




import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest





@require_torchsde
class 					__lowerCamelCase			(       UpperCamelCase__       ):




    """simple docstring"""

    snake_case__   = (DPMSolverSDEScheduler,)
    snake_case__   = 1_0


    def   a (			self	:	Dict  ,      **SCREAMING_SNAKE_CASE__	:	str  ) ->			str:
         lowerCAmelCase__            =					{
             "num_train_timesteps": 1_100,
             "beta_start": 0.0_001,
             "beta_end": 0.02,
             "beta_schedule": "linear",
             "noise_sampler_seed": 0,
         }

         config.update(**SCREAMING_SNAKE_CASE__  )
         return config


    def   a (			self	:	Any  ) ->			int:
         for timesteps in [10, 50, 100, 1_000]:
              self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	Optional[int]  ) ->			Union[str, Any]:
         for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]  ,      [0.0_002, 0.002, 0.02]  ):
              self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE__  ,      beta_end=SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	Tuple  ) ->			int:
         for schedule in ["linear", "scaled_linear"]:
              self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	Optional[int]  ) ->			Dict:
         for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	List[str]  ) ->			Optional[Any]:
         lowerCAmelCase__            =					self.scheduler_classes[0]
         lowerCAmelCase__            =					self.get_scheduler_config()
         lowerCAmelCase__            =					scheduler_class(**SCREAMING_SNAKE_CASE__  )

         scheduler.set_timesteps(self.num_inference_steps  )

         lowerCAmelCase__            =					self.dummy_model()
         lowerCAmelCase__            =					self.dummy_sample_deter * scheduler.init_noise_sigma
         lowerCAmelCase__            =					sample.to(SCREAMING_SNAKE_CASE__  )

         for i, t in enumerate(scheduler.timesteps  ):
              lowerCAmelCase__            =					scheduler.scale_model_input(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )

              lowerCAmelCase__            =					model(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )

              lowerCAmelCase__            =					scheduler.step(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )
              lowerCAmelCase__            =					output.prev_sample

         lowerCAmelCase__            =					torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
         lowerCAmelCase__            =					torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

         if torch_device in ["mps"]:
              assert abs(result_sum.item() - 167.47_821_044_921_875  ) < 1e-2
              assert abs(result_mean.item() - 0.2_178_705_964_565_277  ) < 1e-3
         elif torch_device in ["cuda"]:
              assert abs(result_sum.item() - 171.59_352_111_816_406  ) < 1e-2
              assert abs(result_mean.item() - 0.22_342_906_892_299_652  ) < 1e-3
         else:
              assert abs(result_sum.item() - 162.52_383_422_851_562  ) < 1e-2
              assert abs(result_mean.item() - 0.211_619_570_851_326  ) < 1e-3


    def   a (			self	:	Optional[Any]  ) ->			Optional[int]:
         lowerCAmelCase__            =					self.scheduler_classes[0]
         lowerCAmelCase__            =					self.get_scheduler_config(prediction_type="v_prediction"  )
         lowerCAmelCase__            =					scheduler_class(**SCREAMING_SNAKE_CASE__  )

         scheduler.set_timesteps(self.num_inference_steps  )

         lowerCAmelCase__            =					self.dummy_model()
         lowerCAmelCase__            =					self.dummy_sample_deter * scheduler.init_noise_sigma
         lowerCAmelCase__            =					sample.to(SCREAMING_SNAKE_CASE__  )

         for i, t in enumerate(scheduler.timesteps  ):
              lowerCAmelCase__            =					scheduler.scale_model_input(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )

              lowerCAmelCase__            =					model(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )

              lowerCAmelCase__            =					scheduler.step(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )
              lowerCAmelCase__            =					output.prev_sample

         lowerCAmelCase__            =					torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
         lowerCAmelCase__            =					torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

         if torch_device in ["mps"]:
              assert abs(result_sum.item() - 124.77_149_200_439_453  ) < 1e-2
              assert abs(result_mean.item() - 0.16_226_289_014_816_284  ) < 1e-3
         elif torch_device in ["cuda"]:
              assert abs(result_sum.item() - 128.1_663_360_595_703  ) < 1e-2
              assert abs(result_mean.item() - 0.16_688_326_001_167_297  ) < 1e-3
         else:
              assert abs(result_sum.item() - 119.8_487_548_828_125  ) < 1e-2
              assert abs(result_mean.item() - 0.1_560_530_662_536_621  ) < 1e-3


    def   a (			self	:	Tuple  ) ->			Optional[int]:
         lowerCAmelCase__            =					self.scheduler_classes[0]
         lowerCAmelCase__            =					self.get_scheduler_config()
         lowerCAmelCase__            =					scheduler_class(**SCREAMING_SNAKE_CASE__  )

         scheduler.set_timesteps(self.num_inference_steps  ,      device=SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					self.dummy_model()
         lowerCAmelCase__            =					self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE__  ) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
              lowerCAmelCase__            =					scheduler.scale_model_input(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )

              lowerCAmelCase__            =					model(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )

              lowerCAmelCase__            =					scheduler.step(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )
              lowerCAmelCase__            =					output.prev_sample

         lowerCAmelCase__            =					torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
         lowerCAmelCase__            =					torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

         if torch_device in ["mps"]:
              assert abs(result_sum.item() - 167.46_957_397_460_938  ) < 1e-2
              assert abs(result_mean.item() - 0.21_805_934_607_982_635  ) < 1e-3
         elif torch_device in ["cuda"]:
              assert abs(result_sum.item() - 171.59_353_637_695_312  ) < 1e-2
              assert abs(result_mean.item() - 0.22_342_908_382_415_771  ) < 1e-3
         else:
              assert abs(result_sum.item() - 162.52_383_422_851_562  ) < 1e-2
              assert abs(result_mean.item() - 0.211_619_570_851_326  ) < 1e-3


    def   a (			self	:	List[str]  ) ->			List[str]:
         lowerCAmelCase__            =					self.scheduler_classes[0]
         lowerCAmelCase__            =					self.get_scheduler_config()
         lowerCAmelCase__            =					scheduler_class(**SCREAMING_SNAKE_CASE__  ,      use_karras_sigmas=SCREAMING_SNAKE_CASE__  )

         scheduler.set_timesteps(self.num_inference_steps  ,      device=SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					self.dummy_model()
         lowerCAmelCase__            =					self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE__  ) * scheduler.init_noise_sigma
         lowerCAmelCase__            =					sample.to(SCREAMING_SNAKE_CASE__  )

         for t in scheduler.timesteps:
              lowerCAmelCase__            =					scheduler.scale_model_input(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )

              lowerCAmelCase__            =					model(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )

              lowerCAmelCase__            =					scheduler.step(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )
              lowerCAmelCase__            =					output.prev_sample

         lowerCAmelCase__            =					torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
         lowerCAmelCase__            =					torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

         if torch_device in ["mps"]:
              assert abs(result_sum.item() - 176.66_974_135_742_188  ) < 1e-2
              assert abs(result_mean.item() - 0.23_003_872_730_981_811  ) < 1e-2
         elif torch_device in ["cuda"]:
              assert abs(result_sum.item() - 177.63_653_564_453_125  ) < 1e-2
              assert abs(result_mean.item() - 0.23_003_872_730_981_811  ) < 1e-2
         else:
              assert abs(result_sum.item() - 170.3_135_223_388_672  ) < 1e-2
              assert abs(result_mean.item() - 0.23_003_872_730_981_811  ) < 1e-2



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

@require_sentencepiece
class 							UpperCAmelCase_						(     __lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		List[str]             =       XLMProphetNetTokenizer
							UpperCAmelCase__			:		Optional[int]             =       False
							UpperCAmelCase__			:		List[Any]             =       True


							def 							__lowercase							(     self			)       ->       int:
												super().setUp()

												# We have a SentencePiece fixture for testing
												_a      :       List[Any]   =						XLMProphetNetTokenizer(_a			,				keep_accents=_a			)
												tokenizer.save_pretrained(self.tmpdirname			)


							def 							__lowercase							(     self			)       ->       Any:
												_a      :       Tuple   =						'''[PAD]'''
												_a      :       int   =						0

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


							def 							__lowercase							(     self			)       ->       str:
												_a      :       Any   =						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(_a			)			,				1_0_1_2			)


							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												self.assertEqual(self.get_tokenizer().vocab_size			,				1_0_1_2			)


							def 							__lowercase							(     self			)       ->       str:
												_a      :       Tuple   =						XLMProphetNetTokenizer(_a			,				keep_accents=_a			)

												_a      :       Union[str, Any]   =						tokenizer.tokenize('''This is a test'''			)
												self.assertListEqual(_a			,				['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']			)

												self.assertListEqual(
												    tokenizer.convert_tokens_to_ids(_a			)			,				[value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]			,				)

												_a      :       Optional[int]   =						tokenizer.tokenize('''I was born in 92000, and this is falsé.'''			)
												self.assertListEqual(
												    _a			,				[
												        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''',
												        '''.''',
												    ]			,				)
												_a      :       List[Any]   =						tokenizer.convert_tokens_to_ids(_a			)
												self.assertListEqual(
												    _a			,				[
												        value + tokenizer.fairseq_offset
												        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, -9, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, -9, 4]
												    ]			,				)

												_a      :       List[str]   =						tokenizer.convert_ids_to_tokens(_a			)
												self.assertListEqual(
												    _a			,				[
												        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 							__lowercase							(     self			)       ->       List[str]:
												return XLMProphetNetTokenizer.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased'''			)


							@slow
							def 							__lowercase							(     self			)       ->       Tuple:
												_a      :       str   =						'''Hello World!'''
												_a      :       Tuple   =						[3_5_3_8_9, 6_6_7_2, 4_9, 2]
												self.assertListEqual(_a			,				self.big_tokenizer.encode(_a			)			)




							@slow
							def 							__lowercase							(     self			)       ->       str:
												# fmt: off
												_a      :       str   =						{'''input_ids''': [[1_1_0_7_3, 8_2_7_8_3, 1_8, 2_6, 8_2_7_8_3, 5_4_9, 5_1_5_4_0, 2_4_8, 1_7_2_0_9, 1_3_0_1, 2_1_7, 2_0, 2_1_5_1_8_6, 1_3_2_5, 1_4_7, 1_7_2_0_9, 1_3_0_1, 2_1_7, 2_0, 5_6_3_7_0, 5_3, 1_2_2_0_2_0, 2_0, 1_6_4_7_7, 2_7, 8_7_3_5_5, 4_5_4_8, 2_0, 4_7_2_8, 7_8_3_9_2, 1_7, 1_5_9_9_6_9, 1_8, 2_6, 2_4_4_9_1, 6_2_9, 1_5, 5_3_8, 2_2_7_0_4, 5_4_3_9, 1_5, 2_7_8_8, 2_4_4_9_1, 9_8_8_5, 1_5, 4_3_5_3_4, 6_0_5, 1_5, 8_1_4, 1_8_4_0_3, 3_3_2_0_0, 2_9, 1_5, 4_3_5_3_4, 2_4_4_5_8, 1_2_4_1_0, 1_1_1, 2_4_9_6_6, 8_3_6_6_9, 9_6_3_7, 1_4_4_0_6_8, 2_6, 8_5_0, 2_2_3_4_6, 2_7, 1_4_7, 2_4_9_6_6, 8_3_6_6_9, 8_3_4_9_0, 2_6, 3_9_1_1_3, 7_3_5, 2_7, 6_8_9, 6_5_6, 2_8_0_0, 1_3_3_9, 4_6_0_0, 5_3, 1_2_2_0_2_0, 1_1_5_7_8_5, 3_4, 8_1_6, 1_3_3_9, 4_6_8_8_7, 1_8, 1_4_7, 5_3_9_0_5, 1_9_5_1, 4_2_2_3_8, 4_1_1_7_0, 1_7_7_3_2, 8_3_4, 4_3_6, 1_5, 2_7_5_2_3, 9_8_7_3_3, 2_1_7, 1_4_7, 5_5_4_2, 4_9_8_1, 9_3_0, 1_7_3_4_7, 1_6, 2], [2_0_0_9_1, 6_2_9, 9_4, 8_2_7_8_6, 5_8, 4_9_0, 2_0, 1_5_2_8, 8_4, 5_3_9_0_5, 3_4_4, 8_0_5_9_2, 1_1_0_1_2_8, 1_8_8_2_2, 5_2_6_7, 1_3_0_6, 6_2, 1_5_2_5_3_7, 3_0_8, 7_9_9_7, 4_0_1, 1_2_4_4_2_7, 5_4_9, 3_5_4_4_2, 2_2_5, 1_0_9, 1_5_0_5_5, 2_5_7_4_8, 1_4_7, 7_1_1_9, 4_3_7_1_2, 3_4, 7_6_7, 1_3_5_3_6_6, 1_8, 1_6, 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_9_2, 6_3_7_8_4, 1_1_9_4_6_6, 1_7, 1_4_7_8_0_8, 8_8_2_1_4, 1_8, 6_5_6, 8_1, 3_2, 3_2_9_6, 1_0_2_8_0, 1_6, 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=_a			,				model_name='''microsoft/xprophetnet-large-wiki100-cased'''			,				revision='''1acad1643ddd54a44df6a1b797ada8373685d90e'''			,				)



