



import warnings
from typing import Dict, List, Optional, Tuple

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


__snake_case						:int      		=     logging.get_logger(__name__)

class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    int											=						['''input_ids''', '''attention_mask''']



       def __init__(		self							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]="</s>"     ,		__SCREAMING_SNAKE_CASE							:     str="<unk>"     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]="<pad>"     ,		__SCREAMING_SNAKE_CASE							:     str=125     ,		__SCREAMING_SNAKE_CASE							:     Dict=None     ,		**__SCREAMING_SNAKE_CASE							:     int     ,		):


           '''simple docstring'''

           if extra_ids > 0 and additional_special_tokens is None:
               __a						       =				[F'<extra_id_{i}>' for i in range(__SCREAMING_SNAKE_CASE)]
           elif extra_ids > 0 and additional_special_tokens is not None:
               # Check that we have the right number of extra_id special tokens
               __a						       =				len(set(filter(lambda __SCREAMING_SNAKE_CASE: bool('''extra_id''' in str(__SCREAMING_SNAKE_CASE))     ,		__SCREAMING_SNAKE_CASE)))
               if extra_tokens != extra_ids:
                   raise ValueError(
                       F'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
                       ''' provided to ByT5Tokenizer. In this case the additional_special_tokens must include the'''
                       ''' extra_ids tokens''')

           __a						       =				AddedToken(__SCREAMING_SNAKE_CASE     ,		lstrip=__SCREAMING_SNAKE_CASE     ,		rstrip=__SCREAMING_SNAKE_CASE) if isinstance(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE) else pad_token
           __a						       =				AddedToken(__SCREAMING_SNAKE_CASE     ,		lstrip=__SCREAMING_SNAKE_CASE     ,		rstrip=__SCREAMING_SNAKE_CASE) if isinstance(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE) else eos_token
           __a						       =				AddedToken(__SCREAMING_SNAKE_CASE     ,		lstrip=__SCREAMING_SNAKE_CASE     ,		rstrip=__SCREAMING_SNAKE_CASE) if isinstance(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE) else unk_token

           super().__init__(
               eos_token=__SCREAMING_SNAKE_CASE     ,		unk_token=__SCREAMING_SNAKE_CASE     ,		pad_token=__SCREAMING_SNAKE_CASE     ,		extra_ids=__SCREAMING_SNAKE_CASE     ,		additional_special_tokens=__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE     ,		)

           __a						       =				extra_ids

           __a						       =				2**8  # utf is 8 bits

           # define special tokens dict
           __a						       =				{
               self.pad_token: 0,
               self.eos_token: 1,
               self.unk_token: 2,
           }
           __a						       =				len(self.special_tokens_encoder)
           __a						       =				len(__SCREAMING_SNAKE_CASE)
           for i, token in enumerate(__SCREAMING_SNAKE_CASE):
               __a						       =				self.vocab_size + i - n
           __a						       =				{v: k for k, v in self.special_tokens_encoder.items()}



       @property
       def 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           return self._utf_vocab_size + self._num_special_tokens + self._extra_ids



       def 		_lowerCamelCase			(		self							:     str     ,		__SCREAMING_SNAKE_CASE							:     List[int]     ,		__SCREAMING_SNAKE_CASE							:     Optional[List[int]] = None     ,		__SCREAMING_SNAKE_CASE							:     bool = False):


           '''simple docstring'''

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

           # normal case: some special tokens
           if token_ids_a is None:
               return ([0] * len(__SCREAMING_SNAKE_CASE)) + [1]
           return ([0] * len(__SCREAMING_SNAKE_CASE)) + [1] + ([0] * len(__SCREAMING_SNAKE_CASE)) + [1]



       def 		_lowerCamelCase			(		self							:     str     ,		__SCREAMING_SNAKE_CASE							:     List[int]):


           '''simple docstring'''

           if len(__SCREAMING_SNAKE_CASE) > 0 and token_ids[-1] == self.eos_token_id:
               warnings.warn(
                   F'This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'
                   ''' eos tokens being added.''')
               return token_ids
           else:
               return token_ids + [self.eos_token_id]



       def 		_lowerCamelCase			(		self							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     List[int]     ,		__SCREAMING_SNAKE_CASE							:     Optional[List[int]] = None):


           '''simple docstring'''

           __a						       =				[self.eos_token_id]

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



       def 		_lowerCamelCase			(		self							:     str     ,		__SCREAMING_SNAKE_CASE							:     List[int]     ,		__SCREAMING_SNAKE_CASE							:     Optional[List[int]] = None):


           '''simple docstring'''

           __a						       =				self._add_eos_if_not_present(__SCREAMING_SNAKE_CASE)
           if token_ids_a is None:
               return token_ids_a
           else:
               __a						       =				self._add_eos_if_not_present(__SCREAMING_SNAKE_CASE)
               return token_ids_a + token_ids_a



       def 		_lowerCamelCase			(		self							:     Any     ,		__SCREAMING_SNAKE_CASE							:     str):


           '''simple docstring'''

           __a						       =				[chr(__SCREAMING_SNAKE_CASE) for i in text.encode('''utf-8''')]
           return tokens



       def 		_lowerCamelCase			(		self							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     int):


           '''simple docstring'''

           if token in self.special_tokens_encoder:
               __a						       =				self.special_tokens_encoder[token]
           elif token in self.added_tokens_encoder:
               __a						       =				self.added_tokens_encoder[token]
           elif len(__SCREAMING_SNAKE_CASE) != 1:
               __a						       =				self.unk_token_id
           else:
               __a						       =				ord(__SCREAMING_SNAKE_CASE) + self._num_special_tokens
           return token_id



       def 		_lowerCamelCase			(		self							:     Any     ,		__SCREAMING_SNAKE_CASE							:     int):


           '''simple docstring'''

           if index in self.special_tokens_decoder:
               __a						       =				self.special_tokens_decoder[index]
           else:
               __a						       =				chr(index - self._num_special_tokens)
           return token



       def 		_lowerCamelCase			(		self							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     str):


           '''simple docstring'''

           __a						       =				B''''''
           for token in tokens:
               if token in self.special_tokens_decoder:
                   __a						       =				self.special_tokens_decoder[token].encode('''utf-8''')
               elif token in self.added_tokens_decoder:
                   __a						       =				self.special_tokens_decoder[token].encode('''utf-8''')
               elif token in self.special_tokens_encoder:
                   __a						       =				token.encode('''utf-8''')
               elif token in self.added_tokens_encoder:
                   __a						       =				token.encode('''utf-8''')
               else:
                   __a						       =				bytes([ord(__SCREAMING_SNAKE_CASE)])
               bstring += tok_string
           __a						       =				bstring.decode('''utf-8'''     ,		errors='''ignore''')
           return string





       def 		_lowerCamelCase			(		self							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     str     ,		__SCREAMING_SNAKE_CASE							:     Optional[str] = None):


           '''simple docstring'''

           return ()









'''simple docstring'''


import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):
		assert isinstance(__lowerCAmelCase					,       __lowerCAmelCase      )
		assert dataset.num_rows == 4
		assert dataset.num_columns == 1
		assert dataset.column_names == ["text"]
		for feature, expected_dtype in expected_features.items():
				assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize("keep_in_memory"					,       [False, True]      )
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				Union[str, Any]	    =					tmp_path / "cache"
		_UpperCAmelCase						:				int	    =					{"text": "string"}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				_UpperCAmelCase						:				Union[str, Any]	    =					TextDatasetReader(__lowerCAmelCase					,       cache_dir=__lowerCAmelCase					,       keep_in_memory=__lowerCAmelCase      ).read()
		_check_text_dataset(__lowerCAmelCase					,       __lowerCAmelCase      )






@pytest.mark.parametrize(
    "features"					,       [
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]					,       )
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				List[Any]	    =					tmp_path / "cache"
		_UpperCAmelCase						:				Any	    =					{"text": "string"}
		_UpperCAmelCase						:				Optional[Any]	    =					features.copy() if features else default_expected_features
		_UpperCAmelCase						:				Union[str, Any]	    =					(
		    Features({feature: Value(__lowerCAmelCase      ) for feature, dtype in features.items()}      ) if features is not None else None
		)
		_UpperCAmelCase						:				Union[str, Any]	    =					TextDatasetReader(__lowerCAmelCase					,       features=__lowerCAmelCase					,       cache_dir=__lowerCAmelCase      ).read()
		_check_text_dataset(__lowerCAmelCase					,       __lowerCAmelCase      )






@pytest.mark.parametrize("split"					,       [None, NamedSplit("train"      ), "train", "test"]      )
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				Any	    =					tmp_path / "cache"
		_UpperCAmelCase						:				Dict	    =					{"text": "string"}
		_UpperCAmelCase						:				Union[str, Any]	    =					TextDatasetReader(__lowerCAmelCase					,       cache_dir=__lowerCAmelCase					,       split=__lowerCAmelCase      ).read()
		_check_text_dataset(__lowerCAmelCase					,       __lowerCAmelCase      )
		assert dataset.split == split if split else "train"






@pytest.mark.parametrize("path_type"					,       [str, list]      )
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		if issubclass(__lowerCAmelCase					,       __lowerCAmelCase      ):
				_UpperCAmelCase						:				Union[str, Any]	    =					text_path
		elif issubclass(__lowerCAmelCase					,       __lowerCAmelCase      ):
				_UpperCAmelCase						:				int	    =					[text_path]
		_UpperCAmelCase						:				List[Any]	    =					tmp_path / "cache"
		_UpperCAmelCase						:				Union[str, Any]	    =					{"text": "string"}
		_UpperCAmelCase						:				Optional[int]	    =					TextDatasetReader(__lowerCAmelCase					,       cache_dir=__lowerCAmelCase      ).read()
		_check_text_dataset(__lowerCAmelCase					,       __lowerCAmelCase      )






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase=("train",)      ):
		assert isinstance(__lowerCAmelCase					,       __lowerCAmelCase      )
		for split in splits:
				_UpperCAmelCase						:				List[str]	    =					dataset_dict[split]
				assert dataset.num_rows == 4
				assert dataset.num_columns == 1
				assert dataset.column_names == ["text"]
				for feature, expected_dtype in expected_features.items():
						assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize("keep_in_memory"					,       [False, True]      )
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				List[Any]	    =					tmp_path / "cache"
		_UpperCAmelCase						:				Tuple	    =					{"text": "string"}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				_UpperCAmelCase						:				Any	    =					TextDatasetReader({"train": text_path}					,       cache_dir=__lowerCAmelCase					,       keep_in_memory=__lowerCAmelCase      ).read()
		_check_text_datasetdict(__lowerCAmelCase					,       __lowerCAmelCase      )






@pytest.mark.parametrize(
    "features"					,       [
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]					,       )
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				List[Any]	    =					tmp_path / "cache"
		# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
		_UpperCAmelCase						:				List[Any]	    =					{"text": "string"}
		_UpperCAmelCase						:				List[str]	    =					features.copy() if features else default_expected_features
		_UpperCAmelCase						:				Optional[int]	    =					(
		    Features({feature: Value(__lowerCAmelCase      ) for feature, dtype in features.items()}      ) if features is not None else None
		)
		_UpperCAmelCase						:				Tuple	    =					TextDatasetReader({"train": text_path}					,       features=__lowerCAmelCase					,       cache_dir=__lowerCAmelCase      ).read()
		_check_text_datasetdict(__lowerCAmelCase					,       __lowerCAmelCase      )






@pytest.mark.parametrize("split"					,       [None, NamedSplit("train"      ), "train", "test"]      )
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		if split:
				_UpperCAmelCase						:				int	    =					{split: text_path}
		else:
				_UpperCAmelCase						:				Tuple	    =					"train"
				_UpperCAmelCase						:				List[str]	    =					{"train": text_path, "test": text_path}
		_UpperCAmelCase						:				Optional[Any]	    =					tmp_path / "cache"
		_UpperCAmelCase						:				Optional[int]	    =					{"text": "string"}
		_UpperCAmelCase						:				int	    =					TextDatasetReader(__lowerCAmelCase					,       cache_dir=__lowerCAmelCase      ).read()
		_check_text_datasetdict(__lowerCAmelCase					,       __lowerCAmelCase					,       splits=list(path.keys()      )      )
		assert all(dataset[split].split == split for split in path.keys()      )



