






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest







class      UpperCAmelCase_		(   lowercase					):



      """simple docstring"""
      UpperCamelCase_							:   Union[str, Any] 	=(DPMSolverSinglestepScheduler,)
      UpperCamelCase_							:   Tuple 	=(('num_inference_steps', 25),)
      def        UpperCAmelCase		(							self       ,							**SCREAMING_SNAKE_CASE_					)							-> Optional[Any]:
          UpperCamelCase			:Optional[Any]      				=  {
              '''num_train_timesteps''': 1000,
              '''beta_start''': 0.0001,
              '''beta_end''': 0.02,
              '''beta_schedule''': '''linear''',
              '''solver_order''': 2,
              '''prediction_type''': '''epsilon''',
              '''thresholding''': False,
              '''sample_max_value''': 1.0,
              '''algorithm_type''': '''dpmsolver++''',
              '''solver_type''': '''midpoint''',
              '''lambda_min_clipped''': -float('''inf'''					),
              '''variance_type''': None,
          }

          config.update(**__lowercase					)
          return config
      def        UpperCAmelCase		(							self       ,							SCREAMING_SNAKE_CASE_=0       ,							**SCREAMING_SNAKE_CASE_					)							-> List[str]:
          UpperCamelCase			:str      				=  dict(self.forward_default_kwargs					)
          UpperCamelCase			:Optional[Any]      				=  kwargs.pop('''num_inference_steps'''       ,							__lowercase					)
          UpperCamelCase			:Optional[int]      				=  self.dummy_sample
          UpperCamelCase			:int      				=  0.1 * sample
          UpperCamelCase			:List[Any]      				=  [residual + 0.2, residual + 0.15, residual + 0.10]

          for scheduler_class in self.scheduler_classes:
              UpperCamelCase			:List[Any]      				=  self.get_scheduler_config(**__lowercase					)
              UpperCamelCase			:Optional[Any]      				=  scheduler_class(**__lowercase					)
              scheduler.set_timesteps(__lowercase					)
              # copy over dummy past residuals
              UpperCamelCase			:Optional[Any]      				=  dummy_past_residuals[: scheduler.config.solver_order]

              with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(__lowercase					)
                  UpperCamelCase			:List[Any]      				=  scheduler_class.from_pretrained(__lowercase					)
                  new_scheduler.set_timesteps(__lowercase					)
                  # copy over dummy past residuals
                  UpperCamelCase			:List[Any]      				=  dummy_past_residuals[: new_scheduler.config.solver_order]

              UpperCamelCase				,  UpperCamelCase			:int      				=  sample, sample
              for t in range(__lowercase       ,							time_step + scheduler.config.solver_order + 1					):
                  UpperCamelCase			:Optional[Any]      				=  scheduler.step(__lowercase       ,							__lowercase       ,							__lowercase       ,							**__lowercase					).prev_sample
                  UpperCamelCase			:Any      				=  new_scheduler.step(__lowercase       ,							__lowercase       ,							__lowercase       ,							**__lowercase					).prev_sample

                  assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"
      def        UpperCAmelCase		(							self					)							-> str:
          pass
      def        UpperCAmelCase		(							self       ,							SCREAMING_SNAKE_CASE_=0       ,							**SCREAMING_SNAKE_CASE_					)							-> Optional[int]:
          UpperCamelCase			:Optional[Any]      				=  dict(self.forward_default_kwargs					)
          UpperCamelCase			:Tuple      				=  kwargs.pop('''num_inference_steps'''       ,							__lowercase					)
          UpperCamelCase			:Optional[Any]      				=  self.dummy_sample
          UpperCamelCase			:Tuple      				=  0.1 * sample
          UpperCamelCase			:Optional[Any]      				=  [residual + 0.2, residual + 0.15, residual + 0.10]

          for scheduler_class in self.scheduler_classes:
              UpperCamelCase			:List[Any]      				=  self.get_scheduler_config()
              UpperCamelCase			:Union[str, Any]      				=  scheduler_class(**__lowercase					)
              scheduler.set_timesteps(__lowercase					)

              # copy over dummy past residuals (must be after setting timesteps)
              UpperCamelCase			:Optional[int]      				=  dummy_past_residuals[: scheduler.config.solver_order]

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

                  # copy over dummy past residual (must be after setting timesteps)
                  UpperCamelCase			:Optional[int]      				=  dummy_past_residuals[: new_scheduler.config.solver_order]

              UpperCamelCase			:Optional[int]      				=  scheduler.step(__lowercase       ,							__lowercase       ,							__lowercase       ,							**__lowercase					).prev_sample
              UpperCamelCase			:Optional[Any]      				=  new_scheduler.step(__lowercase       ,							__lowercase       ,							__lowercase       ,							**__lowercase					).prev_sample

              assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"
      def        UpperCAmelCase		(							self       ,							SCREAMING_SNAKE_CASE_=None       ,							**SCREAMING_SNAKE_CASE_					)							-> List[str]:







          if scheduler is None:
              UpperCamelCase			:int      				=  self.scheduler_classes[0]
              UpperCamelCase			:Union[str, Any]      				=  self.get_scheduler_config(**__lowercase					)
              UpperCamelCase			:Union[str, Any]      				=  scheduler_class(**__lowercase					)

          UpperCamelCase			:Tuple      				=  self.scheduler_classes[0]
          UpperCamelCase			:List[str]      				=  self.get_scheduler_config(**__lowercase					)
          UpperCamelCase			:Any      				=  scheduler_class(**__lowercase					)

          UpperCamelCase			:Optional[Any]      				=  10
          UpperCamelCase			:str      				=  self.dummy_model()
          UpperCamelCase			:List[Any]      				=  self.dummy_sample_deter
          scheduler.set_timesteps(__lowercase					)

          for i, t in enumerate(scheduler.timesteps					):
              UpperCamelCase			:Tuple      				=  model(__lowercase       ,							__lowercase					)
              UpperCamelCase			:Optional[Any]      				=  scheduler.step(__lowercase       ,							__lowercase       ,							__lowercase					).prev_sample

          return sample
      def        UpperCAmelCase		(							self					)							-> Union[str, Any]:
          UpperCamelCase			:List[Any]      				=  DPMSolverSinglestepScheduler(**self.get_scheduler_config()					)
          UpperCamelCase			:Optional[Any]      				=  50
          UpperCamelCase			:str      				=  self.dummy_model()
          UpperCamelCase			:Any      				=  self.dummy_sample_deter
          scheduler.set_timesteps(__lowercase					)

          # make sure that the first t is uneven
          for i, t in enumerate(scheduler.timesteps[3:]					):
              UpperCamelCase			:List[str]      				=  model(__lowercase       ,							__lowercase					)
              UpperCamelCase			:Union[str, Any]      				=  scheduler.step(__lowercase       ,							__lowercase       ,							__lowercase					).prev_sample

          UpperCamelCase			:Optional[Any]      				=  torch.mean(torch.abs(__lowercase					)					)

          assert abs(result_mean.item() - 0.2574					) < 1e-3
      def        UpperCAmelCase		(							self					)							-> Optional[int]:







          for timesteps in [25, 50, 100, 999, 1000]:
              self.check_over_configs(num_train_timesteps=__lowercase					)
      def        UpperCAmelCase		(							self					)							-> Tuple:
          UpperCamelCase			:List[Any]      				=  DPMSolverSinglestepScheduler(**self.get_scheduler_config()					)
          UpperCamelCase			:List[str]      				=  self.full_loop(scheduler=__lowercase					)
          UpperCamelCase			:str      				=  torch.mean(torch.abs(__lowercase					)					)

          assert abs(result_mean.item() - 0.2791					) < 1e-3

          UpperCamelCase			:int      				=  DEISMultistepScheduler.from_config(scheduler.config					)
          UpperCamelCase			:Optional[Any]      				=  DPMSolverMultistepScheduler.from_config(scheduler.config					)
          UpperCamelCase			:Union[str, Any]      				=  UniPCMultistepScheduler.from_config(scheduler.config					)
          UpperCamelCase			:Any      				=  DPMSolverSinglestepScheduler.from_config(scheduler.config					)

          UpperCamelCase			:int      				=  self.full_loop(scheduler=__lowercase					)
          UpperCamelCase			:Dict      				=  torch.mean(torch.abs(__lowercase					)					)

          assert abs(result_mean.item() - 0.2791					) < 1e-3
      def        UpperCAmelCase		(							self					)							-> Optional[Any]:
          self.check_over_configs(thresholding=__lowercase					)
          for order in [1, 2, 3]:
              for solver_type in ["midpoint", "heun"]:
                  for threshold in [0.5, 1.0, 2.0]:
                      for prediction_type in ["epsilon", "sample"]:
                          self.check_over_configs(
                              thresholding=__lowercase       ,							prediction_type=__lowercase       ,							sample_max_value=__lowercase       ,							algorithm_type='''dpmsolver++'''       ,							solver_order=__lowercase       ,							solver_type=__lowercase       ,							)
      def        UpperCAmelCase		(							self					)							-> Union[str, Any]:







          for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=__lowercase					)
      def        UpperCAmelCase		(							self					)							-> Optional[int]:







          for algorithm_type in ["dpmsolver", "dpmsolver++"]:
              for solver_type in ["midpoint", "heun"]:
                  for order in [1, 2, 3]:
                      for prediction_type in ["epsilon", "sample"]:
                          self.check_over_configs(
                              solver_order=__lowercase       ,							solver_type=__lowercase       ,							prediction_type=__lowercase       ,							algorithm_type=__lowercase       ,							)
                          UpperCamelCase			:Dict      				=  self.full_loop(
                              solver_order=__lowercase       ,							solver_type=__lowercase       ,							prediction_type=__lowercase       ,							algorithm_type=__lowercase       ,							)
                          assert not torch.isnan(__lowercase					).any(), "Samples have nan numbers"
      def        UpperCAmelCase		(							self					)							-> Union[str, Any]:
          self.check_over_configs(lower_order_final=__lowercase					)
          self.check_over_configs(lower_order_final=__lowercase					)
      def        UpperCAmelCase		(							self					)							-> List[Any]:
          self.check_over_configs(lambda_min_clipped=-float('''inf'''					)					)
          self.check_over_configs(lambda_min_clipped=-5.1					)
      def        UpperCAmelCase		(							self					)							-> Any:
          self.check_over_configs(variance_type=__lowercase					)
          self.check_over_configs(variance_type='''learned_range'''					)
      def        UpperCAmelCase		(							self					)							-> List[Any]:







          for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
              self.check_over_forward(num_inference_steps=__lowercase       ,							time_step=0					)
      def        UpperCAmelCase		(							self					)							-> Union[str, Any]:
          UpperCamelCase			:List[Any]      				=  self.full_loop()
          UpperCamelCase			:Tuple      				=  torch.mean(torch.abs(__lowercase					)					)

          assert abs(result_mean.item() - 0.2791					) < 1e-3
      def        UpperCAmelCase		(							self					)							-> Dict:
          UpperCamelCase			:Union[str, Any]      				=  self.full_loop(use_karras_sigmas=__lowercase					)
          UpperCamelCase			:List[str]      				=  torch.mean(torch.abs(__lowercase					)					)

          assert abs(result_mean.item() - 0.2248					) < 1e-3
      def        UpperCAmelCase		(							self					)							-> Optional[Any]:
          UpperCamelCase			:Optional[int]      				=  self.full_loop(prediction_type='''v_prediction'''					)
          UpperCamelCase			:Any      				=  torch.mean(torch.abs(__lowercase					)					)

          assert abs(result_mean.item() - 0.1453					) < 1e-3
      def        UpperCAmelCase		(							self					)							-> Tuple:
          UpperCamelCase			:int      				=  self.full_loop(prediction_type='''v_prediction'''       ,							use_karras_sigmas=__lowercase					)
          UpperCamelCase			:List[Any]      				=  torch.mean(torch.abs(__lowercase					)					)

          assert abs(result_mean.item() - 0.0649					) < 1e-3




      def        UpperCAmelCase		(							self					)							-> Any:
          UpperCamelCase			:List[Any]      				=  self.scheduler_classes[0]
          UpperCamelCase			:List[str]      				=  self.get_scheduler_config(thresholding=__lowercase       ,							dynamic_thresholding_ratio=0					)
          UpperCamelCase			:Any      				=  scheduler_class(**__lowercase					)

          UpperCamelCase			:Optional[Any]      				=  10
          UpperCamelCase			:List[str]      				=  self.dummy_model()
          UpperCamelCase			:int      				=  self.dummy_sample_deter.half()
          scheduler.set_timesteps(__lowercase					)

          for i, t in enumerate(scheduler.timesteps					):
              UpperCamelCase			:str      				=  model(__lowercase       ,							__lowercase					)
              UpperCamelCase			:List[str]      				=  scheduler.step(__lowercase       ,							__lowercase       ,							__lowercase					).prev_sample

          assert sample.dtype == torch.floataa








'''simple docstring'''

from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate


@dataclass(frozen=A )
class 	lowerCAmelCase  (   A ):
       # `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
       lowerCAmelCase_												=			field(default="summarization" ,      metadata={"include_in_asdict_even_if_is_default": True} )
       lowerCAmelCase_												=			Features({"text": Value("string" )} )
       lowerCAmelCase_												=			Features({"summary": Value("string" )} )
       lowerCAmelCase_												=			"text"
       lowerCAmelCase_												=			"summary"







       @property
       def       snake_case (				self      :			int   ):







            """simple docstring"""







            return {self.text_column: "text", self.summary_column: "summary"}


