

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	:     int		       = None


_UpperCAmelCase	:     Tuple		       = logging.get_logger(__name__)

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

_UpperCAmelCase	:     List[str]		       = {
    """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	:     str		       = {
    """t5-small""": 512,
    """t5-base""": 512,
    """t5-large""": 512,
    """t5-3b""": 512,
    """t5-11b""": 512,
}







class 				lowercase       ( __UpperCAmelCase    ):
				__SCREAMING_SNAKE_CASE      :				Dict     =    VOCAB_FILES_NAMES
				__SCREAMING_SNAKE_CASE      :				List[Any]     =    PRETRAINED_VOCAB_FILES_MAP
				__SCREAMING_SNAKE_CASE      :				Union[str, Any]     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__SCREAMING_SNAKE_CASE      :				Optional[Any]     =    ["input_ids", "attention_mask"]
				__SCREAMING_SNAKE_CASE      :				Any     =    TaTokenizer

				__SCREAMING_SNAKE_CASE      :				List[int]     =    []




				def __init__(      self   ,     snake_case=None   ,     snake_case=None   ,     snake_case="</s>"   ,     snake_case="<unk>"   ,     snake_case="<pad>"   ,     snake_case=100   ,     snake_case=None   ,     **snake_case   ,     ):
											# Add extra_ids to the special token list
											if extra_ids > 0 and additional_special_tokens is None:
																		snake_case_           =					[F'''<extra_id_{i}>''' for i in range(_lowerCamelCase )]
											elif extra_ids > 0 and additional_special_tokens is not None:
																		# Check that we have the right number of extra special tokens
																		snake_case_           =					len(set(filter(lambda snake_case : bool('extra_id_' in str(_lowerCamelCase ) )   ,     _lowerCamelCase ) ) )
																		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__(
											    _lowerCamelCase   ,     tokenizer_file=_lowerCamelCase   ,     eos_token=_lowerCamelCase   ,     unk_token=_lowerCamelCase   ,     pad_token=_lowerCamelCase   ,     extra_ids=_lowerCamelCase   ,     additional_special_tokens=_lowerCamelCase   ,     **_lowerCamelCase   ,     )

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




				@staticmethod
				def 					a							(      snake_case   ,     snake_case   ,     snake_case ):
											if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
																		snake_case_           =					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.'   ,     _lowerCamelCase   ,     )

											return max_model_length




				def 					a							(      self   ,     snake_case   ,     snake_case = None ):
											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(_lowerCamelCase ):
																		logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
																		return
											snake_case_           =					os.path.join(
											    _lowerCamelCase   ,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )

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

											return (out_vocab_file,)




				def 					a							(      self   ,     snake_case   ,     snake_case = None ):
											snake_case_           =					token_ids_a + [self.eos_token_id]
											if token_ids_a is None:
																		return self.prefix_tokens + token_ids_a
											else:
																		snake_case_           =					token_ids_a + [self.eos_token_id]
																		return self.prefix_tokens + token_ids_a + token_ids_a




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




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







import os

try:
					from .build_directory_md import good_file_paths
except ImportError:
					from build_directory_md import good_file_paths  # type: ignore

_UpperCAmelCase	:     Optional[int]		       = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

_UpperCAmelCase	:     Dict		       = [file for file in filepaths if file != file.lower()]
if upper_files:
					print(F'''{len(upper_files)} files contain uppercase characters:''')
					print("""\n""".join(upper_files) + """\n""")

_UpperCAmelCase	:     Tuple		       = [file for file in filepaths if """ """ in file]
if space_files:
					print(F'''{len(space_files)} files contain space characters:''')
					print("""\n""".join(space_files) + """\n""")

_UpperCAmelCase	:     Union[str, Any]		       = [file for file in filepaths if """-""" in file]
if hyphen_files:
					print(F'''{len(hyphen_files)} files contain hyphen characters:''')
					print("""\n""".join(hyphen_files) + """\n""")

_UpperCAmelCase	:     List[Any]		       = [file for file in filepaths if os.sep not in file]
if nodir_files:
					print(F'''{len(nodir_files)} files are not in a directory:''')
					print("""\n""".join(nodir_files) + """\n""")

_UpperCAmelCase	:     List[str]		       = len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
					import sys

					sys.exit(bad_files)




