


import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin




class A    (  unittest.TestCase       ,				UpperCamelCase_	):






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




             _lowerCamelCase			:						Union[str, Any]            =load_tool('text-classification'					)
             self.tool.setup()
             _lowerCamelCase			:						Union[str, Any]            =load_tool('text-classification'     ,  remote=lowercase_					)






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




             _lowerCamelCase			:						Optional[Any]            =self.tool('That\'s quite cool'     ,  ['positive', 'negative']					)
             self.assertEqual(lowercase_     ,  'positive'					)






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




             _lowerCamelCase			:						Optional[Any]            =self.remote_tool('That\'s quite cool'     ,  ['positive', 'negative']					)
             self.assertEqual(lowercase_     ,  'positive'					)






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




             _lowerCamelCase			:						Tuple            =self.tool(text='That\'s quite cool'     ,  labels=['positive', 'negative']					)
             self.assertEqual(lowercase_     ,  'positive'					)






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




             _lowerCamelCase			:						Optional[int]            =self.remote_tool(text='That\'s quite cool'     ,  labels=['positive', 'negative']					)
             self.assertEqual(lowercase_     ,  'positive'					)






from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




class A    (  UpperCamelCase_	):






      def __init__(   self		:	Union[str, Any]     ,  lowercase_		:	NestedDataStructureLike[PathLike]     ,  lowercase_		:	Optional[NamedSplit] = None     ,  lowercase_		:	Optional[Features] = None     ,  lowercase_		:	str = None     ,  lowercase_		:	bool = False     ,  lowercase_		:	bool = False     ,  lowercase_		:	Optional[int] = None     ,  **lowercase_		:	Optional[int]     ,  )   ->		Union[str, Any]:
             """simple docstring"""




             super().__init__(
                 lowercase_     ,  split=lowercase_     ,  features=lowercase_     ,  cache_dir=lowercase_     ,  keep_in_memory=lowercase_     ,  streaming=lowercase_     ,  num_proc=lowercase_     ,  **lowercase_     ,  )
             _lowerCamelCase			:						Optional[int]            =path_or_paths if isinstance(lowercase_     ,  lowercase_					) else {self.split: path_or_paths}
             _lowerCamelCase			:						int            =Text(
                 cache_dir=lowercase_     ,  data_files=lowercase_     ,  features=lowercase_     ,  **lowercase_     ,  )






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




             if self.streaming:
                    _lowerCamelCase			:						Optional[Any]            =self.builder.as_streaming_dataset(split=self.split					)
             # Build regular (map-style) dataset
             else:
                    _lowerCamelCase			:						List[str]            =None
                    _lowerCamelCase			:						int            =None
                    _lowerCamelCase			:						Optional[Any]            =None
                    _lowerCamelCase			:						Any            =None

                    self.builder.download_and_prepare(
                        download_config=lowercase_     ,  download_mode=lowercase_     ,  verification_mode=lowercase_     ,  base_path=lowercase_     ,  num_proc=self.num_proc     ,  )
                    _lowerCamelCase			:						Tuple            =self.builder.as_dataset(
                        split=self.split     ,  verification_mode=lowercase_     ,  in_memory=self.keep_in_memory					)
             return dataset


