




import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 					snake_case__							(_UpperCamelCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							List[Any]											=      (EulerDiscreteScheduler,)
				SCREAMING_SNAKE_CASE_ :							int											=      10





				def 							__UpperCAmelCase							(   self       :  Tuple      ,   **__lowerCamelCase       :  List[str]							)      ->  Tuple:
							a					=    {
							    '''num_train_timesteps''': 11_00,
							    '''beta_start''': 0.0_001,
							    '''beta_end''': 0.02,
							    '''beta_schedule''': '''linear''',
							}

							config.update(**UpperCAmelCase__							)
							return config





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  int:
							for timesteps in [10, 50, 1_00, 10_00]:
										self.check_over_configs(num_train_timesteps=UpperCAmelCase__							)





				def 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  Dict:
							for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]      ,   [0.0_002, 0.002, 0.02]							):
										self.check_over_configs(beta_start=UpperCAmelCase__      ,   beta_end=UpperCAmelCase__							)





				def 							__UpperCAmelCase							(   self       :  Optional[Any]							)      ->  Union[str, Any]:
							for schedule in ["linear", "scaled_linear"]:
										self.check_over_configs(beta_schedule=UpperCAmelCase__							)





				def 							__UpperCAmelCase							(   self       :  str							)      ->  Union[str, Any]:
							for prediction_type in ["epsilon", "v_prediction"]:
										self.check_over_configs(prediction_type=UpperCAmelCase__							)





				def 							__UpperCAmelCase							(   self       :  Optional[Any]							)      ->  Optional[Any]:
							a					=    self.scheduler_classes[0]
							a					=    self.get_scheduler_config()
							a					=    scheduler_class(**UpperCAmelCase__							)

							scheduler.set_timesteps(self.num_inference_steps							)

							a					=    torch.manual_seed(0							)

							a					=    self.dummy_model()
							a					=    self.dummy_sample_deter * scheduler.init_noise_sigma
							a					=    sample.to(UpperCAmelCase__							)

							for i, t in enumerate(scheduler.timesteps							):
										a					=    scheduler.scale_model_input(UpperCAmelCase__      ,   UpperCAmelCase__							)

										a					=    model(UpperCAmelCase__      ,   UpperCAmelCase__							)

										a					=    scheduler.step(UpperCAmelCase__      ,   UpperCAmelCase__      ,   UpperCAmelCase__      ,   generator=UpperCAmelCase__							)
										a					=    output.prev_sample

							a					=    torch.sum(torch.abs(UpperCAmelCase__							)							)
							a					=    torch.mean(torch.abs(UpperCAmelCase__							)							)

							assert abs(result_sum.item() - 10.0_807							) < 1e-2
							assert abs(result_mean.item() - 0.0_131							) < 1e-3





				def 							__UpperCAmelCase							(   self       :  str							)      ->  Optional[Any]:
							a					=    self.scheduler_classes[0]
							a					=    self.get_scheduler_config(prediction_type="v_prediction"							)
							a					=    scheduler_class(**UpperCAmelCase__							)

							scheduler.set_timesteps(self.num_inference_steps							)

							a					=    torch.manual_seed(0							)

							a					=    self.dummy_model()
							a					=    self.dummy_sample_deter * scheduler.init_noise_sigma
							a					=    sample.to(UpperCAmelCase__							)

							for i, t in enumerate(scheduler.timesteps							):
										a					=    scheduler.scale_model_input(UpperCAmelCase__      ,   UpperCAmelCase__							)

										a					=    model(UpperCAmelCase__      ,   UpperCAmelCase__							)

										a					=    scheduler.step(UpperCAmelCase__      ,   UpperCAmelCase__      ,   UpperCAmelCase__      ,   generator=UpperCAmelCase__							)
										a					=    output.prev_sample

							a					=    torch.sum(torch.abs(UpperCAmelCase__							)							)
							a					=    torch.mean(torch.abs(UpperCAmelCase__							)							)

							assert abs(result_sum.item() - 0.0_002							) < 1e-2
							assert abs(result_mean.item() - 2.26_76e-06							) < 1e-3





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  List[Any]:
							a					=    self.scheduler_classes[0]
							a					=    self.get_scheduler_config()
							a					=    scheduler_class(**UpperCAmelCase__							)

							scheduler.set_timesteps(self.num_inference_steps      ,   device=UpperCAmelCase__							)

							a					=    torch.manual_seed(0							)

							a					=    self.dummy_model()
							a					=    self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							a					=    sample.to(UpperCAmelCase__							)

							for t in scheduler.timesteps:
										a					=    scheduler.scale_model_input(UpperCAmelCase__      ,   UpperCAmelCase__							)

										a					=    model(UpperCAmelCase__      ,   UpperCAmelCase__							)

										a					=    scheduler.step(UpperCAmelCase__      ,   UpperCAmelCase__      ,   UpperCAmelCase__      ,   generator=UpperCAmelCase__							)
										a					=    output.prev_sample

							a					=    torch.sum(torch.abs(UpperCAmelCase__							)							)
							a					=    torch.mean(torch.abs(UpperCAmelCase__							)							)

							assert abs(result_sum.item() - 10.0_807							) < 1e-2
							assert abs(result_mean.item() - 0.0_131							) < 1e-3





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]							)      ->  List[str]:
							a					=    self.scheduler_classes[0]
							a					=    self.get_scheduler_config()
							a					=    scheduler_class(**UpperCAmelCase__      ,   use_karras_sigmas=UpperCAmelCase__							)

							scheduler.set_timesteps(self.num_inference_steps      ,   device=UpperCAmelCase__							)

							a					=    torch.manual_seed(0							)

							a					=    self.dummy_model()
							a					=    self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							a					=    sample.to(UpperCAmelCase__							)

							for t in scheduler.timesteps:
										a					=    scheduler.scale_model_input(UpperCAmelCase__      ,   UpperCAmelCase__							)

										a					=    model(UpperCAmelCase__      ,   UpperCAmelCase__							)

										a					=    scheduler.step(UpperCAmelCase__      ,   UpperCAmelCase__      ,   UpperCAmelCase__      ,   generator=UpperCAmelCase__							)
										a					=    output.prev_sample

							a					=    torch.sum(torch.abs(UpperCAmelCase__							)							)
							a					=    torch.mean(torch.abs(UpperCAmelCase__							)							)

							assert abs(result_sum.item() - 124.52_299_499_511_719							) < 1e-2
							assert abs(result_mean.item() - 0.16_213_932_633_399_963							) < 1e-3












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__lowerCAmelCase	:       Dict     =	{
    'configuration_blip': [
        'BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlipConfig',
        'BlipTextConfig',
        'BlipVisionConfig',
    ],
    'processing_blip': ['BlipProcessor'],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase	:       Optional[Any]     =	['BlipImageProcessor']


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase	:       List[Any]     =	[
		    'BLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'BlipModel',
		    'BlipPreTrainedModel',
		    'BlipForConditionalGeneration',
		    'BlipForQuestionAnswering',
		    'BlipVisionModel',
		    'BlipTextModel',
		    'BlipForImageTextRetrieval',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase	:       Any     =	[
		    'TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFBlipModel',
		    'TFBlipPreTrainedModel',
		    'TFBlipForConditionalGeneration',
		    'TFBlipForQuestionAnswering',
		    'TFBlipVisionModel',
		    'TFBlipTextModel',
		    'TFBlipForImageTextRetrieval',
		]

if TYPE_CHECKING:
		from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
		from .processing_blip import BlipProcessor

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_blip import BlipImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_blip import (
				    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BlipForConditionalGeneration,
				    BlipForImageTextRetrieval,
				    BlipForQuestionAnswering,
				    BlipModel,
				    BlipPreTrainedModel,
				    BlipTextModel,
				    BlipVisionModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_blip import (
				    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFBlipForConditionalGeneration,
				    TFBlipForImageTextRetrieval,
				    TFBlipForQuestionAnswering,
				    TFBlipModel,
				    TFBlipPreTrainedModel,
				    TFBlipTextModel,
				    TFBlipVisionModel,
				)

else:
		import sys

		__lowerCAmelCase	:       List[str]     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






