





import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       (DDIMParallelScheduler,)
							lowerCamelCase_							     =       (("eta", 0.0), ("num_inference_steps", 50))


							def   _snake_case	(				self     :List[Any]				,					**__A     :List[str]		) -> Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   {
									    """num_train_timesteps""": 1000,
									    """beta_start""": 0.0_0_0_1,
									    """beta_end""": 0.0_2,
									    """beta_schedule""": """linear""",
									    """clip_sample""": True,
									}

									config.update(**__A		)
									return config


							def   _snake_case	(				self     :int				,					**__A     :List[str]		) -> Optional[int]:



									"""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   _snake_case	(				self     :Tuple		) -> Optional[int]:



									"""simple docstring"""



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


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



									"""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   _snake_case	(				self     :List[str]		) -> Any:



									"""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=__A				,					beta_end=__A		)


							def   _snake_case	(				self     :Dict		) -> Optional[Any]:



									"""simple docstring"""



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


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



									"""simple docstring"""



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


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



									"""simple docstring"""



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


							def   _snake_case	(				self     :Any		) -> Any:



									"""simple docstring"""



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


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



									"""simple docstring"""



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


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



									"""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   _snake_case	(				self     :Optional[int]		) -> List[Any]:



									"""simple docstring"""



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


							def   _snake_case	(				self     :str		) -> Tuple:



									"""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   _snake_case	(				self     :Tuple		) -> str:



									"""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   _snake_case	(				self     :List[str]		) -> Any:



									"""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_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   _snake_case	(				self     :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		)

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


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



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


							def   _snake_case	(				self     :Optional[Any]		) -> List[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() - 5_2.5_3_0_2		) < 1E-2
									assert abs(result_mean.item() - 0.0_6_8_4		) < 1E-3


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.full_loop(set_alpha_to_one=__A				,					beta_start=0.0_1		)
									SCREAMING_SNAKE_CASE__								=   torch.sum(torch.abs(__A		)		)
									SCREAMING_SNAKE_CASE__								=   torch.mean(torch.abs(__A		)		)

									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   _snake_case	(				self     :Tuple		) -> Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.full_loop(set_alpha_to_one=__A				,					beta_start=0.0_1		)
									SCREAMING_SNAKE_CASE__								=   torch.sum(torch.abs(__A		)		)
									SCREAMING_SNAKE_CASE__								=   torch.mean(torch.abs(__A		)		)

									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

# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_snake_case      = {
    '''configuration_efficientnet''': [
        '''EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EfficientNetConfig''',
        '''EfficientNetOnnxConfig''',
    ]
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _snake_case      = ['''EfficientNetImageProcessor''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _snake_case      = [
     '''EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''EfficientNetForImageClassification''',
     '''EfficientNetModel''',
     '''EfficientNetPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_efficientnet import (
     EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
     EfficientNetConfig,
     EfficientNetOnnxConfig,
 )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_efficientnet import EfficientNetImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_efficientnet import (
      EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      EfficientNetForImageClassification,
      EfficientNetModel,
      EfficientNetPreTrainedModel,
  )

else:
 import sys

 _snake_case      = _LazyModule(__name__, globals()['''__file__'''], _import_structure)


