



import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing

class       _SCREAMING_SNAKE_CASE	(snake_case_ ):





     def __init__(       self :							Tuple     ,  UpperCamelCase :							Optional[Any] = "▁"     ,  UpperCamelCase :							Union[str, Any] = True     ,  UpperCamelCase :							List[Any] = "<unk>"     ,  UpperCamelCase :							Union[str, Any] = "</s>"     ,  UpperCamelCase :							List[str] = "<pad>"     ,  )->List[Any]:
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	{
          "pad": {"id": 0, "token": pad_token},
          "eos": {"id": 1, "token": eos_token},
          "unk": {"id": 2, "token": unk_token},
      }

      __SCREAMING_SNAKE_CASE       :    List[str]       =	[None] * len(self.special_tokens				)
      for token_dict in self.special_tokens.values():
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	token_dict["token"]

      __SCREAMING_SNAKE_CASE       :    List[str]       =	Tokenizer(Unigram()				)

      __SCREAMING_SNAKE_CASE       :    int       =	normalizers.Sequence(
          [
              normalizers.Nmt(),
              normalizers.NFKC(),
              normalizers.Replace(Regex(" {2,}"				)     ,  " "				),
              normalizers.Lowercase(),
          ]				)
      __SCREAMING_SNAKE_CASE       :    int       =	pre_tokenizers.Sequence(
          [
              pre_tokenizers.Metaspace(replacement=UpperCamelCase     ,  add_prefix_space=UpperCamelCase				),
              pre_tokenizers.Digits(individual_digits=UpperCamelCase				),
              pre_tokenizers.Punctuation(),
          ]				)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	decoders.Metaspace(replacement=UpperCamelCase     ,  add_prefix_space=UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	TemplateProcessing(
          single=F"""$A {self.special_tokens["eos"]["token"]}"""     ,  special_tokens=[(self.special_tokens["eos"]["token"], self.special_tokens["eos"]["id"])]     ,  )

      __SCREAMING_SNAKE_CASE       :    List[Any]       =	{
          "model": "SentencePieceUnigram",
          "replacement": replacement,
          "add_prefix_space": add_prefix_space,
      }

      super().__init__(UpperCamelCase     ,  UpperCamelCase				)





     def 	__snake_case    (       self :							str     ,  UpperCamelCase :							Optional[int]     ,  UpperCamelCase :							Optional[int] = 8_0_0_0     ,  UpperCamelCase :							Union[str, Any] = True     ,  )->Dict:
      __SCREAMING_SNAKE_CASE       :    Tuple       =	trainers.UnigramTrainer(
          vocab_size=UpperCamelCase     ,  special_tokens=self.special_tokens_list     ,  show_progress=UpperCamelCase     ,  )

      if isinstance(UpperCamelCase     ,  UpperCamelCase				):
       __SCREAMING_SNAKE_CASE       :    Tuple       =	[files]
      self._tokenizer.train(UpperCamelCase     ,  trainer=UpperCamelCase				)

      self.add_unk_id()





     def 	__snake_case    (       self :							int     ,  UpperCamelCase :							List[Any]     ,  UpperCamelCase :							List[Any] = 8_0_0_0     ,  UpperCamelCase :							Union[str, Any] = True     ,  )->List[Any]:
      __SCREAMING_SNAKE_CASE       :    Dict       =	trainers.UnigramTrainer(
          vocab_size=UpperCamelCase     ,  special_tokens=self.special_tokens_list     ,  show_progress=UpperCamelCase     ,  )

      self._tokenizer.train_from_iterator(UpperCamelCase     ,  trainer=UpperCamelCase				)

      self.add_unk_id()






     def 	__snake_case    (       self :							Union[str, Any]				)->List[str]:
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	json.loads(self._tokenizer.to_str()				)

      __SCREAMING_SNAKE_CASE       :    Tuple       =	self.special_tokens["unk"]["id"]

      __SCREAMING_SNAKE_CASE       :    Dict       =	Tokenizer.from_str(json.dumps(UpperCamelCase				)				)











# 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				)






