




import os
from shutil import copyfile
from typing import TYPE_CHECKING, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
from ...utils.versions import require_version


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation

require_version("tokenizers>=0.13.3")

if is_sentencepiece_available():
 from .tokenization_llama import LlamaTokenizer
else:
 a_										=       None

a_										=       logging.get_logger(__name__)
a_										=       {"vocab_file": "tokenizer.model", "tokenizer_file": "tokenizer.json"}

a_						,      a_										=       "[INST]", "[/INST]"
a_						,      a_										=       "<<SYS>>\n", "\n<</SYS>>\n\n"

# fmt: off
a_										=       "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don't know the answer to a question, please don't share false information."

class 	_lowerCamelCase  (     _SCREAMING_SNAKE_CASE				):

      """simple docstring"""

      lowerCAmelCase__     :	Dict      =   VOCAB_FILES_NAMES
      lowerCAmelCase__     :	List[str]      =   LlamaTokenizer
      lowerCAmelCase__     :	Dict      =   "left"
      lowerCAmelCase__     :	List[str]      =   ["input_ids", "attention_mask"]







      def __init__(       self   :	Any	,    snake_case   :	Union[str, Any]=None	,    snake_case   :	List[str]=None	,    snake_case   :	Any=False	,    snake_case   :	str="<unk>"	,    snake_case   :	Union[str, Any]="<s>"	,    snake_case   :	List[Any]="</s>"	,    snake_case   :	str=True	,    snake_case   :	int=False	,    **snake_case   :	Optional[Any]	,    ):
          super().__init__(
              vocab_file=snake_case	,    tokenizer_file=snake_case	,    clean_up_tokenization_spaces=snake_case	,    unk_token=snake_case	,    bos_token=snake_case	,    eos_token=snake_case	,    **snake_case	,    )
          __UpperCamelCase											=  add_bos_token
          __UpperCamelCase											=  add_eos_token
          self.update_post_processor()

          __UpperCamelCase											=  vocab_file
          __UpperCamelCase											=  False if not self.vocab_file else True







      def  snake_case		(       self   :	Any			):
          __UpperCamelCase											=  self.bos_token
          __UpperCamelCase											=  self.bos_token_id

          __UpperCamelCase											=  self.eos_token
          __UpperCamelCase											=  self.eos_token_id

          __UpperCamelCase											=  F"{(bos+':0 ') * self.add_bos_token}$A:0{(' '+eos+':0') * self.add_eos_token}"
          __UpperCamelCase											=  F"{single}{(' '+bos+':1') * self.add_bos_token} $B:1{(' '+eos+':1') * self.add_eos_token}"

          __UpperCamelCase											=  []
          if self.add_bos_token:
              special_tokens.append((bos, bos_token_id)			)
          if self.add_eos_token:
              special_tokens.append((eos, eos_token_id)			)
          __UpperCamelCase											=  processors.TemplateProcessing(
              single=snake_case	,    pair=snake_case	,    special_tokens=snake_case			)







      @property
      def  snake_case		(       self   :	Dict			):
          return self._add_eos_token







      @property
      def  snake_case		(       self   :	Optional[int]			):
          return self._add_bos_token







      @add_eos_token.setter
      def  snake_case		(       self   :	Any	,    snake_case   :	Union[str, Any]			):
          __UpperCamelCase											=  value
          self.update_post_processor()







      @add_bos_token.setter
      def  snake_case		(       self   :	int	,    snake_case   :	str			):
          __UpperCamelCase											=  value
          self.update_post_processor()







      def  snake_case		(       self   :	str	,    snake_case   :	str	,    snake_case   :	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(snake_case			):
              logger.error(F"Vocabulary path ({save_directory}) should be a directory"			)
              return
          __UpperCamelCase											=  os.path.join(
              snake_case	,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

          if os.path.abspath(self.vocab_file			) != os.path.abspath(snake_case			):
              copyfile(self.vocab_file	,    snake_case			)

          return (out_vocab_file,)







      def  snake_case		(       self   :	Tuple	,    snake_case   :	"Conversation"			):
          __UpperCamelCase											=  list(conversation.iter_texts()			)
          if not all([is_user for is_user, msg in dialogue[::2]]			) or not all(
              [not is_user for is_user, msg in dialogue[1::2]]			):
              raise ValueError(
                  '''The model only supports \'user\' and \'assistant\' roles, starting with user and alternating (u/a/u/a/u...)'''			)

          __UpperCamelCase											=  []
          if len(conversation.past_user_inputs			) > 0:
              if not conversation.past_user_inputs[0].startswith(snake_case			) or E_SYS not in conversation.past_user_inputs[0]:
                  __UpperCamelCase											=  (
                      B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
                  )
          elif not dialogue[0][1].startswith(snake_case			) or E_SYS not in dialogue[0][1]:
              __UpperCamelCase											=  (dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

          dialog_tokens += sum(
              [
                  [self.bos_token_id]
                  + self.encode(
                      F"{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} "	,    add_special_tokens=snake_case			)
                  + [self.eos_token_id]
                  for prompt, answer in zip(dialogue[::2]	,    dialogue[1::2]			)
              ]	,    []	,    )
          if not (dialogue[-1][0]):
              raise ValueError(F"Last message must be from user, got {dialogue[-1]['role']}"			)
          dialog_tokens += [self.bos_token_id] + self.encode(
              F"{B_INST} {(dialogue[-1][1]).strip()} {E_INST}"	,    add_special_tokens=snake_case			)
          return dialog_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


a_										=       "Here is a text containing a lot of information: '''{text}'''.\n\nCan you answer this question about the text: '{question}'"

class 	_lowerCamelCase  (     _SCREAMING_SNAKE_CASE				):

      """simple docstring"""

      lowerCAmelCase__     :	Union[str, Any]      =   "google/flan-t5-base"
      lowerCAmelCase__     :	Union[str, Any]      =   (
          "This is a tool that answers questions related to a text. It takes two arguments named `text`, which is the "
          "text where to find the answer, and `question`, which is the question, and returns the answer to the question."
      )
      lowerCAmelCase__     :	List[Any]      =   "text_qa"
      lowerCAmelCase__     :	int      =   AutoTokenizer
      lowerCAmelCase__     :	Optional[Any]      =   AutoModelForSeqaSeqLM

      lowerCAmelCase__     :	Tuple      =   ["text", "text"]
      lowerCAmelCase__     :	Any      =   ["text"]







      def  snake_case		(       self   :	Union[str, Any]	,    snake_case   :	str	,    snake_case   :	str			):
          __UpperCamelCase											=  QA_PROMPT.format(text=snake_case	,    question=snake_case			)
          return self.pre_processor(snake_case	,    return_tensors='''pt'''			)







      def  snake_case		(       self   :	Any	,    snake_case   :	Optional[int]			):
          __UpperCamelCase											=  self.model.generate(**snake_case			)

          __UpperCamelCase						, __UpperCamelCase											=  inputs['''input_ids'''].shape
          __UpperCamelCase											=  output_ids.shape[0]

          return output_ids.reshape(snake_case	,    out_b // in_b	,    *output_ids.shape[1:]			)[0][0]







      def  snake_case		(       self   :	Tuple	,    snake_case   :	Optional[Any]			):
          return self.pre_processor.decode(snake_case	,    skip_special_tokens=snake_case	,    clean_up_tokenization_spaces=snake_case			)






