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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


UpperCAmelCase__    :      Dict       					='''Here is a text containing a lot of information: \'\'\'{text}\'\'\'.

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

class    __A  (    a     ):
				__A  =					"""google/flan-t5-base"""
				__A  =					(
				    """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."""
				)
				__A  =					"""text_qa"""
				__A  =					AutoTokenizer
				__A  =					AutoModelForSeqaSeqLM

				__A  =					["""text""", """text"""]
				__A  =					["""text"""]


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_				):
								lowerCamelCase									=QA_PROMPT.format(text=UpperCAmelCase_ ,			question=UpperCAmelCase_				)
								return self.pre_processor(UpperCAmelCase_ ,			return_tensors="""pt"""				)


				def _snake_case							(		self ,			UpperCAmelCase_				):
								lowerCamelCase									=self.model.generate(**UpperCAmelCase_				)

								lowerCamelCase			,	lowerCamelCase									=inputs["""input_ids"""].shape
								lowerCamelCase									=output_ids.shape[0]

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


				def _snake_case							(		self ,			UpperCAmelCase_				):
								return self.pre_processor.decode(UpperCAmelCase_ ,			skip_special_tokens=UpperCAmelCase_ ,			clean_up_tokenization_spaces=UpperCAmelCase_				)












from sklearn.metrics import accuracy_score

import datasets


UpperCAmelCase__    :      Optional[Any]       					='''
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
 Where:
TP: True positive
TN: True negative
FP: False positive
FN: False negative
'''


UpperCAmelCase__    :      Optional[int]       					='''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {\'accuracy\': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {\'accuracy\': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
        >>> print(results)
        {\'accuracy\': 0.8778625954198473}
'''


UpperCAmelCase__    :      List[str]       					='''
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
'''

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,       _KWARGS_DESCRIPTION     )
class    __A  (    datasets.Metric     ):


				def _snake_case							(		self				):
								return datasets.MetricInfo(
								    description=_DESCRIPTION ,			citation=_CITATION ,			inputs_description=_KWARGS_DESCRIPTION ,			features=datasets.Features(
								        {
								            """predictions""": datasets.Sequence(datasets.Value("""int32"""				)				),
								            """references""": datasets.Sequence(datasets.Value("""int32"""				)				),
								        }
								        if self.config_name == """multilabel"""
								        else {
								            """predictions""": datasets.Value("""int32"""				),
								            """references""": datasets.Value("""int32"""				),
								        }				) ,			reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html"""] ,			)


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_=True ,			UpperCAmelCase_=None				):
								return {
								    "accuracy": float(
								        accuracy_score(UpperCAmelCase_ ,			UpperCAmelCase_ ,			normalize=UpperCAmelCase_ ,			sample_weight=UpperCAmelCase_				)				)
								}





