



import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class 				_a						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							A				:int =       (DDIMParallelScheduler,)
							A				:str =       (("eta", 0.0), ("num_inference_steps", 50))



							def 		_A	(							self ,					**__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Union[str, Any]      						= {
											    "num_train_timesteps": 1000,
											    "beta_start": 0.0_0_0_1,
											    "beta_end": 0.0_2,
											    "beta_schedule": "linear",
											    "clip_sample": True,
											}

											config.update(**__UpperCAmelCase				)
											return config



							def 		_A	(							self ,					**__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Optional[int]      						= self.scheduler_classes[0]
											a__	:							Union[str, Any]      						= self.get_scheduler_config(**__UpperCAmelCase				)
											a__	:							List[Any]      						= scheduler_class(**__UpperCAmelCase				)

											a__						,   a__	:							Any      						= 10, 0.0

											a__	:							str      						= self.dummy_model()
											a__	:							List[str]      						= self.dummy_sample_deter

											scheduler.set_timesteps(__UpperCAmelCase				)

											for t in scheduler.timesteps:
															a__	:							Optional[Any]      						= model(__UpperCAmelCase ,					__UpperCAmelCase				)
															a__	:							int      						= scheduler.step(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				).prev_sample

											return sample



							def 		_A	(							self				):







											"""simple docstring"""



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



							def 		_A	(							self				):







											"""simple docstring"""



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

											a__	:							Optional[Any]      						= self.scheduler_classes[0]
											a__	:							Any      						= self.get_scheduler_config(steps_offset=1				)
											a__	:							Optional[int]      						= scheduler_class(**__UpperCAmelCase				)
											scheduler.set_timesteps(5				)
											assert torch.equal(scheduler.timesteps ,					torch.LongTensor([801, 601, 401, 201, 1]				)				)



							def 		_A	(							self				):







											"""simple docstring"""



											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=__UpperCAmelCase ,					beta_end=__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



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



							def 		_A	(							self				):







											"""simple docstring"""



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



							def 		_A	(							self				):







											"""simple docstring"""



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



							def 		_A	(							self				):







											"""simple docstring"""



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



							def 		_A	(							self				):







											"""simple docstring"""



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



							def 		_A	(							self				):







											"""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 		_A	(							self				):







											"""simple docstring"""



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



							def 		_A	(							self				):







											"""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 		_A	(							self				):







											"""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 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[int]      						= self.scheduler_classes[0]
											a__	:							int      						= self.get_scheduler_config()
											a__	:							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_4_7_7_1				)				) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(980 ,					960				) - 0.3_2_4_6_0				)				) < 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_0_9_7_9				)				) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(999 ,					998				) - 0.0_2				)				) < 1E-5



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							int      						= self.scheduler_classes[0]
											a__	:							Any      						= self.get_scheduler_config()
											a__	:							Optional[int]      						= scheduler_class(**__UpperCAmelCase				)

											a__						,   a__	:							Dict      						= 10, 0.0
											scheduler.set_timesteps(__UpperCAmelCase				)

											a__	:							int      						= self.dummy_model()
											a__	:							int      						= self.dummy_sample_deter
											a__	:							Any      						= self.dummy_sample_deter + 0.1
											a__	:							int      						= self.dummy_sample_deter - 0.1

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

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

											a__	:							Optional[int]      						= torch.sum(torch.abs(__UpperCAmelCase				)				)
											a__	:							Tuple      						= torch.mean(torch.abs(__UpperCAmelCase				)				)

											assert abs(result_sum.item() - 1_1_4_7.7_9_0_4				) < 1E-2
											assert abs(result_mean.item() - 0.4_9_8_2				) < 1E-3



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[Any]      						= self.full_loop()

											a__	:							Optional[int]      						= torch.sum(torch.abs(__UpperCAmelCase				)				)
											a__	:							Dict      						= torch.mean(torch.abs(__UpperCAmelCase				)				)

											assert abs(result_sum.item() - 1_7_2.0_0_6_7				) < 1E-2
											assert abs(result_mean.item() - 0.2_2_3_9_6_7				) < 1E-3



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							int      						= self.full_loop(prediction_type="v_prediction"				)

											a__	:							List[Any]      						= torch.sum(torch.abs(__UpperCAmelCase				)				)
											a__	:							Optional[Any]      						= torch.mean(torch.abs(__UpperCAmelCase				)				)

											assert abs(result_sum.item() - 5_2.5_3_0_2				) < 1E-2
											assert abs(result_mean.item() - 0.0_6_8_4				) < 1E-3



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Tuple      						= self.full_loop(set_alpha_to_one=__UpperCAmelCase ,					beta_start=0.0_1				)
											a__	:							Optional[int]      						= torch.sum(torch.abs(__UpperCAmelCase				)				)
											a__	:							Union[str, Any]      						= torch.mean(torch.abs(__UpperCAmelCase				)				)

											assert abs(result_sum.item() - 1_4_9.8_2_9_5				) < 1E-2
											assert abs(result_mean.item() - 0.1_9_5_1				) < 1E-3




							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[int]      						= self.full_loop(set_alpha_to_one=__UpperCAmelCase ,					beta_start=0.0_1				)
											a__	:							Optional[int]      						= torch.sum(torch.abs(__UpperCAmelCase				)				)
											a__	:							List[Any]      						= torch.mean(torch.abs(__UpperCAmelCase				)				)

											assert abs(result_sum.item() - 1_4_9.0_7_8_4				) < 1E-2
											assert abs(result_mean.item() - 0.1_9_4_1				) < 1E-3







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


lowerCamelCase		       =      {
    """configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
    """processing_trocr""": ["""TrOCRProcessor"""],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase		       =      [
						    """TROCR_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TrOCRForCausalLM""",
						    """TrOCRPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
						from .processing_trocr import TrOCRProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
						import sys

						lowerCamelCase		       =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


