





import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_ta import TaTokenizer
else:
		_lowerCamelCase :    Dict    							= None


_lowerCamelCase :    Dict    							= logging.get_logger(__name__)

_lowerCamelCase :    Dict    							= {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCamelCase :    Dict    							= {
    '''vocab_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/tokenizer.json''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/tokenizer.json''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/tokenizer.json''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/tokenizer.json''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/tokenizer.json''',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
_lowerCamelCase :    Union[str, Any]    							= {
    '''t5-small''': 5_1_2,
    '''t5-base''': 5_1_2,
    '''t5-large''': 5_1_2,
    '''t5-3b''': 5_1_2,
    '''t5-11b''': 5_1_2,
}


class 						lowerCamelCase       (_UpperCAmelCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   VOCAB_FILES_NAMES
							UpperCAmelCase_      						=   PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase_      						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase_      						=   ["input_ids", "attention_mask"]
							UpperCAmelCase_      						=   TaTokenizer

							UpperCAmelCase_      						=   []






							def __init__( self     :					str,			_UpperCAmelCase     :					Any=None,			_UpperCAmelCase     :					List[str]=None,			_UpperCAmelCase     :					int="</s>",			_UpperCAmelCase     :					Tuple="<unk>",			_UpperCAmelCase     :					Optional[int]="<pad>",			_UpperCAmelCase     :					List[str]=1_0_0,			_UpperCAmelCase     :					List[str]=None,			**_UpperCAmelCase     :					List[Any],			)				->			List[str]:





														"""simple docstring"""
														# Add extra_ids to the special token list
														if extra_ids > 0 and additional_special_tokens is None:
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						[F'''<extra_id_{i}>''' for i in range(_UpperCAmelCase						)]
														elif extra_ids > 0 and additional_special_tokens is not None:
																					# Check that we have the right number of extra special tokens
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						len(set(filter(lambda _UpperCAmelCase						: bool("extra_id_" in str(_UpperCAmelCase						)						),			_UpperCAmelCase						)						)						)
																					if extra_tokens != extra_ids:
																												raise ValueError(
																												    F'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
																												    " provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"
																												    " tokens"						)

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

														SCREAMING_SNAKE_CASE__       :      Tuple     =						vocab_file
														SCREAMING_SNAKE_CASE__       :      int     =						False if not self.vocab_file else True
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						extra_ids






							@staticmethod
							def  A_     ( _UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					Any,			_UpperCAmelCase     :					str						)				->			Union[str, Any]:





														"""simple docstring"""
														if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
																					if init_max_model_length is not None and init_max_model_length != max_model_length:
																												return init_max_model_length
																					elif init_max_model_length is None:
																												warnings.warn(
																												    "This tokenizer was incorrectly instantiated with a model max length of"
																												    F''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
																												    " behavior is kept to avoid breaking backwards compatibility when padding/encoding with"
																												    " `truncation is True`.\n- Be aware that you SHOULD NOT rely on"
																												    F''' {pretrained_model_name_or_path} automatically truncating your input to'''
																												    F''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
																												    F''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
																												    " `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"
																												    " instantiate this tokenizer with `model_max_length` set to your preferred value.",			_UpperCAmelCase,			)

														return max_model_length






							def  A_     ( self     :					int,			_UpperCAmelCase     :					str,			_UpperCAmelCase     :					Optional[int] = None						)				->			List[Any]:





														"""simple docstring"""
														if not self.can_save_slow_tokenizer:
																					raise ValueError(
																					    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
																					    "tokenizer."						)

														if not os.path.isdir(_UpperCAmelCase						):
																					logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
																					return
														SCREAMING_SNAKE_CASE__       :      str     =						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						):
																					copyfile(self.vocab_file,			_UpperCAmelCase						)
																					logger.info(F'''Copy vocab file to {out_vocab_file}'''						)

														return (out_vocab_file,)






							def  A_     ( self     :					List[Any],			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					int = None						)				->			Any:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						token_ids_a + [self.eos_token_id]
														if token_ids_a is None:
																					return self.prefix_tokens + token_ids_a
														else:
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						token_ids_a + [self.eos_token_id]
																					return self.prefix_tokens + token_ids_a + token_ids_a






							def  A_     ( self     :					List[str],			_UpperCAmelCase     :					Any,			_UpperCAmelCase     :					Tuple = None						)				->			Union[str, Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						[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  A_     ( self     :					List[str]						)				->			List[Any]:





														"""simple docstring"""
														return list(
														    set(filter(lambda _UpperCAmelCase						: bool(re.search(r"<extra_id_\d+>",			_UpperCAmelCase						)						) is not None,			self.additional_special_tokens						)						)						)






							def  A_     ( self     :					str						)				->			Dict:





														"""simple docstring"""
														return [self.convert_tokens_to_ids(_UpperCAmelCase						) for token in self.get_sentinel_tokens()]













import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict




@pytest.mark.parametrize(
    "split_dict"			,				[
        SplitDict(),
        SplitDict({"train": SplitInfo(name="train"			,				num_bytes=13_37			,				num_examples=42			,				dataset_name="my_dataset"       )}       ),
        SplitDict({"train": SplitInfo(name="train"			,				num_bytes=13_37			,				num_examples=42       )}       ),
        SplitDict({"train": SplitInfo()}       ),
    ]			,				)
def _a (  SCREAMING_SNAKE_CASE__      :  SplitDict       )	->    str:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Dict     =						split_dict._to_yaml_list()
							assert len(SCREAMING_SNAKE_CASE__       ) == len(SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						SplitDict._from_yaml_list(SCREAMING_SNAKE_CASE__       )
							for split_name, split_info in split_dict.items():
														# dataset_name field is deprecated, and is therefore not part of the YAML dump
														SCREAMING_SNAKE_CASE__       :      str     =						None
														# the split name of split_dict takes over the name of the split info object
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						split_name
							assert split_dict == reloaded







@pytest.mark.parametrize(
    "split_info"			,				[SplitInfo(), SplitInfo(dataset_name=SCREAMING_SNAKE_CASE__       ), SplitInfo(dataset_name="my_dataset"       )]       )
def _a (  SCREAMING_SNAKE_CASE__      :  Any       )	->    Dict:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      str     =						asdict(SplitDict({"train": split_info}       )       )
							assert "dataset_name" in split_dict_asdict["train"]
							assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name






