

from typing import TYPE_CHECKING

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


a_      =     {
    'configuration_falcon': ['FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FalconConfig'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     [
			    'FALCON_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'FalconForCausalLM',
			    'FalconModel',
			    'FalconPreTrainedModel',
			    'FalconForSequenceClassification',
			    'FalconForTokenClassification',
			    'FalconForQuestionAnswering',
			]


if TYPE_CHECKING:
			from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_falcon import (
						    FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FalconForCausalLM,
						    FalconForQuestionAnswering,
						    FalconForSequenceClassification,
						    FalconForTokenClassification,
						    FalconModel,
						    FalconPreTrainedModel,
						)


else:
			import sys

			a_      =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





"""simple docstring"""

import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Optional[int]     =      (KDPMaDiscreteScheduler,)
 UpperCAmelCase       :	Any     =      10

 def      lowerCAmelCase_				(					self			:			Dict       ,				**_UpperCAmelCase			:			Optional[Any]						):
       _A     =  {
           'num_train_timesteps': 1_100,
           'beta_start': 0.0001,
           'beta_end': 0.02,
           'beta_schedule': 'linear',
       }

       config.update(**_UpperCAmelCase						)
       return config

 def      lowerCAmelCase_				(					self			:			Any						):
       for timesteps in [10, 50, 100, 1_000]:
             self.check_over_configs(num_train_timesteps=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Dict						):
       for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]       ,				[0.0002, 0.002, 0.02]						):
             self.check_over_configs(beta_start=_UpperCAmelCase       ,				beta_end=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Tuple						):
       for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       _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     =  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						)
             _A     =  output.prev_sample

       _A     =  torch.sum(torch.abs(_UpperCAmelCase						)						)
       _A     =  torch.mean(torch.abs(_UpperCAmelCase						)						)

       if torch_device in ["cpu", "mps"]:
             assert abs(result_sum.item() - 4.6_9_3_4E-0_7						) < 1E-2
             assert abs(result_mean.item() - 6.1_1_1_2E-1_0						) < 1E-3
       else:
             # CUDA
             assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2E-0_7						) < 1E-2
             assert abs(result_mean.item() - 0.0002						) < 1E-3

 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       if torch_device == "mps":
             return
       _A     =  self.scheduler_classes[0]
       _A     =  self.get_scheduler_config()
       _A     =  scheduler_class(**_UpperCAmelCase						)

       scheduler.set_timesteps(self.num_inference_steps						)

       _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						)
             _A     =  output.prev_sample

       _A     =  torch.sum(torch.abs(_UpperCAmelCase						)						)
       _A     =  torch.mean(torch.abs(_UpperCAmelCase						)						)

       if torch_device in ["cpu", "mps"]:
             assert abs(result_sum.item() - 20.4125						) < 1E-2
             assert abs(result_mean.item() - 0.0266						) < 1E-3
       else:
             # CUDA
             assert abs(result_sum.item() - 20.4125						) < 1E-2
             assert abs(result_mean.item() - 0.0266						) < 1E-3



 def      lowerCAmelCase_				(					self			:			Any						):
       if torch_device == "mps":
             return
       _A     =  self.scheduler_classes[0]
       _A     =  self.get_scheduler_config()
       _A     =  scheduler_class(**_UpperCAmelCase						)

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

       _A     =  self.dummy_model()
       _A     =  self.dummy_sample_deter.to(_UpperCAmelCase						) * scheduler.init_noise_sigma

       for t in scheduler.timesteps:
             _A     =  scheduler.scale_model_input(_UpperCAmelCase       ,				_UpperCAmelCase						)

             _A     =  model(_UpperCAmelCase       ,				_UpperCAmelCase						)

             _A     =  scheduler.step(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase						)
             _A     =  output.prev_sample

       _A     =  torch.sum(torch.abs(_UpperCAmelCase						)						)
       _A     =  torch.mean(torch.abs(_UpperCAmelCase						)						)

       if str(_UpperCAmelCase						).startswith('cpu'						):
             # The following sum varies between 148 and 156 on mps. Why?
             assert abs(result_sum.item() - 20.4125						) < 1E-2
             assert abs(result_mean.item() - 0.0266						) < 1E-3
       else:
             # CUDA
             assert abs(result_sum.item() - 20.4125						) < 1E-2
             assert abs(result_mean.item() - 0.0266						) < 1E-3

