



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						    =  None


_lowerCamelCase						    =  logging.get_logger(__name__)

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

_lowerCamelCase						    =  {
    """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						    =  {
    """t5-small""": 512,
    """t5-base""": 512,
    """t5-large""": 512,
    """t5-3b""": 512,
    """t5-11b""": 512,
}

class       _SCREAMING_SNAKE_CASE	(lowercase__ ):
     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 :							Union[str, Any]     ,  UpperCamelCase :							Union[str, Any]=None     ,  UpperCamelCase :							Any=None     ,  UpperCamelCase :							Tuple="</s>"     ,  UpperCamelCase :							str="<unk>"     ,  UpperCamelCase :							int="<pad>"     ,  UpperCamelCase :							Optional[int]=1_0_0     ,  UpperCamelCase :							Optional[Any]=None     ,  **UpperCamelCase :							List[str]     ,  )->List[Any]:
      # Add extra_ids to the special token list
      if extra_ids > 0 and additional_special_tokens is None:
       __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	[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
       __SCREAMING_SNAKE_CASE       :    int       =	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     ,  )

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





     @staticmethod
     def 	__snake_case    (       UpperCamelCase :							List[Any]     ,  UpperCamelCase :							Union[str, Any]     ,  UpperCamelCase :							Dict				)->Optional[int]:
      if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
       __SCREAMING_SNAKE_CASE       :    Optional[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."     ,  UpperCamelCase     ,  )

      return max_model_length





     def 	__snake_case    (       self :							Union[str, Any]     ,  UpperCamelCase :							Tuple     ,  UpperCamelCase :							Tuple = None				)->Union[str, Any]:
      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
      __SCREAMING_SNAKE_CASE       :    int       =	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 	__snake_case    (       self :							Optional[Any]     ,  UpperCamelCase :							Tuple     ,  UpperCamelCase :							Dict = None				)->List[Any]:
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	token_ids_a + [self.eos_token_id]
      if token_ids_a is None:
       return self.prefix_tokens + token_ids_a
      else:
       __SCREAMING_SNAKE_CASE       :    str       =	token_ids_a + [self.eos_token_id]
       return self.prefix_tokens + token_ids_a + token_ids_a





     def 	__snake_case    (       self :							List[Any]     ,  UpperCamelCase :							Union[str, Any]     ,  UpperCamelCase :							List[Any] = None				)->Optional[int]:
      __SCREAMING_SNAKE_CASE       :    Dict       =	[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 :							Optional[Any]				)->Optional[Any]:
      return list(
          set(filter(lambda UpperCamelCase				: bool(re.search(r"<extra_id_\d+>"     ,  UpperCamelCase				)				) is not None     ,  self.additional_special_tokens				)				)				)






     def 	__snake_case    (       self :							Any				)->str:
      return [self.convert_tokens_to_ids(UpperCamelCase				) for token in self.get_sentinel_tokens()]











# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool

class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):
     lowerCAmelCase					=			"""philschmid/bart-large-cnn-samsum"""
     lowerCAmelCase					=			(
         """This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """
         """and returns a summary of the text."""
     )
     lowerCAmelCase					=			"""summarizer"""
     lowerCAmelCase					=			AutoTokenizer
     lowerCAmelCase					=			AutoModelForSeqaSeqLM

     lowerCAmelCase					=			["""text"""]
     lowerCAmelCase					=			["""text"""]





     def 	__snake_case    (       self :							Union[str, Any]     ,  UpperCamelCase :							Tuple				)->Tuple:
      return self.pre_processor(UpperCamelCase     ,  return_tensors="pt"     ,  truncation=UpperCamelCase				)





     def 	__snake_case    (       self :							Dict     ,  UpperCamelCase :							List[Any]				)->Optional[Any]:
      return self.model.generate(**UpperCamelCase				)[0]






     def 	__snake_case    (       self :							Tuple     ,  UpperCamelCase :							Dict				)->Optional[int]:
      return self.pre_processor.decode(UpperCamelCase     ,  skip_special_tokens=UpperCamelCase     ,  clean_up_tokenization_spaces=UpperCamelCase				)






