






"""simple docstring"""






from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


UpperCAmelCase_   : Optional[Any]   			= logging.get_logger(__name__)


UpperCAmelCase_   : List[str]   			= TypeVar("""DatasetType""", Dataset, IterableDataset)



def 					_A			(__a    ,				__a = None    ,				__a = None    ,				__a = None    ,				__a = None    ,				__a = "first_exhausted"    ,				) ->       DatasetType:
    """simple docstring"""






    from .arrow_dataset import Dataset
    from .iterable_dataset import IterableDataset

    if not datasets:
        raise ValueError('''Unable to interleave an empty list of datasets.'''							)
    for i, dataset in enumerate(__a							):
        if not isinstance(__a    ,				(Dataset, IterableDataset)							):
            if isinstance(__a    ,				(DatasetDict, IterableDatasetDict)							):
                if not dataset:
                    raise ValueError(
                        f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
                        '''is an empty dataset dictionary.'''							)
                raise ValueError(
                    f'Dataset at position {i} has at least one split: {list(__a							)}\n'
                    f'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(__a							)							)}\']'							)
            raise ValueError(
                f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__a							).__name__}.'							)
        if i == 0:
            SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		(
                (Dataset, IterableDataset) if isinstance(__a    ,				__a							) else (IterableDataset, Dataset)
            )
        elif not isinstance(__a    ,				__a							):
            raise ValueError(
                f'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.'							)
    if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
        raise ValueError(f'{stopping_strategy} is not supported. Please enter a valid stopping_strategy.'							)
    if dataset_type is Dataset:
        return _interleave_map_style_datasets(
            __a    ,				__a    ,				__a    ,				info=__a    ,				split=__a    ,				stopping_strategy=__a							)
    else:
        return _interleave_iterable_datasets(
            __a    ,				__a    ,				__a    ,				info=__a    ,				split=__a    ,				stopping_strategy=__a							)





def 					_A			(__a    ,				__a = None    ,				__a = None    ,				__a = 0    ,				) ->       DatasetType:
    """simple docstring"""






    if not dsets:
        raise ValueError('''Unable to concatenate an empty list of datasets.'''							)
    for i, dataset in enumerate(__a							):
        if not isinstance(__a    ,				(Dataset, IterableDataset)							):
            if isinstance(__a    ,				(DatasetDict, IterableDatasetDict)							):
                if not dataset:
                    raise ValueError(
                        f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
                        '''is an empty dataset dictionary.'''							)
                raise ValueError(
                    f'Dataset at position {i} has at least one split: {list(__a							)}\n'
                    f'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(__a							)							)}\']'							)
            raise ValueError(
                f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__a							).__name__}.'							)
        if i == 0:
            SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  List[str]    						=		(
                (Dataset, IterableDataset) if isinstance(__a    ,				__a							) else (IterableDataset, Dataset)
            )
        elif not isinstance(__a    ,				__a							):
            raise ValueError(
                f'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.'							)
    if dataset_type is Dataset:
        return _concatenate_map_style_datasets(__a    ,				info=__a    ,				split=__a    ,				axis=__a							)
    else:
        return _concatenate_iterable_datasets(__a    ,				info=__a    ,				split=__a    ,				axis=__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.
import torch

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






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _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 					UpperCamelCase__(    self  ):



     '''simple docstring'''




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

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




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







 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




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




