'''simple docstring'''

# 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


_lowerCamelCase :				Dict														= """Here is a text containing a lot of information: '''{text}'''.

Can you answer this question about the text: '{question}'"""




class    lowerCamelCase__  (   __snake_case     ):
   __UpperCAmelCase           =						"""google/flan-t5-base"""
   __UpperCAmelCase           =						(
       """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."""
   )
   __UpperCAmelCase           =						"""text_qa"""
   __UpperCAmelCase           =						AutoTokenizer
   __UpperCAmelCase           =						AutoModelForSeqaSeqLM

   __UpperCAmelCase           =						["""text""", """text"""]
   __UpperCAmelCase           =						["""text"""]



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     Optional[int]:
        """simple docstring"""





        _UpperCamelCase  :str       =QA_PROMPT.format(text=lowerCAmelCase__					,      question=lowerCAmelCase__     )
        return self.pre_processor(lowerCAmelCase__					,      return_tensors="""pt"""     )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__     )				->     Optional[Any]:
        """simple docstring"""





        _UpperCamelCase  :str       =self.model.generate(**lowerCAmelCase__     )

        _UpperCamelCase       ,      _UpperCamelCase  :List[str]       =inputs["""input_ids"""].shape
        _UpperCamelCase  :List[str]       =output_ids.shape[0]

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




   def  _UpperCamelCase		(    self					,      lowerCAmelCase__     )				->     Any:
        """simple docstring"""





        return self.pre_processor.decode(lowerCAmelCase__					,      skip_special_tokens=lowerCAmelCase__					,      clean_up_tokenization_spaces=lowerCAmelCase__     )
'''simple docstring'''

import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list




class    lowerCamelCase__  (   unittest.TestCase     ):



   def  _UpperCamelCase		(    self     )				->     str:
        """simple docstring"""





        _UpperCamelCase  :Any       ="""<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K ' X J Q Z""".split(""" """     )
        _UpperCamelCase  :Optional[Any]       =dict(zip(lowerCAmelCase__					,      range(len(lowerCAmelCase__     )     )     )     )

        _UpperCamelCase  :int       ={
            """pad_token""": """<pad>""",
            """unk_token""": """<unk>""",
            """bos_token""": """<s>""",
            """eos_token""": """</s>""",
        }
        _UpperCamelCase  :Union[str, Any]       ={
            """feature_size""": 1,
            """padding_value""": 0.0,
            """sampling_rate""": 16_000,
            """return_attention_mask""": False,
            """do_normalize""": True,
        }

        _UpperCamelCase  :List[str]       =tempfile.mkdtemp()
        _UpperCamelCase  :Optional[Any]       =os.path.join(self.tmpdirname					,      VOCAB_FILES_NAMES["""vocab_file"""]     )
        _UpperCamelCase  :Tuple       =os.path.join(self.tmpdirname					,      lowerCAmelCase__     )
        with open(self.vocab_file					,      """w"""					,      encoding="""utf-8"""     ) as fp:
             fp.write(json.dumps(lowerCAmelCase__     ) + """\n"""     )

        with open(self.feature_extraction_file					,      """w"""					,      encoding="""utf-8"""     ) as fp:
             fp.write(json.dumps(lowerCAmelCase__     ) + """\n"""     )



   def  _UpperCamelCase		(    self					,      **lowerCAmelCase__     )				->     List[str]:
        """simple docstring"""





        _UpperCamelCase  :int       =self.add_kwargs_tokens_map.copy()
        kwargs.update(lowerCAmelCase__     )
        return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname					,      **lowerCAmelCase__     )



   def  _UpperCamelCase		(    self					,      **lowerCAmelCase__     )				->     str:
        """simple docstring"""





        return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname					,      **lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     List[str]:
        """simple docstring"""





        shutil.rmtree(self.tmpdirname     )



   def  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





        _UpperCamelCase  :Union[str, Any]       =self.get_tokenizer()
        _UpperCamelCase  :int       =self.get_feature_extractor()

        _UpperCamelCase  :Union[str, Any]       =WavaVecaProcessor(tokenizer=lowerCAmelCase__					,      feature_extractor=lowerCAmelCase__     )

        processor.save_pretrained(self.tmpdirname     )
        _UpperCamelCase  :Dict       =WavaVecaProcessor.from_pretrained(self.tmpdirname     )

        self.assertEqual(processor.tokenizer.get_vocab()					,      tokenizer.get_vocab()     )
        self.assertIsInstance(processor.tokenizer					,      lowerCAmelCase__     )

        self.assertEqual(processor.feature_extractor.to_json_string()					,      feature_extractor.to_json_string()     )
        self.assertIsInstance(processor.feature_extractor					,      lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     Tuple:
        """simple docstring"""





        _UpperCamelCase  :Union[str, Any]       =WavaVecaProcessor(tokenizer=self.get_tokenizer()					,      feature_extractor=self.get_feature_extractor()     )
        processor.save_pretrained(self.tmpdirname     )

        _UpperCamelCase  :Optional[int]       =self.get_tokenizer(bos_token="""(BOS)"""					,      eos_token="""(EOS)"""     )
        _UpperCamelCase  :Optional[int]       =self.get_feature_extractor(do_normalize=lowerCAmelCase__					,      padding_value=1.0     )

        _UpperCamelCase  :Optional[int]       =WavaVecaProcessor.from_pretrained(
            self.tmpdirname					,      bos_token="""(BOS)"""					,      eos_token="""(EOS)"""					,      do_normalize=lowerCAmelCase__					,      padding_value=1.0     )

        self.assertEqual(processor.tokenizer.get_vocab()					,      tokenizer_add_kwargs.get_vocab()     )
        self.assertIsInstance(processor.tokenizer					,      lowerCAmelCase__     )

        self.assertEqual(processor.feature_extractor.to_json_string()					,      feature_extractor_add_kwargs.to_json_string()     )
        self.assertIsInstance(processor.feature_extractor					,      lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





        _UpperCamelCase  :List[Any]       =self.get_feature_extractor()
        _UpperCamelCase  :List[str]       =self.get_tokenizer()

        _UpperCamelCase  :Any       =WavaVecaProcessor(tokenizer=lowerCAmelCase__					,      feature_extractor=lowerCAmelCase__     )

        _UpperCamelCase  :Tuple       =floats_list((3, 1_000)     )

        _UpperCamelCase  :Tuple       =feature_extractor(lowerCAmelCase__					,      return_tensors="""np"""     )
        _UpperCamelCase  :Union[str, Any]       =processor(lowerCAmelCase__					,      return_tensors="""np"""     )

        for key in input_feat_extract.keys():
             self.assertAlmostEqual(input_feat_extract[key].sum()					,      input_processor[key].sum()					,      delta=1e-2     )



   def  _UpperCamelCase		(    self     )				->     Tuple:
        """simple docstring"""





        _UpperCamelCase  :Any       =self.get_feature_extractor()
        _UpperCamelCase  :List[Any]       =self.get_tokenizer()

        _UpperCamelCase  :Tuple       =WavaVecaProcessor(tokenizer=lowerCAmelCase__					,      feature_extractor=lowerCAmelCase__     )

        _UpperCamelCase  :Optional[Any]       ="""This is a test string"""

        _UpperCamelCase  :Union[str, Any]       =processor(text=lowerCAmelCase__     )

        _UpperCamelCase  :Union[str, Any]       =tokenizer(lowerCAmelCase__     )

        for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]					,      encoded_processor[key]     )



   def  _UpperCamelCase		(    self     )				->     Optional[Any]:
        """simple docstring"""





        _UpperCamelCase  :int       =self.get_feature_extractor()
        _UpperCamelCase  :Any       =self.get_tokenizer()

        _UpperCamelCase  :str       =WavaVecaProcessor(tokenizer=lowerCAmelCase__					,      feature_extractor=lowerCAmelCase__     )

        _UpperCamelCase  :Optional[int]       =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        _UpperCamelCase  :List[Any]       =processor.batch_decode(lowerCAmelCase__     )
        _UpperCamelCase  :Any       =tokenizer.batch_decode(lowerCAmelCase__     )

        self.assertListEqual(lowerCAmelCase__					,      lowerCAmelCase__     )




   def  _UpperCamelCase		(    self     )				->     Union[str, Any]:
        """simple docstring"""





        _UpperCamelCase  :List[Any]       =self.get_feature_extractor()
        _UpperCamelCase  :Any       =self.get_tokenizer()

        _UpperCamelCase  :List[str]       =WavaVecaProcessor(tokenizer=lowerCAmelCase__					,      feature_extractor=lowerCAmelCase__     )

        self.assertListEqual(
            processor.model_input_names					,      feature_extractor.model_input_names					,      msg="""`processor` and `feature_extractor` model input names do not match"""					,      )