


import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						(DDIMParallelScheduler,)
						UpperCAmelCase             =						(("eta", 0.0), ("num_inference_steps", 50))




						def 							__UpperCamelCase				(       self					:      int				,    **_a					:      str  )							->     Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  ={
										    '''num_train_timesteps''': 1000,
										    '''beta_start''': 0.00_01,
										    '''beta_end''': 0.02,
										    '''beta_schedule''': '''linear''',
										    '''clip_sample''': True,
										}

										config.update(**_a  )
										return config




						def 							__UpperCamelCase				(       self					:      Optional[Any]				,    **_a					:      List[Any]  )							->     List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.scheduler_classes[0]
										_SCREAMING_SNAKE_CASE			  =self.get_scheduler_config(**_a  )
										_SCREAMING_SNAKE_CASE			  =scheduler_class(**_a  )

										_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =10, 0.0

										_SCREAMING_SNAKE_CASE			  =self.dummy_model()
										_SCREAMING_SNAKE_CASE			  =self.dummy_sample_deter

										scheduler.set_timesteps(_a  )

										for t in scheduler.timesteps:
														_SCREAMING_SNAKE_CASE			  =model(_a				,    _a  )
														_SCREAMING_SNAKE_CASE			  =scheduler.step(_a				,    _a				,    _a				,    _a  ).prev_sample

										return sample




						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     str:


										"""simple docstring"""





										for timesteps in [100, 500, 1000]:
														self.check_over_configs(num_train_timesteps=_a  )




						def 							__UpperCamelCase				(       self					:      Any  )							->     int:


										"""simple docstring"""





										for steps_offset in [0, 1]:
														self.check_over_configs(steps_offset=_a  )

										_SCREAMING_SNAKE_CASE			  =self.scheduler_classes[0]
										_SCREAMING_SNAKE_CASE			  =self.get_scheduler_config(steps_offset=1  )
										_SCREAMING_SNAKE_CASE			  =scheduler_class(**_a  )
										scheduler.set_timesteps(5  )
										assert torch.equal(scheduler.timesteps				,    torch.LongTensor([801, 601, 401, 201, 1]  )  )




						def 							__UpperCamelCase				(       self					:      Dict  )							->     Any:


										"""simple docstring"""





										for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]				,    [0.0_02, 0.02, 0.2, 2]  ):
														self.check_over_configs(beta_start=_a				,    beta_end=_a  )




						def 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     Tuple:


										"""simple docstring"""





										for schedule in ["linear", "squaredcos_cap_v2"]:
														self.check_over_configs(beta_schedule=_a  )




						def 							__UpperCamelCase				(       self					:      Any  )							->     Any:


										"""simple docstring"""





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




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


										"""simple docstring"""





										for clip_sample in [True, False]:
														self.check_over_configs(clip_sample=_a  )




						def 							__UpperCamelCase				(       self					:      Tuple  )							->     Any:


										"""simple docstring"""





										for timestep_spacing in ["trailing", "leading"]:
														self.check_over_configs(timestep_spacing=_a  )




						def 							__UpperCamelCase				(       self					:      str  )							->     List[Any]:


										"""simple docstring"""





										for rescale_betas_zero_snr in [True, False]:
														self.check_over_configs(rescale_betas_zero_snr=_a  )




						def 							__UpperCamelCase				(       self					:      str  )							->     Optional[int]:


										"""simple docstring"""





										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 							__UpperCamelCase				(       self					:      int  )							->     Union[str, Any]:


										"""simple docstring"""





										for t in [1, 10, 49]:
														self.check_over_forward(time_step=_a  )




						def 							__UpperCamelCase				(       self					:      int  )							->     List[str]:


										"""simple docstring"""





										for t, num_inference_steps in zip([1, 10, 50]				,    [10, 50, 500]  ):
														self.check_over_forward(time_step=_a				,    num_inference_steps=_a  )




						def 							__UpperCamelCase				(       self					:      List[str]  )							->     Dict:


										"""simple docstring"""





										for t, eta in zip([1, 10, 49]				,    [0.0, 0.5, 1.0]  ):
														self.check_over_forward(time_step=_a				,    eta=_a  )




						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.scheduler_classes[0]
										_SCREAMING_SNAKE_CASE			  =self.get_scheduler_config()
										_SCREAMING_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(420				,    400  ) - 0.1_47_71  )  ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(980				,    960  ) - 0.3_24_60  )  ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(0				,    0  ) - 0.0  )  ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(487				,    486  ) - 0.0_09_79  )  ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(999				,    998  ) - 0.02  )  ) < 1E-5




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.scheduler_classes[0]
										_SCREAMING_SNAKE_CASE			  =self.get_scheduler_config()
										_SCREAMING_SNAKE_CASE			  =scheduler_class(**_a  )

										_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =10, 0.0
										scheduler.set_timesteps(_a  )

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

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

										_SCREAMING_SNAKE_CASE			  =model(samples.flatten(0				,    1  )				,    timesteps.flatten(0				,    1  )  )
										_SCREAMING_SNAKE_CASE			  =scheduler.batch_step_no_noise(_a				,    timesteps.flatten(0				,    1  )				,    samples.flatten(0				,    1  )				,    _a  )

										_SCREAMING_SNAKE_CASE			  =torch.sum(torch.abs(_a  )  )
										_SCREAMING_SNAKE_CASE			  =torch.mean(torch.abs(_a  )  )

										assert abs(result_sum.item() - 11_47.79_04  ) < 1E-2
										assert abs(result_mean.item() - 0.49_82  ) < 1E-3




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.full_loop()

										_SCREAMING_SNAKE_CASE			  =torch.sum(torch.abs(_a  )  )
										_SCREAMING_SNAKE_CASE			  =torch.mean(torch.abs(_a  )  )

										assert abs(result_sum.item() - 1_72.00_67  ) < 1E-2
										assert abs(result_mean.item() - 0.22_39_67  ) < 1E-3




						def 							__UpperCamelCase				(       self					:      List[str]  )							->     Any:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.full_loop(prediction_type='''v_prediction'''  )

										_SCREAMING_SNAKE_CASE			  =torch.sum(torch.abs(_a  )  )
										_SCREAMING_SNAKE_CASE			  =torch.mean(torch.abs(_a  )  )

										assert abs(result_sum.item() - 52.53_02  ) < 1E-2
										assert abs(result_mean.item() - 0.06_84  ) < 1E-3




						def 							__UpperCamelCase				(       self					:      List[str]  )							->     str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.full_loop(set_alpha_to_one=_a				,    beta_start=0.01  )
										_SCREAMING_SNAKE_CASE			  =torch.sum(torch.abs(_a  )  )
										_SCREAMING_SNAKE_CASE			  =torch.mean(torch.abs(_a  )  )

										assert abs(result_sum.item() - 1_49.82_95  ) < 1E-2
										assert abs(result_mean.item() - 0.19_51  ) < 1E-3




						def 							__UpperCamelCase				(       self					:      str  )							->     Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.full_loop(set_alpha_to_one=_a				,    beta_start=0.01  )
										_SCREAMING_SNAKE_CASE			  =torch.sum(torch.abs(_a  )  )
										_SCREAMING_SNAKE_CASE			  =torch.mean(torch.abs(_a  )  )

										assert abs(result_sum.item() - 1_49.07_84  ) < 1E-2
										assert abs(result_mean.item() - 0.19_41  ) < 1E-3



import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 						A__							(		UpperCamelCase__   ,     unittest.TestCase  ):
						UpperCAmelCase             =						LxmertTokenizer
						UpperCAmelCase             =						LxmertTokenizerFast
						UpperCAmelCase             =						True
						UpperCAmelCase             =						True




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


										"""simple docstring"""





										super().setUp()

										_SCREAMING_SNAKE_CASE			  =[
										    '''[UNK]''',
										    '''[CLS]''',
										    '''[SEP]''',
										    '''want''',
										    '''##want''',
										    '''##ed''',
										    '''wa''',
										    '''un''',
										    '''runn''',
										    '''##ing''',
										    ''',''',
										    '''low''',
										    '''lowest''',
										]
										_SCREAMING_SNAKE_CASE			  =os.path.join(self.tmpdirname				,    VOCAB_FILES_NAMES['''vocab_file''']  )
										with open(self.vocab_file				,    '''w'''				,    encoding='''utf-8'''  ) as vocab_writer:
														vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]  )  )




						def 							__UpperCamelCase				(       self					:      List[str]				,    _a					:      Tuple  )							->     Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  ='''UNwant\u00E9d,running'''
										_SCREAMING_SNAKE_CASE			  ='''unwanted, running'''
										return input_text, output_text




						def 							__UpperCamelCase				(       self					:      Tuple  )							->     List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.tokenizer_class(self.vocab_file  )

										_SCREAMING_SNAKE_CASE			  =tokenizer.tokenize('''UNwant\u00E9d,running'''  )
										self.assertListEqual(_a				,    ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']  )
										self.assertListEqual(tokenizer.convert_tokens_to_ids(_a  )				,    [7, 4, 5, 10, 8, 9]  )




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


										"""simple docstring"""





										if not self.test_rust_tokenizer:
														return

										_SCREAMING_SNAKE_CASE			  =self.get_tokenizer()
										_SCREAMING_SNAKE_CASE			  =self.get_rust_tokenizer()

										_SCREAMING_SNAKE_CASE			  ='''I was born in 92000, and this is falsé.'''

										_SCREAMING_SNAKE_CASE			  =tokenizer.tokenize(_a  )
										_SCREAMING_SNAKE_CASE			  =rust_tokenizer.tokenize(_a  )
										self.assertListEqual(_a				,    _a  )

										_SCREAMING_SNAKE_CASE			  =tokenizer.encode(_a				,    add_special_tokens=_a  )
										_SCREAMING_SNAKE_CASE			  =rust_tokenizer.encode(_a				,    add_special_tokens=_a  )
										self.assertListEqual(_a				,    _a  )

										_SCREAMING_SNAKE_CASE			  =self.get_rust_tokenizer()
										_SCREAMING_SNAKE_CASE			  =tokenizer.encode(_a  )
										_SCREAMING_SNAKE_CASE			  =rust_tokenizer.encode(_a  )
										self.assertListEqual(_a				,    _a  )