
"""simple docstring"""

import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):
      __SCREAMING_SNAKE_CASE :				List[str]				   =						(IPNDMScheduler,)
      __SCREAMING_SNAKE_CASE :				Any				   =						(("num_inference_steps", 5_0),)




      def      a_   (				self ,							**lowercase_				)    ->       int:
          UpperCAmelCase          =       {'num_train_timesteps': 1_0_0_0}
          config.update(**lowercase_				)
          return config




      def      a_   (				self ,							lowercase_=0 ,							**lowercase_				)    ->       Optional[int]:
          UpperCAmelCase          =       dict(self.forward_default_kwargs				)
          UpperCAmelCase          =       kwargs.pop('num_inference_steps' ,							lowercase_				)
          UpperCAmelCase          =       self.dummy_sample
          UpperCAmelCase          =       0.1 * sample
          UpperCAmelCase          =       [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

          for scheduler_class in self.scheduler_classes:
              UpperCAmelCase          =       self.get_scheduler_config(**lowercase_				)
              UpperCAmelCase          =       scheduler_class(**lowercase_				)
              scheduler.set_timesteps(lowercase_				)
              # copy over dummy past residuals
              UpperCAmelCase          =       dummy_past_residuals[:]

              if time_step is None:
                  UpperCAmelCase          =       scheduler.timesteps[len(scheduler.timesteps				) // 2]

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

              UpperCAmelCase          =       scheduler.step(lowercase_ ,							lowercase_ ,							lowercase_ ,							**lowercase_				).prev_sample
              UpperCAmelCase          =       new_scheduler.step(lowercase_ ,							lowercase_ ,							lowercase_ ,							**lowercase_				).prev_sample

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

              UpperCAmelCase          =       scheduler.step(lowercase_ ,							lowercase_ ,							lowercase_ ,							**lowercase_				).prev_sample
              UpperCAmelCase          =       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      a_   (				self				)    ->       Any:
          pass




      def      a_   (				self ,							lowercase_=0 ,							**lowercase_				)    ->       Union[str, Any]:
          UpperCAmelCase          =       dict(self.forward_default_kwargs				)
          UpperCAmelCase          =       kwargs.pop('num_inference_steps' ,							lowercase_				)
          UpperCAmelCase          =       self.dummy_sample
          UpperCAmelCase          =       0.1 * sample
          UpperCAmelCase          =       [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

          for scheduler_class in self.scheduler_classes:
              UpperCAmelCase          =       self.get_scheduler_config()
              UpperCAmelCase          =       scheduler_class(**lowercase_				)
              scheduler.set_timesteps(lowercase_				)

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

              if time_step is None:
                  UpperCAmelCase          =       scheduler.timesteps[len(scheduler.timesteps				) // 2]

              with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(lowercase_				)
                  UpperCAmelCase          =       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          =       dummy_past_residuals[:]

              UpperCAmelCase          =       scheduler.step(lowercase_ ,							lowercase_ ,							lowercase_ ,							**lowercase_				).prev_sample
              UpperCAmelCase          =       new_scheduler.step(lowercase_ ,							lowercase_ ,							lowercase_ ,							**lowercase_				).prev_sample

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

              UpperCAmelCase          =       scheduler.step(lowercase_ ,							lowercase_ ,							lowercase_ ,							**lowercase_				).prev_sample
              UpperCAmelCase          =       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      a_   (				self ,							**lowercase_				)    ->       int:
          UpperCAmelCase          =       self.scheduler_classes[0]
          UpperCAmelCase          =       self.get_scheduler_config(**lowercase_				)
          UpperCAmelCase          =       scheduler_class(**lowercase_				)

          UpperCAmelCase          =       1_0
          UpperCAmelCase          =       self.dummy_model()
          UpperCAmelCase          =       self.dummy_sample_deter
          scheduler.set_timesteps(lowercase_				)

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

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

          return sample




      def      a_   (				self				)    ->       str:
          UpperCAmelCase          =       dict(self.forward_default_kwargs				)

          UpperCAmelCase          =       kwargs.pop('num_inference_steps' ,							lowercase_				)

          for scheduler_class in self.scheduler_classes:
              UpperCAmelCase          =       self.get_scheduler_config()
              UpperCAmelCase          =       scheduler_class(**lowercase_				)

              UpperCAmelCase          =       self.dummy_sample
              UpperCAmelCase          =       0.1 * sample

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

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

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

              UpperCAmelCase          =       scheduler.step(lowercase_ ,							lowercase_ ,							lowercase_ ,							**lowercase_				).prev_sample
              UpperCAmelCase          =       scheduler.step(lowercase_ ,							lowercase_ ,							lowercase_ ,							**lowercase_				).prev_sample

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

              UpperCAmelCase          =       scheduler.step(lowercase_ ,							lowercase_ ,							lowercase_ ,							**lowercase_				).prev_sample
              UpperCAmelCase          =       scheduler.step(lowercase_ ,							lowercase_ ,							lowercase_ ,							**lowercase_				).prev_sample

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




      def      a_   (				self				)    ->       List[str]:
          for timesteps in [1_0_0, 1_0_0_0]:
              self.check_over_configs(num_train_timesteps=lowercase_ ,							time_step=lowercase_				)




      def      a_   (				self				)    ->       List[str]:
          for t, num_inference_steps in zip([1, 5, 1_0] ,							[1_0, 5_0, 1_0_0]				):
              self.check_over_forward(num_inference_steps=lowercase_ ,							time_step=lowercase_				)




      def      a_   (				self				)    ->       Optional[int]:
          UpperCAmelCase          =       self.full_loop()
          UpperCAmelCase          =       torch.mean(torch.abs(lowercase_				)				)

          assert abs(result_mean.item() - 2_5_4_0_5_2_9				) < 1_0





"""simple docstring"""

from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_            =    {
    '''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
    '''tokenization_roc_bert''': ['''RoCBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   pass

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    [
       '''ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''RoCBertForCausalLM''',
       '''RoCBertForMaskedLM''',
       '''RoCBertForMultipleChoice''',
       '''RoCBertForPreTraining''',
       '''RoCBertForQuestionAnswering''',
       '''RoCBertForSequenceClassification''',
       '''RoCBertForTokenClassification''',
       '''RoCBertLayer''',
       '''RoCBertModel''',
       '''RoCBertPreTrainedModel''',
       '''load_tf_weights_in_roc_bert''',
   ]

if TYPE_CHECKING:
   from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
   from .tokenization_roc_bert import RoCBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      raise OptionalDependencyNotAvailable()

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roc_bert import (
          ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          RoCBertForCausalLM,
          RoCBertForMaskedLM,
          RoCBertForMultipleChoice,
          RoCBertForPreTraining,
          RoCBertForQuestionAnswering,
          RoCBertForSequenceClassification,
          RoCBertForTokenClassification,
          RoCBertLayer,
          RoCBertModel,
          RoCBertPreTrainedModel,
          load_tf_weights_in_roc_bert,
      )


else:
   import sys

   SCREAMING_SNAKE_CASE_            =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



