



'''simple docstring'''

import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						    =						(DDIMParallelScheduler,)
					A_						    =						(("""eta""", 0.0), ("""num_inference_steps""", 50))




					def 						__UpperCAmelCase     (  self				:	Any     ,  **UpperCamelCase_				:	Dict					)  ->       Union[str, Any]:



						'''simple docstring'''





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

						config.update(**UpperCamelCase_					)
						return config




					def 						__UpperCAmelCase     (  self				:	List[str]     ,  **UpperCamelCase_				:	Optional[Any]					)  ->       Dict:



						'''simple docstring'''





						_lowercase					:       List[str]       =		self.scheduler_classes[0]
						_lowercase					:       List[str]       =		self.get_scheduler_config(**UpperCamelCase_					)
						_lowercase					:       Optional[Any]       =		scheduler_class(**UpperCamelCase_					)

						_lowercase  ,    _lowercase					:       Dict       =		10, 0.0

						_lowercase					:       Any       =		self.dummy_model()
						_lowercase					:       Any       =		self.dummy_sample_deter

						scheduler.set_timesteps(UpperCamelCase_					)

						for t in scheduler.timesteps:
							_lowercase					:       List[Any]       =		model(UpperCamelCase_     ,  UpperCamelCase_					)
							_lowercase					:       List[Any]       =		scheduler.step(UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					).prev_sample

						return sample




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



						'''simple docstring'''





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




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



						'''simple docstring'''





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

						_lowercase					:       List[str]       =		self.scheduler_classes[0]
						_lowercase					:       List[Any]       =		self.get_scheduler_config(steps_offset=1					)
						_lowercase					:       str       =		scheduler_class(**UpperCamelCase_					)
						scheduler.set_timesteps(5					)
						assert torch.equal(scheduler.timesteps     ,  torch.LongTensor([801, 601, 401, 201, 1]					)					)




					def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       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=UpperCamelCase_     ,  beta_end=UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       Tuple:



						'''simple docstring'''





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




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



						'''simple docstring'''





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




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



						'''simple docstring'''





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




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



						'''simple docstring'''





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




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



						'''simple docstring'''





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




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



						'''simple docstring'''





						self.check_over_configs(thresholding=UpperCamelCase_					)
						for threshold in [0.5, 1.0, 2.0]:
							for prediction_type in ["epsilon", "v_prediction"]:
								self.check_over_configs(
								    thresholding=UpperCamelCase_     ,  prediction_type=UpperCamelCase_     ,  sample_max_value=UpperCamelCase_     ,  )




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



						'''simple docstring'''





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




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



						'''simple docstring'''





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




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



						'''simple docstring'''





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




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



						'''simple docstring'''





						_lowercase					:       Optional[Any]       =		self.scheduler_classes[0]
						_lowercase					:       List[Any]       =		self.get_scheduler_config()
						_lowercase					:       str       =		scheduler_class(**UpperCamelCase_					)

						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				:	int					)  ->       Dict:



						'''simple docstring'''





						_lowercase					:       Any       =		self.scheduler_classes[0]
						_lowercase					:       Optional[Any]       =		self.get_scheduler_config()
						_lowercase					:       List[Any]       =		scheduler_class(**UpperCamelCase_					)

						_lowercase  ,    _lowercase					:       Tuple       =		10, 0.0
						scheduler.set_timesteps(UpperCamelCase_					)

						_lowercase					:       List[str]       =		self.dummy_model()
						_lowercase					:       List[str]       =		self.dummy_sample_deter
						_lowercase					:       Optional[Any]       =		self.dummy_sample_deter + 0.1
						_lowercase					:       str       =		self.dummy_sample_deter - 0.1

						_lowercase					:       str       =		samplea.shape[0]
						_lowercase					:       Tuple       =		torch.stack([samplea, samplea, samplea]     ,  dim=0					)
						_lowercase					:       str       =		torch.arange(UpperCamelCase_					)[0:3, None].repeat(1     ,  UpperCamelCase_					)

						_lowercase					:       int       =		model(samples.flatten(0     ,  1					)     ,  timesteps.flatten(0     ,  1					)					)
						_lowercase					:       List[str]       =		scheduler.batch_step_no_noise(UpperCamelCase_     ,  timesteps.flatten(0     ,  1					)     ,  samples.flatten(0     ,  1					)     ,  UpperCamelCase_					)

						_lowercase					:       Dict       =		torch.sum(torch.abs(UpperCamelCase_					)					)
						_lowercase					:       Union[str, Any]       =		torch.mean(torch.abs(UpperCamelCase_					)					)

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




					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       Tuple:



						'''simple docstring'''





						_lowercase					:       Any       =		self.full_loop()

						_lowercase					:       Optional[Any]       =		torch.sum(torch.abs(UpperCamelCase_					)					)
						_lowercase					:       Tuple       =		torch.mean(torch.abs(UpperCamelCase_					)					)

						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]					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       int       =		self.full_loop(prediction_type='v_prediction'					)

						_lowercase					:       Tuple       =		torch.sum(torch.abs(UpperCamelCase_					)					)
						_lowercase					:       List[Any]       =		torch.mean(torch.abs(UpperCamelCase_					)					)

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




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



						'''simple docstring'''





						_lowercase					:       List[Any]       =		self.full_loop(set_alpha_to_one=UpperCamelCase_     ,  beta_start=0.01					)
						_lowercase					:       Union[str, Any]       =		torch.sum(torch.abs(UpperCamelCase_					)					)
						_lowercase					:       Union[str, Any]       =		torch.mean(torch.abs(UpperCamelCase_					)					)

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




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



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		self.full_loop(set_alpha_to_one=UpperCamelCase_     ,  beta_start=0.01					)
						_lowercase					:       str       =		torch.sum(torch.abs(UpperCamelCase_					)					)
						_lowercase					:       Tuple       =		torch.mean(torch.abs(UpperCamelCase_					)					)

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





'''simple docstring'''

import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
		import jax.numpy as jnp

		from transformers.models.roformer.modeling_flax_roformer import (
		    FlaxRoFormerForMaskedLM,
		    FlaxRoFormerForMultipleChoice,
		    FlaxRoFormerForQuestionAnswering,
		    FlaxRoFormerForSequenceClassification,
		    FlaxRoFormerForTokenClassification,
		    FlaxRoFormerModel,
		)



class 		lowerCamelCase__      (						unittest.TestCase     ):




					'''simple docstring'''



					def __init__(  self				:	Dict     ,  UpperCamelCase_				:	Union[str, Any]     ,  UpperCamelCase_				:	List[str]=13     ,  UpperCamelCase_				:	Union[str, Any]=7     ,  UpperCamelCase_				:	Union[str, Any]=True     ,  UpperCamelCase_				:	Any=True     ,  UpperCamelCase_				:	Dict=True     ,  UpperCamelCase_				:	List[str]=True     ,  UpperCamelCase_				:	int=99     ,  UpperCamelCase_				:	Tuple=32     ,  UpperCamelCase_				:	List[str]=5     ,  UpperCamelCase_				:	Dict=4     ,  UpperCamelCase_				:	Tuple=37     ,  UpperCamelCase_				:	int="gelu"     ,  UpperCamelCase_				:	int=0.1     ,  UpperCamelCase_				:	Dict=0.1     ,  UpperCamelCase_				:	Any=512     ,  UpperCamelCase_				:	List[Any]=16     ,  UpperCamelCase_				:	Optional[int]=2     ,  UpperCamelCase_				:	Tuple=0.02     ,  UpperCamelCase_				:	Union[str, Any]=4     ,  )  ->       Tuple:



						'''simple docstring'''





						_lowercase					:       int       =		parent
						_lowercase					:       str       =		batch_size
						_lowercase					:       List[str]       =		seq_length
						_lowercase					:       Dict       =		is_training
						_lowercase					:       Optional[int]       =		use_attention_mask
						_lowercase					:       List[Any]       =		use_token_type_ids
						_lowercase					:       Union[str, Any]       =		use_labels
						_lowercase					:       Dict       =		vocab_size
						_lowercase					:       List[Any]       =		hidden_size
						_lowercase					:       Any       =		num_hidden_layers
						_lowercase					:       int       =		num_attention_heads
						_lowercase					:       Optional[int]       =		intermediate_size
						_lowercase					:       Any       =		hidden_act
						_lowercase					:       List[str]       =		hidden_dropout_prob
						_lowercase					:       Union[str, Any]       =		attention_probs_dropout_prob
						_lowercase					:       Optional[int]       =		max_position_embeddings
						_lowercase					:       int       =		type_vocab_size
						_lowercase					:       Any       =		type_sequence_label_size
						_lowercase					:       Any       =		initializer_range
						_lowercase					:       str       =		num_choices




					def 						__UpperCAmelCase     (  self				:	str					)  ->       int:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		ids_tensor([self.batch_size, self.seq_length]     ,  self.vocab_size					)

						_lowercase					:       int       =		None
						if self.use_attention_mask:
							_lowercase					:       Optional[int]       =		random_attention_mask([self.batch_size, self.seq_length]					)

						_lowercase					:       Any       =		None
						if self.use_token_type_ids:
							_lowercase					:       Optional[Any]       =		ids_tensor([self.batch_size, self.seq_length]     ,  self.type_vocab_size					)

						_lowercase					:       str       =		RoFormerConfig(
						    vocab_size=self.vocab_size     ,  hidden_size=self.hidden_size     ,  num_hidden_layers=self.num_hidden_layers     ,  num_attention_heads=self.num_attention_heads     ,  intermediate_size=self.intermediate_size     ,  hidden_act=self.hidden_act     ,  hidden_dropout_prob=self.hidden_dropout_prob     ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,  max_position_embeddings=self.max_position_embeddings     ,  type_vocab_size=self.type_vocab_size     ,  is_decoder=UpperCamelCase_     ,  initializer_range=self.initializer_range     ,  )

						return config, input_ids, token_type_ids, attention_mask




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



						'''simple docstring'''





						_lowercase					:       Dict       =		self.prepare_config_and_inputs()
						_lowercase  ,    _lowercase  ,    _lowercase  ,    _lowercase					:       Union[str, Any]       =		config_and_inputs
						_lowercase					:       Optional[int]       =		{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
						return config, inputs_dict






@require_flax
class 		lowerCamelCase__      (						A	,   unittest.TestCase     ):




					'''simple docstring'''



					A_						    =						True

					A_						    =						(
					    (
					        FlaxRoFormerModel,
					        FlaxRoFormerForMaskedLM,
					        FlaxRoFormerForSequenceClassification,
					        FlaxRoFormerForTokenClassification,
					        FlaxRoFormerForMultipleChoice,
					        FlaxRoFormerForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)




					def 						__UpperCAmelCase     (  self				:	str					)  ->       int:



						'''simple docstring'''





						_lowercase					:       Tuple       =		FlaxRoFormerModelTester(self					)




					@slow
					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       Optional[int]:



						'''simple docstring'''





						for model_class_name in self.all_model_classes:
							_lowercase					:       Optional[int]       =		model_class_name.from_pretrained('junnyu/roformer_chinese_small'     ,  from_pt=UpperCamelCase_					)
							_lowercase					:       str       =		model(np.ones((1, 1)					)					)
							self.assertIsNotNone(UpperCamelCase_					)






@require_flax
class 		lowerCamelCase__      (						unittest.TestCase     ):




					'''simple docstring'''



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



						'''simple docstring'''





						_lowercase					:       Dict       =		FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'					)
						_lowercase					:       Any       =		jnp.array([[0, 1, 2, 3, 4, 5]]					)
						_lowercase					:       int       =		model(UpperCamelCase_					)[0]

						_lowercase					:       Union[str, Any]       =		5_0000

						_lowercase					:       str       =		(1, 6, vocab_size)
						self.assertEqual(output.shape     ,  UpperCamelCase_					)

						_lowercase					:       int       =		jnp.array(
						    [[[-0.12_05, -1.02_65, 0.29_22], [-1.51_34, 0.19_74, 0.15_19], [-5.01_35, -3.90_03, -0.84_04]]]					)

						self.assertTrue(jnp.allclose(output[:, :3, :3]     ,  UpperCamelCase_     ,  atol=1E-4					)					)
