


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     Dict        =						(DEISMultistepScheduler,)
      snake_case__				:     str        =						(('''num_inference_steps''', 25),)
      def        SCREAMING_SNAKE_CASE							(       self				:       str     ,							**SCREAMING_SNAKE_CASE__				:       Any     )		->			Optional[Any]:
             a_					:		Optional[Any]							=  {
                 'num_train_timesteps': 1_0_0_0,
                 'beta_start': 0.0001,
                 'beta_end': 0.02,
                 'beta_schedule': 'linear',
                 'solver_order': 2,
             }

             config.update(**SCREAMING_SNAKE_CASE__     )
             return config
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=0     ,							**SCREAMING_SNAKE_CASE__				:       Optional[Any]     )		->			Tuple:
             a_					:		Optional[Any]							=  dict(self.forward_default_kwargs     )
             a_					:		Dict							=  kwargs.pop('num_inference_steps'     ,							SCREAMING_SNAKE_CASE__     )
             a_					:		Tuple							=  self.dummy_sample
             a_					:		str							=  0.1 * sample
             a_					:		Tuple							=  [residual + 0.2, residual + 0.15, residual + 0.10]

             for scheduler_class in self.scheduler_classes:
                    a_					:		Union[str, Any]							=  self.get_scheduler_config(**SCREAMING_SNAKE_CASE__     )
                    a_					:		Any							=  scheduler_class(**SCREAMING_SNAKE_CASE__     )
                    scheduler.set_timesteps(SCREAMING_SNAKE_CASE__     )
                    # copy over dummy past residuals
                    a_					:		List[str]							=  dummy_past_residuals[: scheduler.config.solver_order]

                    with tempfile.TemporaryDirectory() as tmpdirname:
                           scheduler.save_config(SCREAMING_SNAKE_CASE__     )
                           a_					:		Optional[Any]							=  scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE__     )
                           new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE__     )
                           # copy over dummy past residuals
                           a_					:		Dict							=  dummy_past_residuals[: new_scheduler.config.solver_order]

                    a_							,		a_					:		Union[str, Any]							=  sample, sample
                    for t in range(SCREAMING_SNAKE_CASE__     ,							time_step + scheduler.config.solver_order + 1     ):
                           a_					:		Tuple							=  scheduler.step(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     ).prev_sample
                           a_					:		Tuple							=  new_scheduler.step(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     ).prev_sample

                           assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			str:
             pass
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=0     ,							**SCREAMING_SNAKE_CASE__				:       List[Any]     )		->			List[Any]:
             a_					:		int							=  dict(self.forward_default_kwargs     )
             a_					:		Tuple							=  kwargs.pop('num_inference_steps'     ,							SCREAMING_SNAKE_CASE__     )
             a_					:		int							=  self.dummy_sample
             a_					:		Any							=  0.1 * sample
             a_					:		Union[str, Any]							=  [residual + 0.2, residual + 0.15, residual + 0.10]

             for scheduler_class in self.scheduler_classes:
                    a_					:		Tuple							=  self.get_scheduler_config()
                    a_					:		Optional[int]							=  scheduler_class(**SCREAMING_SNAKE_CASE__     )
                    scheduler.set_timesteps(SCREAMING_SNAKE_CASE__     )

                    # copy over dummy past residuals (must be after setting timesteps)
                    a_					:		Tuple							=  dummy_past_residuals[: scheduler.config.solver_order]

                    with tempfile.TemporaryDirectory() as tmpdirname:
                           scheduler.save_config(SCREAMING_SNAKE_CASE__     )
                           a_					:		Tuple							=  scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE__     )
                           # copy over dummy past residuals
                           new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE__     )

                           # copy over dummy past residual (must be after setting timesteps)
                           a_					:		int							=  dummy_past_residuals[: new_scheduler.config.solver_order]

                    a_					:		Any							=  scheduler.step(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     ).prev_sample
                    a_					:		Tuple							=  new_scheduler.step(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     ).prev_sample

                    assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=None     ,							**SCREAMING_SNAKE_CASE__				:       Tuple     )		->			Union[str, Any]:
             if scheduler is None:
                    a_					:		Dict							=  self.scheduler_classes[0]
                    a_					:		Union[str, Any]							=  self.get_scheduler_config(**SCREAMING_SNAKE_CASE__     )
                    a_					:		Tuple							=  scheduler_class(**SCREAMING_SNAKE_CASE__     )

             a_					:		int							=  self.scheduler_classes[0]
             a_					:		str							=  self.get_scheduler_config(**SCREAMING_SNAKE_CASE__     )
             a_					:		str							=  scheduler_class(**SCREAMING_SNAKE_CASE__     )

             a_					:		List[Any]							=  1_0
             a_					:		str							=  self.dummy_model()
             a_					:		Optional[Any]							=  self.dummy_sample_deter
             scheduler.set_timesteps(SCREAMING_SNAKE_CASE__     )

             for i, t in enumerate(scheduler.timesteps     ):
                    a_					:		Union[str, Any]							=  model(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
                    a_					:		Optional[Any]							=  scheduler.step(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ).prev_sample

             return sample
      def        SCREAMING_SNAKE_CASE							(       self				:       int     )		->			Union[str, Any]:
             a_					:		int							=  dict(self.forward_default_kwargs     )

             a_					:		str							=  kwargs.pop('num_inference_steps'     ,							SCREAMING_SNAKE_CASE__     )

             for scheduler_class in self.scheduler_classes:
                    a_					:		Union[str, Any]							=  self.get_scheduler_config()
                    a_					:		Optional[int]							=  scheduler_class(**SCREAMING_SNAKE_CASE__     )

                    a_					:		Any							=  self.dummy_sample
                    a_					:		List[Any]							=  0.1 * sample

                    if num_inference_steps is not None and hasattr(SCREAMING_SNAKE_CASE__     ,							'set_timesteps'     ):
                           scheduler.set_timesteps(SCREAMING_SNAKE_CASE__     )
                    elif num_inference_steps is not None and not hasattr(SCREAMING_SNAKE_CASE__     ,							'set_timesteps'     ):
                           a_					:		Optional[Any]							=  num_inference_steps

                    # copy over dummy past residuals (must be done after set_timesteps)
                    a_					:		Union[str, Any]							=  [residual + 0.2, residual + 0.15, residual + 0.10]
                    a_					:		str							=  dummy_past_residuals[: scheduler.config.solver_order]

                    a_					:		Any							=  scheduler.timesteps[5]
                    a_					:		Dict							=  scheduler.timesteps[6]

                    a_					:		int							=  scheduler.step(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     ).prev_sample
                    a_					:		int							=  scheduler.step(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     ).prev_sample

                    self.assertEqual(output_a.shape     ,							sample.shape     )
                    self.assertEqual(output_a.shape     ,							output_a.shape     )
      def        SCREAMING_SNAKE_CASE							(       self				:       int     )		->			str:
             # make sure that iterating over schedulers with same config names gives same results
             # for defaults
             a_					:		int							=  DEISMultistepScheduler(**self.get_scheduler_config()     )
             a_					:		List[Any]							=  self.full_loop(scheduler=SCREAMING_SNAKE_CASE__     )
             a_					:		Any							=  torch.mean(torch.abs(SCREAMING_SNAKE_CASE__     )     )

             assert abs(result_mean.item() - 0.23916     ) < 1E-3

             a_					:		List[Any]							=  DPMSolverSinglestepScheduler.from_config(scheduler.config     )
             a_					:		Union[str, Any]							=  DPMSolverMultistepScheduler.from_config(scheduler.config     )
             a_					:		Optional[Any]							=  UniPCMultistepScheduler.from_config(scheduler.config     )
             a_					:		Optional[Any]							=  DEISMultistepScheduler.from_config(scheduler.config     )

             a_					:		Dict							=  self.full_loop(scheduler=SCREAMING_SNAKE_CASE__     )
             a_					:		Optional[int]							=  torch.mean(torch.abs(SCREAMING_SNAKE_CASE__     )     )

             assert abs(result_mean.item() - 0.23916     ) < 1E-3
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			List[str]:
             for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                    self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			int:
             self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE__     )
             for order in [1, 2, 3]:
                    for solver_type in ["logrho"]:
                           for threshold in [0.5, 1.0, 2.0]:
                                  for prediction_type in ["epsilon", "sample"]:
                                         self.check_over_configs(
                                             thresholding=SCREAMING_SNAKE_CASE__     ,							prediction_type=SCREAMING_SNAKE_CASE__     ,							sample_max_value=SCREAMING_SNAKE_CASE__     ,							algorithm_type='deis'     ,							solver_order=SCREAMING_SNAKE_CASE__     ,							solver_type=SCREAMING_SNAKE_CASE__     ,							)
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			Union[str, Any]:
             for prediction_type in ["epsilon", "v_prediction"]:
                    self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			Dict:
             for algorithm_type in ["deis"]:
                    for solver_type in ["logrho"]:
                           for order in [1, 2, 3]:
                                  for prediction_type in ["epsilon", "sample"]:
                                         self.check_over_configs(
                                             solver_order=SCREAMING_SNAKE_CASE__     ,							solver_type=SCREAMING_SNAKE_CASE__     ,							prediction_type=SCREAMING_SNAKE_CASE__     ,							algorithm_type=SCREAMING_SNAKE_CASE__     ,							)
                                         a_					:		Optional[Any]							=  self.full_loop(
                                             solver_order=SCREAMING_SNAKE_CASE__     ,							solver_type=SCREAMING_SNAKE_CASE__     ,							prediction_type=SCREAMING_SNAKE_CASE__     ,							algorithm_type=SCREAMING_SNAKE_CASE__     ,							)
                                         assert not torch.isnan(SCREAMING_SNAKE_CASE__     ).any(), "Samples have nan numbers"
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			Union[str, Any]:
             self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE__     )
             self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			Optional[Any]:
             for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                    self.check_over_forward(num_inference_steps=SCREAMING_SNAKE_CASE__     ,							time_step=0     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			str:
             a_					:		List[str]							=  self.full_loop()
             a_					:		List[Any]							=  torch.mean(torch.abs(SCREAMING_SNAKE_CASE__     )     )

             assert abs(result_mean.item() - 0.23916     ) < 1E-3
      def        SCREAMING_SNAKE_CASE							(       self				:       int     )		->			str:
             a_					:		Dict							=  self.full_loop(prediction_type='v_prediction'     )
             a_					:		Union[str, Any]							=  torch.mean(torch.abs(SCREAMING_SNAKE_CASE__     )     )

             assert abs(result_mean.item() - 0.091     ) < 1E-3

      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			int:
             a_					:		int							=  self.scheduler_classes[0]
             a_					:		Union[str, Any]							=  self.get_scheduler_config(thresholding=SCREAMING_SNAKE_CASE__     ,							dynamic_thresholding_ratio=0     )
             a_					:		List[str]							=  scheduler_class(**SCREAMING_SNAKE_CASE__     )

             a_					:		Union[str, Any]							=  1_0
             a_					:		List[str]							=  self.dummy_model()
             a_					:		Optional[int]							=  self.dummy_sample_deter.half()
             scheduler.set_timesteps(SCREAMING_SNAKE_CASE__     )

             for i, t in enumerate(scheduler.timesteps     ):
                    a_					:		Any							=  model(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
                    a_					:		List[Any]							=  scheduler.step(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ).prev_sample

             assert sample.dtype == torch.floataa






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.
UpperCAmelCase_      : Dict  					=       {'LayoutLMv2Config', 'LayoutLMv3Config'}
@is_pipeline_test
class  SCREAMING_SNAKE_CASE__  (				unittest.TestCase			):
      snake_case__				:     List[str]        =						MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
      snake_case__				:     Optional[Any]        =						TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

      if model_mapping is not None:
            snake_case__				:     str        =						{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
      if tf_model_mapping is not None:
            snake_case__				:     List[Any]        =						{
                config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
            }
      @require_torch
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Tuple:
             a_					:		List[Any]							=  pipeline(
                 task='text-classification'     ,							model='hf-internal-testing/tiny-random-distilbert'     ,							framework='pt'     )

             a_					:		int							=  text_classifier('This is great !'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'LABEL_0', 'score': 0.504}]     )

             a_					:		Tuple							=  text_classifier('This is great !'     ,							top_k=2     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}]     )

             a_					:		List[str]							=  text_classifier(['This is great !', 'This is bad']     ,							top_k=2     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[
                     [{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}],
                     [{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}],
                 ]     ,							)

             a_					:		Tuple							=  text_classifier('This is great !'     ,							top_k=1     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'LABEL_0', 'score': 0.504}]     )

             # Legacy behavior
             a_					:		Union[str, Any]							=  text_classifier('This is great !'     ,							return_all_scores=SCREAMING_SNAKE_CASE__     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'LABEL_0', 'score': 0.504}]     )

             a_					:		List[str]							=  text_classifier('This is great !'     ,							return_all_scores=SCREAMING_SNAKE_CASE__     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[[{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}]]     )

             a_					:		int							=  text_classifier(['This is great !', 'Something else']     ,							return_all_scores=SCREAMING_SNAKE_CASE__     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[
                     [{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}],
                     [{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}],
                 ]     ,							)

             a_					:		str							=  text_classifier(['This is great !', 'Something else']     ,							return_all_scores=SCREAMING_SNAKE_CASE__     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[
                     {'label': 'LABEL_0', 'score': 0.504},
                     {'label': 'LABEL_0', 'score': 0.504},
                 ]     ,							)
      @require_torch
      def        SCREAMING_SNAKE_CASE							(       self				:       int     )		->			Dict:
             import torch

             a_					:		List[Any]							=  pipeline(
                 task='text-classification'     ,							model='hf-internal-testing/tiny-random-distilbert'     ,							framework='pt'     ,							device=torch.device('cpu'     )     ,							)

             a_					:		Any							=  text_classifier('This is great !'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'LABEL_0', 'score': 0.504}]     )
      @require_tf
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			Optional[Any]:
             a_					:		List[str]							=  pipeline(
                 task='text-classification'     ,							model='hf-internal-testing/tiny-random-distilbert'     ,							framework='tf'     )

             a_					:		Optional[int]							=  text_classifier('This is great !'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'LABEL_0', 'score': 0.504}]     )
      @slow
      @require_torch
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			Optional[int]:
             a_					:		List[str]							=  pipeline('text-classification'     )

             a_					:		Dict							=  text_classifier('This is great !'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'POSITIVE', 'score': 1.0}]     )
             a_					:		Union[str, Any]							=  text_classifier('This is bad !'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'NEGATIVE', 'score': 1.0}]     )
             a_					:		Tuple							=  text_classifier('Birds are a type of animal'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'POSITIVE', 'score': 0.988}]     )
      @slow
      @require_tf
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			List[Any]:
             a_					:		Dict							=  pipeline('text-classification'     ,							framework='tf'     )

             a_					:		Optional[Any]							=  text_classifier('This is great !'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'POSITIVE', 'score': 1.0}]     )
             a_					:		int							=  text_classifier('This is bad !'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'NEGATIVE', 'score': 1.0}]     )
             a_					:		Optional[int]							=  text_classifier('Birds are a type of animal'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'POSITIVE', 'score': 0.988}]     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       Any     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     )		->			Any:
             a_					:		Optional[Any]							=  TextClassificationPipeline(model=SCREAMING_SNAKE_CASE__     ,							tokenizer=SCREAMING_SNAKE_CASE__     )
             return text_classifier, ["HuggingFace is in", "This is another test"]

      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       Any     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]     )		->			Union[str, Any]:
             a_					:		List[str]							=  text_classifier.model
             # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
             a_					:		Union[str, Any]							=  'HuggingFace is in'
             a_					:		int							=  text_classifier(SCREAMING_SNAKE_CASE__     )

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

             a_					:		Union[str, Any]							=  ['HuggingFace is in ', 'Paris is in France']
             a_					:		int							=  text_classifier(SCREAMING_SNAKE_CASE__     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': ANY(SCREAMING_SNAKE_CASE__     ), 'score': ANY(SCREAMING_SNAKE_CASE__     )}, {'label': ANY(SCREAMING_SNAKE_CASE__     ), 'score': ANY(SCREAMING_SNAKE_CASE__     )}]     ,							)
             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
             a_					:		List[Any]							=  text_classifier(SCREAMING_SNAKE_CASE__     ,							top_k=SCREAMING_SNAKE_CASE__     )
             a_					:		Dict							=  len(model.config.idalabel.values()     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[[{'label': ANY(SCREAMING_SNAKE_CASE__     ), 'score': ANY(SCREAMING_SNAKE_CASE__     )}] * N, [{'label': ANY(SCREAMING_SNAKE_CASE__     ), 'score': ANY(SCREAMING_SNAKE_CASE__     )}] * N]     ,							)

             a_					:		int							=  {'text': 'HuggingFace is in ', 'text_pair': 'Paris is in France'}
             a_					:		Optional[int]							=  text_classifier(SCREAMING_SNAKE_CASE__     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							{'label': ANY(SCREAMING_SNAKE_CASE__     ), 'score': ANY(SCREAMING_SNAKE_CASE__     )}     ,							)
             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.
             a_					:		Any							=  [['HuggingFace is in ', 'Paris is in France']]
             with self.assertRaises(SCREAMING_SNAKE_CASE__     ):
                    text_classifier(SCREAMING_SNAKE_CASE__     )

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


