



"""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:
					SCREAMING_SNAKE_CASE_				= None


SCREAMING_SNAKE_CASE_				= logging.get_logger(__name__)

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

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




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					VOCAB_FILES_NAMES
							__lowerCAmelCase    					=					PRETRAINED_VOCAB_FILES_MAP
							__lowerCAmelCase    					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__lowerCAmelCase    					=					["input_ids", "attention_mask"]
							__lowerCAmelCase    					=					TaTokenizer

							__lowerCAmelCase    					=					[]






							def __init__(   self			,     a_=None			,     a_=None			,     a_="</s>"			,     a_="<unk>"			,     a_="<pad>"			,     a_=1_0_0			,     a_=None			,     **a_			,     ):
									# Add extra_ids to the special token list
									if extra_ids > 0 and additional_special_tokens is None:
											a_     :      Tuple 						=     [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
											a_     :      List[str] 						=     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_			,     )

									a_     :      Optional[Any] 						=     vocab_file
									a_     :      Tuple 						=     False if not self.vocab_file else True
									a_     :      Optional[int] 						=     extra_ids






							@staticmethod
							def snake_case_  (   a_			,     a_			,     a_      ):
									if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
											a_     :      List[Any] 						=     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 snake_case_  (   self			,     a_			,     a_ = 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(a_      ):
											logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
											return
									a_     :      int 						=     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 snake_case_  (   self			,     a_			,     a_ = None      ):
									a_     :      Optional[int] 						=     token_ids_a + [self.eos_token_id]
									if token_ids_a is None:
											return self.prefix_tokens + token_ids_a
									else:
											a_     :      str 						=     token_ids_a + [self.eos_token_id]
											return self.prefix_tokens + token_ids_a + token_ids_a






							def snake_case_  (   self			,     a_			,     a_ = None      ):
									a_     :      List[str] 						=     [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 snake_case_  (   self      ):
									return list(
									    set(filter(lambda a_      : bool(re.search(R"<extra_id_\d+>"			,     a_      )      ) is not None			,     self.additional_special_tokens      )      )      )






							def snake_case_  (   self      ):
									return [self.convert_tokens_to_ids(a_      ) for token in self.get_sentinel_tokens()]




"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE_				= {
    """configuration_bigbird_pegasus""": [
        """BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BigBirdPegasusConfig""",
        """BigBirdPegasusOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= [
					    """BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """BigBirdPegasusForCausalLM""",
					    """BigBirdPegasusForConditionalGeneration""",
					    """BigBirdPegasusForQuestionAnswering""",
					    """BigBirdPegasusForSequenceClassification""",
					    """BigBirdPegasusModel""",
					    """BigBirdPegasusPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_bigbird_pegasus import (
					    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    BigBirdPegasusConfig,
					    BigBirdPegasusOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_bigbird_pegasus import (
										    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BigBirdPegasusForCausalLM,
										    BigBirdPegasusForConditionalGeneration,
										    BigBirdPegasusForQuestionAnswering,
										    BigBirdPegasusForSequenceClassification,
										    BigBirdPegasusModel,
										    BigBirdPegasusPreTrainedModel,
										)


else:
					import sys

					SCREAMING_SNAKE_CASE_				= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)