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#
# 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


SCREAMING_SNAKE_CASE								=  'Here is a text containing a lot of information: \'\'\'{text}\'\'\'.\n\nCan you answer this question about the text: \'{question}\''
class 							A_      (  __A     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     Union[str, Any]	=   "google/flan-t5-base"
 _SCREAMING_SNAKE_CASE							:     Optional[int]	=   (
     "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."
 )
 _SCREAMING_SNAKE_CASE							:     List[Any]	=   "text_qa"
 _SCREAMING_SNAKE_CASE							:     Dict	=   AutoTokenizer
 _SCREAMING_SNAKE_CASE							:     Union[str, Any]	=   AutoModelForSeqaSeqLM

 _SCREAMING_SNAKE_CASE							:     Dict	=   ["text", "text"]
 _SCREAMING_SNAKE_CASE							:     Optional[Any]	=   ["text"]


 def 	snake_case__			(		self							,					_A							,					_A)	->						Union[str, Any]:

     """simple docstring"""



     _UpperCAmelCase			: Dict     					=			QA_PROMPT.format(text=_A							,					question=_A)
     return self.pre_processor(_A							,					return_tensors='''pt''')


 def 	snake_case__			(		self							,					_A)	->						str:

     """simple docstring"""



     _UpperCAmelCase			: List[str]     					=			self.model.generate(**_A)

     _UpperCAmelCase			: Dict     					=			inputs["input_ids"].shape
     _UpperCAmelCase			: Optional[Any]     					=			output_ids.shape[0]

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


 def 	snake_case__			(		self							,					_A)	->						Optional[int]:

     """simple docstring"""



     return self.pre_processor.decode(_A							,					skip_special_tokens=_A							,					clean_up_tokenization_spaces=_A)












from unittest.mock import patch

import datasets
from datasets import Dataset

def        _lowerCamelCase		(  ) ->   Tuple:
    _UpperCAmelCase			: Union[str, Any]     					=			Dataset.from_dict({'''col_1''': [3, 2, 0, 1]}	)

    with patch('''tqdm.auto.tqdm'''	) as mock_tqdm:
        datasets.disable_progress_bar()
        dset.map(lambda __A	: {"col_2": x["col_1"] + 1}	)
        mock_tqdm.assert_not_called()

        mock_tqdm.reset_mock()

        datasets.enable_progress_bar()
        dset.map(lambda __A	: {"col_2": x["col_1"] + 1}	)
        mock_tqdm.assert_called()




