"""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''': 512,
    '''t5-base''': 512,
    '''t5-large''': 512,
    '''t5-3b''': 512,
    '''t5-11b''': 512,
}







class      UpperCAmelCase_       (						_lowercase):
							snake_case__        =      VOCAB_FILES_NAMES
							snake_case__        =      PRETRAINED_VOCAB_FILES_MAP
							snake_case__        =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							snake_case__        =      ["input_ids", "attention_mask"]
							snake_case__        =      TaTokenizer

							snake_case__        =      []






							def __init__(		self :      Tuple     ,						__UpperCamelCase :      Tuple=None     ,						__UpperCamelCase :      int=None     ,						__UpperCamelCase :      str="</s>"     ,						__UpperCamelCase :      str="<unk>"     ,						__UpperCamelCase :      str="<pad>"     ,						__UpperCamelCase :      int=100     ,						__UpperCamelCase :      Optional[int]=None     ,						**__UpperCamelCase :      Tuple     ,						)       ->       Optional[int]:
												# Add extra_ids to the special token list
												if extra_ids > 0 and additional_special_tokens is None:
																	_UpperCamelCase      						=	[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
																	_UpperCamelCase      						=	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__     ,						)

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






							@staticmethod
							def       _UpperCamelCase			(		__UpperCamelCase :      List[Any]     ,						__UpperCamelCase :      Optional[int]     ,						__UpperCamelCase :      Optional[Any]  )       ->       List[str]:
												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.'''     ,						UpperCAmelCase__     ,						)

												return max_model_length






							def       _UpperCamelCase			(		self :      Optional[int]     ,						__UpperCamelCase :      str     ,						__UpperCamelCase :      Optional[str] = None  )       ->       Tuple[str]:
												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
												_UpperCamelCase      						=	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       _UpperCamelCase			(		self :      List[Any]     ,						__UpperCamelCase :      List[int]     ,						__UpperCamelCase :      Optional[List[int]] = None  )       ->       List[int]:
												_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       _UpperCamelCase			(		self :      Union[str, Any]     ,						__UpperCamelCase :      List[int]     ,						__UpperCamelCase :      Optional[List[int]] = None  )       ->       List[int]:
												_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       _UpperCamelCase			(		self :      int  )       ->       Union[str, Any]:
												return list(
												    set(filter(lambda __UpperCamelCase  : bool(re.search(R'''<extra_id_\d+>'''     ,						UpperCAmelCase__  )  ) is not None     ,						self.additional_special_tokens  )  )  )






							def       _UpperCamelCase			(		self :      List[str]  )       ->       Tuple:
												return [self.convert_tokens_to_ids(UpperCAmelCase__  ) for token in self.get_sentinel_tokens()]




"""simple docstring"""







import pytest

from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs




@pytest.mark.parametrize(
    "kwargs, expected"  ,						[
        ({"num_shards": 0, "max_num_jobs": 1}, []),
        ({"num_shards": 10, "max_num_jobs": 1}, [range(10   )]),
        ({"num_shards": 10, "max_num_jobs": 10}, [range(lowerCAmelCase_  ,						i + 1   ) for i in range(10   )]),
        ({"num_shards": 1, "max_num_jobs": 10}, [range(1   )]),
        ({"num_shards": 10, "max_num_jobs": 3}, [range(0  ,						4   ), range(4  ,						7   ), range(7  ,						10   )]),
        ({"num_shards": 3, "max_num_jobs": 10}, [range(0  ,						1   ), range(1  ,						2   ), range(2  ,						3   )]),
    ]  ,						)
def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				_distribute_shards(**lowerCAmelCase_   )
					assert out == expected




@pytest.mark.parametrize(
    "gen_kwargs, max_num_jobs, expected"  ,						[
        ({"foo": 0}, 10, [{"foo": 0}]),
        ({"shards": [0, 1, 2, 3]}, 1, [{"shards": [0, 1, 2, 3]}]),
        ({"shards": [0, 1, 2, 3]}, 4, [{"shards": [0]}, {"shards": [1]}, {"shards": [2]}, {"shards": [3]}]),
        ({"shards": [0, 1]}, 4, [{"shards": [0]}, {"shards": [1]}]),
        ({"shards": [0, 1, 2, 3]}, 2, [{"shards": [0, 1]}, {"shards": [2, 3]}]),
    ]  ,						)
def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				_split_gen_kwargs(lowerCAmelCase_  ,						lowerCAmelCase_   )
					assert out == expected




@pytest.mark.parametrize(
    "gen_kwargs, expected"  ,						[
        ({"foo": 0}, 1),
        ({"shards": [0]}, 1),
        ({"shards": [0, 1, 2, 3]}, 4),
        ({"shards": [0, 1, 2, 3], "foo": 0}, 4),
        ({"shards": [0, 1, 2, 3], "other": (0, 1)}, 4),
        ({"shards": [0, 1, 2, 3], "shards2": [0, 1]}, RuntimeError),
    ]  ,						)
def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					if expected is RuntimeError:
										with pytest.raises(lowerCAmelCase_   ):
															_number_of_shards_in_gen_kwargs(lowerCAmelCase_   )
					else:
										__SCREAMING_SNAKE_CASE						   =				_number_of_shards_in_gen_kwargs(lowerCAmelCase_   )
										assert out == expected
