




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      lowerCAmelCase__	:       List[Any]			=    (DPMSolverMultistepScheduler,)
      lowerCAmelCase__	:       List[Any]			=    (("num_inference_steps", 25),)







      def 					snake_case		(   self    :       Dict       ,				**snake_case    :       Dict ):
          __UpperCamelCase     						=  {
              '''num_train_timesteps''': 1000,
              '''beta_start''': 0.00_01,
              '''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''',
              '''lower_order_final''': False,
              '''lambda_min_clipped''': -float('''inf''' ),
              '''variance_type''': None,
          }

          config.update(**snake_case )
          return config







      def 					snake_case		(   self    :       str       ,				snake_case    :       str=0       ,				**snake_case    :       Optional[int] ):
          __UpperCamelCase     						=  dict(self.forward_default_kwargs )
          __UpperCamelCase     						=  kwargs.pop('''num_inference_steps'''       ,				snake_case )
          __UpperCamelCase     						=  self.dummy_sample
          __UpperCamelCase     						=  0.1 * sample
          __UpperCamelCase     						=  [residual + 0.2, residual + 0.15, residual + 0.10]

          for scheduler_class in self.scheduler_classes:
              __UpperCamelCase     						=  self.get_scheduler_config(**snake_case )
              __UpperCamelCase     						=  scheduler_class(**snake_case )
              scheduler.set_timesteps(snake_case )
              # copy over dummy past residuals
              __UpperCamelCase     						=  dummy_past_residuals[: scheduler.config.solver_order]

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

              __UpperCamelCase						,					__UpperCamelCase     						=  sample, sample
              for t in range(snake_case       ,				time_step + scheduler.config.solver_order + 1 ):
                  __UpperCamelCase     						=  scheduler.step(snake_case       ,				snake_case       ,				snake_case       ,				**snake_case ).prev_sample
                  __UpperCamelCase     						=  new_scheduler.step(snake_case       ,				snake_case       ,				snake_case       ,				**snake_case ).prev_sample

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







      def 					snake_case		(   self    :       Optional[Any] ):
          pass







      def 					snake_case		(   self    :       Any       ,				snake_case    :       int=0       ,				**snake_case    :       Dict ):
          __UpperCamelCase     						=  dict(self.forward_default_kwargs )
          __UpperCamelCase     						=  kwargs.pop('''num_inference_steps'''       ,				snake_case )
          __UpperCamelCase     						=  self.dummy_sample
          __UpperCamelCase     						=  0.1 * sample
          __UpperCamelCase     						=  [residual + 0.2, residual + 0.15, residual + 0.10]

          for scheduler_class in self.scheduler_classes:
              __UpperCamelCase     						=  self.get_scheduler_config()
              __UpperCamelCase     						=  scheduler_class(**snake_case )
              scheduler.set_timesteps(snake_case )

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

              with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(snake_case )
                  __UpperCamelCase     						=  scheduler_class.from_pretrained(snake_case )
                  # copy over dummy past residuals
                  new_scheduler.set_timesteps(snake_case )

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

              __UpperCamelCase     						=  scheduler.step(snake_case       ,				snake_case       ,				snake_case       ,				**snake_case ).prev_sample
              __UpperCamelCase     						=  new_scheduler.step(snake_case       ,				snake_case       ,				snake_case       ,				**snake_case ).prev_sample

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







      def 					snake_case		(   self    :       int       ,				snake_case    :       List[str]=None       ,				**snake_case    :       Dict ):
          if scheduler is None:
              __UpperCamelCase     						=  self.scheduler_classes[0]
              __UpperCamelCase     						=  self.get_scheduler_config(**snake_case )
              __UpperCamelCase     						=  scheduler_class(**snake_case )

          __UpperCamelCase     						=  10
          __UpperCamelCase     						=  self.dummy_model()
          __UpperCamelCase     						=  self.dummy_sample_deter
          scheduler.set_timesteps(snake_case )

          for i, t in enumerate(scheduler.timesteps ):
              __UpperCamelCase     						=  model(snake_case       ,				snake_case )
              __UpperCamelCase     						=  scheduler.step(snake_case       ,				snake_case       ,				snake_case ).prev_sample

          return sample







      def 					snake_case		(   self    :       List[str] ):
          __UpperCamelCase     						=  dict(self.forward_default_kwargs )

          __UpperCamelCase     						=  kwargs.pop('''num_inference_steps'''       ,				snake_case )

          for scheduler_class in self.scheduler_classes:
              __UpperCamelCase     						=  self.get_scheduler_config()
              __UpperCamelCase     						=  scheduler_class(**snake_case )

              __UpperCamelCase     						=  self.dummy_sample
              __UpperCamelCase     						=  0.1 * sample

              if num_inference_steps is not None and hasattr(snake_case       ,				'''set_timesteps''' ):
                  scheduler.set_timesteps(snake_case )
              elif num_inference_steps is not None and not hasattr(snake_case       ,				'''set_timesteps''' ):
                  __UpperCamelCase     						=  num_inference_steps

              # copy over dummy past residuals (must be done after set_timesteps)
              __UpperCamelCase     						=  [residual + 0.2, residual + 0.15, residual + 0.10]
              __UpperCamelCase     						=  dummy_past_residuals[: scheduler.config.solver_order]

              __UpperCamelCase     						=  scheduler.timesteps[5]
              __UpperCamelCase     						=  scheduler.timesteps[6]

              __UpperCamelCase     						=  scheduler.step(snake_case       ,				snake_case       ,				snake_case       ,				**snake_case ).prev_sample
              __UpperCamelCase     						=  scheduler.step(snake_case       ,				snake_case       ,				snake_case       ,				**snake_case ).prev_sample

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







      def 					snake_case		(   self    :       List[str] ):
          for timesteps in [25, 50, 100, 999, 1000]:
              self.check_over_configs(num_train_timesteps=snake_case )







      def 					snake_case		(   self    :       Any ):
          self.check_over_configs(thresholding=snake_case )
          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=snake_case       ,				prediction_type=snake_case       ,				sample_max_value=snake_case       ,				algorithm_type='''dpmsolver++'''       ,				solver_order=snake_case       ,				solver_type=snake_case       ,				)







      def 					snake_case		(   self    :       List[Any] ):
          for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=snake_case )







      def 					snake_case		(   self    :       int ):
          for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
              for solver_type in ["midpoint", "heun"]:
                  for order in [1, 2, 3]:
                      for prediction_type in ["epsilon", "sample"]:
                          if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                              if order == 3:
                                  continue
                          else:
                              self.check_over_configs(
                                  solver_order=snake_case       ,				solver_type=snake_case       ,				prediction_type=snake_case       ,				algorithm_type=snake_case       ,				)
                          __UpperCamelCase     						=  self.full_loop(
                              solver_order=snake_case       ,				solver_type=snake_case       ,				prediction_type=snake_case       ,				algorithm_type=snake_case       ,				)
                          assert not torch.isnan(snake_case ).any(), "Samples have nan numbers"







      def 					snake_case		(   self    :       Any ):
          self.check_over_configs(lower_order_final=snake_case )
          self.check_over_configs(lower_order_final=snake_case )







      def 					snake_case		(   self    :       Optional[int] ):
          self.check_over_configs(lambda_min_clipped=-float('''inf''' ) )
          self.check_over_configs(lambda_min_clipped=-5.1 )







      def 					snake_case		(   self    :       str ):
          self.check_over_configs(variance_type=snake_case )
          self.check_over_configs(variance_type='''learned_range''' )







      def 					snake_case		(   self    :       Dict ):
          for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
              self.check_over_forward(num_inference_steps=snake_case       ,				time_step=0 )







      def 					snake_case		(   self    :       Any ):
          __UpperCamelCase     						=  self.full_loop()
          __UpperCamelCase     						=  torch.mean(torch.abs(snake_case ) )

          assert abs(result_mean.item() - 0.33_01 ) < 1E-3







      def 					snake_case		(   self    :       List[str] ):
          __UpperCamelCase     						=  self.full_loop(thresholding=snake_case       ,				dynamic_thresholding_ratio=0.87       ,				sample_max_value=0.5 )
          __UpperCamelCase     						=  torch.mean(torch.abs(snake_case ) )

          assert abs(result_mean.item() - 1.13_64 ) < 1E-3







      def 					snake_case		(   self    :       List[Any] ):
          __UpperCamelCase     						=  self.full_loop(prediction_type='''v_prediction''' )
          __UpperCamelCase     						=  torch.mean(torch.abs(snake_case ) )

          assert abs(result_mean.item() - 0.22_51 ) < 1E-3







      def 					snake_case		(   self    :       List[str] ):
          __UpperCamelCase     						=  self.full_loop(prediction_type='''v_prediction'''       ,				use_karras_sigmas=snake_case )
          __UpperCamelCase     						=  torch.mean(torch.abs(snake_case ) )

          assert abs(result_mean.item() - 0.20_96 ) < 1E-3







      def 					snake_case		(   self    :       int ):
          # make sure that iterating over schedulers with same config names gives same results
          # for defaults
          __UpperCamelCase     						=  DPMSolverMultistepScheduler(**self.get_scheduler_config() )
          __UpperCamelCase     						=  self.full_loop(scheduler=snake_case )
          __UpperCamelCase     						=  torch.mean(torch.abs(snake_case ) )

          assert abs(result_mean.item() - 0.33_01 ) < 1E-3

          __UpperCamelCase     						=  DPMSolverSinglestepScheduler.from_config(scheduler.config )
          __UpperCamelCase     						=  UniPCMultistepScheduler.from_config(scheduler.config )
          __UpperCamelCase     						=  DEISMultistepScheduler.from_config(scheduler.config )
          __UpperCamelCase     						=  DPMSolverMultistepScheduler.from_config(scheduler.config )

          __UpperCamelCase     						=  self.full_loop(scheduler=snake_case )
          __UpperCamelCase     						=  torch.mean(torch.abs(snake_case ) )

          assert abs(result_mean.item() - 0.33_01 ) < 1E-3







      def 					snake_case		(   self    :       List[str] ):
          __UpperCamelCase     						=  self.scheduler_classes[0]
          __UpperCamelCase     						=  self.get_scheduler_config(thresholding=snake_case       ,				dynamic_thresholding_ratio=0 )
          __UpperCamelCase     						=  scheduler_class(**snake_case )

          __UpperCamelCase     						=  10
          __UpperCamelCase     						=  self.dummy_model()
          __UpperCamelCase     						=  self.dummy_sample_deter.half()
          scheduler.set_timesteps(snake_case )

          for i, t in enumerate(scheduler.timesteps ):
              __UpperCamelCase     						=  model(snake_case       ,				snake_case )
              __UpperCamelCase     						=  scheduler.step(snake_case       ,				snake_case       ,				snake_case ).prev_sample

          assert sample.dtype == torch.floataa







      def 					snake_case		(   self    :       Any       ,				**snake_case    :       int ):
          for scheduler_class in self.scheduler_classes:
              __UpperCamelCase     						=  self.get_scheduler_config(**snake_case )
              __UpperCamelCase     						=  scheduler_class(**snake_case )

              scheduler.set_timesteps(scheduler.config.num_train_timesteps )
              assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCamelCase_	       =   {
    "configuration_esm": ["ESM_PRETRAINED_CONFIG_ARCHIVE_MAP", "EsmConfig"],
    "tokenization_esm": ["EsmTokenizer"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_	       =   [
							    "ESM_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "EsmForMaskedLM",
							    "EsmForSequenceClassification",
							    "EsmForTokenClassification",
							    "EsmModel",
							    "EsmPreTrainedModel",
							]
							UpperCamelCase_	       =   ["EsmForProteinFolding", "EsmFoldPreTrainedModel"]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_	       =   [
							    "TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFEsmForMaskedLM",
							    "TFEsmForSequenceClassification",
							    "TFEsmForTokenClassification",
							    "TFEsmModel",
							    "TFEsmPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
							from .tokenization_esm import EsmTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_esm import (
														    ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    EsmForMaskedLM,
														    EsmForSequenceClassification,
														    EsmForTokenClassification,
														    EsmModel,
														    EsmPreTrainedModel,
														)
														from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_esm import (
														    TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFEsmForMaskedLM,
														    TFEsmForSequenceClassification,
														    TFEsmForTokenClassification,
														    TFEsmModel,
														    TFEsmPreTrainedModel,
														)


else:
							import sys

							UpperCamelCase_	       =   _LazyModule(__name__, globals()["__file__"], _import_structure)





