




import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest
class      _UpperCamelCase (			lowerCAmelCase						):
      UpperCAmelCase_            =						(DDIMScheduler,)
      UpperCAmelCase_            =						(("""eta""", 0.0), ("""num_inference_steps""", 50))
      def 		UpperCAmelCase_  (     self				:Union[str, Any]	, **lowerCamelCase				:Optional[Any] )       ->     Tuple:
       UpperCAmelCase__								=  {
           "num_train_timesteps": 1000,
           "beta_start": 0.00_01,
           "beta_end": 0.02,
           "beta_schedule": "linear",
           "clip_sample": True,
       }

       config.update(**lowerCamelCase )
       return config
      def 		UpperCAmelCase_  (     self				:Any	, **lowerCamelCase				:Tuple )       ->     str:
       UpperCAmelCase__								=  self.scheduler_classes[0]
       UpperCAmelCase__								=  self.get_scheduler_config(**lowerCamelCase )
       UpperCAmelCase__								=  scheduler_class(**lowerCamelCase )

       UpperCAmelCase__	,	UpperCAmelCase__								=  10, 0.0

       UpperCAmelCase__								=  self.dummy_model()
       UpperCAmelCase__								=  self.dummy_sample_deter

       scheduler.set_timesteps(lowerCamelCase )

       for t in scheduler.timesteps:
        UpperCAmelCase__								=  model(lowerCamelCase	, lowerCamelCase )
        UpperCAmelCase__								=  scheduler.step(lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, lowerCamelCase ).prev_sample

       return sample
      def 		UpperCAmelCase_  (     self				:Optional[Any] )       ->     Dict:
       for timesteps in [100, 500, 1000]:
        self.check_over_configs(num_train_timesteps=lowerCamelCase )
      def 		UpperCAmelCase_  (     self				:int )       ->     Union[str, Any]:
       for steps_offset in [0, 1]:
        self.check_over_configs(steps_offset=lowerCamelCase )

       UpperCAmelCase__								=  self.scheduler_classes[0]
       UpperCAmelCase__								=  self.get_scheduler_config(steps_offset=1 )
       UpperCAmelCase__								=  scheduler_class(**lowerCamelCase )
       scheduler.set_timesteps(5 )
       assert torch.equal(scheduler.timesteps	, torch.LongTensor([801, 601, 401, 201, 1] ) )
      def 		UpperCAmelCase_  (     self				:Dict )       ->     Any:
       for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]	, [0.0_02, 0.02, 0.2, 2] ):
        self.check_over_configs(beta_start=lowerCamelCase	, beta_end=lowerCamelCase )
      def 		UpperCAmelCase_  (     self				:Dict )       ->     Union[str, Any]:
       for schedule in ["linear", "squaredcos_cap_v2"]:
        self.check_over_configs(beta_schedule=lowerCamelCase )
      def 		UpperCAmelCase_  (     self				:Any )       ->     Tuple:
       for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=lowerCamelCase )
      def 		UpperCAmelCase_  (     self				:Tuple )       ->     List[str]:
       for clip_sample in [True, False]:
        self.check_over_configs(clip_sample=lowerCamelCase )
      def 		UpperCAmelCase_  (     self				:Union[str, Any] )       ->     Any:
       for timestep_spacing in ["trailing", "leading"]:
        self.check_over_configs(timestep_spacing=lowerCamelCase )
      def 		UpperCAmelCase_  (     self				:Optional[int] )       ->     Union[str, Any]:
       for rescale_betas_zero_snr in [True, False]:
        self.check_over_configs(rescale_betas_zero_snr=lowerCamelCase )
      def 		UpperCAmelCase_  (     self				:List[Any] )       ->     str:
       self.check_over_configs(thresholding=lowerCamelCase )
       for threshold in [0.5, 1.0, 2.0]:
        for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(
             thresholding=lowerCamelCase	, prediction_type=lowerCamelCase	, sample_max_value=lowerCamelCase	, )
      def 		UpperCAmelCase_  (     self				:int )       ->     Union[str, Any]:
       for t in [1, 10, 49]:
        self.check_over_forward(time_step=lowerCamelCase )
      def 		UpperCAmelCase_  (     self				:Tuple )       ->     Optional[int]:
       for t, num_inference_steps in zip([1, 10, 50]	, [10, 50, 500] ):
        self.check_over_forward(time_step=lowerCamelCase	, num_inference_steps=lowerCamelCase )
      def 		UpperCAmelCase_  (     self				:List[Any] )       ->     Any:
       for t, eta in zip([1, 10, 49]	, [0.0, 0.5, 1.0] ):
        self.check_over_forward(time_step=lowerCamelCase	, eta=lowerCamelCase )
      def 		UpperCAmelCase_  (     self				:Tuple )       ->     Optional[int]:
       UpperCAmelCase__								=  self.scheduler_classes[0]
       UpperCAmelCase__								=  self.get_scheduler_config()
       UpperCAmelCase__								=  scheduler_class(**lowerCamelCase )

       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_47_71 ) ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(980	, 960 ) - 0.3_24_60 ) ) < 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_09_79 ) ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(999	, 998 ) - 0.02 ) ) < 1e-5
      def 		UpperCAmelCase_  (     self				:str )       ->     Dict:
       UpperCAmelCase__								=  self.full_loop()

       UpperCAmelCase__								=  torch.sum(torch.abs(lowerCamelCase ) )
       UpperCAmelCase__								=  torch.mean(torch.abs(lowerCamelCase ) )

       assert abs(result_sum.item() - 1_72.00_67 ) < 1e-2
       assert abs(result_mean.item() - 0.22_39_67 ) < 1e-3
      def 		UpperCAmelCase_  (     self				:Any )       ->     Optional[Any]:
       UpperCAmelCase__								=  self.full_loop(prediction_type="v_prediction" )

       UpperCAmelCase__								=  torch.sum(torch.abs(lowerCamelCase ) )
       UpperCAmelCase__								=  torch.mean(torch.abs(lowerCamelCase ) )

       assert abs(result_sum.item() - 52.53_02 ) < 1e-2
       assert abs(result_mean.item() - 0.06_84 ) < 1e-3
      def 		UpperCAmelCase_  (     self				:Optional[int] )       ->     Optional[Any]:
       # We specify different beta, so that the first alpha is 0.99
       UpperCAmelCase__								=  self.full_loop(set_alpha_to_one=lowerCamelCase	, beta_start=0.01 )
       UpperCAmelCase__								=  torch.sum(torch.abs(lowerCamelCase ) )
       UpperCAmelCase__								=  torch.mean(torch.abs(lowerCamelCase ) )

       assert abs(result_sum.item() - 1_49.82_95 ) < 1e-2
       assert abs(result_mean.item() - 0.19_51 ) < 1e-3







      def 		UpperCAmelCase_  (     self				:int )       ->     List[str]:
       # We specify different beta, so that the first alpha is 0.99
       UpperCAmelCase__								=  self.full_loop(set_alpha_to_one=lowerCamelCase	, beta_start=0.01 )
       UpperCAmelCase__								=  torch.sum(torch.abs(lowerCamelCase ) )
       UpperCAmelCase__								=  torch.mean(torch.abs(lowerCamelCase ) )

       assert abs(result_sum.item() - 1_49.07_84 ) < 1e-2
       assert abs(result_mean.item() - 0.19_41 ) < 1e-3











from typing import TYPE_CHECKING

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


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

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

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase  :				str						=				[
      "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

  _lowerCAmelCase  :				List[Any]						=				_LazyModule(__name__, globals()["__file__"], _import_structure)





