


"""simple docstring"""







import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 __lowerCAmelCase			 =  (IPNDMScheduler,)
 __lowerCAmelCase			 =  (("num_inference_steps", 50),)


 def 				SCREAMING_SNAKE_CASE							(		self			,   **__A						) ->					str:
     a					={'''num_train_timesteps''': 1000}
     config.update(**__A						)
     return config


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A=0			,   **__A						) ->					Optional[int]:
     a					=dict(self.forward_default_kwargs						)
     a					=kwargs.pop('''num_inference_steps'''			,   __A						)
     a					=self.dummy_sample
     a					=0.1 * sample
     a					=[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

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

         a					=scheduler.step(__A			,   __A			,   __A			,   **__A						).prev_sample
         a					=new_scheduler.step(__A			,   __A			,   __A			,   **__A						).prev_sample

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

         a					=scheduler.step(__A			,   __A			,   __A			,   **__A						).prev_sample
         a					=new_scheduler.step(__A			,   __A			,   __A			,   **__A						).prev_sample

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


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Union[str, Any]:
     pass


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A=0			,   **__A						) ->					Tuple:
     a					=dict(self.forward_default_kwargs						)
     a					=kwargs.pop('''num_inference_steps'''			,   __A						)
     a					=self.dummy_sample
     a					=0.1 * sample
     a					=[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

     for scheduler_class in self.scheduler_classes:
         a					=self.get_scheduler_config()
         a					=scheduler_class(**__A						)
         scheduler.set_timesteps(__A						)

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

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

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

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

         a					=scheduler.step(__A			,   __A			,   __A			,   **__A						).prev_sample
         a					=new_scheduler.step(__A			,   __A			,   __A			,   **__A						).prev_sample

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

         a					=scheduler.step(__A			,   __A			,   __A			,   **__A						).prev_sample
         a					=new_scheduler.step(__A			,   __A			,   __A			,   **__A						).prev_sample

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


 def 				SCREAMING_SNAKE_CASE							(		self			,   **__A						) ->					str:
     a					=self.scheduler_classes[0]
     a					=self.get_scheduler_config(**__A						)
     a					=scheduler_class(**__A						)

     a					=10
     a					=self.dummy_model()
     a					=self.dummy_sample_deter
     scheduler.set_timesteps(__A						)

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

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

     return sample


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
     a					=dict(self.forward_default_kwargs						)

     a					=kwargs.pop('''num_inference_steps'''			,   __A						)

     for scheduler_class in self.scheduler_classes:
         a					=self.get_scheduler_config()
         a					=scheduler_class(**__A						)

         a					=self.dummy_sample
         a					=0.1 * sample

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

         # copy over dummy past residuals (must be done after set_timesteps)
         a					=[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
         a					=dummy_past_residuals[:]

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

         a					=scheduler.step(__A			,   __A			,   __A			,   **__A						).prev_sample
         a					=scheduler.step(__A			,   __A			,   __A			,   **__A						).prev_sample

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

         a					=scheduler.step(__A			,   __A			,   __A			,   **__A						).prev_sample
         a					=scheduler.step(__A			,   __A			,   __A			,   **__A						).prev_sample

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


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     for timesteps in [100, 1000]:
         self.check_over_configs(num_train_timesteps=__A			,   time_step=__A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
     for t, num_inference_steps in zip([1, 5, 10]			,   [10, 50, 100]						):
         self.check_over_forward(num_inference_steps=__A			,   time_step=__A						)





 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[Any]:
     a					=self.full_loop()
     a					=torch.mean(torch.abs(__A						)						)

     assert abs(result_mean.item() - 254_0529						) < 10



"""simple docstring"""







from typing import TYPE_CHECKING

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


lowerCamelCase_       :       int      						=  {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       Dict      						=  ["""MBartTokenizer"""]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       str      						=  ["""MBartTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       List[str]      						=  [
           """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """MBartForCausalLM""",
           """MBartForConditionalGeneration""",
           """MBartForQuestionAnswering""",
           """MBartForSequenceClassification""",
           """MBartModel""",
           """MBartPreTrainedModel""",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       List[Any]      						=  [
           """TFMBartForConditionalGeneration""",
           """TFMBartModel""",
           """TFMBartPreTrainedModel""",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       str      						=  [
           """FlaxMBartForConditionalGeneration""",
           """FlaxMBartForQuestionAnswering""",
           """FlaxMBartForSequenceClassification""",
           """FlaxMBartModel""",
           """FlaxMBartPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mbart import MBartTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mbart_fast import MBartTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mbart import (
                  MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MBartForCausalLM,
                  MBartForConditionalGeneration,
                  MBartForQuestionAnswering,
                  MBartForSequenceClassification,
                  MBartModel,
                  MBartPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_mbart import (
                  FlaxMBartForConditionalGeneration,
                  FlaxMBartForQuestionAnswering,
                  FlaxMBartForSequenceClassification,
                  FlaxMBartModel,
                  FlaxMBartPreTrainedModel,
              )

else:
       import sys

       lowerCamelCase_       :       List[Any]      						=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)