

import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   str     					=				(DDPMParallelScheduler,)




	def        lowerCAmelCase_       (     self						:     List[Any]					,      **__A						:     int							):
		__A :			int		    =			{
		    """num_train_timesteps""": 1000,
		    """beta_start""": 0.0_0_0_1,
		    """beta_end""": 0.0_2,
		    """beta_schedule""": """linear""",
		    """variance_type""": """fixed_small""",
		    """clip_sample""": True,
		}

		config.update(**__A							)
		return config




	def        lowerCAmelCase_       (     self						:     List[str]							):
		for timesteps in [1, 5, 100, 1000]:
			self.check_over_configs(num_train_timesteps=__A							)




	def        lowerCAmelCase_       (     self						:     int							):
		for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]					,      [0.0_0_2, 0.0_2, 0.2, 2]							):
			self.check_over_configs(beta_start=__A					,      beta_end=__A							)




	def        lowerCAmelCase_       (     self						:     Any							):
		for schedule in ["linear", "squaredcos_cap_v2"]:
			self.check_over_configs(beta_schedule=__A							)




	def        lowerCAmelCase_       (     self						:     Any							):
		for variance in ["fixed_small", "fixed_large", "other"]:
			self.check_over_configs(variance_type=__A							)




	def        lowerCAmelCase_       (     self						:     List[Any]							):
		for clip_sample in [True, False]:
			self.check_over_configs(clip_sample=__A							)




	def        lowerCAmelCase_       (     self						:     int							):
		self.check_over_configs(thresholding=__A							)
		for threshold in [0.5, 1.0, 2.0]:
			for prediction_type in ["epsilon", "sample", "v_prediction"]:
				self.check_over_configs(
				    thresholding=__A					,      prediction_type=__A					,      sample_max_value=__A					,      )




	def        lowerCAmelCase_       (     self						:     int							):
		for prediction_type in ["epsilon", "sample", "v_prediction"]:
			self.check_over_configs(prediction_type=__A							)




	def        lowerCAmelCase_       (     self						:     int							):
		for t in [0, 500, 999]:
			self.check_over_forward(time_step=__A							)




	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		__A :			str		    =			self.scheduler_classes[0]
		__A :			Tuple		    =			self.get_scheduler_config()
		__A :			int		    =			scheduler_class(**__A							)

		assert torch.sum(torch.abs(scheduler._get_variance(0							) - 0.0							)							) < 1e-5
		assert torch.sum(torch.abs(scheduler._get_variance(487							) - 0.0_0_9_7_9							)							) < 1e-5
		assert torch.sum(torch.abs(scheduler._get_variance(999							) - 0.0_2							)							) < 1e-5




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		__A :			int		    =			self.scheduler_classes[0]
		__A :			Optional[int]		    =			self.get_scheduler_config()
		__A :			List[Any]		    =			scheduler_class(**__A							)

		__A :			Any		    =			len(__A							)

		__A :			Tuple		    =			self.dummy_model()
		__A :			Union[str, Any]		    =			self.dummy_sample_deter
		__A :			List[Any]		    =			self.dummy_sample_deter + 0.1
		__A :			Optional[Any]		    =			self.dummy_sample_deter - 0.1

		__A :			Dict		    =			samplea.shape[0]
		__A :			Optional[int]		    =			torch.stack([samplea, samplea, samplea]					,      dim=0							)
		__A :			Dict		    =			torch.arange(__A							)[0:3, None].repeat(1					,      __A							)

		__A :			Optional[Any]		    =			model(samples.flatten(0					,      1							)					,      timesteps.flatten(0					,      1							)							)
		__A :			Dict		    =			scheduler.batch_step_no_noise(__A					,      timesteps.flatten(0					,      1							)					,      samples.flatten(0					,      1							)							)

		__A :			List[str]		    =			torch.sum(torch.abs(__A							)							)
		__A :			List[str]		    =			torch.mean(torch.abs(__A							)							)

		assert abs(result_sum.item() - 1_1_5_3.1_8_3_3							) < 1e-2
		assert abs(result_mean.item() - 0.5_0_0_5							) < 1e-3




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		__A :			int		    =			self.scheduler_classes[0]
		__A :			int		    =			self.get_scheduler_config()
		__A :			Dict		    =			scheduler_class(**__A							)

		__A :			Optional[Any]		    =			len(__A							)

		__A :			Tuple		    =			self.dummy_model()
		__A :			Optional[Any]		    =			self.dummy_sample_deter
		__A :			List[str]		    =			torch.manual_seed(0							)

		for t in reversed(range(__A							)							):
			# 1. predict noise residual
			__A :			str		    =			model(__A					,      __A							)

			# 2. predict previous mean of sample x_t-1
			__A :			List[str]		    =			scheduler.step(__A					,      __A					,      __A					,      generator=__A							).prev_sample

			__A :			Union[str, Any]		    =			pred_prev_sample

		__A :			List[Any]		    =			torch.sum(torch.abs(__A							)							)
		__A :			Union[str, Any]		    =			torch.mean(torch.abs(__A							)							)

		assert abs(result_sum.item() - 2_5_8.9_6_0_6							) < 1e-2
		assert abs(result_mean.item() - 0.3_3_7_2							) < 1e-3




	def        lowerCAmelCase_       (     self						:     int							):
		__A :			int		    =			self.scheduler_classes[0]
		__A :			int		    =			self.get_scheduler_config(prediction_type="""v_prediction"""							)
		__A :			int		    =			scheduler_class(**__A							)

		__A :			Dict		    =			len(__A							)

		__A :			Any		    =			self.dummy_model()
		__A :			str		    =			self.dummy_sample_deter
		__A :			Union[str, Any]		    =			torch.manual_seed(0							)

		for t in reversed(range(__A							)							):
			# 1. predict noise residual
			__A :			Optional[Any]		    =			model(__A					,      __A							)

			# 2. predict previous mean of sample x_t-1
			__A :			Tuple		    =			scheduler.step(__A					,      __A					,      __A					,      generator=__A							).prev_sample

			__A :			Dict		    =			pred_prev_sample

		__A :			Union[str, Any]		    =			torch.sum(torch.abs(__A							)							)
		__A :			Union[str, Any]		    =			torch.mean(torch.abs(__A							)							)

		assert abs(result_sum.item() - 2_0_2.0_2_9_6							) < 1e-2
		assert abs(result_mean.item() - 0.2_6_3_1							) < 1e-3




	def        lowerCAmelCase_       (     self						:     Any							):
		__A :			List[str]		    =			self.scheduler_classes[0]
		__A :			Any		    =			self.get_scheduler_config()
		__A :			Optional[Any]		    =			scheduler_class(**__A							)

		__A :			Optional[Any]		    =			[100, 87, 50, 1, 0]

		scheduler.set_timesteps(timesteps=__A							)

		__A :			List[Any]		    =			scheduler.timesteps

		for i, timestep in enumerate(__A							):
			if i == len(__A							) - 1:
				__A :			List[str]		    =			-1
			else:
				__A :			int		    =			timesteps[i + 1]

			__A :			Any		    =			scheduler.previous_timestep(__A							)
			__A :			Any		    =			prev_t.item()

			self.assertEqual(__A					,      __A							)




	def        lowerCAmelCase_       (     self						:     Any							):
		__A :			str		    =			self.scheduler_classes[0]
		__A :			int		    =			self.get_scheduler_config()
		__A :			Optional[int]		    =			scheduler_class(**__A							)

		__A :			Any		    =			[100, 87, 50, 51, 0]

		with self.assertRaises(__A					,      msg="""`custom_timesteps` must be in descending order."""							):
			scheduler.set_timesteps(timesteps=__A							)




	def        lowerCAmelCase_       (     self						:     Any							):
		__A :			List[str]		    =			self.scheduler_classes[0]
		__A :			Dict		    =			self.get_scheduler_config()
		__A :			Tuple		    =			scheduler_class(**__A							)

		__A :			Tuple		    =			[100, 87, 50, 1, 0]
		__A :			Union[str, Any]		    =			len(__A							)

		with self.assertRaises(__A					,      msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""							):
			scheduler.set_timesteps(num_inference_steps=__A					,      timesteps=__A							)




	def        lowerCAmelCase_       (     self						:     Any							):
		__A :			Tuple		    =			self.scheduler_classes[0]
		__A :			Optional[int]		    =			self.get_scheduler_config()
		__A :			Dict		    =			scheduler_class(**__A							)

		__A :			List[Any]		    =			[scheduler.config.num_train_timesteps]

		with self.assertRaises(
		    __A					,      msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""					,      ):
			scheduler.set_timesteps(timesteps=__A							)








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'''			,				)



