

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							:  List[Any]   	=						None


lowerCAmelCase							:  str   	=						logging.get_logger(__name__)

lowerCAmelCase							:  Union[str, Any]   	=						{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCAmelCase							:  Optional[Any]   	=						{
    """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							:  str   	=						{
    """t5-small""": 512,
    """t5-base""": 512,
    """t5-large""": 512,
    """t5-3b""": 512,
    """t5-11b""": 512,
}



class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							Any										=     VOCAB_FILES_NAMES
			_UpperCAmelCase   :							Dict										=     PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase   :							int										=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCAmelCase   :							str										=     ['''input_ids''', '''attention_mask''']
			_UpperCAmelCase   :							Optional[int]										=     TaTokenizer

			_UpperCAmelCase   :							List[int]										=     []



			def __init__( self  :     Any   ,						lowerCAmelCase__  :     Optional[int]=None   ,						lowerCAmelCase__  :     Dict=None   ,						lowerCAmelCase__  :     int="</s>"   ,						lowerCAmelCase__  :     List[Any]="<unk>"   ,						lowerCAmelCase__  :     str="<pad>"   ,						lowerCAmelCase__  :     List[Any]=100   ,						lowerCAmelCase__  :     List[Any]=None   ,						**lowerCAmelCase__  :     Tuple   ,						):
									# Add extra_ids to the special token list
									if extra_ids > 0 and additional_special_tokens is None:
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       [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
															SCREAMING_SNAKE_CASE_: List[str]  		=       len(set(filter(lambda lowerCAmelCase__: 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__   ,						)

									SCREAMING_SNAKE_CASE_: Dict  		=       vocab_file
									SCREAMING_SNAKE_CASE_: Dict  		=       False if not self.vocab_file else True
									SCREAMING_SNAKE_CASE_: List[str]  		=       extra_ids



			@staticmethod
			def        _SCREAMING_SNAKE_CASE			( lowerCAmelCase__  :     List[str]   ,						lowerCAmelCase__  :     Dict   ,						lowerCAmelCase__  :     Any):
									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."   ,						lowerCAmelCase__   ,						)

									return max_model_length



			def        _SCREAMING_SNAKE_CASE			( self  :     Any   ,						lowerCAmelCase__  :     str   ,						lowerCAmelCase__  :     Optional[str] = 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
									SCREAMING_SNAKE_CASE_: Tuple  		=       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        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     List[int]   ,						lowerCAmelCase__  :     Optional[List[int]] = None):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       token_ids_a + [self.eos_token_id]
									if token_ids_a is None:
															return self.prefix_tokens + token_ids_a
									else:
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       token_ids_a + [self.eos_token_id]
															return self.prefix_tokens + token_ids_a + token_ids_a



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]   ,						lowerCAmelCase__  :     List[int]   ,						lowerCAmelCase__  :     Optional[List[int]] = None):
									SCREAMING_SNAKE_CASE_: List[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        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									return list(
									    set(filter(lambda lowerCAmelCase__: bool(re.search(R"<extra_id_\d+>"   ,						lowerCAmelCase__)) is not None   ,						self.additional_special_tokens)))




			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									return [self.convert_tokens_to_ids(lowerCAmelCase__) for token in self.get_sentinel_tokens()]









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
						from ..ta.tokenization_ta import TaTokenizer
else:
						from ...utils.dummy_sentencepiece_objects import TaTokenizer

lowerCAmelCase							:  Any   	=						TaTokenizer

if is_tokenizers_available():
						from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
						from ...utils.dummy_tokenizers_objects import TaTokenizerFast

lowerCAmelCase							:  Dict   	=						TaTokenizerFast

lowerCAmelCase							:  Optional[int]   	=						{"""configuration_mt5""": ["""MT5Config""", """MT5OnnxConfig"""]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  int   	=						[
						    """MT5EncoderModel""",
						    """MT5ForConditionalGeneration""",
						    """MT5ForQuestionAnswering""",
						    """MT5Model""",
						    """MT5PreTrainedModel""",
						    """MT5Stack""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  Tuple   	=						["""TFMT5EncoderModel""", """TFMT5ForConditionalGeneration""", """TFMT5Model"""]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  List[str]   	=						["""FlaxMT5EncoderModel""", """FlaxMT5ForConditionalGeneration""", """FlaxMT5Model"""]


if TYPE_CHECKING:
						from .configuration_mta import MTaConfig, MTaOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mta import (
												    MTaEncoderModel,
												    MTaForConditionalGeneration,
												    MTaForQuestionAnswering,
												    MTaModel,
												    MTaPreTrainedModel,
												    MTaStack,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
						import sys

						lowerCAmelCase							:  Optional[Any]   	=						_LazyModule(
						    __name__,
						    globals()["""__file__"""],
						    _import_structure,
						    extra_objects={"""MT5Tokenizer""": MTaTokenizer, """MT5TokenizerFast""": MTaTokenizerFast},
						    module_spec=__spec__,
						)






