

'''simple docstring'''
import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest


class   a__    (				a_    ):
   _SCREAMING_SNAKE_CASE       :	Tuple      			=				(IPNDMScheduler,)
   _SCREAMING_SNAKE_CASE       :	Union[str, Any]      			=				(("num_inference_steps", 50),)


   def 		_lowerCamelCase  (					self     ,							**_UpperCamelCase    ):





      """simple docstring"""





      _lowercase     : Any     	=  {"num_train_timesteps": 1000}
      config.update(**_UpperCamelCase    )
      return config


   def 		_lowerCamelCase  (					self     ,							_UpperCamelCase=0     ,							**_UpperCamelCase    ):





      """simple docstring"""





      _lowercase     : Any     	=  dict(self.forward_default_kwargs    )
      _lowercase     : int     	=  kwargs.pop("num_inference_steps"     ,							_UpperCamelCase    )
      _lowercase     : Optional[int]     	=  self.dummy_sample
      _lowercase     : str     	=  0.1 * sample
      _lowercase     : Union[str, Any]     	=  [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

      for scheduler_class in self.scheduler_classes:
         _lowercase     : List[str]     	=  self.get_scheduler_config(**_UpperCamelCase    )
         _lowercase     : Union[str, Any]     	=  scheduler_class(**_UpperCamelCase    )
         scheduler.set_timesteps(_UpperCamelCase    )
         # copy over dummy past residuals
         _lowercase     : int     	=  dummy_past_residuals[:]

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

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

         _lowercase     : int     	=  scheduler.step(_UpperCamelCase     ,							_UpperCamelCase     ,							_UpperCamelCase     ,							**_UpperCamelCase    ).prev_sample
         _lowercase     : Any     	=  new_scheduler.step(_UpperCamelCase     ,							_UpperCamelCase     ,							_UpperCamelCase     ,							**_UpperCamelCase    ).prev_sample

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

         _lowercase     : List[Any]     	=  scheduler.step(_UpperCamelCase     ,							_UpperCamelCase     ,							_UpperCamelCase     ,							**_UpperCamelCase    ).prev_sample
         _lowercase     : int     	=  new_scheduler.step(_UpperCamelCase     ,							_UpperCamelCase     ,							_UpperCamelCase     ,							**_UpperCamelCase    ).prev_sample

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


   def 		_lowerCamelCase  (					self    ):





      """simple docstring"""





      pass


   def 		_lowerCamelCase  (					self     ,							_UpperCamelCase=0     ,							**_UpperCamelCase    ):





      """simple docstring"""





      _lowercase     : Dict     	=  dict(self.forward_default_kwargs    )
      _lowercase     : Any     	=  kwargs.pop("num_inference_steps"     ,							_UpperCamelCase    )
      _lowercase     : Union[str, Any]     	=  self.dummy_sample
      _lowercase     : Optional[int]     	=  0.1 * sample
      _lowercase     : Optional[int]     	=  [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

      for scheduler_class in self.scheduler_classes:
         _lowercase     : Any     	=  self.get_scheduler_config()
         _lowercase     : Any     	=  scheduler_class(**_UpperCamelCase    )
         scheduler.set_timesteps(_UpperCamelCase    )

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

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

         with tempfile.TemporaryDirectory() as tmpdirname:
            scheduler.save_config(_UpperCamelCase    )
            _lowercase     : Optional[Any]     	=  scheduler_class.from_pretrained(_UpperCamelCase    )
            # copy over dummy past residuals
            new_scheduler.set_timesteps(_UpperCamelCase    )

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

         _lowercase     : Optional[int]     	=  scheduler.step(_UpperCamelCase     ,							_UpperCamelCase     ,							_UpperCamelCase     ,							**_UpperCamelCase    ).prev_sample
         _lowercase     : List[Any]     	=  new_scheduler.step(_UpperCamelCase     ,							_UpperCamelCase     ,							_UpperCamelCase     ,							**_UpperCamelCase    ).prev_sample

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

         _lowercase     : Dict     	=  scheduler.step(_UpperCamelCase     ,							_UpperCamelCase     ,							_UpperCamelCase     ,							**_UpperCamelCase    ).prev_sample
         _lowercase     : List[str]     	=  new_scheduler.step(_UpperCamelCase     ,							_UpperCamelCase     ,							_UpperCamelCase     ,							**_UpperCamelCase    ).prev_sample

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


   def 		_lowerCamelCase  (					self     ,							**_UpperCamelCase    ):





      """simple docstring"""





      _lowercase     : Dict     	=  self.scheduler_classes[0]
      _lowercase     : Dict     	=  self.get_scheduler_config(**_UpperCamelCase    )
      _lowercase     : List[Any]     	=  scheduler_class(**_UpperCamelCase    )

      _lowercase     : Union[str, Any]     	=  10
      _lowercase     : Any     	=  self.dummy_model()
      _lowercase     : List[str]     	=  self.dummy_sample_deter
      scheduler.set_timesteps(_UpperCamelCase    )

      for i, t in enumerate(scheduler.timesteps    ):
         _lowercase     : Union[str, Any]     	=  model(_UpperCamelCase     ,							_UpperCamelCase    )
         _lowercase     : Any     	=  scheduler.step(_UpperCamelCase     ,							_UpperCamelCase     ,							_UpperCamelCase    ).prev_sample

      for i, t in enumerate(scheduler.timesteps    ):
         _lowercase     : Tuple     	=  model(_UpperCamelCase     ,							_UpperCamelCase    )
         _lowercase     : List[Any]     	=  scheduler.step(_UpperCamelCase     ,							_UpperCamelCase     ,							_UpperCamelCase    ).prev_sample

      return sample


   def 		_lowerCamelCase  (					self    ):





      """simple docstring"""





      _lowercase     : Optional[Any]     	=  dict(self.forward_default_kwargs    )

      _lowercase     : str     	=  kwargs.pop("num_inference_steps"     ,							_UpperCamelCase    )

      for scheduler_class in self.scheduler_classes:
         _lowercase     : int     	=  self.get_scheduler_config()
         _lowercase     : Tuple     	=  scheduler_class(**_UpperCamelCase    )

         _lowercase     : Optional[int]     	=  self.dummy_sample
         _lowercase     : Union[str, Any]     	=  0.1 * sample

         if num_inference_steps is not None and hasattr(_UpperCamelCase     ,							"set_timesteps"    ):
            scheduler.set_timesteps(_UpperCamelCase    )
         elif num_inference_steps is not None and not hasattr(_UpperCamelCase     ,							"set_timesteps"    ):
            _lowercase     : Dict     	=  num_inference_steps

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

         _lowercase     : Any     	=  scheduler.timesteps[5]
         _lowercase     : List[Any]     	=  scheduler.timesteps[6]

         _lowercase     : int     	=  scheduler.step(_UpperCamelCase     ,							_UpperCamelCase     ,							_UpperCamelCase     ,							**_UpperCamelCase    ).prev_sample
         _lowercase     : Dict     	=  scheduler.step(_UpperCamelCase     ,							_UpperCamelCase     ,							_UpperCamelCase     ,							**_UpperCamelCase    ).prev_sample

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

         _lowercase     : Dict     	=  scheduler.step(_UpperCamelCase     ,							_UpperCamelCase     ,							_UpperCamelCase     ,							**_UpperCamelCase    ).prev_sample
         _lowercase     : Optional[int]     	=  scheduler.step(_UpperCamelCase     ,							_UpperCamelCase     ,							_UpperCamelCase     ,							**_UpperCamelCase    ).prev_sample

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


   def 		_lowerCamelCase  (					self    ):





      """simple docstring"""





      for timesteps in [100, 1000]:
         self.check_over_configs(num_train_timesteps=_UpperCamelCase     ,							time_step=_UpperCamelCase    )


   def 		_lowerCamelCase  (					self    ):





      """simple docstring"""





      for t, num_inference_steps in zip([1, 5, 10]     ,							[10, 50, 100]    ):
         self.check_over_forward(num_inference_steps=_UpperCamelCase     ,							time_step=_UpperCamelCase    )






   def 		_lowerCamelCase  (					self    ):





      """simple docstring"""





      _lowercase     : Any     	=  self.full_loop()
      _lowercase     : str     	=  torch.mean(torch.abs(_UpperCamelCase    )    )

      assert abs(result_mean.item() - 2540529    ) < 10










import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


UpperCAmelCase   :    Optional[Any]     =		logging.getLogger(__name__)







@dataclass
class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					Optional[float]			       =		field(
		    default=0.0			,							metadata={"help": "The label smoothing epsilon to apply (if not zero)."}  )
		UpperCamelCase     :					bool			       =		field(default=a_			,							metadata={"help": "Whether to SortishSamler or not."}  )
		UpperCamelCase     :					bool			       =		field(
		    default=a_			,							metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}  )
		UpperCamelCase     :					bool			       =		field(default=a_			,							metadata={"help": "whether to use adafactor"}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Encoder layer dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Decoder layer dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(default=a_			,							metadata={"help": "Dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Attention dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[str]			       =		field(
		    default="linear"			,							metadata={"help": f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()  )}'''}			,							)

