import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class _a  (			lowerCAmelCase__   ):

	'''simple docstring'''



	lowerCamelCase_       :     Optional[int]          =			(DEISMultistepScheduler,)
	lowerCamelCase_       :     Dict          =			(("num_inference_steps", 2_5),)

	def 	__UpperCAmelCase(  self				,					**__UpperCAmelCase       ):
			__A				:       Optional[int]			    = {
			    "num_train_timesteps": 1_000,
			    "beta_start": 0.00_01,
			    "beta_end": 0.02,
			    "beta_schedule": "linear",
			    "solver_order": 2,
			}

			config.update(**__UpperCAmelCase       )
			return config

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

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

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

					__A    ,  __A				:       int			    = sample, sample
					for t in range(__UpperCAmelCase				,					time_step + scheduler.config.solver_order + 1       ):
							__A				:       str			    = scheduler.step(__UpperCAmelCase				,					__UpperCAmelCase				,					__UpperCAmelCase				,					**__UpperCAmelCase       ).prev_sample
							__A				:       Tuple			    = 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 	__UpperCAmelCase(  self       ):
			pass

	def 	__UpperCAmelCase(  self				,					__UpperCAmelCase=0				,					**__UpperCAmelCase       ):
			__A				:       Union[str, Any]			    = dict(self.forward_default_kwargs       )
			__A				:       Dict			    = kwargs.pop("num_inference_steps"				,					__UpperCAmelCase       )
			__A				:       List[str]			    = self.dummy_sample
			__A				:       List[Any]			    = 0.1 * sample
			__A				:       Optional[Any]			    = [residual + 0.2, residual + 0.15, residual + 0.10]

			for scheduler_class in self.scheduler_classes:
					__A				:       Tuple			    = self.get_scheduler_config()
					__A				:       Union[str, Any]			    = scheduler_class(**__UpperCAmelCase       )
					scheduler.set_timesteps(__UpperCAmelCase       )

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

					with tempfile.TemporaryDirectory() as tmpdirname:
							scheduler.save_config(__UpperCAmelCase       )
							__A				:       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)
							__A				:       Union[str, Any]			    = dummy_past_residuals[: new_scheduler.config.solver_order]

					__A				:       Optional[Any]			    = scheduler.step(__UpperCAmelCase				,					__UpperCAmelCase				,					__UpperCAmelCase				,					**__UpperCAmelCase       ).prev_sample
					__A				:       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 	__UpperCAmelCase(  self				,					__UpperCAmelCase=None				,					**__UpperCAmelCase       ):

			if scheduler is None:
					__A				:       Optional[int]			    = self.scheduler_classes[0]
					__A				:       Optional[int]			    = self.get_scheduler_config(**__UpperCAmelCase       )
					__A				:       List[Any]			    = scheduler_class(**__UpperCAmelCase       )

			__A				:       Tuple			    = self.scheduler_classes[0]
			__A				:       str			    = self.get_scheduler_config(**__UpperCAmelCase       )
			__A				:       Optional[Any]			    = scheduler_class(**__UpperCAmelCase       )

			__A				:       Dict			    = 10
			__A				:       Optional[int]			    = self.dummy_model()
			__A				:       Optional[Any]			    = self.dummy_sample_deter
			scheduler.set_timesteps(__UpperCAmelCase       )

			for i, t in enumerate(scheduler.timesteps       ):
					__A				:       Dict			    = model(__UpperCAmelCase				,					__UpperCAmelCase       )
					__A				:       Any			    = scheduler.step(__UpperCAmelCase				,					__UpperCAmelCase				,					__UpperCAmelCase       ).prev_sample

			return sample

	def 	__UpperCAmelCase(  self       ):
			__A				:       Union[str, Any]			    = dict(self.forward_default_kwargs       )

			__A				:       Dict			    = kwargs.pop("num_inference_steps"				,					__UpperCAmelCase       )

			for scheduler_class in self.scheduler_classes:
					__A				:       Any			    = self.get_scheduler_config()
					__A				:       Union[str, Any]			    = scheduler_class(**__UpperCAmelCase       )

					__A				:       Optional[Any]			    = self.dummy_sample
					__A				:       int			    = 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"       ):
							__A				:       Any			    = num_inference_steps

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

					__A				:       Dict			    = scheduler.timesteps[5]
					__A				:       List[str]			    = scheduler.timesteps[6]

					__A				:       int			    = scheduler.step(__UpperCAmelCase				,					__UpperCAmelCase				,					__UpperCAmelCase				,					**__UpperCAmelCase       ).prev_sample
					__A				:       List[str]			    = scheduler.step(__UpperCAmelCase				,					__UpperCAmelCase				,					__UpperCAmelCase				,					**__UpperCAmelCase       ).prev_sample

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

	def 	__UpperCAmelCase(  self       ):
			__A				:       Any			    = DEISMultistepScheduler(**self.get_scheduler_config()       )
			__A				:       List[Any]			    = self.full_loop(scheduler=__UpperCAmelCase       )
			__A				:       Dict			    = torch.mean(torch.abs(__UpperCAmelCase       )       )

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

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

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

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

	def 	__UpperCAmelCase(  self       ):

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

	def 	__UpperCAmelCase(  self       ):
			self.check_over_configs(thresholding=__UpperCAmelCase       )
			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=__UpperCAmelCase				,					prediction_type=__UpperCAmelCase				,					sample_max_value=__UpperCAmelCase				,					algorithm_type="deis"				,					solver_order=__UpperCAmelCase				,					solver_type=__UpperCAmelCase				,					)

	def 	__UpperCAmelCase(  self       ):

			for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(prediction_type=__UpperCAmelCase       )

	def 	__UpperCAmelCase(  self       ):

			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=__UpperCAmelCase				,					solver_type=__UpperCAmelCase				,					prediction_type=__UpperCAmelCase				,					algorithm_type=__UpperCAmelCase				,					)
											__A				:       Union[str, Any]			    = 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 	__UpperCAmelCase(  self       ):
			self.check_over_configs(lower_order_final=__UpperCAmelCase       )
			self.check_over_configs(lower_order_final=__UpperCAmelCase       )

	def 	__UpperCAmelCase(  self       ):

			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 	__UpperCAmelCase(  self       ):
			__A				:       List[Any]			    = self.full_loop()
			__A				:       Tuple			    = torch.mean(torch.abs(__UpperCAmelCase       )       )

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

	def 	__UpperCAmelCase(  self       ):
			__A				:       Union[str, Any]			    = self.full_loop(prediction_type="v_prediction"       )
			__A				:       List[str]			    = torch.mean(torch.abs(__UpperCAmelCase       )       )

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







	def 	__UpperCAmelCase(  self       ):
			__A				:       List[Any]			    = self.scheduler_classes[0]
			__A				:       List[Any]			    = self.get_scheduler_config(thresholding=__UpperCAmelCase				,					dynamic_thresholding_ratio=0       )
			__A				:       int			    = scheduler_class(**__UpperCAmelCase       )

			__A				:       int			    = 10
			__A				:       str			    = self.dummy_model()
			__A				:       str			    = self.dummy_sample_deter.half()
			scheduler.set_timesteps(__UpperCAmelCase       )

			for i, t in enumerate(scheduler.timesteps       ):
					__A				:       Any			    = model(__UpperCAmelCase				,					__UpperCAmelCase       )
					__A				:       Any			    = scheduler.step(__UpperCAmelCase				,					__UpperCAmelCase				,					__UpperCAmelCase       ).prev_sample

			assert sample.dtype == torch.floataa










import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


a__   :					List[Any] 						=      get_tests_dir("fixtures/test_sentencepiece.model")



@require_sentencepiece
class    UpperCAmelCase__(				lowerCamelCase  ,				unittest.TestCase						):

   '''simple docstring'''




   A     :	Tuple												=     XLMProphetNetTokenizer
   A     :	List[str]												=     False
   A     :	Union[str, Any]												=     True


   def       UpperCAmelCase		(    self	:      Any)						->					int:




         """simple docstring"""

         super().setUp()

         # We have a SentencePiece fixture for testing
         lowercase__  =	XLMProphetNetTokenizer(lowerCAmelCase      ,					keep_accents=lowerCAmelCase)
         tokenizer.save_pretrained(self.tmpdirname)


   def       UpperCAmelCase		(    self	:      Optional[int])						->					Optional[Any]:




         """simple docstring"""

         lowercase__  =	'[PAD]'
         lowercase__  =	0

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


   def       UpperCAmelCase		(    self	:      Dict)						->					List[Any]:




         """simple docstring"""

         lowercase__  =	list(self.get_tokenizer().get_vocab().keys())

         self.assertEqual(vocab_keys[0]      ,					'[PAD]')
         self.assertEqual(vocab_keys[1]      ,					'[CLS]')
         self.assertEqual(vocab_keys[-1]      ,					'j')
         self.assertEqual(len(lowerCAmelCase)      ,					10_12)


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




         """simple docstring"""

         self.assertEqual(self.get_tokenizer().vocab_size      ,					10_12)


   def       UpperCAmelCase		(    self	:      Tuple)						->					Optional[int]:




         """simple docstring"""

         lowercase__  =	XLMProphetNetTokenizer(lowerCAmelCase      ,					keep_accents=lowerCAmelCase)

         lowercase__  =	tokenizer.tokenize('This is a test')
         self.assertListEqual(lowerCAmelCase      ,					['▁This', '▁is', '▁a', '▁t', 'est'])

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(lowerCAmelCase)      ,					[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]      ,					)

         lowercase__  =	tokenizer.tokenize('I was born in 92000, and this is falsé.')
         self.assertListEqual(
             lowerCAmelCase      ,					[
                 SPIECE_UNDERLINE + 'I',
                 SPIECE_UNDERLINE + 'was',
                 SPIECE_UNDERLINE + 'b',
                 'or',
                 'n',
                 SPIECE_UNDERLINE + 'in',
                 SPIECE_UNDERLINE + '',
                 '9',
                 '2',
                 '0',
                 '0',
                 '0',
                 ',',
                 SPIECE_UNDERLINE + 'and',
                 SPIECE_UNDERLINE + 'this',
                 SPIECE_UNDERLINE + 'is',
                 SPIECE_UNDERLINE + 'f',
                 'al',
                 's',
                 'é',
                 '.',
             ]      ,					)
         lowercase__  =	tokenizer.convert_tokens_to_ids(lowerCAmelCase)
         self.assertListEqual(
             lowerCAmelCase      ,					[
                 value + tokenizer.fairseq_offset
                 for value in [8, 21, 84, 55, 24, 19, 7, -9, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, -9, 4]
             ]      ,					)

         lowercase__  =	tokenizer.convert_ids_to_tokens(lowerCAmelCase)
         self.assertListEqual(
             lowerCAmelCase      ,					[
                 SPIECE_UNDERLINE + 'I',
                 SPIECE_UNDERLINE + 'was',
                 SPIECE_UNDERLINE + 'b',
                 'or',
                 'n',
                 SPIECE_UNDERLINE + 'in',
                 SPIECE_UNDERLINE + '',
                 '[UNK]',
                 '2',
                 '0',
                 '0',
                 '0',
                 ',',
                 SPIECE_UNDERLINE + 'and',
                 SPIECE_UNDERLINE + 'this',
                 SPIECE_UNDERLINE + 'is',
                 SPIECE_UNDERLINE + 'f',
                 'al',
                 's',
                 '[UNK]',
                 '.',
             ]      ,					)


   @cached_property
   def       UpperCAmelCase		(    self	:      str)						->					List[Any]:




         """simple docstring"""

         return XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased')


   @slow
   def       UpperCAmelCase		(    self	:      List[Any])						->					List[str]:




         """simple docstring"""

         lowercase__  =	'Hello World!'
         lowercase__  =	[3_53_89, 66_72, 49, 2]
         self.assertListEqual(lowerCAmelCase      ,					self.big_tokenizer.encode(lowerCAmelCase))



   @slow
   def       UpperCAmelCase		(    self	:      Tuple)						->					str:




         """simple docstring"""

         lowercase__  =	{'input_ids': [[1_10_73, 8_27_83, 18, 26, 8_27_83, 5_49, 5_15_40, 2_48, 1_72_09, 13_01, 2_17, 20, 21_51_86, 13_25, 1_47, 1_72_09, 13_01, 2_17, 20, 5_63_70, 53, 12_20_20, 20, 1_64_77, 27, 8_73_55, 45_48, 20, 47_28, 7_83_92, 17, 15_99_69, 18, 26, 2_44_91, 6_29, 15, 5_38, 2_27_04, 54_39, 15, 27_88, 2_44_91, 98_85, 15, 4_35_34, 6_05, 15, 8_14, 1_84_03, 3_32_00, 29, 15, 4_35_34, 2_44_58, 1_24_10, 1_11, 2_49_66, 8_36_69, 96_37, 14_40_68, 26, 8_50, 2_23_46, 27, 1_47, 2_49_66, 8_36_69, 8_34_90, 26, 3_91_13, 7_35, 27, 6_89, 6_56, 28_00, 13_39, 46_00, 53, 12_20_20, 11_57_85, 34, 8_16, 13_39, 4_68_87, 18, 1_47, 5_39_05, 19_51, 4_22_38, 4_11_70, 1_77_32, 8_34, 4_36, 15, 2_75_23, 9_87_33, 2_17, 1_47, 55_42, 49_81, 9_30, 1_73_47, 16, 2], [2_00_91, 6_29, 94, 8_27_86, 58, 4_90, 20, 15_28, 84, 5_39_05, 3_44, 8_05_92, 11_01_28, 1_88_22, 52_67, 13_06, 62, 15_25_37, 3_08, 79_97, 4_01, 12_44_27, 5_49, 3_54_42, 2_25, 1_09, 1_50_55, 2_57_48, 1_47, 71_19, 4_37_12, 34, 7_67, 13_53_66, 18, 16, 2, 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], [5_92, 6_37_84, 11_94_66, 17, 14_78_08, 8_82_14, 18, 6_56, 81, 32, 32_96, 1_02_80, 16, 2, 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, 0]], '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, 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], [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, 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=lowerCAmelCase      ,					model_name='microsoft/xprophetnet-large-wiki100-cased'      ,					revision='1acad1643ddd54a44df6a1b797ada8373685d90e'      ,					)
