

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

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


A_       : Tuple         =			logging.get_logger(__name__)

A_       : str         =			'▁'

A_       : Union[str, Any]         =			{'vocab_file': 'prophetnet.tokenizer'}

A_       : Union[str, Any]         =			{
    'vocab_file': {
        'microsoft/xprophetnet-large-wiki100-cased': (
            'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'
        ),
    }
}

A_       : int         =			{
    'microsoft/xprophetnet-large-wiki100-cased': {'do_lower_case': False},
}

A_       : List[str]         =			{
    'microsoft/xprophetnet-large-wiki100-cased': 512,
}

def 						UpperCamelCase					(lowercase_: Dict					)     ->      Optional[int]:
	A__							:    str        =		collections.OrderedDict()
	with open(lowercase_    ,      """r"""    ,      encoding="""utf-8"""					) as reader:
		A__							:    Optional[int]        =		reader.readlines()
	for index, token in enumerate(lowercase_					):
		A__							:    Any        =		token.rstrip("""\n"""					)
		A__							:    int        =		index
	return vocab



class     _a						(__magic_name__				):


					'''simple docstring'''






					UpperCAmelCase__:   List[str]  							=       VOCAB_FILES_NAMES
					UpperCAmelCase__:   Tuple  							=       PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__:   Any  							=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__:   Any  							=       ['''input_ids''', '''attention_mask''']






					def __init__( self    ,       A__    ,       A__="[SEP]"    ,       A__="[SEP]"    ,       A__="[SEP]"    ,       A__="[UNK]"    ,       A__="[PAD]"    ,       A__="[CLS]"    ,       A__="[MASK]"    ,       A__ = None    ,       **A__    ,       ):
						A__							:    List[Any]        =		{} if sp_model_kwargs is None else sp_model_kwargs

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

						try:
							import sentencepiece as spm
						except ImportError:
							logger.warning(
							    """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
							    """ pip install sentencepiece"""			)
							raise

						A__							:    List[Any]        =		spm.SentencePieceProcessor(**self.sp_model_kwargs			)
						self.sp_model.Load(str(A__			)			)
						A__							:    List[str]        =		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'

						# put special tokens and [unused] tokens into the vocab
						A__							:    str        =		{"""[PAD]""": 0, """[CLS]""": 1, """[SEP]""": 2, """[UNK]""": 3, """[MASK]""": 4}

						for i in range(10			):
							A__							:    int        =		F"""[unused{i}]"""
							A__							:    Union[str, Any]        =		5 + i

						# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
						A__							:    Dict        =		12
						A__							:    Optional[int]        =		{v: k for k, v in self.fairseq_tokens_to_ids.items()}
						for k in self.fairseq_tokens_to_ids.keys():
							self.unique_no_split_tokens.append(A__			)






					def __getstate__( self			):
						A__							:    Optional[int]        =		self.__dict__.copy()
						A__							:    Union[str, Any]        =		None
						return state






					def __setstate__( self    ,       A__			):
						A__							:    Union[str, Any]        =		d
						try:
							import sentencepiece as spm
						except ImportError:
							logger.warning(
							    """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
							    """ pip install sentencepiece"""			)
							raise

						# for backward compatibility
						if not hasattr(self    ,       """sp_model_kwargs"""			):
							A__							:    Union[str, Any]        =		{}

						A__							:    List[Any]        =		spm.SentencePieceProcessor(**self.sp_model_kwargs			)
						self.sp_model.Load(self.vocab_file			)






					def   __A			( self    ,       A__    ,       A__ = None    ,       A__ = False			):

						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 ([0] * len(A__			)) + [1]
						return ([0] * len(A__			)) + [1] + ([0] * len(A__			)) + [1]






					def   __A			( self    ,       A__    ,       A__ = None			):
						A__							:    Any        =		[self.sep_token_id]

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






					@property
					def   __A			( self			):
						return len(self.sp_model			) + self.fairseq_offset






					def   __A			( self			):
						A__							:    int        =		{self.convert_ids_to_tokens(A__			): i for i in range(self.vocab_size			)}
						vocab.update(self.added_tokens_encoder			)
						return vocab






					def   __A			( self    ,       A__			):
						return self.sp_model.encode(A__    ,       out_type=A__			)






					def   __A			( self    ,       A__			):
						if token in self.fairseq_tokens_to_ids:
							return self.fairseq_tokens_to_ids[token]
						A__							:    Any        =		self.sp_model.PieceToId(A__			)

						# 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   __A			( self    ,       A__			):
						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   __A			( self    ,       A__			):
						A__							:    int        =		"""""".join(A__			).replace(A__    ,       """ """			).strip()
						return out_string






					def   __A			( self    ,       A__    ,       A__ = None			):
						if not os.path.isdir(A__			):
							logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""			)
							return
						A__							:    List[str]        =		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:
								A__							:    Optional[int]        =		self.sp_model.serialized_model_proto()
								fi.write(A__			)

						return (out_vocab_file,)






					def   __A			( self    ,       A__    ,       A__ = None			):

						if token_ids_a is None:
							return token_ids_a + [self.sep_token_id]
						A__							:    Any        =		[self.sep_token_id]
						return token_ids_a + sep + token_ids_a + sep






import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict

@pytest.mark.parametrize(
    """files"""    ,      [
        ["""full:README.md""", """dataset_infos.json"""],
        ["""empty:README.md""", """dataset_infos.json"""],
        ["""dataset_infos.json"""],
        ["""full:README.md"""],
    ]    ,      )
def 						UpperCamelCase					(lowercase_: List[str]    ,      lowercase_: Optional[int]					)     ->      List[str]:
	A__							:    Tuple        =		tmp_path_factory.mktemp("""dset_infos_dir"""					)
	if "full:README.md" in files:
		with open(dataset_infos_dir / """README.md"""    ,      """w"""					) as f:
			f.write("""---\ndataset_info:\n  dataset_size: 42\n---"""					)
	if "empty:README.md" in files:
		with open(dataset_infos_dir / """README.md"""    ,      """w"""					) as f:
			f.write(""""""					)
    # we want to support dataset_infos.json for backward compatibility
	if "dataset_infos.json" in files:
		with open(dataset_infos_dir / """dataset_infos.json"""    ,      """w"""					) as f:
			f.write("""{\"default\": {\"dataset_size\": 42}}"""					)
	A__							:    List[Any]        =		DatasetInfosDict.from_directory(lowercase_					)
	assert dataset_infos
	assert dataset_infos["default"].dataset_size == 42

@pytest.mark.parametrize(
    """dataset_info"""    ,      [
        DatasetInfo(),
        DatasetInfo(
            description="""foo"""    ,      features=Features({"""a""": Value("""int32"""					)}					)    ,      builder_name="""builder"""    ,      config_name="""config"""    ,      version="""1.0.0"""    ,      splits=[{"""name""": """train"""}]    ,      download_size=42    ,      ),
    ]    ,      )
def 						UpperCamelCase					(lowercase_: str    ,      lowercase_: DatasetInfo					)     ->      List[Any]:
	A__							:    Union[str, Any]        =		str(lowercase_					)
	dataset_info.write_to_directory(lowercase_					)
	A__							:    List[Any]        =		DatasetInfo.from_directory(lowercase_					)
	assert dataset_info == reloaded
	assert os.path.exists(os.path.join(lowercase_    ,      """dataset_info.json"""					)					)

def 						UpperCamelCase					()     ->      List[Any]:
	A__							:    Union[str, Any]        =		DatasetInfo(
	    description="""foo"""    ,      citation="""bar"""    ,      homepage="""https://foo.bar"""    ,      license="""CC0"""    ,      features=Features({"""a""": Value("""int32"""					)}					)    ,      post_processed={}    ,      supervised_keys=()    ,      task_templates=[]    ,      builder_name="""builder"""    ,      config_name="""config"""    ,      version="""1.0.0"""    ,      splits=[{"""name""": """train""", """num_examples""": 42}]    ,      download_checksums={}    ,      download_size=1337    ,      post_processing_size=442    ,      dataset_size=1234    ,      size_in_bytes=1337 + 442 + 1234    ,      )
	A__							:    Dict        =		dataset_info._to_yaml_dict()
	assert sorted(lowercase_					) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML					)
	for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
		assert key in dataset_info_yaml_dict
		assert isinstance(dataset_info_yaml_dict[key]    ,      (list, dict, int, str)					)
	A__							:    Union[str, Any]        =		yaml.safe_dump(lowercase_					)
	A__							:    List[Any]        =		yaml.safe_load(lowercase_					)
	assert dataset_info_yaml_dict == reloaded

def 						UpperCamelCase					()     ->      List[str]:
	A__							:    Optional[int]        =		DatasetInfo()
	A__							:    List[Any]        =		dataset_info._to_yaml_dict()
	assert dataset_info_yaml_dict == {}


@pytest.mark.parametrize(
    """dataset_infos_dict"""    ,      [
        DatasetInfosDict(),
        DatasetInfosDict({"""default""": DatasetInfo()}					),
        DatasetInfosDict({"""my_config_name""": DatasetInfo()}					),
        DatasetInfosDict(
            {
                """default""": DatasetInfo(
                    description="""foo"""    ,      features=Features({"""a""": Value("""int32"""					)}					)    ,      builder_name="""builder"""    ,      config_name="""config"""    ,      version="""1.0.0"""    ,      splits=[{"""name""": """train"""}]    ,      download_size=42    ,      )
            }					),
        DatasetInfosDict(
            {
                """v1""": DatasetInfo(dataset_size=42					),
                """v2""": DatasetInfo(dataset_size=1337					),
            }					),
    ]    ,      )
def 						UpperCamelCase					(lowercase_: Tuple    ,      lowercase_: DatasetInfosDict					)     ->      Optional[Any]:
	A__							:    List[Any]        =		str(lowercase_					)
	dataset_infos_dict.write_to_directory(lowercase_					)
	A__							:    Dict        =		DatasetInfosDict.from_directory(lowercase_					)

	# the config_name of the dataset_infos_dict take over the attribute
	for config_name, dataset_info in dataset_infos_dict.items():
		A__							:    Optional[int]        =		config_name
		# the yaml representation doesn't include fields like description or citation
		# so we just test that we can recover what we can from the yaml
		A__							:    List[str]        =		DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()					)
	assert dataset_infos_dict == reloaded

	if dataset_infos_dict:
		assert os.path.exists(os.path.join(lowercase_    ,      """README.md"""					)					)



