


'''simple docstring'''







import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 			__lowerCamelCase			(	a_	):
    """simple docstring"""
    a													=   (KDPMaDiscreteScheduler,)
    a													=   10


    def        A   (  self      :							str				,							**SCREAMING_SNAKE_CASE      :							Optional[Any]):
           _A					:     Union[str, Any]     = {
               'num_train_timesteps': 1100,
               'beta_start': 0.0001,
               'beta_end': 0.02,
               'beta_schedule': 'linear',
           }

           config.update(**SCREAMING_SNAKE_CASE)
           return config


    def        A   (  self      :							Tuple):
           for timesteps in [10, 50, 100, 1000]:
                  self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE)


    def        A   (  self      :							int):
           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=SCREAMING_SNAKE_CASE				,							beta_end=SCREAMING_SNAKE_CASE)


    def        A   (  self      :							int):
           for schedule in ["linear", "scaled_linear"]:
                  self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE)


    def        A   (  self      :							int):
           for prediction_type in ["epsilon", "v_prediction"]:
                  self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE)


    def        A   (  self      :							str):
           _A					:     Optional[Any]     = self.scheduler_classes[0]
           _A					:     Any     = self.get_scheduler_config(prediction_type='v_prediction')
           _A					:     int     = scheduler_class(**SCREAMING_SNAKE_CASE)

           scheduler.set_timesteps(self.num_inference_steps)

           _A					:     str     = self.dummy_model()
           _A					:     Tuple     = self.dummy_sample_deter * scheduler.init_noise_sigma
           _A					:     Tuple     = sample.to(SCREAMING_SNAKE_CASE)

           for i, t in enumerate(scheduler.timesteps):
                  _A					:     Dict     = scheduler.scale_model_input(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)

                  _A					:     List[str]     = model(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)

                  _A					:     str     = scheduler.step(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
                  _A					:     List[str]     = output.prev_sample

           _A					:     Tuple     = torch.sum(torch.abs(SCREAMING_SNAKE_CASE))
           _A					:     List[str]     = torch.mean(torch.abs(SCREAMING_SNAKE_CASE))

           if torch_device in ["cpu", "mps"]:
                  assert abs(result_sum.item() - 4.69_34e-07) < 1e-2
                  assert abs(result_mean.item() - 6.11_12e-10) < 1e-3
           else:
                  # CUDA
                  assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72e-07) < 1e-2
                  assert abs(result_mean.item() - 0.0002) < 1e-3


    def        A   (  self      :							Tuple):
           if torch_device == "mps":
                  return
           _A					:     int     = self.scheduler_classes[0]
           _A					:     Optional[int]     = self.get_scheduler_config()
           _A					:     Optional[Any]     = scheduler_class(**SCREAMING_SNAKE_CASE)

           scheduler.set_timesteps(self.num_inference_steps)

           _A					:     List[str]     = self.dummy_model()
           _A					:     int     = self.dummy_sample_deter * scheduler.init_noise_sigma
           _A					:     Union[str, Any]     = sample.to(SCREAMING_SNAKE_CASE)

           for i, t in enumerate(scheduler.timesteps):
                  _A					:     Any     = scheduler.scale_model_input(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)

                  _A					:     Dict     = model(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)

                  _A					:     List[Any]     = scheduler.step(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
                  _A					:     int     = output.prev_sample

           _A					:     Any     = torch.sum(torch.abs(SCREAMING_SNAKE_CASE))
           _A					:     Optional[int]     = torch.mean(torch.abs(SCREAMING_SNAKE_CASE))

           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        A   (  self      :							Dict):
           if torch_device == "mps":
                  return
           _A					:     str     = self.scheduler_classes[0]
           _A					:     Tuple     = self.get_scheduler_config()
           _A					:     List[Any]     = scheduler_class(**SCREAMING_SNAKE_CASE)

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

           _A					:     Tuple     = self.dummy_model()
           _A					:     Union[str, Any]     = self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE) * scheduler.init_noise_sigma

           for t in scheduler.timesteps:
                  _A					:     Optional[int]     = scheduler.scale_model_input(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)

                  _A					:     Union[str, Any]     = model(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)

                  _A					:     int     = scheduler.step(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
                  _A					:     Any     = output.prev_sample

           _A					:     int     = torch.sum(torch.abs(SCREAMING_SNAKE_CASE))
           _A					:     int     = torch.mean(torch.abs(SCREAMING_SNAKE_CASE))

           if str(SCREAMING_SNAKE_CASE).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










'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A				:					List[str]    						=			{
    '''configuration_electra''': ['''ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ElectraConfig''', '''ElectraOnnxConfig'''],
    '''tokenization_electra''': ['''ElectraTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					int    						=			['''ElectraTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					List[str]    						=			[
       '''ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ElectraForCausalLM''',
       '''ElectraForMaskedLM''',
       '''ElectraForMultipleChoice''',
       '''ElectraForPreTraining''',
       '''ElectraForQuestionAnswering''',
       '''ElectraForSequenceClassification''',
       '''ElectraForTokenClassification''',
       '''ElectraModel''',
       '''ElectraPreTrainedModel''',
       '''load_tf_weights_in_electra''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					Dict    						=			[
       '''TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFElectraForMaskedLM''',
       '''TFElectraForMultipleChoice''',
       '''TFElectraForPreTraining''',
       '''TFElectraForQuestionAnswering''',
       '''TFElectraForSequenceClassification''',
       '''TFElectraForTokenClassification''',
       '''TFElectraModel''',
       '''TFElectraPreTrainedModel''',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					List[Any]    						=			[
       '''FlaxElectraForCausalLM''',
       '''FlaxElectraForMaskedLM''',
       '''FlaxElectraForMultipleChoice''',
       '''FlaxElectraForPreTraining''',
       '''FlaxElectraForQuestionAnswering''',
       '''FlaxElectraForSequenceClassification''',
       '''FlaxElectraForTokenClassification''',
       '''FlaxElectraModel''',
       '''FlaxElectraPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
   from .tokenization_electra import ElectraTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_electra_fast import ElectraTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_electra import (
          ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
          ElectraForCausalLM,
          ElectraForMaskedLM,
          ElectraForMultipleChoice,
          ElectraForPreTraining,
          ElectraForQuestionAnswering,
          ElectraForSequenceClassification,
          ElectraForTokenClassification,
          ElectraModel,
          ElectraPreTrainedModel,
          load_tf_weights_in_electra,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_electra import (
          TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFElectraForMaskedLM,
          TFElectraForMultipleChoice,
          TFElectraForPreTraining,
          TFElectraForQuestionAnswering,
          TFElectraForSequenceClassification,
          TFElectraForTokenClassification,
          TFElectraModel,
          TFElectraPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_electra import (
          FlaxElectraForCausalLM,
          FlaxElectraForMaskedLM,
          FlaxElectraForMultipleChoice,
          FlaxElectraForPreTraining,
          FlaxElectraForQuestionAnswering,
          FlaxElectraForSequenceClassification,
          FlaxElectraForTokenClassification,
          FlaxElectraModel,
          FlaxElectraPreTrainedModel,
      )

else:
   import sys

   A				:					Tuple    						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






