperCamelCase	:   Optional[Any]			   =					(
																													    encoded_inputs["global_attention_mask"] + [-1] * difference
																													)
																						elif self.padding_side == "left":
																													__UpperCamelCase	:   Union[str, Any]			   =					[-1] * difference + encoded_inputs[
																													    "global_attention_mask"
																													]
																						else:
																													raise ValueError("Invalid padding strategy:" + str(self.padding_side						)						)

								return encoded_inputs

# 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)