



'''simple docstring'''





import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
 from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCAmelCase      						=	get_tests_dir('''fixtures/test_sentencepiece.model''')

_lowerCAmelCase      						=	{'''target_lang''': '''fi''', '''source_lang''': '''en'''}
_lowerCAmelCase      						=	'''>>zh<<'''
_lowerCAmelCase      						=	'''Helsinki-NLP/'''

if is_torch_available():
 _lowerCAmelCase      						=	'''pt'''
elif is_tf_available():
 _lowerCAmelCase      						=	'''tf'''
else:
 _lowerCAmelCase      						=	'''jax'''






@require_sentencepiece
class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_						, unittest.TestCase     ):
  '''simple docstring'''




  __lowercase							:      Any				=      MarianTokenizer
  __lowercase							:      Optional[int]				=      False
  __lowercase							:      Any				=      True




  def 							UpperCAmelCase_	(    self      )    ->		str:
      super().setUp()
      lowerCAmelCase__ :  Dict							      =					["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
      lowerCAmelCase__ :  Optional[int]							      =					dict(zip(__UpperCAmelCase  ,range(len(__UpperCAmelCase      )      )      )      )
      lowerCAmelCase__ :  Tuple							      =					Path(self.tmpdirname      )
      save_json(__UpperCAmelCase  ,save_dir / VOCAB_FILES_NAMES["""vocab"""]      )
      save_json(__UpperCAmelCase  ,save_dir / VOCAB_FILES_NAMES["""tokenizer_config_file"""]      )
      if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
          copyfile(__UpperCAmelCase  ,save_dir / VOCAB_FILES_NAMES["""source_spm"""]      )
          copyfile(__UpperCAmelCase  ,save_dir / VOCAB_FILES_NAMES["""target_spm"""]      )

      lowerCAmelCase__ :  List[Any]							      =					MarianTokenizer.from_pretrained(self.tmpdirname      )
      tokenizer.save_pretrained(self.tmpdirname      )




  def 							UpperCAmelCase_	(    self  ,**__UpperCAmelCase      )    ->		MarianTokenizer:
      return MarianTokenizer.from_pretrained(self.tmpdirname  ,**__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		int:
      return (
          "This is a test",
          "This is a test",
      )




  def 							UpperCAmelCase_	(    self      )    ->		Any:
      lowerCAmelCase__ :  Optional[int]							      =					"""</s>"""
      lowerCAmelCase__ :  Optional[Any]							      =					0

      self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCAmelCase      )  ,__UpperCAmelCase      )
      self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCAmelCase      )  ,__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		List[Any]:
      lowerCAmelCase__ :  Union[str, Any]							      =					list(self.get_tokenizer().get_vocab().keys()      )

      self.assertEqual(vocab_keys[0]  ,"""</s>"""      )
      self.assertEqual(vocab_keys[1]  ,"""<unk>"""      )
      self.assertEqual(vocab_keys[-1]  ,"""<pad>"""      )
      self.assertEqual(len(__UpperCAmelCase      )  ,9      )




  def 							UpperCAmelCase_	(    self      )    ->		Tuple:
      self.assertEqual(self.get_tokenizer().vocab_size  ,9      )




  def 							UpperCAmelCase_	(    self      )    ->		Any:
      lowerCAmelCase__ :  Optional[int]							      =					MarianTokenizer.from_pretrained(F"""{ORG_NAME}opus-mt-en-de"""      )
      lowerCAmelCase__ :  Optional[Any]							      =					en_de_tokenizer(["""I am a small frog"""]  ,return_tensors=__UpperCAmelCase      )
      self.assertIsInstance(__UpperCAmelCase  ,__UpperCAmelCase      )
      lowerCAmelCase__ :  int							      =					[38, 121, 14, 697, 3_8848, 0]
      self.assertListEqual(__UpperCAmelCase  ,batch.input_ids[0]      )

      lowerCAmelCase__ :  List[str]							      =					tempfile.mkdtemp()
      en_de_tokenizer.save_pretrained(__UpperCAmelCase      )
      lowerCAmelCase__ :  Optional[Any]							      =					[x.name for x in Path(__UpperCAmelCase      ).glob("""*"""      )]
      self.assertIn("""source.spm"""  ,__UpperCAmelCase      )
      MarianTokenizer.from_pretrained(__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		str:
      lowerCAmelCase__ :  Union[str, Any]							      =					self.get_tokenizer()

      lowerCAmelCase__ :  Union[str, Any]							      =					tok(
          ["""I am a small frog""" * 1000, """I am a small frog"""]  ,padding=__UpperCAmelCase  ,truncation=__UpperCAmelCase  ,return_tensors=__UpperCAmelCase      )
      self.assertIsInstance(__UpperCAmelCase  ,__UpperCAmelCase      )
      self.assertEqual(batch.input_ids.shape  ,(2, 512)      )




  def 							UpperCAmelCase_	(    self      )    ->		Any:
      lowerCAmelCase__ :  Any							      =					self.get_tokenizer()
      lowerCAmelCase__ :  Dict							      =					tok(["""I am a tiny frog""", """I am a small frog"""]  ,padding=__UpperCAmelCase  ,return_tensors=__UpperCAmelCase      )
      self.assertIsInstance(__UpperCAmelCase  ,__UpperCAmelCase      )
      self.assertEqual(batch_smaller.input_ids.shape  ,(2, 10)      )




  @slow
  def 							UpperCAmelCase_	(    self      )    ->		Tuple:
      # fmt: off
      lowerCAmelCase__ :  List[str]							      =					{"""input_ids""": [[4_3495, 462, 20, 4_2164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 3_8999, 6, 8, 464, 132, 1703, 492, 13, 4669, 3_7867, 13, 7525, 27, 1593, 988, 13, 3_3972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 1_2338, 2, 1_3958, 387, 2, 3629, 6953, 188, 2900, 2, 1_3958, 8011, 1_1501, 23, 8460, 4073, 3_4009, 20, 435, 1_1439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 3_7867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 2_6453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 1_0767, 6, 316, 304, 4239, 3, 0], [148, 1_5722, 19, 1839, 12, 1350, 13, 2_2327, 5082, 5418, 4_7567, 3_5938, 59, 318, 1_9552, 108, 2183, 54, 1_4976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 1_9088, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100], [36, 6395, 1_2570, 3_9147, 1_1597, 6, 266, 4, 4_5405, 7296, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
      # fmt: on

      self.tokenizer_integration_test_util(
          expected_encoding=__UpperCAmelCase  ,model_name="""Helsinki-NLP/opus-mt-en-de"""  ,revision="""1a8c2263da11e68e50938f97e10cd57820bd504c"""  ,decode_kwargs={"""use_source_tokenizer""": True}  ,)






  def 							UpperCAmelCase_	(    self      )    ->		List[str]:
      lowerCAmelCase__ :  Optional[int]							      =					MarianTokenizer.from_pretrained("""hf-internal-testing/test-marian-two-vocabs"""      )

      lowerCAmelCase__ :  Tuple							      =					"""Tämä on testi"""
      lowerCAmelCase__ :  str							      =					"""This is a test"""

      lowerCAmelCase__ :  Tuple							      =					[76, 7, 2047, 2]
      lowerCAmelCase__ :  Any							      =					[69, 12, 11, 940, 2]

      lowerCAmelCase__ :  Union[str, Any]							      =					tokenizer(__UpperCAmelCase      ).input_ids
      self.assertListEqual(__UpperCAmelCase  ,__UpperCAmelCase      )

      lowerCAmelCase__ :  List[Any]							      =					tokenizer(text_target=__UpperCAmelCase      ).input_ids
      self.assertListEqual(__UpperCAmelCase  ,__UpperCAmelCase      )

      lowerCAmelCase__ :  List[str]							      =					tokenizer.decode(__UpperCAmelCase  ,skip_special_tokens=__UpperCAmelCase      )
      self.assertEqual(__UpperCAmelCase  ,__UpperCAmelCase      )






import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
A      :    Union[str, Any]		=					{"LayoutLMv2Config", "LayoutLMv3Config"}







@is_pipeline_test
class 			_lowercase (							unittest.TestCase):


       """simple docstring"""




       A__											=			MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
       A__											=			TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

       if model_mapping is not None:
              A__											=			{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
       if tf_model_mapping is not None:
              A__											=			{
                  config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
              }





       @require_torch
       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Tuple    =  pipeline(
                 task="text-classification"					,      model="hf-internal-testing/tiny-random-distilbert"					,      framework="pt"       )

             lowerCamelCase__		:				Dict    =  text_classifier("This is great !"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "LABEL_0", "score": 0.5_0_4}]       )

             lowerCamelCase__		:				List[str]    =  text_classifier("This is great !"					,      top_k=2       )
             self.assertEqual(
                 nested_simplify(__lowerCamelCase       )					,      [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}]       )

             lowerCamelCase__		:				Optional[int]    =  text_classifier(["This is great !", "This is bad"]					,      top_k=2       )
             self.assertEqual(
                 nested_simplify(__lowerCamelCase       )					,      [
                     [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}],
                     [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}],
                 ]					,      )

             lowerCamelCase__		:				Any    =  text_classifier("This is great !"					,      top_k=1       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "LABEL_0", "score": 0.5_0_4}]       )

             # Legacy behavior
             lowerCamelCase__		:				Dict    =  text_classifier("This is great !"					,      return_all_scores=__lowerCamelCase       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "LABEL_0", "score": 0.5_0_4}]       )

             lowerCamelCase__		:				str    =  text_classifier("This is great !"					,      return_all_scores=__lowerCamelCase       )
             self.assertEqual(
                 nested_simplify(__lowerCamelCase       )					,      [[{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}]]       )

             lowerCamelCase__		:				Optional[Any]    =  text_classifier(["This is great !", "Something else"]					,      return_all_scores=__lowerCamelCase       )
             self.assertEqual(
                 nested_simplify(__lowerCamelCase       )					,      [
                     [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}],
                     [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}],
                 ]					,      )

             lowerCamelCase__		:				Any    =  text_classifier(["This is great !", "Something else"]					,      return_all_scores=__lowerCamelCase       )
             self.assertEqual(
                 nested_simplify(__lowerCamelCase       )					,      [
                     {"label": "LABEL_0", "score": 0.5_0_4},
                     {"label": "LABEL_0", "score": 0.5_0_4},
                 ]					,      )





       @require_torch
       def 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             import torch

             lowerCamelCase__		:				int    =  pipeline(
                 task="text-classification"					,      model="hf-internal-testing/tiny-random-distilbert"					,      framework="pt"					,      device=torch.device("cpu"       )					,      )

             lowerCamelCase__		:				Any    =  text_classifier("This is great !"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "LABEL_0", "score": 0.5_0_4}]       )





       @require_tf
       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[str]    =  pipeline(
                 task="text-classification"					,      model="hf-internal-testing/tiny-random-distilbert"					,      framework="tf"       )

             lowerCamelCase__		:				List[str]    =  text_classifier("This is great !"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "LABEL_0", "score": 0.5_0_4}]       )





       @slow
       @require_torch
       def 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[Any]    =  pipeline("text-classification"       )

             lowerCamelCase__		:				List[str]    =  text_classifier("This is great !"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "POSITIVE", "score": 1.0}]       )
             lowerCamelCase__		:				Optional[int]    =  text_classifier("This is bad !"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "NEGATIVE", "score": 1.0}]       )
             lowerCamelCase__		:				Tuple    =  text_classifier("Birds are a type of animal"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "POSITIVE", "score": 0.9_8_8}]       )





       @slow
       @require_tf
       def 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  pipeline("text-classification"					,      framework="tf"       )

             lowerCamelCase__		:				Optional[int]    =  text_classifier("This is great !"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "POSITIVE", "score": 1.0}]       )
             lowerCamelCase__		:				Optional[Any]    =  text_classifier("This is bad !"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "NEGATIVE", "score": 1.0}]       )
             lowerCamelCase__		:				Dict    =  text_classifier("Birds are a type of animal"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "POSITIVE", "score": 0.9_8_8}]       )





       def 						lowerCAmelCase     (   self							:			Tuple					,      __lowerCamelCase							:			Any					,      __lowerCamelCase							:			str					,      __lowerCamelCase							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Tuple    =  TextClassificationPipeline(model=__lowerCamelCase					,      tokenizer=__lowerCamelCase       )
             return text_classifier, ["HuggingFace is in", "This is another test"]





       def 						lowerCAmelCase     (   self							:			Optional[Any]					,      __lowerCamelCase							:			Tuple					,      __lowerCamelCase							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  text_classifier.model
             # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
             lowerCamelCase__		:				List[Any]    =  "HuggingFace is in"
             lowerCamelCase__		:				Tuple    =  text_classifier(__lowerCamelCase       )

             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": ANY(__lowerCamelCase       ), "score": ANY(__lowerCamelCase       )}]       )
             self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()       )

             lowerCamelCase__		:				Optional[int]    =  ["HuggingFace is in ", "Paris is in France"]
             lowerCamelCase__		:				Dict    =  text_classifier(__lowerCamelCase       )
             self.assertEqual(
                 nested_simplify(__lowerCamelCase       )					,      [{"label": ANY(__lowerCamelCase       ), "score": ANY(__lowerCamelCase       )}, {"label": ANY(__lowerCamelCase       ), "score": ANY(__lowerCamelCase       )}]					,      )
             self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()       )
             self.assertTrue(outputs[1]["label"] in model.config.idalabel.values()       )

             # Forcing to get all results with `top_k=None`
             # This is NOT the legacy format
             lowerCamelCase__		:				List[Any]    =  text_classifier(__lowerCamelCase					,      top_k=__lowerCamelCase       )
             lowerCamelCase__		:				Union[str, Any]    =  len(model.config.idalabel.values()       )
             self.assertEqual(
                 nested_simplify(__lowerCamelCase       )					,      [[{"label": ANY(__lowerCamelCase       ), "score": ANY(__lowerCamelCase       )}] * N, [{"label": ANY(__lowerCamelCase       ), "score": ANY(__lowerCamelCase       )}] * N]					,      )

             lowerCamelCase__		:				Optional[int]    =  {"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
             lowerCamelCase__		:				List[Any]    =  text_classifier(__lowerCamelCase       )
             self.assertEqual(
                 nested_simplify(__lowerCamelCase       )					,      {"label": ANY(__lowerCamelCase       ), "score": ANY(__lowerCamelCase       )}					,      )
             self.assertTrue(outputs["label"] in model.config.idalabel.values()       )

             # This might be used a text pair, but tokenizer + pipe interaction
             # makes it hard to understand that it's not using the pair properly
             # https://github.com/huggingface/transformers/issues/17305
             # We disabled this usage instead as it was outputting wrong outputs.
             lowerCamelCase__		:				Any    =  [["HuggingFace is in ", "Paris is in France"]]
             with self.assertRaises(__lowerCamelCase       ):
                   text_classifier(__lowerCamelCase       )

             # This used to be valid for doing text pairs
             # We're keeping it working because of backward compatibility
             lowerCamelCase__		:				int    =  text_classifier([[["HuggingFace is in ", "Paris is in France"]]]       )
             self.assertEqual(
                 nested_simplify(__lowerCamelCase       )					,      [{"label": ANY(__lowerCamelCase       ), "score": ANY(__lowerCamelCase       )}]					,      )
             self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()       )


