



"""simple docstring"""





import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase			  =       (DDPMParallelScheduler,)





		def    UpperCAmelCase__							(					self							,    **_lowercase   ) ->						List[Any]:







								'''simple docstring'''

								snake_case_   : Optional[int]						=			{
								    """num_train_timesteps""": 1_0_0_0,
								    """beta_start""": 0.0001,
								    """beta_end""": 0.02,
								    """beta_schedule""": """linear""",
								    """variance_type""": """fixed_small""",
								    """clip_sample""": True,
								}

								config.update(**_lowercase   )
								return config





		def    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

								for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
														self.check_over_configs(num_train_timesteps=_lowercase   )





		def    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

								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=_lowercase							,    beta_end=_lowercase   )





		def    UpperCAmelCase__							(					self   ) ->						Dict:







								'''simple docstring'''

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





		def    UpperCAmelCase__							(					self   ) ->						Optional[Any]:







								'''simple docstring'''

								for variance in ["fixed_small", "fixed_large", "other"]:
														self.check_over_configs(variance_type=_lowercase   )





		def    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

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





		def    UpperCAmelCase__							(					self   ) ->						Tuple:







								'''simple docstring'''

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





		def    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

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





		def    UpperCAmelCase__							(					self   ) ->						Tuple:







								'''simple docstring'''

								for t in [0, 5_0_0, 9_9_9]:
														self.check_over_forward(time_step=_lowercase   )





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







								'''simple docstring'''

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

								assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 0.0   )   ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(4_8_7   ) - 0.0_0979   )   ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(9_9_9   ) - 0.02   )   ) < 1E-5





		def    UpperCAmelCase__							(					self   ) ->						Tuple:







								'''simple docstring'''

								snake_case_   : int						=			self.scheduler_classes[0]
								snake_case_   : int						=			self.get_scheduler_config()
								snake_case_   : str						=			scheduler_class(**_lowercase   )

								snake_case_   : str						=			len(_lowercase   )

								snake_case_   : Optional[Any]						=			self.dummy_model()
								snake_case_   : Optional[int]						=			self.dummy_sample_deter
								snake_case_   : Tuple						=			self.dummy_sample_deter + 0.1
								snake_case_   : int						=			self.dummy_sample_deter - 0.1

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

								snake_case_   : List[Any]						=			model(samples.flatten(0							,    1   )							,    timesteps.flatten(0							,    1   )   )
								snake_case_   : Any						=			scheduler.batch_step_no_noise(_lowercase							,    timesteps.flatten(0							,    1   )							,    samples.flatten(0							,    1   )   )

								snake_case_   : Tuple						=			torch.sum(torch.abs(_lowercase   )   )
								snake_case_   : Dict						=			torch.mean(torch.abs(_lowercase   )   )

								assert abs(result_sum.item() - 1153.1833   ) < 1E-2
								assert abs(result_mean.item() - 0.5005   ) < 1E-3





		def    UpperCAmelCase__							(					self   ) ->						List[Any]:







								'''simple docstring'''

								snake_case_   : Union[str, Any]						=			self.scheduler_classes[0]
								snake_case_   : Tuple						=			self.get_scheduler_config()
								snake_case_   : Any						=			scheduler_class(**_lowercase   )

								snake_case_   : int						=			len(_lowercase   )

								snake_case_   : Optional[Any]						=			self.dummy_model()
								snake_case_   : Any						=			self.dummy_sample_deter
								snake_case_   : Optional[int]						=			torch.manual_seed(0   )

								for t in reversed(range(_lowercase   )   ):
														# 1. predict noise residual
														snake_case_   : Tuple						=			model(_lowercase							,    _lowercase   )

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

														snake_case_   : List[str]						=			pred_prev_sample

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

								assert abs(result_sum.item() - 258.9606   ) < 1E-2
								assert abs(result_mean.item() - 0.3372   ) < 1E-3





		def    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

								snake_case_   : List[Any]						=			self.scheduler_classes[0]
								snake_case_   : Dict						=			self.get_scheduler_config(prediction_type="""v_prediction"""   )
								snake_case_   : Any						=			scheduler_class(**_lowercase   )

								snake_case_   : str						=			len(_lowercase   )

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

								for t in reversed(range(_lowercase   )   ):
														# 1. predict noise residual
														snake_case_   : Dict						=			model(_lowercase							,    _lowercase   )

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

														snake_case_   : Union[str, Any]						=			pred_prev_sample

								snake_case_   : Any						=			torch.sum(torch.abs(_lowercase   )   )
								snake_case_   : List[str]						=			torch.mean(torch.abs(_lowercase   )   )

								assert abs(result_sum.item() - 202.0296   ) < 1E-2
								assert abs(result_mean.item() - 0.2631   ) < 1E-3





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







								'''simple docstring'''

								snake_case_   : Optional[int]						=			self.scheduler_classes[0]
								snake_case_   : Optional[int]						=			self.get_scheduler_config()
								snake_case_   : Tuple						=			scheduler_class(**_lowercase   )

								snake_case_   : str						=			[1_0_0, 8_7, 5_0, 1, 0]

								scheduler.set_timesteps(timesteps=_lowercase   )

								snake_case_   : List[str]						=			scheduler.timesteps

								for i, timestep in enumerate(_lowercase   ):
														if i == len(_lowercase   ) - 1:
																				snake_case_   : Dict						=			-1
														else:
																				snake_case_   : str						=			timesteps[i + 1]

														snake_case_   : Union[str, Any]						=			scheduler.previous_timestep(_lowercase   )
														snake_case_   : Dict						=			prev_t.item()

														self.assertEqual(_lowercase							,    _lowercase   )





		def    UpperCAmelCase__							(					self   ) ->						List[Any]:







								'''simple docstring'''

								snake_case_   : Optional[int]						=			self.scheduler_classes[0]
								snake_case_   : List[str]						=			self.get_scheduler_config()
								snake_case_   : Tuple						=			scheduler_class(**_lowercase   )

								snake_case_   : Union[str, Any]						=			[1_0_0, 8_7, 5_0, 5_1, 0]

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





		def    UpperCAmelCase__							(					self   ) ->						Dict:







								'''simple docstring'''

								snake_case_   : Optional[Any]						=			self.scheduler_classes[0]
								snake_case_   : int						=			self.get_scheduler_config()
								snake_case_   : Union[str, Any]						=			scheduler_class(**_lowercase   )

								snake_case_   : List[str]						=			[1_0_0, 8_7, 5_0, 1, 0]
								snake_case_   : Union[str, Any]						=			len(_lowercase   )

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





		def    UpperCAmelCase__							(					self   ) ->						List[Any]:







								'''simple docstring'''

								snake_case_   : str						=			self.scheduler_classes[0]
								snake_case_   : List[str]						=			self.get_scheduler_config()
								snake_case_   : Dict						=			scheduler_class(**_lowercase   )

								snake_case_   : Tuple						=			[scheduler.config.num_train_timesteps]

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






"""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							,    _lowercase							,    _lowercase=1_3							,    _lowercase=7							,    _lowercase=True							,    _lowercase=True							,    _lowercase=True							,    _lowercase=True							,    _lowercase=9_9							,    _lowercase=3_2							,    _lowercase=5							,    _lowercase=4							,    _lowercase=3_7							,    _lowercase="gelu"							,    _lowercase=0.1							,    _lowercase=0.1							,    _lowercase=5_1_2							,    _lowercase=1_6							,    _lowercase=2							,    _lowercase=0.02							,    _lowercase=4							,    ) ->						List[str]:







								'''simple docstring'''

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





		def    UpperCAmelCase__							(					self   ) ->						List[Any]:







								'''simple docstring'''

								snake_case_   : List[Any]						=			ids_tensor([self.batch_size, self.seq_length]							,    self.vocab_size   )

								snake_case_   : Tuple						=			None
								if self.use_attention_mask:
														snake_case_   : Union[str, Any]						=			random_attention_mask([self.batch_size, self.seq_length]   )

								snake_case_   : Optional[int]						=			None
								if self.use_token_type_ids:
														snake_case_   : str						=			ids_tensor([self.batch_size, self.seq_length]							,    self.type_vocab_size   )

								snake_case_   : Union[str, Any]						=			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=_lowercase							,    initializer_range=self.initializer_range							,    )

								return config, input_ids, token_type_ids, attention_mask





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







								'''simple docstring'''

								snake_case_   : str						=			self.prepare_config_and_inputs()
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_   : List[Any]						=			config_and_inputs
								snake_case_   : Tuple						=			{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
								return config, inputs_dict




@require_flax
class    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__  ,				unittest.TestCase		):





		"""simple docstring"""






		_lowerCamelCase			  =       True

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





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







								'''simple docstring'''

								snake_case_   : Dict						=			FlaxRoFormerModelTester(self   )





		@slow
		def    UpperCAmelCase__							(					self   ) ->						Union[str, Any]:







								'''simple docstring'''

								for model_class_name in self.all_model_classes:
														snake_case_   : Tuple						=			model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""							,    from_pt=_lowercase   )
														snake_case_   : List[str]						=			model(np.ones((1, 1)   )   )
														self.assertIsNotNone(_lowercase   )




@require_flax
class    _lowerCAmelCase    (				unittest.TestCase		):





		"""simple docstring"""






		@slow
		def    UpperCAmelCase__							(					self   ) ->						Tuple:







								'''simple docstring'''

								snake_case_   : Dict						=			FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""   )
								snake_case_   : Tuple						=			jnp.array([[0, 1, 2, 3, 4, 5]]   )
								snake_case_   : Dict						=			model(_lowercase   )[0]

								snake_case_   : Optional[int]						=			5_0_0_0_0

								snake_case_   : Union[str, Any]						=			(1, 6, vocab_size)
								self.assertEqual(output.shape							,    _lowercase   )

								snake_case_   : Dict						=			jnp.array(
								    [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]   )

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

