





"""simple docstring"""


import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


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

__magic_name__       :					List[Any]						=			"""▁"""

__magic_name__       :					str						=			{
    """vocab_file""": """vocab.json""",
    """spm_file""": """sentencepiece.bpe.model""",
}

__magic_name__       :					Optional[Any]						=			{
    """vocab_file""": {
        """facebook/s2t-small-librispeech-asr""": (
            """https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json"""
        ),
    },
    """spm_file""": {
        """facebook/s2t-small-librispeech-asr""": (
            """https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model"""
        )
    },
}

__magic_name__       :					Union[str, Any]						=			{
    """facebook/s2t-small-librispeech-asr""": 1_0_2_4,
}

__magic_name__       :					Optional[int]						=			["""pt""", """fr""", """ru""", """nl""", """ro""", """it""", """es""", """de"""]

__magic_name__       :					Tuple						=			{"""mustc""": MUSTC_LANGS}






class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			__lowerCAmelCase     :				Any										=  VOCAB_FILES_NAMES
			__lowerCAmelCase     :				Tuple										=  PRETRAINED_VOCAB_FILES_MAP
			__lowerCAmelCase     :				List[Any]										=  MAX_MODEL_INPUT_SIZES
			__lowerCAmelCase     :				int										=  ["""input_ids""", """attention_mask"""]

			__lowerCAmelCase     :				List[int]										=  []







			def __init__(      self						,   _A						,   _A						,   _A="<s>"						,   _A="</s>"						,   _A="<pad>"						,   _A="<unk>"						,   _A=False						,   _A=False						,   _A=None						,   _A=None						,   _A = None						,   **_A						,   ):






									'''simple docstring'''







									UpperCamelCase				:		Union[str, Any]				   =			{} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    bos_token=_A						,   eos_token=_A						,   unk_token=_A						,   pad_token=_A						,   do_upper_case=_A						,   do_lower_case=_A						,   tgt_lang=_A						,   lang_codes=_A						,   sp_model_kwargs=self.sp_model_kwargs						,   **_A						,   )
									UpperCamelCase				:		Any				   =			do_upper_case
									UpperCamelCase				:		Tuple				   =			do_lower_case

									UpperCamelCase				:		Tuple				   =			load_json(_A    )
									UpperCamelCase				:		List[str]				   =			{v: k for k, v in self.encoder.items()}
									UpperCamelCase				:		Union[str, Any]				   =			spm_file
									UpperCamelCase				:		Any				   =			load_spm(_A						,   self.sp_model_kwargs    )

									if lang_codes is not None:
															UpperCamelCase				:		List[str]				   =			lang_codes
															UpperCamelCase				:		Tuple				   =			LANGUAGES[lang_codes]
															UpperCamelCase				:		List[str]				   =			[f"""<lang:{lang}>""" for lang in self.langs]
															UpperCamelCase				:		str				   =			{lang: self.sp_model.PieceToId(f"""<lang:{lang}>"""    ) for lang in self.langs}

															UpperCamelCase				:		Dict				   =			self.lang_tokens
															UpperCamelCase				:		Any				   =			tgt_lang if tgt_lang is not None else self.langs[0]

															self.set_tgt_lang_special_tokens(self._tgt_lang    )
									else:
															UpperCamelCase				:		List[Any]				   =			{}







			@property
			def   _a      (      self    ):






									'''simple docstring'''







									return len(self.encoder    )







			@property
			def   _a      (      self    ):






									'''simple docstring'''







									return self._tgt_lang







			@tgt_lang.setter
			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		List[Any]				   =			new_tgt_lang
									self.set_tgt_lang_special_tokens(_A    )







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		Dict				   =			self.lang_code_to_id[tgt_lang]
									UpperCamelCase				:		int				   =			[lang_code_id]







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									return self.sp_model.encode(_A						,   out_type=_A    )







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									return self.encoder.get(_A						,   self.encoder[self.unk_token]    )







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									return self.decoder.get(_A						,   self.unk_token    )







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		Any				   =			[]
									UpperCamelCase				:		Any				   =			""""""
									for token in tokens:
															# make sure that special tokens are not decoded using sentencepiece model
															if token in self.all_special_tokens:
																					UpperCamelCase				:		Any				   =			self.sp_model.decode(_A    )
																					out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
																					UpperCamelCase				:		List[Any]				   =			[]
															else:
																					current_sub_tokens.append(_A    )
									UpperCamelCase				:		List[Any]				   =			self.sp_model.decode(_A    )
									out_string += decoded.upper() if self.do_upper_case else decoded
									return out_string.strip()







			def   _a      (      self						,   _A						,   _A=None    ):






									'''simple docstring'''







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







			def   _a      (      self						,   _A						,   _A = None						,   _A = False    ):






									'''simple docstring'''







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

									UpperCamelCase				:		Tuple				   =			[1] * len(self.prefix_tokens    )
									UpperCamelCase				:		List[str]				   =			[1]
									if token_ids_a is None:
															return prefix_ones + ([0] * len(_A    )) + suffix_ones
									return prefix_ones + ([0] * len(_A    )) + ([0] * len(_A    )) + suffix_ones







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		List[Any]				   =			self.encoder.copy()
									vocab.update(self.added_tokens_encoder    )
									return vocab







			def __getstate__(      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[int]				   =			self.__dict__.copy()
									UpperCamelCase				:		Optional[Any]				   =			None
									return state







			def __setstate__(      self						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		List[str]				   =			d

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

									UpperCamelCase				:		int				   =			load_spm(self.spm_file						,   self.sp_model_kwargs    )







			def   _a      (      self						,   _A						,   _A = None    ):






									'''simple docstring'''







									UpperCamelCase				:		int				   =			Path(_A    )
									assert save_dir.is_dir(), f"""{save_directory} should be a directory"""
									UpperCamelCase				:		Dict				   =			save_dir / (
									    (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
									)
									UpperCamelCase				:		Dict				   =			save_dir / (
									    (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
									)

									save_json(self.encoder						,   _A    )

									if os.path.abspath(self.spm_file    ) != os.path.abspath(_A    ) and os.path.isfile(self.spm_file    ):
															copyfile(self.spm_file						,   _A    )
									elif not os.path.isfile(self.spm_file    ):
															with open(_A						,   """wb"""    ) as fi:
																					UpperCamelCase				:		Tuple				   =			self.sp_model.serialized_model_proto()
																					fi.write(_A    )

									return (str(_A    ), str(_A    ))


def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Dict				   =			sentencepiece.SentencePieceProcessor(**SCREAMING_SNAKE_CASE     )
						spm.Load(str(SCREAMING_SNAKE_CASE     )     )
						return spm

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						with open(SCREAMING_SNAKE_CASE							,     """r"""     ) as f:
												return json.load(SCREAMING_SNAKE_CASE     )

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						with open(SCREAMING_SNAKE_CASE							,     """w"""     ) as f:
												json.dump(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     indent=2     )











import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


UpperCamelCase    							=      namedtuple(
    '_TestCommandArgs',
    [
        'dataset',
        'name',
        'cache_dir',
        'data_dir',
        'all_configs',
        'save_infos',
        'ignore_verifications',
        'force_redownload',
        'clear_cache',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)


def 				__lowerCamelCase     (							__lowerCAmelCase							:					List[Any]					,							__lowerCAmelCase							:					Any							)     ->  Optional[int]:
   return (abs(source - target							) / target) < 0.01



@pytest.mark.integration
def 				__lowerCamelCase     (							__lowerCAmelCase							:					str							)     ->  Union[str, Any]:
   __UpperCamelCase	:			Dict					=					_TestCommandArgs(dataset=__lowerCAmelCase					,							all_configs=__lowerCAmelCase					,							save_infos=__lowerCAmelCase							)
   __UpperCamelCase	:			List[Any]					=					TestCommand(*__lowerCAmelCase							)
   test_command.run()
   __UpperCamelCase	:			Any					=					os.path.join(__lowerCAmelCase					,							"""README.md"""							)
   assert os.path.exists(__lowerCAmelCase							)
   __UpperCamelCase	:			Optional[int]					=					DatasetInfosDict.from_directory(__lowerCAmelCase							)
   __UpperCamelCase	:			List[Any]					=					DatasetInfosDict(
       {
           """default""": DatasetInfo(
               features=Features(
                   {
                       """tokens""": Sequence(Value("""string"""							)							),
                       """ner_tags""": Sequence(
                           ClassLabel(names=["""O""", """B-PER""", """I-PER""", """B-ORG""", """I-ORG""", """B-LOC""", """I-LOC"""]							)							),
                       """langs""": Sequence(Value("""string"""							)							),
                       """spans""": Sequence(Value("""string"""							)							),
                   }							)					,							splits=[
                   {
                       """name""": """train""",
                       """num_bytes""": 2351563,
                       """num_examples""": 10000,
                   },
                   {
                       """name""": """validation""",
                       """num_bytes""": 238418,
                       """num_examples""": 1000,
                   },
               ]					,							download_size=3940680					,							dataset_size=2589981					,							)
       }							)
   assert dataset_infos.keys() == expected_dataset_infos.keys()
   for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
      __UpperCamelCase	,     __UpperCamelCase	:			Union[str, Any]					=					getattr(dataset_infos["""default"""]					,							__lowerCAmelCase							), getattr(expected_dataset_infos["""default"""]					,							__lowerCAmelCase							)
      if key == "num_bytes":
         assert is_apercent_close(__lowerCAmelCase					,							__lowerCAmelCase							)
      elif key == "splits":
         assert list(__lowerCAmelCase							) == list(__lowerCAmelCase							)
         for split in result:
            assert result[split].name == expected[split].name
            assert result[split].num_examples == expected[split].num_examples
            assert is_apercent_close(result[split].num_bytes					,							expected[split].num_bytes							)
      else:
         result == expected



