
import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest







class      A__							(     A__	):
				lowercase			    =  (PNDMScheduler,)
				lowercase			    =  (('''num_inference_steps''', 50),)






				def 							_lowerCamelCase		(						self					:      List[str]    ,     **a					:      Union[str, Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[int]         =		{
							    "num_train_timesteps": 1_000,
							    "beta_start": 0.0_0_0_1,
							    "beta_end": 0.0_2,
							    "beta_schedule": "linear",
							}

							config.update(**a   )
							return config






				def 							_lowerCamelCase		(						self					:      List[Any]    ,     a					:      str=0    ,     **a					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Any         =		dict(self.forward_default_kwargs   )
							lowerCAmelCase__      :      int         =		kwargs.pop('num_inference_steps'    ,     a   )
							lowerCAmelCase__      :      List[str]         =		self.dummy_sample
							lowerCAmelCase__      :      Optional[int]         =		0.1 * sample
							lowerCAmelCase__      :      List[Any]         =		[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

							for scheduler_class in self.scheduler_classes:
										lowerCAmelCase__      :      Tuple         =		self.get_scheduler_config(**a   )
										lowerCAmelCase__      :      Optional[Any]         =		scheduler_class(**a   )
										scheduler.set_timesteps(a   )
										# copy over dummy past residuals
										lowerCAmelCase__      :      Any         =		dummy_past_residuals[:]

										with tempfile.TemporaryDirectory() as tmpdirname:
													scheduler.save_config(a   )
													lowerCAmelCase__      :      Optional[int]         =		scheduler_class.from_pretrained(a   )
													new_scheduler.set_timesteps(a   )
													# copy over dummy past residuals
													lowerCAmelCase__      :      str         =		dummy_past_residuals[:]

										lowerCAmelCase__      :      Any         =		scheduler.step_prk(a    ,     a    ,     a    ,     **a   ).prev_sample
										lowerCAmelCase__      :      Optional[int]         =		new_scheduler.step_prk(a    ,     a    ,     a    ,     **a   ).prev_sample

										assert torch.sum(torch.abs(output - new_output   )   ) < 1E-5, "Scheduler outputs are not identical"

										lowerCAmelCase__      :      Optional[Any]         =		scheduler.step_plms(a    ,     a    ,     a    ,     **a   ).prev_sample
										lowerCAmelCase__      :      Union[str, Any]         =		new_scheduler.step_plms(a    ,     a    ,     a    ,     **a   ).prev_sample

										assert torch.sum(torch.abs(output - new_output   )   ) < 1E-5, "Scheduler outputs are not identical"






				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							pass






				def 							_lowerCamelCase		(						self					:      Any    ,     a					:      Dict=0    ,     **a					:      List[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      str         =		dict(self.forward_default_kwargs   )
							lowerCAmelCase__      :      Optional[int]         =		kwargs.pop('num_inference_steps'    ,     a   )
							lowerCAmelCase__      :      Any         =		self.dummy_sample
							lowerCAmelCase__      :      List[Any]         =		0.1 * sample
							lowerCAmelCase__      :      List[str]         =		[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

							for scheduler_class in self.scheduler_classes:
										lowerCAmelCase__      :      List[str]         =		self.get_scheduler_config()
										lowerCAmelCase__      :      Tuple         =		scheduler_class(**a   )
										scheduler.set_timesteps(a   )

										# copy over dummy past residuals (must be after setting timesteps)
										lowerCAmelCase__      :      List[Any]         =		dummy_past_residuals[:]

										with tempfile.TemporaryDirectory() as tmpdirname:
													scheduler.save_config(a   )
													lowerCAmelCase__      :      Optional[Any]         =		scheduler_class.from_pretrained(a   )
													# copy over dummy past residuals
													new_scheduler.set_timesteps(a   )

													# copy over dummy past residual (must be after setting timesteps)
													lowerCAmelCase__      :      Optional[int]         =		dummy_past_residuals[:]

										lowerCAmelCase__      :      int         =		scheduler.step_prk(a    ,     a    ,     a    ,     **a   ).prev_sample
										lowerCAmelCase__      :      Dict         =		new_scheduler.step_prk(a    ,     a    ,     a    ,     **a   ).prev_sample

										assert torch.sum(torch.abs(output - new_output   )   ) < 1E-5, "Scheduler outputs are not identical"

										lowerCAmelCase__      :      int         =		scheduler.step_plms(a    ,     a    ,     a    ,     **a   ).prev_sample
										lowerCAmelCase__      :      str         =		new_scheduler.step_plms(a    ,     a    ,     a    ,     **a   ).prev_sample

										assert torch.sum(torch.abs(output - new_output   )   ) < 1E-5, "Scheduler outputs are not identical"






				def 							_lowerCamelCase		(						self					:      Union[str, Any]    ,     **a					:      List[str]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      str         =		self.scheduler_classes[0]
							lowerCAmelCase__      :      Tuple         =		self.get_scheduler_config(**a   )
							lowerCAmelCase__      :      Optional[int]         =		scheduler_class(**a   )

							lowerCAmelCase__      :      Union[str, Any]         =		10
							lowerCAmelCase__      :      Optional[Any]         =		self.dummy_model()
							lowerCAmelCase__      :      List[str]         =		self.dummy_sample_deter
							scheduler.set_timesteps(a   )

							for i, t in enumerate(scheduler.prk_timesteps   ):
										lowerCAmelCase__      :      Union[str, Any]         =		model(a    ,     a   )
										lowerCAmelCase__      :      Union[str, Any]         =		scheduler.step_prk(a    ,     a    ,     a   ).prev_sample

							for i, t in enumerate(scheduler.plms_timesteps   ):
										lowerCAmelCase__      :      Tuple         =		model(a    ,     a   )
										lowerCAmelCase__      :      Optional[int]         =		scheduler.step_plms(a    ,     a    ,     a   ).prev_sample

							return sample






				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Dict         =		dict(self.forward_default_kwargs   )

							lowerCAmelCase__      :      Optional[int]         =		kwargs.pop('num_inference_steps'    ,     a   )

							for scheduler_class in self.scheduler_classes:
										lowerCAmelCase__      :      Any         =		self.get_scheduler_config()
										lowerCAmelCase__      :      List[Any]         =		scheduler_class(**a   )

										lowerCAmelCase__      :      List[str]         =		self.dummy_sample
										lowerCAmelCase__      :      Optional[int]         =		0.1 * sample

										if num_inference_steps is not None and hasattr(a    ,     'set_timesteps'   ):
													scheduler.set_timesteps(a   )
										elif num_inference_steps is not None and not hasattr(a    ,     'set_timesteps'   ):
													lowerCAmelCase__      :      List[Any]         =		num_inference_steps

										# copy over dummy past residuals (must be done after set_timesteps)
										lowerCAmelCase__      :      List[Any]         =		[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
										lowerCAmelCase__      :      List[str]         =		dummy_past_residuals[:]

										lowerCAmelCase__      :      Tuple         =		scheduler.step_prk(a    ,     0    ,     a    ,     **a   ).prev_sample
										lowerCAmelCase__      :      Optional[int]         =		scheduler.step_prk(a    ,     1    ,     a    ,     **a   ).prev_sample

										self.assertEqual(output_a.shape    ,     sample.shape   )
										self.assertEqual(output_a.shape    ,     output_a.shape   )

										lowerCAmelCase__      :      Any         =		scheduler.step_plms(a    ,     0    ,     a    ,     **a   ).prev_sample
										lowerCAmelCase__      :      str         =		scheduler.step_plms(a    ,     1    ,     a    ,     **a   ).prev_sample

										self.assertEqual(output_a.shape    ,     sample.shape   )
										self.assertEqual(output_a.shape    ,     output_a.shape   )






				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							for timesteps in [100, 1_000]:
										self.check_over_configs(num_train_timesteps=a   )






				def 							_lowerCamelCase		(						self					:      Dict   ):






							'''simple docstring'''







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

							lowerCAmelCase__      :      Any         =		self.scheduler_classes[0]
							lowerCAmelCase__      :      List[str]         =		self.get_scheduler_config(steps_offset=1   )
							lowerCAmelCase__      :      Dict         =		scheduler_class(**a   )
							scheduler.set_timesteps(10   )
							assert torch.equal(
							    scheduler.timesteps    ,     torch.LongTensor(
							        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]   )    ,     )






				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]    ,     [0.0_0_2, 0.0_2]   ):
										self.check_over_configs(beta_start=a    ,     beta_end=a   )






				def 							_lowerCamelCase		(						self					:      Dict   ):






							'''simple docstring'''







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






				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







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






				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							for t in [1, 5, 10]:
										self.check_over_forward(time_step=a   )






				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							for t, num_inference_steps in zip([1, 5, 10]    ,     [10, 50, 100]   ):
										self.check_over_forward(num_inference_steps=a   )






				def 							_lowerCamelCase		(						self					:      Dict   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		27

							for scheduler_class in self.scheduler_classes:
										lowerCAmelCase__      :      Union[str, Any]         =		self.dummy_sample
										lowerCAmelCase__      :      List[str]         =		0.1 * sample

										lowerCAmelCase__      :      Union[str, Any]         =		self.get_scheduler_config()
										lowerCAmelCase__      :      Optional[int]         =		scheduler_class(**a   )

										scheduler.set_timesteps(a   )

										# before power of 3 fix, would error on first step, so we only need to do two
										for i, t in enumerate(scheduler.prk_timesteps[:2]   ):
													lowerCAmelCase__      :      List[Any]         =		scheduler.step_prk(a    ,     a    ,     a   ).prev_sample






				def 							_lowerCamelCase		(						self					:      Union[str, Any]   ):






							'''simple docstring'''







							with self.assertRaises(a   ):
										lowerCAmelCase__      :      List[str]         =		self.scheduler_classes[0]
										lowerCAmelCase__      :      List[Any]         =		self.get_scheduler_config()
										lowerCAmelCase__      :      Dict         =		scheduler_class(**a   )

										scheduler.step_plms(self.dummy_sample    ,     1    ,     self.dummy_sample   ).prev_sample






				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		self.full_loop()
							lowerCAmelCase__      :      Any         =		torch.sum(torch.abs(a   )   )
							lowerCAmelCase__      :      Optional[Any]         =		torch.mean(torch.abs(a   )   )

							assert abs(result_sum.item() - 198.1_318   ) < 1E-2
							assert abs(result_mean.item() - 0.2_5_8_0   ) < 1E-3






				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Tuple         =		self.full_loop(prediction_type='v_prediction'   )
							lowerCAmelCase__      :      List[str]         =		torch.sum(torch.abs(a   )   )
							lowerCAmelCase__      :      List[str]         =		torch.mean(torch.abs(a   )   )

							assert abs(result_sum.item() - 67.3_986   ) < 1E-2
							assert abs(result_mean.item() - 0.0_8_7_8   ) < 1E-3






				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Tuple         =		self.full_loop(set_alpha_to_one=a    ,     beta_start=0.0_1   )
							lowerCAmelCase__      :      Tuple         =		torch.sum(torch.abs(a   )   )
							lowerCAmelCase__      :      Tuple         =		torch.mean(torch.abs(a   )   )

							assert abs(result_sum.item() - 230.0_399   ) < 1E-2
							assert abs(result_mean.item() - 0.2_9_9_5   ) < 1E-3






				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







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

							assert abs(result_sum.item() - 186.9_482   ) < 1E-2
							assert abs(result_mean.item() - 0.2_4_3_4   ) < 1E-3







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase	:							Tuple				={
    '''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
    '''tokenization_luke''': ['''LukeTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase	:							Union[str, Any]				=[
     '''LUKE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''LukeForEntityClassification''',
     '''LukeForEntityPairClassification''',
     '''LukeForEntitySpanClassification''',
     '''LukeForMultipleChoice''',
     '''LukeForQuestionAnswering''',
     '''LukeForSequenceClassification''',
     '''LukeForTokenClassification''',
     '''LukeForMaskedLM''',
     '''LukeModel''',
     '''LukePreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
 from .tokenization_luke import LukeTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_luke import (
      LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
      LukeForEntityClassification,
      LukeForEntityPairClassification,
      LukeForEntitySpanClassification,
      LukeForMaskedLM,
      LukeForMultipleChoice,
      LukeForQuestionAnswering,
      LukeForSequenceClassification,
      LukeForTokenClassification,
      LukeModel,
      LukePreTrainedModel,
  )

else:
 import sys

 lowerCamelCase	:							Dict				=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)