




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			(DEISMultistepScheduler,)
			snake_case_  						=			(('''num_inference_steps''', 25),)


			def  lowercase_       ( self     ,		**lowerCamelCase__				)       -> List[str]:



							'''simple docstring'''
							__lowerCamelCase									=       {
							    'num_train_timesteps': 1_000,
							    'beta_start': 0.00_01,
							    'beta_end': 0.02,
							    'beta_schedule': 'linear',
							    'solver_order': 2,
							}

							config.update(**lowerCamelCase__				)
							return config


			def  lowercase_       ( self     ,		lowerCamelCase__=0     ,		**lowerCamelCase__				)       -> int:



							'''simple docstring'''
							__lowerCamelCase									=       dict(self.forward_default_kwargs				)
							__lowerCamelCase									=       kwargs.pop('num_inference_steps'     ,		lowerCamelCase__				)
							__lowerCamelCase									=       self.dummy_sample
							__lowerCamelCase									=       0.1 * sample
							__lowerCamelCase									=       [residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
											__lowerCamelCase									=       self.get_scheduler_config(**lowerCamelCase__				)
											__lowerCamelCase									=       scheduler_class(**lowerCamelCase__				)
											scheduler.set_timesteps(lowerCamelCase__				)
											# copy over dummy past residuals
											__lowerCamelCase									=       dummy_past_residuals[: scheduler.config.solver_order]

											with tempfile.TemporaryDirectory() as tmpdirname:
															scheduler.save_config(lowerCamelCase__				)
															__lowerCamelCase									=       scheduler_class.from_pretrained(lowerCamelCase__				)
															new_scheduler.set_timesteps(lowerCamelCase__				)
															# copy over dummy past residuals
															__lowerCamelCase									=       dummy_past_residuals[: new_scheduler.config.solver_order]

											__lowerCamelCase			,		__lowerCamelCase									=       sample, sample
											for t in range(lowerCamelCase__     ,		time_step + scheduler.config.solver_order + 1				):
															__lowerCamelCase									=       scheduler.step(lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		**lowerCamelCase__				).prev_sample
															__lowerCamelCase									=       new_scheduler.step(lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		**lowerCamelCase__				).prev_sample

															assert torch.sum(torch.abs(output - new_output				)				) < 1e-5, "Scheduler outputs are not identical"


			def  lowercase_       ( self				)       -> Any:



							'''simple docstring'''
							pass


			def  lowercase_       ( self     ,		lowerCamelCase__=0     ,		**lowerCamelCase__				)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       dict(self.forward_default_kwargs				)
							__lowerCamelCase									=       kwargs.pop('num_inference_steps'     ,		lowerCamelCase__				)
							__lowerCamelCase									=       self.dummy_sample
							__lowerCamelCase									=       0.1 * sample
							__lowerCamelCase									=       [residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
											__lowerCamelCase									=       self.get_scheduler_config()
											__lowerCamelCase									=       scheduler_class(**lowerCamelCase__				)
											scheduler.set_timesteps(lowerCamelCase__				)

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

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

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

											__lowerCamelCase									=       scheduler.step(lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		**lowerCamelCase__				).prev_sample
											__lowerCamelCase									=       new_scheduler.step(lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		**lowerCamelCase__				).prev_sample

											assert torch.sum(torch.abs(output - new_output				)				) < 1e-5, "Scheduler outputs are not identical"


			def  lowercase_       ( self     ,		lowerCamelCase__=None     ,		**lowerCamelCase__				)       -> Optional[int]:



							'''simple docstring'''
							if scheduler is None:
											__lowerCamelCase									=       self.scheduler_classes[0]
											__lowerCamelCase									=       self.get_scheduler_config(**lowerCamelCase__				)
											__lowerCamelCase									=       scheduler_class(**lowerCamelCase__				)

							__lowerCamelCase									=       self.scheduler_classes[0]
							__lowerCamelCase									=       self.get_scheduler_config(**lowerCamelCase__				)
							__lowerCamelCase									=       scheduler_class(**lowerCamelCase__				)

							__lowerCamelCase									=       10
							__lowerCamelCase									=       self.dummy_model()
							__lowerCamelCase									=       self.dummy_sample_deter
							scheduler.set_timesteps(lowerCamelCase__				)

							for i, t in enumerate(scheduler.timesteps				):
											__lowerCamelCase									=       model(lowerCamelCase__     ,		lowerCamelCase__				)
											__lowerCamelCase									=       scheduler.step(lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__				).prev_sample

							return sample


			def  lowercase_       ( self				)       -> Optional[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       dict(self.forward_default_kwargs				)

							__lowerCamelCase									=       kwargs.pop('num_inference_steps'     ,		lowerCamelCase__				)

							for scheduler_class in self.scheduler_classes:
											__lowerCamelCase									=       self.get_scheduler_config()
											__lowerCamelCase									=       scheduler_class(**lowerCamelCase__				)

											__lowerCamelCase									=       self.dummy_sample
											__lowerCamelCase									=       0.1 * sample

											if num_inference_steps is not None and hasattr(lowerCamelCase__     ,		'set_timesteps'				):
															scheduler.set_timesteps(lowerCamelCase__				)
											elif num_inference_steps is not None and not hasattr(lowerCamelCase__     ,		'set_timesteps'				):
															__lowerCamelCase									=       num_inference_steps

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

											__lowerCamelCase									=       scheduler.timesteps[5]
											__lowerCamelCase									=       scheduler.timesteps[6]

											__lowerCamelCase									=       scheduler.step(lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		**lowerCamelCase__				).prev_sample
											__lowerCamelCase									=       scheduler.step(lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		**lowerCamelCase__				).prev_sample

											self.assertEqual(output_a.shape     ,		sample.shape				)
											self.assertEqual(output_a.shape     ,		output_a.shape				)


			def  lowercase_       ( self				)       -> Dict:



							'''simple docstring'''
							# make sure that iterating over schedulers with same config names gives same results
							# for defaults
							__lowerCamelCase									=       DEISMultistepScheduler(**self.get_scheduler_config()				)
							__lowerCamelCase									=       self.full_loop(scheduler=lowerCamelCase__				)
							__lowerCamelCase									=       torch.mean(torch.abs(lowerCamelCase__				)				)

							assert abs(result_mean.item() - 0.2_39_16				) < 1e-3

							__lowerCamelCase									=       DPMSolverSinglestepScheduler.from_config(scheduler.config				)
							__lowerCamelCase									=       DPMSolverMultistepScheduler.from_config(scheduler.config				)
							__lowerCamelCase									=       UniPCMultistepScheduler.from_config(scheduler.config				)
							__lowerCamelCase									=       DEISMultistepScheduler.from_config(scheduler.config				)

							__lowerCamelCase									=       self.full_loop(scheduler=lowerCamelCase__				)
							__lowerCamelCase									=       torch.mean(torch.abs(lowerCamelCase__				)				)

							assert abs(result_mean.item() - 0.2_39_16				) < 1e-3


			def  lowercase_       ( self				)       -> Any:



							'''simple docstring'''
							for timesteps in [25, 50, 100, 999, 1_000]:
											self.check_over_configs(num_train_timesteps=lowerCamelCase__				)


			def  lowercase_       ( self				)       -> int:



							'''simple docstring'''
							self.check_over_configs(thresholding=lowerCamelCase__				)
							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=lowerCamelCase__     ,		prediction_type=lowerCamelCase__     ,		sample_max_value=lowerCamelCase__     ,		algorithm_type='deis'     ,		solver_order=lowerCamelCase__     ,		solver_type=lowerCamelCase__     ,		)


			def  lowercase_       ( self				)       -> List[str]:



							'''simple docstring'''
							for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=lowerCamelCase__				)


			def  lowercase_       ( self				)       -> Tuple:



							'''simple docstring'''
							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=lowerCamelCase__     ,		solver_type=lowerCamelCase__     ,		prediction_type=lowerCamelCase__     ,		algorithm_type=lowerCamelCase__     ,		)
																							__lowerCamelCase									=       self.full_loop(
																							    solver_order=lowerCamelCase__     ,		solver_type=lowerCamelCase__     ,		prediction_type=lowerCamelCase__     ,		algorithm_type=lowerCamelCase__     ,		)
																							assert not torch.isnan(lowerCamelCase__				).any(), "Samples have nan numbers"


			def  lowercase_       ( self				)       -> int:



							'''simple docstring'''
							self.check_over_configs(lower_order_final=lowerCamelCase__				)
							self.check_over_configs(lower_order_final=lowerCamelCase__				)


			def  lowercase_       ( self				)       -> Optional[Any]:



							'''simple docstring'''
							for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
											self.check_over_forward(num_inference_steps=lowerCamelCase__     ,		time_step=0				)


			def  lowercase_       ( self				)       -> Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase									=       self.full_loop()
							__lowerCamelCase									=       torch.mean(torch.abs(lowerCamelCase__				)				)

							assert abs(result_mean.item() - 0.2_39_16				) < 1e-3


			def  lowercase_       ( self				)       -> List[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       self.full_loop(prediction_type='v_prediction'				)
							__lowerCamelCase									=       torch.mean(torch.abs(lowerCamelCase__				)				)

							assert abs(result_mean.item() - 0.0_91				) < 1e-3






			def  lowercase_       ( self				)       -> Dict:



							'''simple docstring'''
							__lowerCamelCase									=       self.scheduler_classes[0]
							__lowerCamelCase									=       self.get_scheduler_config(thresholding=lowerCamelCase__     ,		dynamic_thresholding_ratio=0				)
							__lowerCamelCase									=       scheduler_class(**lowerCamelCase__				)

							__lowerCamelCase									=       10
							__lowerCamelCase									=       self.dummy_model()
							__lowerCamelCase									=       self.dummy_sample_deter.half()
							scheduler.set_timesteps(lowerCamelCase__				)

							for i, t in enumerate(scheduler.timesteps				):
											__lowerCamelCase									=       model(lowerCamelCase__     ,		lowerCamelCase__				)
											__lowerCamelCase									=       scheduler.step(lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__				).prev_sample

							assert sample.dtype == torch.floataa








import gc
import unittest

from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_gpu,
    slow,
)

from .test_pipelines_common import ANY







@is_pipeline_test
class        __lowerCAmelCase					(			unittest.TestCase					):

			"""simple docstring"""







			snake_case_  						=			MODEL_FOR_MASKED_LM_MAPPING
			snake_case_  						=			TF_MODEL_FOR_MASKED_LM_MAPPING


			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							super().tearDown()
							# clean-up as much as possible GPU memory occupied by PyTorch
							gc.collect()
							if is_torch_available():
											import torch

											torch.cuda.empty_cache()


			@require_tf
			def  lowercase_       ( self				)       -> Any:



							'''simple docstring'''
							__lowerCamelCase									=       pipeline(task='fill-mask'     ,		model='sshleifer/tiny-distilroberta-base'     ,		top_k=2     ,		framework='tf'				)
							__lowerCamelCase									=       unmasker('My name is <mask>'				)
							self.assertEqual(
							    nested_simplify(lowerCamelCase__     ,		decimals=6				)     ,		[
							        {'sequence': 'My name is grouped', 'score': 2.1e-05, 'token': 38_015, 'token_str': ' grouped'},
							        {'sequence': 'My name is accuser', 'score': 2.1e-05, 'token': 25_506, 'token_str': ' accuser'},
							    ]     ,		)

							__lowerCamelCase									=       unmasker('The largest city in France is <mask>'				)
							self.assertEqual(
							    nested_simplify(lowerCamelCase__     ,		decimals=6				)     ,		[
							        {
							            'sequence': 'The largest city in France is grouped',
							            'score': 2.1e-05,
							            'token': 38_015,
							            'token_str': ' grouped',
							        },
							        {
							            'sequence': 'The largest city in France is accuser',
							            'score': 2.1e-05,
							            'token': 25_506,
							            'token_str': ' accuser',
							        },
							    ]     ,		)

							__lowerCamelCase									=       unmasker('My name is <mask>'     ,		targets=[' Patrick', ' Clara', ' Teven']     ,		top_k=3				)
							self.assertEqual(
							    nested_simplify(lowerCamelCase__     ,		decimals=6				)     ,		[
							        {'sequence': 'My name is Clara', 'score': 2e-05, 'token': 13_606, 'token_str': ' Clara'},
							        {'sequence': 'My name is Patrick', 'score': 2e-05, 'token': 3_499, 'token_str': ' Patrick'},
							        {'sequence': 'My name is Te', 'score': 1.9e-05, 'token': 2_941, 'token_str': ' Te'},
							    ]     ,		)


			@require_torch
			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       pipeline(task='fill-mask'     ,		model='sshleifer/tiny-distilroberta-base'     ,		top_k=2     ,		framework='pt'				)

							__lowerCamelCase									=       unmasker('My name is <mask>'				)
							self.assertEqual(
							    nested_simplify(lowerCamelCase__     ,		decimals=6				)     ,		[
							        {'sequence': 'My name is Maul', 'score': 2.2e-05, 'token': 35_676, 'token_str': ' Maul'},
							        {'sequence': 'My name isELS', 'score': 2.2e-05, 'token': 16_416, 'token_str': 'ELS'},
							    ]     ,		)

							__lowerCamelCase									=       unmasker('The largest city in France is <mask>'				)
							self.assertEqual(
							    nested_simplify(lowerCamelCase__     ,		decimals=6				)     ,		[
							        {
							            'sequence': 'The largest city in France is Maul',
							            'score': 2.2e-05,
							            'token': 35_676,
							            'token_str': ' Maul',
							        },
							        {'sequence': 'The largest city in France isELS', 'score': 2.2e-05, 'token': 16_416, 'token_str': 'ELS'},
							    ]     ,		)

							__lowerCamelCase									=       unmasker('My name is <mask>'     ,		targets=[' Patrick', ' Clara', ' Teven']     ,		top_k=3				)
							self.assertEqual(
							    nested_simplify(lowerCamelCase__     ,		decimals=6				)     ,		[
							        {'sequence': 'My name is Patrick', 'score': 2.1e-05, 'token': 3_499, 'token_str': ' Patrick'},
							        {'sequence': 'My name is Te', 'score': 2e-05, 'token': 2_941, 'token_str': ' Te'},
							        {'sequence': 'My name is Clara', 'score': 2e-05, 'token': 13_606, 'token_str': ' Clara'},
							    ]     ,		)

							__lowerCamelCase									=       unmasker('My name is <mask> <mask>'     ,		top_k=2				)

							self.assertEqual(
							    nested_simplify(lowerCamelCase__     ,		decimals=6				)     ,		[
							        [
							            {
							                'score': 2.2e-05,
							                'token': 35_676,
							                'token_str': ' Maul',
							                'sequence': '<s>My name is Maul<mask></s>',
							            },
							            {'score': 2.2e-05, 'token': 16_416, 'token_str': 'ELS', 'sequence': '<s>My name isELS<mask></s>'},
							        ],
							        [
							            {
							                'score': 2.2e-05,
							                'token': 35_676,
							                'token_str': ' Maul',
							                'sequence': '<s>My name is<mask> Maul</s>',
							            },
							            {'score': 2.2e-05, 'token': 16_416, 'token_str': 'ELS', 'sequence': '<s>My name is<mask>ELS</s>'},
							        ],
							    ]     ,		)


			@require_torch_gpu
			def  lowercase_       ( self				)       -> int:



							'''simple docstring'''
							__lowerCamelCase									=       pipeline('fill-mask'     ,		model='hf-internal-testing/tiny-random-distilbert'     ,		device=0     ,		framework='pt'				)

							# convert model to fp16
							pipe.model.half()

							__lowerCamelCase									=       pipe('Paris is the [MASK] of France.'				)
							# We actually don't care about the result, we just want to make sure
							# it works, meaning the float16 tensor got casted back to float32
							# for postprocessing.
							self.assertIsInstance(lowerCamelCase__     ,		lowerCamelCase__				)


			@slow
			@require_torch
			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       pipeline(task='fill-mask'     ,		model='distilroberta-base'     ,		top_k=2     ,		framework='pt'				)
							self.run_large_test(lowerCamelCase__				)


			@slow
			@require_tf
			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       pipeline(task='fill-mask'     ,		model='distilroberta-base'     ,		top_k=2     ,		framework='tf'				)
							self.run_large_test(lowerCamelCase__				)


			def  lowercase_       ( self     ,		lowerCamelCase__				)       -> Optional[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       unmasker('My name is <mask>'				)
							self.assertEqual(
							    nested_simplify(lowerCamelCase__				)     ,		[
							        {'sequence': 'My name is John', 'score': 0.0_08, 'token': 610, 'token_str': ' John'},
							        {'sequence': 'My name is Chris', 'score': 0.0_07, 'token': 1_573, 'token_str': ' Chris'},
							    ]     ,		)
							__lowerCamelCase									=       unmasker('The largest city in France is <mask>'				)
							self.assertEqual(
							    nested_simplify(lowerCamelCase__				)     ,		[
							        {
							            'sequence': 'The largest city in France is Paris',
							            'score': 0.2_51,
							            'token': 2_201,
							            'token_str': ' Paris',
							        },
							        {
							            'sequence': 'The largest city in France is Lyon',
							            'score': 0.2_14,
							            'token': 12_790,
							            'token_str': ' Lyon',
							        },
							    ]     ,		)

							__lowerCamelCase									=       unmasker('My name is <mask>'     ,		targets=[' Patrick', ' Clara', ' Teven']     ,		top_k=3				)
							self.assertEqual(
							    nested_simplify(lowerCamelCase__				)     ,		[
							        {'sequence': 'My name is Patrick', 'score': 0.0_05, 'token': 3_499, 'token_str': ' Patrick'},
							        {'sequence': 'My name is Clara', 'score': 0.0_00, 'token': 13_606, 'token_str': ' Clara'},
							        {'sequence': 'My name is Te', 'score': 0.0_00, 'token': 2_941, 'token_str': ' Te'},
							    ]     ,		)


			@require_torch
			def  lowercase_       ( self				)       -> int:



							'''simple docstring'''
							__lowerCamelCase									=       pipeline(task='fill-mask'     ,		model='sshleifer/tiny-distilroberta-base'     ,		framework='pt'				)
							__lowerCamelCase									=       None
							__lowerCamelCase									=       None
							self.run_pipeline_test(lowerCamelCase__     ,		[]				)


			@require_tf
			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       pipeline(task='fill-mask'     ,		model='sshleifer/tiny-distilroberta-base'     ,		framework='tf'				)
							__lowerCamelCase									=       None
							__lowerCamelCase									=       None
							self.run_pipeline_test(lowerCamelCase__     ,		[]				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> List[str]:



							'''simple docstring'''
							if tokenizer is None or tokenizer.mask_token_id is None:
											self.skipTest('The provided tokenizer has no mask token, (probably reformer or wav2vec2)'				)

							__lowerCamelCase									=       FillMaskPipeline(model=lowerCamelCase__     ,		tokenizer=lowerCamelCase__				)
							__lowerCamelCase									=       [
							    f"""This is another {tokenizer.mask_token} test""",
							]
							return fill_masker, examples


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> Optional[int]:



							'''simple docstring'''
							__lowerCamelCase									=       fill_masker.tokenizer
							__lowerCamelCase									=       fill_masker.model

							__lowerCamelCase									=       fill_masker(
							    f"""This is a {tokenizer.mask_token}"""     ,		)
							self.assertEqual(
							    lowerCamelCase__     ,		[
							        {'sequence': ANY(lowerCamelCase__				), 'score': ANY(lowerCamelCase__				), 'token': ANY(lowerCamelCase__				), 'token_str': ANY(lowerCamelCase__				)},
							        {'sequence': ANY(lowerCamelCase__				), 'score': ANY(lowerCamelCase__				), 'token': ANY(lowerCamelCase__				), 'token_str': ANY(lowerCamelCase__				)},
							        {'sequence': ANY(lowerCamelCase__				), 'score': ANY(lowerCamelCase__				), 'token': ANY(lowerCamelCase__				), 'token_str': ANY(lowerCamelCase__				)},
							        {'sequence': ANY(lowerCamelCase__				), 'score': ANY(lowerCamelCase__				), 'token': ANY(lowerCamelCase__				), 'token_str': ANY(lowerCamelCase__				)},
							        {'sequence': ANY(lowerCamelCase__				), 'score': ANY(lowerCamelCase__				), 'token': ANY(lowerCamelCase__				), 'token_str': ANY(lowerCamelCase__				)},
							    ]     ,		)

							__lowerCamelCase									=       fill_masker([f"""This is a {tokenizer.mask_token}"""]				)
							self.assertEqual(
							    lowerCamelCase__     ,		[
							        {'sequence': ANY(lowerCamelCase__				), 'score': ANY(lowerCamelCase__				), 'token': ANY(lowerCamelCase__				), 'token_str': ANY(lowerCamelCase__				)},
							        {'sequence': ANY(lowerCamelCase__				), 'score': ANY(lowerCamelCase__				), 'token': ANY(lowerCamelCase__				), 'token_str': ANY(lowerCamelCase__				)},
							        {'sequence': ANY(lowerCamelCase__				), 'score': ANY(lowerCamelCase__				), 'token': ANY(lowerCamelCase__				), 'token_str': ANY(lowerCamelCase__				)},
							        {'sequence': ANY(lowerCamelCase__				), 'score': ANY(lowerCamelCase__				), 'token': ANY(lowerCamelCase__				), 'token_str': ANY(lowerCamelCase__				)},
							        {'sequence': ANY(lowerCamelCase__				), 'score': ANY(lowerCamelCase__				), 'token': ANY(lowerCamelCase__				), 'token_str': ANY(lowerCamelCase__				)},
							    ]     ,		)

							__lowerCamelCase									=       fill_masker([f"""This is a {tokenizer.mask_token}""", f"""Another {tokenizer.mask_token} great test."""]				)
							self.assertEqual(
							    lowerCamelCase__     ,		[
							        [
							            {'sequence': ANY(lowerCamelCase__				), 'score': ANY(lowerCamelCase__				), 'token': ANY(lowerCamelCase__				), 'token_str': ANY(lowerCamelCase__				)},
							            {'sequence': ANY(lowerCamelCase__				), 'score': ANY(lowerCamelCase__				), 'token': ANY(lowerCamelCase__				), 'token_str': ANY(lowerCamelCase__				)},
							            {'sequence': ANY(lowerCamelCase__				), 'score': ANY(lowerCamelCase__				), 'token': ANY(lowerCamelCase__				), 'token_str': ANY(lowerCamelCase__				)},
							            {'sequence': ANY(lowerCamelCase__				), 'score': ANY(lowerCamelCase__				), 'token': ANY(lowerCamelCase__				), 'token_str': ANY(lowerCamelCase__				)},
							            {'sequence': ANY(lowerCamelCase__				), 'score': ANY(lowerCamelCase__				), 'token': ANY(lowerCamelCase__				), 'token_str': ANY(lowerCamelCase__				)},
							        ],
							        [
							            {'sequence': ANY(lowerCamelCase__				), 'score': ANY(lowerCamelCase__				), 'token': ANY(lowerCamelCase__				), 'token_str': ANY(lowerCamelCase__				)},
							            {'sequence': ANY(lowerCamelCase__				), 'score': ANY(lowerCamelCase__				), 'token': ANY(lowerCamelCase__				), 'token_str': ANY(lowerCamelCase__				)},
							            {'sequence': ANY(lowerCamelCase__				), 'score': ANY(lowerCamelCase__				), 'token': ANY(lowerCamelCase__				), 'token_str': ANY(lowerCamelCase__				)},
							            {'sequence': ANY(lowerCamelCase__				), 'score': ANY(lowerCamelCase__				), 'token': ANY(lowerCamelCase__				), 'token_str': ANY(lowerCamelCase__				)},
							            {'sequence': ANY(lowerCamelCase__				), 'score': ANY(lowerCamelCase__				), 'token': ANY(lowerCamelCase__				), 'token_str': ANY(lowerCamelCase__				)},
							        ],
							    ]     ,		)

							with self.assertRaises(lowerCamelCase__				):
											fill_masker([None]				)
							# No mask_token is not supported
							with self.assertRaises(lowerCamelCase__				):
											fill_masker('This is'				)

							self.run_test_top_k(lowerCamelCase__     ,		lowerCamelCase__				)
							self.run_test_targets(lowerCamelCase__     ,		lowerCamelCase__				)
							self.run_test_top_k_targets(lowerCamelCase__     ,		lowerCamelCase__				)
							self.fill_mask_with_duplicate_targets_and_top_k(lowerCamelCase__     ,		lowerCamelCase__				)
							self.fill_mask_with_multiple_masks(lowerCamelCase__     ,		lowerCamelCase__				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> Dict:



							'''simple docstring'''
							__lowerCamelCase									=       tokenizer.get_vocab()
							__lowerCamelCase									=       sorted(vocab.keys()				)[:2]
							# Pipeline argument
							__lowerCamelCase									=       FillMaskPipeline(model=lowerCamelCase__     ,		tokenizer=lowerCamelCase__     ,		targets=lowerCamelCase__				)
							__lowerCamelCase									=       fill_masker(f"""This is a {tokenizer.mask_token}"""				)
							self.assertEqual(
							    lowerCamelCase__     ,		[
							        {'sequence': ANY(lowerCamelCase__				), 'score': ANY(lowerCamelCase__				), 'token': ANY(lowerCamelCase__				), 'token_str': ANY(lowerCamelCase__				)},
							        {'sequence': ANY(lowerCamelCase__				), 'score': ANY(lowerCamelCase__				), 'token': ANY(lowerCamelCase__				), 'token_str': ANY(lowerCamelCase__				)},
							    ]     ,		)
							__lowerCamelCase									=       {vocab[el] for el in targets}
							self.assertEqual({el['token'] for el in outputs}     ,		lowerCamelCase__				)
							__lowerCamelCase									=       [tokenizer.decode([x]				) for x in target_ids]
							self.assertEqual({el['token_str'] for el in outputs}     ,		set(lowerCamelCase__				)				)

							# Call argument
							__lowerCamelCase									=       FillMaskPipeline(model=lowerCamelCase__     ,		tokenizer=lowerCamelCase__				)
							__lowerCamelCase									=       fill_masker(f"""This is a {tokenizer.mask_token}"""     ,		targets=lowerCamelCase__				)
							self.assertEqual(
							    lowerCamelCase__     ,		[
							        {'sequence': ANY(lowerCamelCase__				), 'score': ANY(lowerCamelCase__				), 'token': ANY(lowerCamelCase__				), 'token_str': ANY(lowerCamelCase__				)},
							        {'sequence': ANY(lowerCamelCase__				), 'score': ANY(lowerCamelCase__				), 'token': ANY(lowerCamelCase__				), 'token_str': ANY(lowerCamelCase__				)},
							    ]     ,		)
							__lowerCamelCase									=       {vocab[el] for el in targets}
							self.assertEqual({el['token'] for el in outputs}     ,		lowerCamelCase__				)
							__lowerCamelCase									=       [tokenizer.decode([x]				) for x in target_ids]
							self.assertEqual({el['token_str'] for el in outputs}     ,		set(lowerCamelCase__				)				)

							# Score equivalence
							__lowerCamelCase									=       fill_masker(f"""This is a {tokenizer.mask_token}"""     ,		targets=lowerCamelCase__				)
							__lowerCamelCase									=       [top_mask['token_str'] for top_mask in outputs]
							__lowerCamelCase									=       [top_mask['score'] for top_mask in outputs]

							# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
							if set(lowerCamelCase__				) == set(lowerCamelCase__				):
											__lowerCamelCase									=       fill_masker(f"""This is a {tokenizer.mask_token}"""     ,		targets=lowerCamelCase__				)
											__lowerCamelCase									=       [top_mask['score'] for top_mask in unmasked_targets]
											self.assertEqual(nested_simplify(lowerCamelCase__				)     ,		nested_simplify(lowerCamelCase__				)				)

							# Raises with invalid
							with self.assertRaises(lowerCamelCase__				):
											__lowerCamelCase									=       fill_masker(f"""This is a {tokenizer.mask_token}"""     ,		targets=[]				)
							# For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
							if "" not in tokenizer.get_vocab():
											with self.assertRaises(lowerCamelCase__				):
															__lowerCamelCase									=       fill_masker(f"""This is a {tokenizer.mask_token}"""     ,		targets=['']				)
											with self.assertRaises(lowerCamelCase__				):
															__lowerCamelCase									=       fill_masker(f"""This is a {tokenizer.mask_token}"""     ,		targets=''				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> Optional[int]:



							'''simple docstring'''
							__lowerCamelCase									=       FillMaskPipeline(model=lowerCamelCase__     ,		tokenizer=lowerCamelCase__     ,		top_k=2				)
							__lowerCamelCase									=       fill_masker(f"""This is a {tokenizer.mask_token}"""				)
							self.assertEqual(
							    lowerCamelCase__     ,		[
							        {'sequence': ANY(lowerCamelCase__				), 'score': ANY(lowerCamelCase__				), 'token': ANY(lowerCamelCase__				), 'token_str': ANY(lowerCamelCase__				)},
							        {'sequence': ANY(lowerCamelCase__				), 'score': ANY(lowerCamelCase__				), 'token': ANY(lowerCamelCase__				), 'token_str': ANY(lowerCamelCase__				)},
							    ]     ,		)

							__lowerCamelCase									=       FillMaskPipeline(model=lowerCamelCase__     ,		tokenizer=lowerCamelCase__				)
							__lowerCamelCase									=       fill_masker(f"""This is a {tokenizer.mask_token}"""     ,		top_k=2				)
							self.assertEqual(
							    lowerCamelCase__     ,		[
							        {'sequence': ANY(lowerCamelCase__				), 'score': ANY(lowerCamelCase__				), 'token': ANY(lowerCamelCase__				), 'token_str': ANY(lowerCamelCase__				)},
							        {'sequence': ANY(lowerCamelCase__				), 'score': ANY(lowerCamelCase__				), 'token': ANY(lowerCamelCase__				), 'token_str': ANY(lowerCamelCase__				)},
							    ]     ,		)
							self.assertEqual(nested_simplify(lowerCamelCase__				)     ,		nested_simplify(lowerCamelCase__				)				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> int:



							'''simple docstring'''
							__lowerCamelCase									=       tokenizer.get_vocab()
							__lowerCamelCase									=       FillMaskPipeline(model=lowerCamelCase__     ,		tokenizer=lowerCamelCase__				)

							# top_k=2, ntargets=3
							__lowerCamelCase									=       sorted(vocab.keys()				)[:3]
							__lowerCamelCase									=       fill_masker(f"""This is a {tokenizer.mask_token}"""     ,		top_k=2     ,		targets=lowerCamelCase__				)

							# If we use the most probably targets, and filter differently, we should still
							# have the same results
							__lowerCamelCase									=       [el['token_str'] for el in sorted(lowerCamelCase__     ,		key=lambda lowerCamelCase__				: x["score"]     ,		reverse=lowerCamelCase__				)]
							# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
							if set(lowerCamelCase__				).issubset(lowerCamelCase__				):
											__lowerCamelCase									=       fill_masker(f"""This is a {tokenizer.mask_token}"""     ,		top_k=3     ,		targets=lowerCamelCase__				)
											# They should yield exactly the same result
											self.assertEqual(nested_simplify(lowerCamelCase__				)     ,		nested_simplify(lowerCamelCase__				)				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> List[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       FillMaskPipeline(model=lowerCamelCase__     ,		tokenizer=lowerCamelCase__				)
							__lowerCamelCase									=       tokenizer.get_vocab()
							# String duplicates + id duplicates
							__lowerCamelCase									=       sorted(vocab.keys()				)[:3]
							__lowerCamelCase									=       [targets[0], targets[1], targets[0], targets[2], targets[1]]
							__lowerCamelCase									=       fill_masker(f"""My name is {tokenizer.mask_token}"""     ,		targets=lowerCamelCase__     ,		top_k=10				)

							# The target list contains duplicates, so we can't output more
							# than them
							self.assertEqual(len(lowerCamelCase__				)     ,		3				)






			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase									=       FillMaskPipeline(model=lowerCamelCase__     ,		tokenizer=lowerCamelCase__				)

							__lowerCamelCase									=       fill_masker(
							    f"""This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}"""     ,		top_k=2				)
							self.assertEqual(
							    lowerCamelCase__     ,		[
							        [
							            {'sequence': ANY(lowerCamelCase__				), 'score': ANY(lowerCamelCase__				), 'token': ANY(lowerCamelCase__				), 'token_str': ANY(lowerCamelCase__				)},
							            {'sequence': ANY(lowerCamelCase__				), 'score': ANY(lowerCamelCase__				), 'token': ANY(lowerCamelCase__				), 'token_str': ANY(lowerCamelCase__				)},
							        ],
							        [
							            {'sequence': ANY(lowerCamelCase__				), 'score': ANY(lowerCamelCase__				), 'token': ANY(lowerCamelCase__				), 'token_str': ANY(lowerCamelCase__				)},
							            {'sequence': ANY(lowerCamelCase__				), 'score': ANY(lowerCamelCase__				), 'token': ANY(lowerCamelCase__				), 'token_str': ANY(lowerCamelCase__				)},
							        ],
							        [
							            {'sequence': ANY(lowerCamelCase__				), 'score': ANY(lowerCamelCase__				), 'token': ANY(lowerCamelCase__				), 'token_str': ANY(lowerCamelCase__				)},
							            {'sequence': ANY(lowerCamelCase__				), 'score': ANY(lowerCamelCase__				), 'token': ANY(lowerCamelCase__				), 'token_str': ANY(lowerCamelCase__				)},
							        ],
							    ]     ,		)


