





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:
				UpperCamelCase__   : List[str]					  = None


UpperCamelCase__   : Dict					  = logging.get_logger(__name__)

UpperCamelCase__   : str					  = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

UpperCamelCase__   : 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
UpperCamelCase__   : Tuple					  = {
    """t5-small""": 5_12,
    """t5-base""": 5_12,
    """t5-large""": 5_12,
    """t5-3b""": 5_12,
    """t5-11b""": 5_12,
}







class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					lowerCamelCase     :     List[str]			    =       VOCAB_FILES_NAMES
					lowerCamelCase     :     Any			    =       PRETRAINED_VOCAB_FILES_MAP
					lowerCamelCase     :     int			    =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCamelCase     :     str			    =       ['input_ids', 'attention_mask']
					lowerCamelCase     :     List[str]			    =       TaTokenizer

					lowerCamelCase     :     List[int]			    =       []





					def __init__(						self    :  List[str] ,					__lowercase    :  Optional[Any]=None ,					__lowercase    :  Tuple=None ,					__lowercase    :  int="</s>" ,					__lowercase    :  Any="<unk>" ,					__lowercase    :  Union[str, Any]="<pad>" ,					__lowercase    :  Optional[Any]=1_00 ,					__lowercase    :  Union[str, Any]=None ,					**__lowercase    :  Union[str, Any] ,					):






						'''simple docstring'''







						if extra_ids > 0 and additional_special_tokens is None:
							UpperCAmelCase_								=     [F"""<extra_id_{i}>""" for i in range(__lowercase		)]
						elif extra_ids > 0 and additional_special_tokens is not None:
							# Check that we have the right number of extra special tokens
							UpperCAmelCase_								=     len(set(filter(lambda __lowercase		: bool("""extra_id_""" in str(__lowercase		)		) ,					__lowercase		)		)		)
							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__(
						    __lowercase ,					tokenizer_file=__lowercase ,					eos_token=__lowercase ,					unk_token=__lowercase ,					pad_token=__lowercase ,					extra_ids=__lowercase ,					additional_special_tokens=__lowercase ,					**__lowercase ,					)

						UpperCAmelCase_								=     vocab_file
						UpperCAmelCase_								=     False if not self.vocab_file else True
						UpperCAmelCase_								=     extra_ids





					@staticmethod
					def        SCREAMING_SNAKE_CASE							(						__lowercase    :  int ,					__lowercase    :  Tuple ,					__lowercase    :  Any		):






						'''simple docstring'''







						if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
							UpperCAmelCase_								=     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.""" ,					__lowercase ,					)

						return max_model_length





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any] ,					__lowercase    :  str ,					__lowercase    :  Optional[str] = None		):






						'''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(__lowercase		):
							logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
							return
						UpperCAmelCase_								=     os.path.join(
						    __lowercase ,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)

						if os.path.abspath(self.vocab_file		) != os.path.abspath(__lowercase		):
							copyfile(self.vocab_file ,					__lowercase		)
							logger.info(F"""Copy vocab file to {out_vocab_file}"""		)

						return (out_vocab_file,)





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






						'''simple docstring'''







						UpperCAmelCase_								=     token_ids_a + [self.eos_token_id]
						if token_ids_a is None:
							return self.prefix_tokens + token_ids_a
						else:
							UpperCAmelCase_								=     token_ids_a + [self.eos_token_id]
							return self.prefix_tokens + token_ids_a + token_ids_a





					def        SCREAMING_SNAKE_CASE							(						self    :  List[str] ,					__lowercase    :  List[int] ,					__lowercase    :  Optional[List[int]] = None		):






						'''simple docstring'''







						UpperCAmelCase_								=     [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        SCREAMING_SNAKE_CASE							(						self    :  Union[str, Any]		):






						'''simple docstring'''







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





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[int]		):






						'''simple docstring'''







						return [self.convert_tokens_to_ids(__lowercase		) for token in self.get_sentinel_tokens()]









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    A_(			A				,      A   ):
	assert isinstance(A				,      A   )
	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    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     tmp_path / """cache"""
	UpperCAmelCase_								=     {"""text""": """string"""}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		UpperCAmelCase_								=     TextDatasetReader(A				,      cache_dir=A				,      keep_in_memory=A   ).read()
	_check_text_dataset(A				,      A   )




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




@pytest.mark.parametrize("""split"""				,      [None, NamedSplit("""train"""   ), """train""", """test"""]   )
def    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     tmp_path / """cache"""
	UpperCAmelCase_								=     {"""text""": """string"""}
	UpperCAmelCase_								=     TextDatasetReader(A				,      cache_dir=A				,      split=A   ).read()
	_check_text_dataset(A				,      A   )
	assert dataset.split == split if split else "train"




@pytest.mark.parametrize("""path_type"""				,      [str, list]   )
def    A_(			A				,      A				,      A   ):
	if issubclass(A				,      A   ):
		UpperCAmelCase_								=     text_path
	elif issubclass(A				,      A   ):
		UpperCAmelCase_								=     [text_path]
	UpperCAmelCase_								=     tmp_path / """cache"""
	UpperCAmelCase_								=     {"""text""": """string"""}
	UpperCAmelCase_								=     TextDatasetReader(A				,      cache_dir=A   ).read()
	_check_text_dataset(A				,      A   )




def    A_(			A				,      A				,      A=("train",)   ):
	assert isinstance(A				,      A   )
	for split in splits:
		UpperCAmelCase_								=     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    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     tmp_path / """cache"""
	UpperCAmelCase_								=     {"""text""": """string"""}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		UpperCAmelCase_								=     TextDatasetReader({"""train""": text_path}				,      cache_dir=A				,      keep_in_memory=A   ).read()
	_check_text_datasetdict(A				,      A   )




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




@pytest.mark.parametrize("""split"""				,      [None, NamedSplit("""train"""   ), """train""", """test"""]   )
def    A_(			A				,      A				,      A   ):
	if split:
		UpperCAmelCase_								=     {split: text_path}
	else:
		UpperCAmelCase_								=     """train"""
		UpperCAmelCase_								=     {"""train""": text_path, """test""": text_path}
	UpperCAmelCase_								=     tmp_path / """cache"""
	UpperCAmelCase_								=     {"""text""": """string"""}
	UpperCAmelCase_								=     TextDatasetReader(A				,      cache_dir=A   ).read()
	_check_text_datasetdict(A				,      A				,      splits=list(path.keys()   )   )
	assert all(dataset[split].split == split for split in path.keys()   )


