






'''simple docstring'''







import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class _lowercase      (							_lowercase						):
							a							 =			(DPMSolverMultistepScheduler,)
							a							 =			(("""num_inference_steps""", 25),)




							def 						lowerCamelCase_							(				self:							Optional[int]     ,				**UpperCamelCase__:							Optional[Any]	):
														lowerCamelCase__   :      List[str]			    =			{
														    """num_train_timesteps""": 1_000,
														    """beta_start""": 0.0_001,
														    """beta_end""": 0.02,
														    """beta_schedule""": """linear""",
														    """solver_order""": 2,
														    """prediction_type""": """epsilon""",
														    """thresholding""": False,
														    """sample_max_value""": 1.0,
														    """algorithm_type""": """dpmsolver++""",
														    """solver_type""": """midpoint""",
														    """lower_order_final""": False,
														    """lambda_min_clipped""": -float("""inf"""	),
														    """variance_type""": None,
														}

														config.update(**UpperCamelCase__	)
														return config




							def 						lowerCamelCase_							(				self:							Optional[int]     ,				UpperCamelCase__:							Optional[Any]=0     ,				**UpperCamelCase__:							Optional[Any]	):
														lowerCamelCase__   :      Any			    =			dict(self.forward_default_kwargs	)
														lowerCamelCase__   :      Any			    =			kwargs.pop("""num_inference_steps"""     ,				UpperCamelCase__	)
														lowerCamelCase__   :      List[str]			    =			self.dummy_sample
														lowerCamelCase__   :      Tuple			    =			0.1 * sample
														lowerCamelCase__   :      str			    =			[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

																					lowerCamelCase__    ,						lowerCamelCase__   :      Tuple			    =			sample, sample
																					for t in range(UpperCamelCase__     ,				time_step + scheduler.config.solver_order + 1	):
																												lowerCamelCase__   :      List[Any]			    =			scheduler.step(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				**UpperCamelCase__	).prev_sample
																												lowerCamelCase__   :      Dict			    =			new_scheduler.step(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				**UpperCamelCase__	).prev_sample

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




							def 						lowerCamelCase_							(				self:							List[str]	):
														pass




							def 						lowerCamelCase_							(				self:							Optional[Any]     ,				UpperCamelCase__:							Any=0     ,				**UpperCamelCase__:							Any	):
														lowerCamelCase__   :      Optional[Any]			    =			dict(self.forward_default_kwargs	)
														lowerCamelCase__   :      int			    =			kwargs.pop("""num_inference_steps"""     ,				UpperCamelCase__	)
														lowerCamelCase__   :      str			    =			self.dummy_sample
														lowerCamelCase__   :      Any			    =			0.1 * sample
														lowerCamelCase__   :      str			    =			[residual + 0.2, residual + 0.15, residual + 0.10]

														for scheduler_class in self.scheduler_classes:
																					lowerCamelCase__   :      List[Any]			    =			self.get_scheduler_config()
																					lowerCamelCase__   :      List[Any]			    =			scheduler_class(**UpperCamelCase__	)
																					scheduler.set_timesteps(UpperCamelCase__	)

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

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

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

																					lowerCamelCase__   :      Union[str, Any]			    =			scheduler.step(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				**UpperCamelCase__	).prev_sample
																					lowerCamelCase__   :      Optional[Any]			    =			new_scheduler.step(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				**UpperCamelCase__	).prev_sample

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




							def 						lowerCamelCase_							(				self:							Any     ,				UpperCamelCase__:							List[str]=None     ,				**UpperCamelCase__:							int	):
														if scheduler is None:
																					lowerCamelCase__   :      Any			    =			self.scheduler_classes[0]
																					lowerCamelCase__   :      Union[str, Any]			    =			self.get_scheduler_config(**UpperCamelCase__	)
																					lowerCamelCase__   :      Optional[Any]			    =			scheduler_class(**UpperCamelCase__	)

														lowerCamelCase__   :      Any			    =			10
														lowerCamelCase__   :      str			    =			self.dummy_model()
														lowerCamelCase__   :      List[str]			    =			self.dummy_sample_deter
														scheduler.set_timesteps(UpperCamelCase__	)

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

														return sample




							def 						lowerCamelCase_							(				self:							Dict	):
														lowerCamelCase__   :      Any			    =			dict(self.forward_default_kwargs	)

														lowerCamelCase__   :      int			    =			kwargs.pop("""num_inference_steps"""     ,				UpperCamelCase__	)

														for scheduler_class in self.scheduler_classes:
																					lowerCamelCase__   :      str			    =			self.get_scheduler_config()
																					lowerCamelCase__   :      List[str]			    =			scheduler_class(**UpperCamelCase__	)

																					lowerCamelCase__   :      Union[str, Any]			    =			self.dummy_sample
																					lowerCamelCase__   :      Union[str, Any]			    =			0.1 * sample

																					if num_inference_steps is not None and hasattr(UpperCamelCase__     ,				"""set_timesteps"""	):
																												scheduler.set_timesteps(UpperCamelCase__	)
																					elif num_inference_steps is not None and not hasattr(UpperCamelCase__     ,				"""set_timesteps"""	):
																												lowerCamelCase__   :      Any			    =			num_inference_steps

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

																					lowerCamelCase__   :      List[str]			    =			scheduler.timesteps[5]
																					lowerCamelCase__   :      Optional[Any]			    =			scheduler.timesteps[6]

																					lowerCamelCase__   :      Union[str, Any]			    =			scheduler.step(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				**UpperCamelCase__	).prev_sample
																					lowerCamelCase__   :      Optional[Any]			    =			scheduler.step(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				**UpperCamelCase__	).prev_sample

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




							def 						lowerCamelCase_							(				self:							str	):
														for timesteps in [25, 50, 100, 999, 1_000]:
																					self.check_over_configs(num_train_timesteps=UpperCamelCase__	)




							def 						lowerCamelCase_							(				self:							int	):
														self.check_over_configs(thresholding=UpperCamelCase__	)
														for order in [1, 2, 3]:
																					for solver_type in ["midpoint", "heun"]:
																												for threshold in [0.5, 1.0, 2.0]:
																																			for prediction_type in ["epsilon", "sample"]:
																																										self.check_over_configs(
																																										    thresholding=UpperCamelCase__     ,				prediction_type=UpperCamelCase__     ,				sample_max_value=UpperCamelCase__     ,				algorithm_type="""dpmsolver++"""     ,				solver_order=UpperCamelCase__     ,				solver_type=UpperCamelCase__     ,				)




							def 						lowerCamelCase_							(				self:							Any	):
														for prediction_type in ["epsilon", "v_prediction"]:
																					self.check_over_configs(prediction_type=UpperCamelCase__	)




							def 						lowerCamelCase_							(				self:							Tuple	):
														for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
																					for solver_type in ["midpoint", "heun"]:
																												for order in [1, 2, 3]:
																																			for prediction_type in ["epsilon", "sample"]:
																																										if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
																																																	if order == 3:
																																																								continue
																																										else:
																																																	self.check_over_configs(
																																																	    solver_order=UpperCamelCase__     ,				solver_type=UpperCamelCase__     ,				prediction_type=UpperCamelCase__     ,				algorithm_type=UpperCamelCase__     ,				)
																																										lowerCamelCase__   :      List[str]			    =			self.full_loop(
																																										    solver_order=UpperCamelCase__     ,				solver_type=UpperCamelCase__     ,				prediction_type=UpperCamelCase__     ,				algorithm_type=UpperCamelCase__     ,				)
																																										assert not torch.isnan(UpperCamelCase__	).any(), "Samples have nan numbers"




							def 						lowerCamelCase_							(				self:							Optional[int]	):
														self.check_over_configs(lower_order_final=UpperCamelCase__	)
														self.check_over_configs(lower_order_final=UpperCamelCase__	)




							def 						lowerCamelCase_							(				self:							List[str]	):
														self.check_over_configs(lambda_min_clipped=-float("""inf"""	)	)
														self.check_over_configs(lambda_min_clipped=-5.1	)




							def 						lowerCamelCase_							(				self:							Union[str, Any]	):
														self.check_over_configs(variance_type=UpperCamelCase__	)
														self.check_over_configs(variance_type="""learned_range"""	)




							def 						lowerCamelCase_							(				self:							Any	):
														for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
																					self.check_over_forward(num_inference_steps=UpperCamelCase__     ,				time_step=0	)




							def 						lowerCamelCase_							(				self:							List[str]	):
														lowerCamelCase__   :      Tuple			    =			self.full_loop()
														lowerCamelCase__   :      str			    =			torch.mean(torch.abs(UpperCamelCase__	)	)

														assert abs(result_mean.item() - 0.3_301	) < 1e-3




							def 						lowerCamelCase_							(				self:							Optional[int]	):
														lowerCamelCase__   :      Optional[Any]			    =			self.full_loop(thresholding=UpperCamelCase__     ,				dynamic_thresholding_ratio=0.87     ,				sample_max_value=0.5	)
														lowerCamelCase__   :      Tuple			    =			torch.mean(torch.abs(UpperCamelCase__	)	)

														assert abs(result_mean.item() - 1.1_364	) < 1e-3




							def 						lowerCamelCase_							(				self:							str	):
														lowerCamelCase__   :      Optional[int]			    =			self.full_loop(prediction_type="""v_prediction"""	)
														lowerCamelCase__   :      Optional[Any]			    =			torch.mean(torch.abs(UpperCamelCase__	)	)

														assert abs(result_mean.item() - 0.2_251	) < 1e-3




							def 						lowerCamelCase_							(				self:							Optional[Any]	):
														lowerCamelCase__   :      Tuple			    =			self.full_loop(prediction_type="""v_prediction"""     ,				use_karras_sigmas=UpperCamelCase__	)
														lowerCamelCase__   :      int			    =			torch.mean(torch.abs(UpperCamelCase__	)	)

														assert abs(result_mean.item() - 0.2_096	) < 1e-3




							def 						lowerCamelCase_							(				self:							List[str]	):
														# make sure that iterating over schedulers with same config names gives same results
														# for defaults
														lowerCamelCase__   :      List[Any]			    =			DPMSolverMultistepScheduler(**self.get_scheduler_config()	)
														lowerCamelCase__   :      Optional[int]			    =			self.full_loop(scheduler=UpperCamelCase__	)
														lowerCamelCase__   :      Dict			    =			torch.mean(torch.abs(UpperCamelCase__	)	)

														assert abs(result_mean.item() - 0.3_301	) < 1e-3

														lowerCamelCase__   :      Optional[Any]			    =			DPMSolverSinglestepScheduler.from_config(scheduler.config	)
														lowerCamelCase__   :      List[str]			    =			UniPCMultistepScheduler.from_config(scheduler.config	)
														lowerCamelCase__   :      Dict			    =			DEISMultistepScheduler.from_config(scheduler.config	)
														lowerCamelCase__   :      Optional[Any]			    =			DPMSolverMultistepScheduler.from_config(scheduler.config	)

														lowerCamelCase__   :      str			    =			self.full_loop(scheduler=UpperCamelCase__	)
														lowerCamelCase__   :      List[Any]			    =			torch.mean(torch.abs(UpperCamelCase__	)	)

														assert abs(result_mean.item() - 0.3_301	) < 1e-3




							def 						lowerCamelCase_							(				self:							Dict	):
														lowerCamelCase__   :      Any			    =			self.scheduler_classes[0]
														lowerCamelCase__   :      str			    =			self.get_scheduler_config(thresholding=UpperCamelCase__     ,				dynamic_thresholding_ratio=0	)
														lowerCamelCase__   :      List[str]			    =			scheduler_class(**UpperCamelCase__	)

														lowerCamelCase__   :      Dict			    =			10
														lowerCamelCase__   :      Union[str, Any]			    =			self.dummy_model()
														lowerCamelCase__   :      int			    =			self.dummy_sample_deter.half()
														scheduler.set_timesteps(UpperCamelCase__	)

														for i, t in enumerate(scheduler.timesteps	):
																					lowerCamelCase__   :      Tuple			    =			model(UpperCamelCase__     ,				UpperCamelCase__	)
																					lowerCamelCase__   :      List[str]			    =			scheduler.step(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__	).prev_sample

														assert sample.dtype == torch.floataa




							def 						lowerCamelCase_							(				self:							str     ,				**UpperCamelCase__:							Tuple	):
														for scheduler_class in self.scheduler_classes:
																					lowerCamelCase__   :      Optional[Any]			    =			self.get_scheduler_config(**UpperCamelCase__	)
																					lowerCamelCase__   :      List[str]			    =			scheduler_class(**UpperCamelCase__	)

																					scheduler.set_timesteps(scheduler.config.num_train_timesteps	)
																					assert len(scheduler.timesteps.unique()	) == scheduler.num_inference_steps









'''simple docstring'''







import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors

@require_tokenizers
class _lowercase      (							_lowercase							, unittest.TestCase						):
							a							 =			BartTokenizer
							a							 =			BartTokenizerFast
							a							 =			True
							a							 =			filter_roberta_detectors




							def 						lowerCamelCase_							(				self:							Union[str, Any]	):
														super().setUp()
														lowerCamelCase__   :      int			    =			[
														    """l""",
														    """o""",
														    """w""",
														    """e""",
														    """r""",
														    """s""",
														    """t""",
														    """i""",
														    """d""",
														    """n""",
														    """\u0120""",
														    """\u0120l""",
														    """\u0120n""",
														    """\u0120lo""",
														    """\u0120low""",
														    """er""",
														    """\u0120lowest""",
														    """\u0120newer""",
														    """\u0120wider""",
														    """<unk>""",
														]
														lowerCamelCase__   :      List[str]			    =			dict(zip(UpperCamelCase__     ,				range(len(UpperCamelCase__	)	)	)	)
														lowerCamelCase__   :      List[str]			    =			["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
														lowerCamelCase__   :      List[Any]			    =			{"""unk_token""": """<unk>"""}

														lowerCamelCase__   :      List[str]			    =			os.path.join(self.tmpdirname     ,				VOCAB_FILES_NAMES["""vocab_file"""]	)
														lowerCamelCase__   :      List[str]			    =			os.path.join(self.tmpdirname     ,				VOCAB_FILES_NAMES["""merges_file"""]	)
														with open(self.vocab_file     ,				"""w"""     ,				encoding="""utf-8"""	) as fp:
																					fp.write(json.dumps(UpperCamelCase__	) + """\n"""	)
														with open(self.merges_file     ,				"""w"""     ,				encoding="""utf-8"""	) as fp:
																					fp.write("""\n""".join(UpperCamelCase__	)	)




							def 						lowerCamelCase_							(				self:							Tuple     ,				**UpperCamelCase__:							Dict	):
														kwargs.update(self.special_tokens_map	)
														return self.tokenizer_class.from_pretrained(self.tmpdirname     ,				**UpperCamelCase__	)




							def 						lowerCamelCase_							(				self:							Optional[int]     ,				**UpperCamelCase__:							Tuple	):
														kwargs.update(self.special_tokens_map	)
														return self.rust_tokenizer_class.from_pretrained(self.tmpdirname     ,				**UpperCamelCase__	)




							def 						lowerCamelCase_							(				self:							List[str]     ,				UpperCamelCase__:							List[Any]	):
														return "lower newer", "lower newer"




							@cached_property
							def 						lowerCamelCase_							(				self:							str	):
														return BartTokenizer.from_pretrained("""facebook/bart-large"""	)




							@cached_property
							def 						lowerCamelCase_							(				self:							Union[str, Any]	):
														return BartTokenizerFast.from_pretrained("""facebook/bart-large"""	)




							@require_torch
							def 						lowerCamelCase_							(				self:							Optional[int]	):
														lowerCamelCase__   :      Any			    =			["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
														lowerCamelCase__   :      List[Any]			    =			[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					lowerCamelCase__   :      Tuple			    =			tokenizer(UpperCamelCase__     ,				max_length=len(UpperCamelCase__	)     ,				padding=UpperCamelCase__     ,				return_tensors="""pt"""	)
																					self.assertIsInstance(UpperCamelCase__     ,				UpperCamelCase__	)

																					self.assertEqual((2, 9)     ,				batch.input_ids.shape	)
																					self.assertEqual((2, 9)     ,				batch.attention_mask.shape	)
																					lowerCamelCase__   :      Optional[Any]			    =			batch.input_ids.tolist()[0]
																					self.assertListEqual(UpperCamelCase__     ,				UpperCamelCase__	)
																					# Test that special tokens are reset




							@require_torch
							def 						lowerCamelCase_							(				self:							Tuple	):
														lowerCamelCase__   :      str			    =			["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					lowerCamelCase__   :      Union[str, Any]			    =			tokenizer(UpperCamelCase__     ,				padding=UpperCamelCase__     ,				return_tensors="""pt"""	)
																					# check if input_ids are returned and no labels
																					self.assertIn("""input_ids"""     ,				UpperCamelCase__	)
																					self.assertIn("""attention_mask"""     ,				UpperCamelCase__	)
																					self.assertNotIn("""labels"""     ,				UpperCamelCase__	)
																					self.assertNotIn("""decoder_attention_mask"""     ,				UpperCamelCase__	)




							@require_torch
							def 						lowerCamelCase_							(				self:							Dict	):
														lowerCamelCase__   :      str			    =			[
														    """Summary of the text.""",
														    """Another summary.""",
														]
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					lowerCamelCase__   :      Any			    =			tokenizer(text_target=UpperCamelCase__     ,				max_length=32     ,				padding="""max_length"""     ,				return_tensors="""pt"""	)
																					self.assertEqual(32     ,				targets["""input_ids"""].shape[1]	)




							@require_torch
							def 						lowerCamelCase_							(				self:							str	):
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					lowerCamelCase__   :      Union[str, Any]			    =			tokenizer(
																					    ["""I am a small frog""" * 1_024, """I am a small frog"""]     ,				padding=UpperCamelCase__     ,				truncation=UpperCamelCase__     ,				return_tensors="""pt"""	)
																					self.assertIsInstance(UpperCamelCase__     ,				UpperCamelCase__	)
																					self.assertEqual(batch.input_ids.shape     ,				(2, 1_024)	)




							@require_torch
							def 						lowerCamelCase_							(				self:							Optional[Any]	):
														lowerCamelCase__   :      Optional[Any]			    =			["""A long paragraph for summarization."""]
														lowerCamelCase__   :      Any			    =			[
														    """Summary of the text.""",
														]
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					lowerCamelCase__   :      Union[str, Any]			    =			tokenizer(UpperCamelCase__     ,				return_tensors="""pt"""	)
																					lowerCamelCase__   :      Union[str, Any]			    =			tokenizer(text_target=UpperCamelCase__     ,				return_tensors="""pt"""	)
																					lowerCamelCase__   :      List[str]			    =			inputs["""input_ids"""]
																					lowerCamelCase__   :      Optional[int]			    =			targets["""input_ids"""]
																					self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()	)
																					self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()	)
																					self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()	)
																					self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()	)




							def 						lowerCamelCase_							(				self:							Optional[int]	):
														pass




							def 						lowerCamelCase_							(				self:							List[str]	):
														for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																					with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
																												lowerCamelCase__   :      int			    =			self.rust_tokenizer_class.from_pretrained(UpperCamelCase__     ,				**UpperCamelCase__	)
																												lowerCamelCase__   :      int			    =			self.tokenizer_class.from_pretrained(UpperCamelCase__     ,				**UpperCamelCase__	)
																												lowerCamelCase__   :      Dict			    =			"""A, <mask> AllenNLP sentence."""
																												lowerCamelCase__   :      Dict			    =			tokenizer_r.encode_plus(UpperCamelCase__     ,				add_special_tokens=UpperCamelCase__     ,				return_token_type_ids=UpperCamelCase__	)
																												lowerCamelCase__   :      Union[str, Any]			    =			tokenizer_p.encode_plus(UpperCamelCase__     ,				add_special_tokens=UpperCamelCase__     ,				return_token_type_ids=UpperCamelCase__	)

																												# token_type_ids should put 0 everywhere
																												self.assertEqual(sum(tokens_r["""token_type_ids"""]	)     ,				sum(tokens_p["""token_type_ids"""]	)	)

																												# attention_mask should put 1 everywhere, so sum over length should be 1
																												self.assertEqual(
																												    sum(tokens_r["""attention_mask"""]	) / len(tokens_r["""attention_mask"""]	)     ,				sum(tokens_p["""attention_mask"""]	) / len(tokens_p["""attention_mask"""]	)     ,				)

																												lowerCamelCase__   :      Any			    =			tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]	)
																												lowerCamelCase__   :      Any			    =			tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]	)

																												# Rust correctly handles the space before the mask while python doesnt
																												self.assertSequenceEqual(tokens_p["""input_ids"""]     ,				[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]	)
																												self.assertSequenceEqual(tokens_r["""input_ids"""]     ,				[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]	)

																												self.assertSequenceEqual(
																												    UpperCamelCase__     ,				["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]	)
																												self.assertSequenceEqual(
																												    UpperCamelCase__     ,				["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]	)

