

# 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.
import re

from ..utils import cached_file


# docstyle-ignore
_SCREAMING_SNAKE_CASE  :						Optional[Any]         =    '''
Human: <<task>>

Assistant: '''


_SCREAMING_SNAKE_CASE  :						Optional[Any]         =    '''huggingface-tools/default-prompts'''
_SCREAMING_SNAKE_CASE  :						List[Any]         =    {'''chat''': '''chat_prompt_template.txt''', '''run''': '''run_prompt_template.txt'''}
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A="run"       ):





     '''simple docstring'''




     if prompt_or_repo_id is None:
          SCREAMING_SNAKE_CASE__					=					DEFAULT_PROMPTS_REPO

     # prompt is considered a repo ID when it does not contain any kind of space
     if re.search('''\\s'''   ,	_A       ) is not None:
          return prompt_or_repo_id

     SCREAMING_SNAKE_CASE__					=					cached_file(
         _A   ,	PROMPT_FILES[mode]   ,	repo_type='''dataset'''   ,	user_agent={'''agent''': agent_name}       )
     with open(_A   ,	'''r'''   ,	encoding='''utf-8'''       ) as f:
          return f.read()





from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Sequence, Value
from .base import TaskTemplate






@dataclass(frozen=A__ )
class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  a     =      field(default="question-answering-extractive"		,  metadata={"include_in_asdict_even_if_is_default": True} )
  a     =      Features({"question": Value("string" ), "context": Value("string" )} )
  a     =      Features(
      {
          "answers": Sequence(
              {
                  "text": Value("string" ),
                  "answer_start": Value("int32" ),
              } )
      } )
  a     =      "question"
  a     =      "context"
  a     =      "answers"






  @property
  def    lowercase_	(							self		:	Dict      )	->							Dict[str, str]:
       return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}


