
"""simple docstring"""
import numpy as np
def 					a__ ( a :					List[Any]							,  a :					List[Any]		):

  """simple docstring"""







  _snake_case  :    List[str]            = np.array(a		)
  _snake_case  :    Optional[int]            = np.array(a		)

  _snake_case  :    Union[str, Any]            = abs(predict - actual		)
  _snake_case  :    Any            = difference.mean()

  return score
def 					a__ ( a :					Any							,  a :					str		):

  """simple docstring"""







  _snake_case  :    List[str]            = np.array(a		)
  _snake_case  :    Tuple            = np.array(a		)

  _snake_case  :    str            = predict - actual
  _snake_case  :    Optional[int]            = np.square(a		)

  _snake_case  :    Dict            = square_diff.mean()
  return score
def 					a__ ( a :					Tuple							,  a :					int		):

  """simple docstring"""







  _snake_case  :    str            = np.array(a		)
  _snake_case  :    Optional[Any]            = np.array(a		)

  _snake_case  :    List[str]            = predict - actual
  _snake_case  :    Optional[int]            = np.square(a		)
  _snake_case  :    Union[str, Any]            = square_diff.mean()
  _snake_case  :    Optional[int]            = np.sqrt(a		)
  return score
def 					a__ ( a :					int							,  a :					Union[str, Any]		):

  """simple docstring"""







  _snake_case  :    Optional[int]            = np.array(a		)
  _snake_case  :    Union[str, Any]            = np.array(a		)

  _snake_case  :    Union[str, Any]            = np.log(predict + 1		)
  _snake_case  :    int            = np.log(actual + 1		)

  _snake_case  :    Tuple            = log_predict - log_actual
  _snake_case  :    Tuple            = np.square(a		)
  _snake_case  :    Optional[int]            = square_diff.mean()

  _snake_case  :    Dict            = np.sqrt(a		)

  return score
def 					a__ ( a :					str							,  a :					Dict		):

  """simple docstring"""







  _snake_case  :    List[str]            = np.array(a		)
  _snake_case  :    Union[str, Any]            = np.array(a		)

  _snake_case  :    int            = predict - actual
  _snake_case  :    str            = np.sum(a		) / len(a		)
  _snake_case  :    Any            = np.sum(a		) / len(a		)
  # print(numerator, denumerator)
  _snake_case  :    Tuple            = float(a		) / denumerator * 100

  return score



def 					a__ ( a :					str							,  a :					List[str]		):

  """simple docstring"""







  return np.mean(np.array(a		) == np.array(a		)		)


"""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


_a	:	Union[str, Any]=					"Here is a text containing a lot of information: '''{text}'''.\n\nCan you answer this question about the text: '{question}'"

class 						UpperCamelCase     (       lowercase		):
				UpperCAmelCase							: str       =						"""google/flan-t5-base"""
				UpperCAmelCase							: 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."""
				)
				UpperCAmelCase							: Dict       =						"""text_qa"""
				UpperCAmelCase							: Dict       =						AutoTokenizer
				UpperCAmelCase							: Optional[Any]       =						AutoModelForSeqaSeqLM

				UpperCAmelCase							: List[str]       =						["""text""", """text"""]
				UpperCAmelCase							: Any       =						["""text"""]



				def    _lowercase	(self							: Tuple      ,    _A							: str      ,    _A							: str)       ->      Tuple:
					__snake_case     :				List[Any] 						=					QA_PROMPT.format(text=_A      ,    question=_A)
					return self.pre_processor(_A      ,    return_tensors='pt')



				def    _lowercase	(self							: Tuple      ,    _A							: Optional[Any])       ->      List[Any]:
					__snake_case     :				List[Any] 						=					self.model.generate(**_A)

					__snake_case						,							__snake_case     :				Any 						=					inputs['input_ids'].shape
					__snake_case     :				Any 						=					output_ids.shape[0]

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






				def    _lowercase	(self							: Optional[int]      ,    _A							: int)       ->      str:
					return self.pre_processor.decode(_A      ,    skip_special_tokens=_A      ,    clean_up_tokenization_spaces=_A)





