






'''simple docstring'''

import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate




@dataclass(frozen=SCREAMING_SNAKE_CASE__    )
class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    str			  =    field(default='''image-classification''', metadata={'''include_in_asdict_even_if_is_default''': True}    )
 __a      :    ClassVar[Features]			  =    Features({'''image''': Image()}    )
 __a      :    ClassVar[Features]			  =    Features({'''labels''': ClassLabel}    )
 __a      :    str			  =    "image"
 __a      :    str			  =    "labels"
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase			):
       if self.label_column not in features:
             raise ValueError(F"Column {self.label_column} is not present in features."			)
       if not isinstance(features[self.label_column]      ,  __lowerCAmelCase			):
             raise ValueError(F"Column {self.label_column} is not a ClassLabel."			)
       UpperCamelCase_					: Optional[int]            =				copy.deepcopy(self			)
       UpperCamelCase_					: Optional[Any]            =				self.label_schema.copy()
       UpperCamelCase_					: Any            =				features[self.label_column]
       UpperCamelCase_					: int            =				label_schema
       return task_template





 @property
 def 	_UpperCAmelCase							(				self			):
       return {
           self.image_column: "image",
           self.label_column: "labels",
       }














'''simple docstring'''

from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest




@require_tokenizers
class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    Optional[int]			  =    DistilBertTokenizer
 __a      :    Union[str, Any]			  =    DistilBertTokenizerFast
 __a      :    Any			  =    True
 @slow
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: List[Any]            =				DistilBertTokenizer.from_pretrained("""distilbert-base-uncased"""			)

       UpperCamelCase_					: Dict            =				tokenizer.encode("""sequence builders"""      ,  add_special_tokens=__lowerCAmelCase			)
       UpperCamelCase_					: int            =				tokenizer.encode("""multi-sequence build"""      ,  add_special_tokens=__lowerCAmelCase			)

       UpperCamelCase_					: Union[str, Any]            =				tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase			)
       UpperCamelCase_					: Dict            =				tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase      ,  __lowerCAmelCase			)

       assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
       assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
           tokenizer.sep_token_id
       ]






