

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

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



@dataclass(frozen=__lowercase    )
class    _a						(__lowercase    ):


					'''simple docstring'''






					UpperCAmelCase__:       str  							=	field(default='''text-classification'''		,       metadata={'''include_in_asdict_even_if_is_default''': True}    )
					UpperCAmelCase__:       ClassVar[Features]  							=	Features({'''text''': Value('''string'''    )}    )
					UpperCAmelCase__:       ClassVar[Features]  							=	Features({'''labels''': ClassLabel}    )
					UpperCAmelCase__:       str  							=	"text"
					UpperCAmelCase__:       str  							=	"labels"






					def   __A      (			self   ,	A__      ):
						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]   ,	UpperCAmelCase__      ):
							raise ValueError(F"""Column {self.label_column} is not a ClassLabel."""      )
						A__							:   Union[str, Any]					       =		copy.deepcopy(self      )
						A__							:   str					       =		self.label_schema.copy()
						A__							:   List[str]					       =		features[self.label_column]
						A__							:   Dict					       =		label_schema
						return task_template






					@property
					def   __A      (			self      ):
						return {
						    self.text_column: "text",
						    self.label_column: "labels",
						}






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
	from transformers import AutoModelForSeqaSeqLM, AutoTokenizer



@require_torch
@require_sentencepiece
@require_tokenizers
class    _a						(unittest.TestCase    ):


					'''simple docstring'''






					@slow
					def   __A      (			self      ):
						A__							:   Optional[Any]					       =		AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""   ,	return_dict=A__      ).to(A__      )
						A__							:   str					       =		AutoTokenizer.from_pretrained("""google/mt5-small"""      )

						A__							:   int					       =		tokenizer("""Hello there"""   ,	return_tensors="""pt"""      ).input_ids
						A__							:   List[Any]					       =		tokenizer("""Hi I am"""   ,	return_tensors="""pt"""      ).input_ids

						A__							:   Union[str, Any]					       =		model(input_ids.to(A__      )   ,	labels=labels.to(A__      )      ).loss
						A__							:   Union[str, Any]					       =		-(labels.shape[-1] * loss.item())

						A__							:   Any					       =		-8_4.9_1_2_7
						self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 1e-4      )



