




'''simple docstring'''


import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class 						a_				(			__lowercase						):
	lowercase_       :     int			  =      (DDIMParallelScheduler,)
	lowercase_       :     Union[str, Any]			  =      (('''eta''', 0.0), ('''num_inference_steps''', 50))




	def   lowercase__   (       self     :  Dict       ,		**__lowerCAmelCase     :  Any      ):
							__snake_case						 =   {
							    'num_train_timesteps': 1_0_0_0,
							    'beta_start': 0.0001,
							    'beta_end': 0.02,
							    'beta_schedule': 'linear',
							    'clip_sample': True,
							}

							config.update(**_A      )
							return config




	def   lowercase__   (       self     :  str       ,		**__lowerCAmelCase     :  int      ):
							__snake_case						 =   self.scheduler_classes[0]
							__snake_case						 =   self.get_scheduler_config(**_A      )
							__snake_case						 =   scheduler_class(**_A      )

							__snake_case ,       __snake_case						 =   1_0, 0.0

							__snake_case						 =   self.dummy_model()
							__snake_case						 =   self.dummy_sample_deter

							scheduler.set_timesteps(_A      )

							for t in scheduler.timesteps:
													__snake_case						 =   model(_A       ,		_A      )
													__snake_case						 =   scheduler.step(_A       ,		_A       ,		_A       ,		_A      ).prev_sample

							return sample




	def   lowercase__   (       self     :  List[str]      ):
							for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
													self.check_over_configs(num_train_timesteps=_A      )




	def   lowercase__   (       self     :  Union[str, Any]      ):
							for steps_offset in [0, 1]:
													self.check_over_configs(steps_offset=_A      )

							__snake_case						 =   self.scheduler_classes[0]
							__snake_case						 =   self.get_scheduler_config(steps_offset=1      )
							__snake_case						 =   scheduler_class(**_A      )
							scheduler.set_timesteps(5      )
							assert torch.equal(scheduler.timesteps       ,		torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]      )      )




	def   lowercase__   (       self     :  Union[str, Any]      ):
							for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]       ,		[0.002, 0.02, 0.2, 2]      ):
													self.check_over_configs(beta_start=_A       ,		beta_end=_A      )




	def   lowercase__   (       self     :  Optional[int]      ):
							for schedule in ["linear", "squaredcos_cap_v2"]:
													self.check_over_configs(beta_schedule=_A      )




	def   lowercase__   (       self     :  Tuple      ):
							for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=_A      )




	def   lowercase__   (       self     :  Union[str, Any]      ):
							for clip_sample in [True, False]:
													self.check_over_configs(clip_sample=_A      )




	def   lowercase__   (       self     :  Tuple      ):
							for timestep_spacing in ["trailing", "leading"]:
													self.check_over_configs(timestep_spacing=_A      )




	def   lowercase__   (       self     :  List[Any]      ):
							for rescale_betas_zero_snr in [True, False]:
													self.check_over_configs(rescale_betas_zero_snr=_A      )




	def   lowercase__   (       self     :  Union[str, Any]      ):
							self.check_over_configs(thresholding=_A      )
							for threshold in [0.5, 1.0, 2.0]:
													for prediction_type in ["epsilon", "v_prediction"]:
																			self.check_over_configs(
																			    thresholding=_A       ,		prediction_type=_A       ,		sample_max_value=_A       ,		)




	def   lowercase__   (       self     :  str      ):
							for t in [1, 1_0, 4_9]:
													self.check_over_forward(time_step=_A      )




	def   lowercase__   (       self     :  int      ):
							for t, num_inference_steps in zip([1, 1_0, 5_0]       ,		[1_0, 5_0, 5_0_0]      ):
													self.check_over_forward(time_step=_A       ,		num_inference_steps=_A      )




	def   lowercase__   (       self     :  Dict      ):
							for t, eta in zip([1, 1_0, 4_9]       ,		[0.0, 0.5, 1.0]      ):
													self.check_over_forward(time_step=_A       ,		eta=_A      )




	def   lowercase__   (       self     :  List[Any]      ):
							__snake_case						 =   self.scheduler_classes[0]
							__snake_case						 =   self.get_scheduler_config()
							__snake_case						 =   scheduler_class(**_A      )

							assert torch.sum(torch.abs(scheduler._get_variance(0       ,		0      ) - 0.0      )      ) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_2_0       ,		4_0_0      ) - 0.14771      )      ) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_8_0       ,		9_6_0      ) - 0.32460      )      ) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(0       ,		0      ) - 0.0      )      ) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_8_7       ,		4_8_6      ) - 0.00979      )      ) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_9_9       ,		9_9_8      ) - 0.02      )      ) < 1E-5




	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   self.scheduler_classes[0]
							__snake_case						 =   self.get_scheduler_config()
							__snake_case						 =   scheduler_class(**_A      )

							__snake_case ,       __snake_case						 =   1_0, 0.0
							scheduler.set_timesteps(_A      )

							__snake_case						 =   self.dummy_model()
							__snake_case						 =   self.dummy_sample_deter
							__snake_case						 =   self.dummy_sample_deter + 0.1
							__snake_case						 =   self.dummy_sample_deter - 0.1

							__snake_case						 =   samplea.shape[0]
							__snake_case						 =   torch.stack([samplea, samplea, samplea]       ,		dim=0      )
							__snake_case						 =   torch.arange(_A      )[0:3, None].repeat(1       ,		_A      )

							__snake_case						 =   model(samples.flatten(0       ,		1      )       ,		timesteps.flatten(0       ,		1      )      )
							__snake_case						 =   scheduler.batch_step_no_noise(_A       ,		timesteps.flatten(0       ,		1      )       ,		samples.flatten(0       ,		1      )       ,		_A      )

							__snake_case						 =   torch.sum(torch.abs(_A      )      )
							__snake_case						 =   torch.mean(torch.abs(_A      )      )

							assert abs(result_sum.item() - 1147.7904      ) < 1E-2
							assert abs(result_mean.item() - 0.4982      ) < 1E-3




	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   self.full_loop()

							__snake_case						 =   torch.sum(torch.abs(_A      )      )
							__snake_case						 =   torch.mean(torch.abs(_A      )      )

							assert abs(result_sum.item() - 172.0067      ) < 1E-2
							assert abs(result_mean.item() - 0.223967      ) < 1E-3




	def   lowercase__   (       self     :  str      ):
							__snake_case						 =   self.full_loop(prediction_type='v_prediction'      )

							__snake_case						 =   torch.sum(torch.abs(_A      )      )
							__snake_case						 =   torch.mean(torch.abs(_A      )      )

							assert abs(result_sum.item() - 52.5302      ) < 1E-2
							assert abs(result_mean.item() - 0.0684      ) < 1E-3




	def   lowercase__   (       self     :  Any      ):
							# We specify different beta, so that the first alpha is 0.99
							__snake_case						 =   self.full_loop(set_alpha_to_one=_A       ,		beta_start=0.01      )
							__snake_case						 =   torch.sum(torch.abs(_A      )      )
							__snake_case						 =   torch.mean(torch.abs(_A      )      )

							assert abs(result_sum.item() - 149.8295      ) < 1E-2
							assert abs(result_mean.item() - 0.1951      ) < 1E-3




	def   lowercase__   (       self     :  List[Any]      ):
							# We specify different beta, so that the first alpha is 0.99
							__snake_case						 =   self.full_loop(set_alpha_to_one=_A       ,		beta_start=0.01      )
							__snake_case						 =   torch.sum(torch.abs(_A      )      )
							__snake_case						 =   torch.mean(torch.abs(_A      )      )

							assert abs(result_sum.item() - 149.0784      ) < 1E-2
							assert abs(result_mean.item() - 0.1941      ) < 1E-3


import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCAmelCase       =		"▁"

_lowerCAmelCase       =		get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
class     lowerCAmelCase_     (       __lowercase,  unittest.TestCase ):
							UpperCAmelCase				   =     BertGenerationTokenizer
							UpperCAmelCase				   =     False
							UpperCAmelCase				   =     True







							def  UpperCamelCase_						(   self      :     List[str]		):
												super().setUp()

												_UpperCamelCase			     =     BertGenerationTokenizer(_A ,			keep_accents=_A		)
												tokenizer.save_pretrained(self.tmpdirname		)







							def  UpperCamelCase_						(   self      :     Dict		):
												_UpperCamelCase			     =     '''<s>'''
												_UpperCamelCase			     =     1

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







							def  UpperCamelCase_						(   self      :     Any		):
												_UpperCamelCase			     =     list(self.get_tokenizer().get_vocab().keys()		)

												self.assertEqual(vocab_keys[0] ,			'''<unk>'''		)
												self.assertEqual(vocab_keys[1] ,			'''<s>'''		)
												self.assertEqual(vocab_keys[-1] ,			'''<pad>'''		)
												self.assertEqual(len(_A		) ,			1002		)







							def  UpperCamelCase_						(   self      :     Dict		):
												self.assertEqual(self.get_tokenizer().vocab_size ,			1000		)







							def  UpperCamelCase_						(   self      :     int		):
												_UpperCamelCase			     =     BertGenerationTokenizer(_A ,			keep_accents=_A		)

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

												self.assertListEqual(
												    tokenizer.convert_tokens_to_ids(_A		) ,			[285, 46, 10, 170, 382] ,			)

												_UpperCamelCase			     =     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''',
												        '''.''',
												    ] ,			)
												_UpperCamelCase			     =     tokenizer.convert_tokens_to_ids(_A		)
												self.assertListEqual(
												    _A ,			[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] ,			)

												_UpperCamelCase			     =     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  UpperCamelCase_						(   self      :     Union[str, Any]		):
												return BertGenerationTokenizer.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''		)







							@slow
							def  UpperCamelCase_						(   self      :     Optional[Any]		):
												_UpperCamelCase			     =     '''Hello World!'''
												_UpperCamelCase			     =     [1_8536, 2260, 101]

												self.assertListEqual(_A ,			self.big_tokenizer.encode(_A		)		)







							@slow
							def  UpperCamelCase_						(   self      :     int		):
												_UpperCamelCase			     =     (
												    '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
												    ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
												)
												_UpperCamelCase			     =     [
												    871,
												    419,
												    358,
												    946,
												    991,
												    2521,
												    452,
												    358,
												    1357,
												    387,
												    7751,
												    3536,
												    112,
												    985,
												    456,
												    126,
												    865,
												    938,
												    5400,
												    5734,
												    458,
												    1368,
												    467,
												    786,
												    2462,
												    5246,
												    1159,
												    633,
												    865,
												    4519,
												    457,
												    582,
												    852,
												    2557,
												    427,
												    916,
												    508,
												    405,
												    3_4324,
												    497,
												    391,
												    408,
												    1_1342,
												    1244,
												    385,
												    100,
												    938,
												    985,
												    456,
												    574,
												    362,
												    1_2597,
												    3200,
												    3129,
												    1172,
												]

												self.assertListEqual(_A ,			self.big_tokenizer.encode(_A		)		)







							@require_torch
							@slow
							def  UpperCamelCase_						(   self      :     Dict		):
												import torch

												from transformers import BertGenerationConfig, BertGenerationEncoder

												# Build sequence
												_UpperCamelCase			     =     list(self.big_tokenizer.get_vocab().keys()		)[:10]
												_UpperCamelCase			     =     ''' '''.join(_A		)
												_UpperCamelCase			     =     self.big_tokenizer.encode_plus(_A ,			return_tensors='''pt''' ,			return_token_type_ids=_A		)
												_UpperCamelCase			     =     self.big_tokenizer.batch_encode_plus(
												    [sequence + ''' ''' + sequence] ,			return_tensors='''pt''' ,			return_token_type_ids=_A		)

												_UpperCamelCase			     =     BertGenerationConfig()
												_UpperCamelCase			     =     BertGenerationEncoder(_A		)

												assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

												with torch.no_grad():
																	model(**_A		)
																	model(**_A		)







							@slow
							def  UpperCamelCase_						(   self      :     Dict		):
												# fmt: off
												_UpperCamelCase			     =     {'''input_ids''': [[3_9286, 458, 3_6335, 2001, 456, 1_3073, 1_3266, 455, 113, 7746, 1741, 1_1157, 391, 1_3073, 1_3266, 455, 113, 3967, 3_5412, 113, 4936, 109, 3870, 2377, 113, 3_0084, 4_5720, 458, 134, 1_7496, 112, 503, 1_1672, 113, 118, 112, 5665, 1_3347, 3_8687, 112, 1496, 3_1389, 112, 3268, 4_7264, 134, 962, 112, 1_6377, 8035, 2_3130, 430, 1_2169, 1_5518, 2_8592, 458, 146, 4_1697, 109, 391, 1_2169, 1_5518, 1_6689, 458, 146, 4_1358, 109, 452, 726, 4034, 111, 763, 3_5412, 5082, 388, 1903, 111, 9051, 391, 2870, 4_8918, 1900, 1123, 550, 998, 112, 9586, 1_5985, 455, 391, 410, 2_2955, 3_7636, 114], [448, 1_7496, 419, 3663, 385, 763, 113, 2_7533, 2870, 3283, 1_3043, 1639, 2_4713, 523, 656, 2_4013, 1_8550, 2521, 517, 2_7014, 2_1244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 1_1786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 2_1932, 1_8146, 726, 363, 1_7032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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='''google/bert_for_seq_generation_L-24_bbc_encoder''' ,			revision='''c817d1fd1be2ffa69431227a1fe320544943d4db''' ,			)
