from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset




class    a__				(       lowerCamelCase_			):






     def        SCREAMING_SNAKE_CASE__       (			self		:		Union[str, Any]   ):





         """simple docstring"""



         return [
             {"col_1": 3, "col_2": "a"},
             {"col_1": 2, "col_2": "b"},
             {"col_1": 1, "col_2": "c"},
             {"col_1": 0, "col_2": "d"},
         ]






     def        SCREAMING_SNAKE_CASE__       (			self		:		Any   ):





         """simple docstring"""



         __lowerCamelCase          =  {'''col_1''': [3, 2, 1, 0], '''col_2''': ['''a''', '''b''', '''c''', '''d''']}
         return Dataset.from_dict(__snake_case   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  self._create_example_records()
         __lowerCamelCase          =  Dataset.from_list(__snake_case   )
         self.assertListEqual(dset.column_names  ,    ['''col_1''', '''col_2''']   )
         for i, r in enumerate(__snake_case   ):
             self.assertDictEqual(__snake_case  ,    example_records[i]   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Union[str, Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  self._create_example_records()
         __lowerCamelCase          =  Dataset.from_list(__snake_case   )
         __lowerCamelCase          =  Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]}   )
         self.assertEqual(dset.info  ,    dset_from_dict.info   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]   ):  # checks what happens with missing columns





         """simple docstring"""



         __lowerCamelCase          =  [{'''col_1''': 1}, {'''col_2''': '''x'''}]
         __lowerCamelCase          =  Dataset.from_list(__snake_case   )
         self.assertDictEqual(dset[0]  ,    {'''col_1''': 1}   )
         self.assertDictEqual(dset[1]  ,    {'''col_1''': None}   )  # NB: first record is used for columns






     def        SCREAMING_SNAKE_CASE__       (			self		:		Any   ):  # checks if the type can be inferred from the second record





         """simple docstring"""



         __lowerCamelCase          =  [{'''col_1''': []}, {'''col_1''': [1, 2]}]
         __lowerCamelCase          =  Dataset.from_list(__snake_case   )
         self.assertEqual(dset.info.features['''col_1''']  ,    Sequence(Value('''int64'''   )   )   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]   ):





         """simple docstring"""



         __lowerCamelCase          =  Dataset.from_list([]   )
         self.assertEqual(len(__snake_case   )  ,    0   )
         self.assertListEqual(dset.column_names  ,    []   )

'''simple docstring'''




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												=None


__UpperCAmelCase												=logging.get_logger(__name__)

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

__UpperCAmelCase												={
    "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												={
    "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    a__				(       UpperCAmelCase__			):
     lowerCamelCase  :					Dict							=VOCAB_FILES_NAMES
     lowerCamelCase  :					Optional[Any]							=PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase  :					Dict							=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase  :					int							=["input_ids", "attention_mask"]
     lowerCamelCase  :					Tuple							=TaTokenizer

     lowerCamelCase  :					List[int]							=[]






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





         """simple docstring"""



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

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






     @staticmethod
     def        SCREAMING_SNAKE_CASE__       (			a		:		Optional[int]  ,    a		:		List[str]  ,    a		:		Union[str, Any]   ):





         """simple docstring"""



         if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
             __lowerCamelCase          =  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.'''  ,    a  ,    )

         return max_model_length






     def        SCREAMING_SNAKE_CASE__       (			self		:		Tuple  ,    a		:		str  ,    a		:		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(a   ):
             logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
             return
         __lowerCamelCase          =  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   ):
             copyfile(self.vocab_file  ,    a   )
             logger.info(f"""Copy vocab file to {out_vocab_file}"""   )

         return (out_vocab_file,)






     def        SCREAMING_SNAKE_CASE__       (			self		:		int  ,    a		:		List[int]  ,    a		:		Optional[List[int]] = None   ):





         """simple docstring"""



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






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]  ,    a		:		List[int]  ,    a		:		Optional[List[int]] = None   ):





         """simple docstring"""



         __lowerCamelCase          =  [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		:		Any   ):





         """simple docstring"""



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






     def        SCREAMING_SNAKE_CASE__       (			self		:		Union[str, Any]   ):





         """simple docstring"""



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