



"""simple docstring"""


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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__A   :							Dict										=   logging.get_logger(__name__)

__A   :							Optional[Any]										=   "▁"

__A   :							Tuple										=   {"vocab_file": "sentencepiece.bpe.model"}

__A   :							Optional[Any]										=   {
    "vocab_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

__A   :							Any										=   {
    "xlm-roberta-base": 512,
    "xlm-roberta-large": 512,
    "xlm-roberta-large-finetuned-conll02-dutch": 512,
    "xlm-roberta-large-finetuned-conll02-spanish": 512,
    "xlm-roberta-large-finetuned-conll03-english": 512,
    "xlm-roberta-large-finetuned-conll03-german": 512,
}
class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	VOCAB_FILES_NAMES
			UpperCamelCase__			      =	PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__			      =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__			      =	["""input_ids""", """attention_mask"""]



			def __init__(		self     :    Optional[Any]				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    Dict="<s>"				,   __UpperCamelCase     :    Optional[int]="</s>"				,   __UpperCamelCase     :    Union[str, Any]="</s>"				,   __UpperCamelCase     :    Optional[Any]="<s>"				,   __UpperCamelCase     :    List[str]="<unk>"				,   __UpperCamelCase     :    Optional[Any]="<pad>"				,   __UpperCamelCase     :    Tuple="<mask>"				,   __UpperCamelCase     :    Optional[Dict[str, Any]] = None				,   **__UpperCamelCase     :    Optional[int]				,   )->None:
							# Mask token behave like a normal word, i.e. include the space before it
							_UpperCAmelCase					=    AddedToken(__UpperCamelCase				,   lstrip=__UpperCamelCase				,   rstrip=__UpperCamelCase   ) if isinstance(__UpperCamelCase				,   __UpperCamelCase   ) else mask_token

							_UpperCAmelCase					=    {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=__UpperCamelCase				,   eos_token=__UpperCamelCase				,   unk_token=__UpperCamelCase				,   sep_token=__UpperCamelCase				,   cls_token=__UpperCamelCase				,   pad_token=__UpperCamelCase				,   mask_token=__UpperCamelCase				,   sp_model_kwargs=self.sp_model_kwargs				,   **__UpperCamelCase				,   )

							_UpperCAmelCase					=    spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.Load(str(__UpperCamelCase   )   )
							_UpperCAmelCase					=    vocab_file

							# Original fairseq vocab and spm vocab must be "aligned":
							# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
							# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
							# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
							# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

							# Mimic fairseq token-to-id alignment for the first 4 token
							_UpperCAmelCase					=    {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

							# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
							_UpperCAmelCase					=    1

							_UpperCAmelCase					=    len(self.sp_model   ) + self.fairseq_offset
							_UpperCAmelCase					=    {v: k for k, v in self.fairseq_tokens_to_ids.items()}



			def __getstate__(		self     :    str   )->Union[str, Any]:
							_UpperCAmelCase					=    self.__dict__.copy()
							_UpperCAmelCase					=    None
							_UpperCAmelCase					=    self.sp_model.serialized_model_proto()
							return state



			def __setstate__(		self     :    List[Any]				,   __UpperCamelCase     :    Optional[Any]   )->Any:
							_UpperCAmelCase					=    d

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

							_UpperCAmelCase					=    spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )



			def  lowercase__						(		self     :    int				,   __UpperCamelCase     :    List[int]				,   __UpperCamelCase     :    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					=    [self.cls_token_id]
							_UpperCAmelCase					=    [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep



			def  lowercase__						(		self     :    Any				,   __UpperCamelCase     :    List[int]				,   __UpperCamelCase     :    Optional[List[int]] = None				,   __UpperCamelCase     :    bool = False   )->List[int]:

							if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=__UpperCamelCase				,   token_ids_a=__UpperCamelCase				,   already_has_special_tokens=__UpperCamelCase   )

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



			def  lowercase__						(		self     :    Optional[int]				,   __UpperCamelCase     :    List[int]				,   __UpperCamelCase     :    Optional[List[int]] = None   )->List[int]:
							_UpperCAmelCase					=    [self.sep_token_id]
							_UpperCAmelCase					=    [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]



			@property
			def  lowercase__						(		self     :    Dict   )->Optional[Any]:
							return len(self.sp_model   ) + self.fairseq_offset + 1  # Add the <mask> token



			def  lowercase__						(		self     :    Union[str, Any]   )->Optional[int]:
							_UpperCAmelCase					=    {self.convert_ids_to_tokens(__UpperCamelCase   ): i for i in range(self.vocab_size   )}
							vocab.update(self.added_tokens_encoder   )
							return vocab



			def  lowercase__						(		self     :    int				,   __UpperCamelCase     :    str   )->List[str]:
							return self.sp_model.encode(__UpperCamelCase				,   out_type=__UpperCamelCase   )



			def  lowercase__						(		self     :    Optional[int]				,   __UpperCamelCase     :    str   )->Dict:
							if token in self.fairseq_tokens_to_ids:
											return self.fairseq_tokens_to_ids[token]
							_UpperCAmelCase					=    self.sp_model.PieceToId(__UpperCamelCase   )

							# Need to return unknown token if the SP model returned 0
							return spm_id + self.fairseq_offset if spm_id else self.unk_token_id



			def  lowercase__						(		self     :    Dict				,   __UpperCamelCase     :    str   )->Union[str, Any]:
							if index in self.fairseq_ids_to_tokens:
											return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(index - self.fairseq_offset   )



			def  lowercase__						(		self     :    Dict				,   __UpperCamelCase     :    List[str]   )->int:
							_UpperCAmelCase					=    ''''''.join(__UpperCamelCase   ).replace(__UpperCamelCase				,   ''' '''   ).strip()
							return out_string



			def  lowercase__						(		self     :    str				,   __UpperCamelCase     :    str				,   __UpperCamelCase     :    Optional[str] = None   )->Tuple[str]:
							if not os.path.isdir(__UpperCamelCase   ):
											logger.error(F'Vocabulary path ({save_directory}) should be a directory'   )
											return
							_UpperCAmelCase					=    os.path.join(
							    __UpperCamelCase				,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

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

							return (out_vocab_file,)








"""simple docstring"""


from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml
class 			_a (					yaml.SafeLoader):
			"""simple docstring"""



			def  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    Any   )->List[Any]:
							_UpperCAmelCase					=    [self.constructed_objects[key_node] for key_node, _ in node.value]
							_UpperCAmelCase					=    [tuple(__UpperCamelCase   ) if isinstance(__UpperCamelCase				,   __UpperCamelCase   ) else key for key in keys]
							_UpperCAmelCase					=    Counter(__UpperCamelCase   )
							_UpperCAmelCase					=    [key for key in counter if counter[key] > 1]
							if duplicate_keys:
											raise TypeError(F'Got duplicate yaml keys: {duplicate_keys}'   )



			def  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    Optional[Any]				,   __UpperCamelCase     :    str=False   )->Dict:
							_UpperCAmelCase					=    super().construct_mapping(__UpperCamelCase				,   deep=__UpperCamelCase   )
							self._check_no_duplicates_on_constructed_node(__UpperCamelCase   )
							return mapping







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str				):






				'''simple docstring'''



				_UpperCAmelCase					=    list(readme_content.splitlines()				)
				if full_content and full_content[0] == "---" and "---" in full_content[1:]:
								_UpperCAmelCase					=    full_content[1:].index('''---'''				) + 1
								_UpperCAmelCase					=    '''\n'''.join(full_content[1:sep_idx]				)
								return yamlblock, "\n".join(full_content[sep_idx + 1 :]				)

				return None, "\n".join(_SCREAMING_SNAKE_CASE				)
class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			# class attributes
			UpperCamelCase__			      =	{"""train_eval_index"""}  # train-eval-index in the YAML metadata



			@classmethod
			def  lowercase__						(		cls     :    List[Any]				,   __UpperCamelCase     :    Path   )->"DatasetMetadata":
							with open(__UpperCamelCase				,   encoding='''utf-8'''   ) as readme_file:
											_UpperCAmelCase		,						_UpperCAmelCase					=    _split_yaml_from_readme(readme_file.read()   )
							if yaml_string is not None:
											return cls.from_yaml_string(__UpperCamelCase   )
							else:
											return cls()



			def  lowercase__						(		self     :    Tuple				,   __UpperCamelCase     :    Path   )->List[Any]:
							if path.exists():
											with open(__UpperCamelCase				,   encoding='''utf-8'''   ) as readme_file:
															_UpperCAmelCase					=    readme_file.read()
							else:
											_UpperCAmelCase					=    None
							_UpperCAmelCase					=    self._to_readme(__UpperCamelCase   )
							with open(__UpperCamelCase				,   '''w'''				,   encoding='''utf-8'''   ) as readme_file:
											readme_file.write(__UpperCamelCase   )



			def  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    Optional[str] = None   )->str:
							if readme_content is not None:
											_UpperCAmelCase		,						_UpperCAmelCase					=    _split_yaml_from_readme(__UpperCamelCase   )
											_UpperCAmelCase					=    '''---\n''' + self.to_yaml_string() + '''---\n''' + content
							else:
											_UpperCAmelCase					=    '''---\n''' + self.to_yaml_string() + '''---\n'''
							return full_content



			@classmethod
			def  lowercase__						(		cls     :    str				,   __UpperCamelCase     :    str   )->"DatasetMetadata":
							_UpperCAmelCase					=    yaml.load(__UpperCamelCase				,   Loader=_NoDuplicateSafeLoader   ) or {}

							# Convert the YAML keys to DatasetMetadata fields
							_UpperCAmelCase					=    {
							    (key.replace('''-'''				,   '''_'''   ) if key.replace('''-'''				,   '''_'''   ) in cls._FIELDS_WITH_DASHES else key): value
							    for key, value in metadata_dict.items()
							}
							return cls(**__UpperCamelCase   )



			def  lowercase__						(		self     :    str   )->str:
							return yaml.safe_dump(
							    {
							        (key.replace('''_'''				,   '''-'''   ) if key in self._FIELDS_WITH_DASHES else key): value
							        for key, value in self.items()
							    }				,   sort_keys=__UpperCamelCase				,   allow_unicode=__UpperCamelCase				,   encoding='''utf-8'''				,   ).decode('''utf-8'''   )



__A   :							str										=   {
    "image-classification": [],
    "translation": [],
    "image-segmentation": [],
    "fill-mask": [],
    "automatic-speech-recognition": [],
    "token-classification": [],
    "sentence-similarity": [],
    "audio-classification": [],
    "question-answering": [],
    "summarization": [],
    "zero-shot-classification": [],
    "table-to-text": [],
    "feature-extraction": [],
    "other": [],
    "multiple-choice": [],
    "text-classification": [],
    "text-to-image": [],
    "text2text-generation": [],
    "zero-shot-image-classification": [],
    "tabular-classification": [],
    "tabular-regression": [],
    "image-to-image": [],
    "tabular-to-text": [],
    "unconditional-image-generation": [],
    "text-retrieval": [],
    "text-to-speech": [],
    "object-detection": [],
    "audio-to-audio": [],
    "text-generation": [],
    "conversational": [],
    "table-question-answering": [],
    "visual-question-answering": [],
    "image-to-text": [],
    "reinforcement-learning": [],
    "voice-activity-detection": [],
    "time-series-forecasting": [],
    "document-question-answering": [],
}


if __name__ == "__main__":
					from argparse import ArgumentParser

					__A   :							str										=   ArgumentParser(usage="Validate the yaml metadata block of a README.md file.")
					ap.add_argument("readme_filepath")
					__A   :							Union[str, Any]										=   ap.parse_args()

					__A   :							Dict										=   Path(args.readme_filepath)
					__A   :							Tuple										=   DatasetMetadata.from_readme(readme_filepath)
					print(dataset_metadata)
					dataset_metadata.to_readme(readme_filepath)



