





'''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.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool


class  SCREAMING_SNAKE_CASE    (a__							):
     lowerCAmelCase					=       '''facebook/bart-large-mnli'''
     lowerCAmelCase					=       (
         '''This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which '''
         '''should be the text to classify, and `labels`, which should be the list of labels to use for classification. '''
         '''It returns the most likely label in the list of provided `labels` for the input text.'''
     )
     lowerCAmelCase					=       '''text_classifier'''
     lowerCAmelCase					=       AutoTokenizer
     lowerCAmelCase					=       AutoModelForSequenceClassification

     lowerCAmelCase					=       ['''text''', ['''text''']]
     lowerCAmelCase					=       ['''text''']


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          super().setup()
          __A						: List[Any]      =       self.model.config
          __A						: Any      =       -1
          for idx, label in config.idalabel.items():
               if label.lower().startswith('entail'):
                    __A						: Optional[Any]      =       int(_UpperCAmelCase)
          if self.entailment_id == -1:
               raise ValueError('Could not determine the entailment ID from the model config, please pass it at init.')


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: Dict      =       labels
          return self.pre_processor(
              [text] * len(_UpperCAmelCase)						,	[F'This example is {label}' for label in labels]						,	return_tensors='pt'						,	padding='max_length'						,	)


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: Optional[int]      =       outputs.logits
          __A						: Any      =       torch.argmax(logits[:, 2]).item()
          return self._labels[label_id]






'''simple docstring'''




import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast


@dataclass
class  SCREAMING_SNAKE_CASE    (datasets.BuilderConfig							):
     lowerCAmelCase					=       None




class  SCREAMING_SNAKE_CASE    (datasets.ArrowBasedBuilder							):
     lowerCAmelCase					=       PandasConfig


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return datasets.DatasetInfo(features=self.config.features)


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase):

          '''simple docstring'''






          if not self.config.data_files:
               raise ValueError(F'At least one data file must be specified, but got data_files={self.config.data_files}')
          __A						: Dict      =       dl_manager.download_and_extract(self.config.data_files)
          if isinstance(_UpperCAmelCase						,	(str, list, tuple)):
               __A						: Union[str, Any]      =       data_files
               if isinstance(_UpperCAmelCase						,	_UpperCAmelCase):
                    __A						: Optional[int]      =       [files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               __A						: Optional[Any]      =       [dl_manager.iter_files(_UpperCAmelCase) for file in files]
               return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,	gen_kwargs={'files': files})]
          __A						: Tuple      =       []
          for split_name, files in data_files.items():
               if isinstance(_UpperCAmelCase						,	_UpperCAmelCase):
                    __A						: Dict      =       [files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               __A						: Optional[Any]      =       [dl_manager.iter_files(_UpperCAmelCase) for file in files]
               splits.append(datasets.SplitGenerator(name=_UpperCAmelCase						,	gen_kwargs={'files': files}))
          return splits


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase):

          '''simple docstring'''






          if self.config.features is not None:
               # more expensive cast to support nested features with keys in a different order
               # allows str <-> int/float or str to Audio for example
               __A						: List[str]      =       table_cast(_UpperCAmelCase						,	self.config.features.arrow_schema)
          return pa_table


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase):

          '''simple docstring'''






          for i, file in enumerate(itertools.chain.from_iterable(_UpperCAmelCase)):
               with open(_UpperCAmelCase						,	'rb') as f:
                    __A						: Optional[int]      =       pa.Table.from_pandas(pd.read_pickle(_UpperCAmelCase))
                    yield i, self._cast_table(_UpperCAmelCase)