


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							Dict	   =       (UniPCMultistepScheduler,)
     __lowercase				:							Dict	   =       (('num_inference_steps', 25),)

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		**_a:int   ):
       snake_case__						= {
           '''num_train_timesteps''': 10_00,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
           '''solver_order''': 2,
           '''solver_type''': '''bh2''',
       }

       config.update(**_a   )
       return config

     def 	SCREAMING_SNAKE_CASE__						(					self:str     ,		_a:Dict=0     ,		**_a:Tuple   ):
       snake_case__						= dict(self.forward_default_kwargs   )
       snake_case__						= kwargs.pop('''num_inference_steps'''     ,		_a   )
       snake_case__						= self.dummy_sample
       snake_case__						= 0.1 * sample
       snake_case__						= [residual + 0.2, residual + 0.15, residual + 0.10]

       for scheduler_class in self.scheduler_classes:
         snake_case__						= self.get_scheduler_config(**_a   )
         snake_case__						= scheduler_class(**_a   )
         scheduler.set_timesteps(_a   )
         # copy over dummy past residuals
         snake_case__						= dummy_past_residuals[: scheduler.config.solver_order]

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

         snake_case__				,				snake_case__						= sample, sample
         for t in range(_a     ,		time_step + scheduler.config.solver_order + 1   ):
           snake_case__						= scheduler.step(_a     ,		_a     ,		_a     ,		**_a   ).prev_sample
           snake_case__						= 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:Dict     ,		_a:str=0     ,		**_a:List[str]   ):
       snake_case__						= dict(self.forward_default_kwargs   )
       snake_case__						= kwargs.pop('''num_inference_steps'''     ,		_a   )
       snake_case__						= self.dummy_sample
       snake_case__						= 0.1 * sample
       snake_case__						= [residual + 0.2, residual + 0.15, residual + 0.10]

       for scheduler_class in self.scheduler_classes:
         snake_case__						= self.get_scheduler_config()
         snake_case__						= scheduler_class(**_a   )
         scheduler.set_timesteps(_a   )

         # copy over dummy past residuals (must be after setting timesteps)
         snake_case__						= dummy_past_residuals[: scheduler.config.solver_order]

         with tempfile.TemporaryDirectory() as tmpdirname:
           scheduler.save_config(_a   )
           snake_case__						= 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)
           snake_case__						= dummy_past_residuals[: new_scheduler.config.solver_order]

         snake_case__						= scheduler.step(_a     ,		_a     ,		_a     ,		**_a   ).prev_sample
         snake_case__						= 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:Tuple     ,		_a:List[Any]=None     ,		**_a:List[str]   ):
       if scheduler is None:
         snake_case__						= self.scheduler_classes[0]
         snake_case__						= self.get_scheduler_config(**_a   )
         snake_case__						= scheduler_class(**_a   )

       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config(**_a   )
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= 10
       snake_case__						= self.dummy_model()
       snake_case__						= self.dummy_sample_deter
       scheduler.set_timesteps(_a   )

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

       return sample

     def 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       snake_case__						= dict(self.forward_default_kwargs   )

       snake_case__						= kwargs.pop('''num_inference_steps'''     ,		_a   )

       for scheduler_class in self.scheduler_classes:
         snake_case__						= self.get_scheduler_config()
         snake_case__						= scheduler_class(**_a   )

         snake_case__						= self.dummy_sample
         snake_case__						= 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'''   ):
           snake_case__						= num_inference_steps

         # copy over dummy past residuals (must be done after set_timesteps)
         snake_case__						= [residual + 0.2, residual + 0.15, residual + 0.10]
         snake_case__						= dummy_past_residuals[: scheduler.config.solver_order]

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

         snake_case__						= scheduler.step(_a     ,		_a     ,		_a     ,		**_a   ).prev_sample
         snake_case__						= 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:int   ):
       # make sure that iterating over schedulers with same config names gives same results
       # for defaults
       snake_case__						= UniPCMultistepScheduler(**self.get_scheduler_config()   )
       snake_case__						= self.full_loop(scheduler=_a   )
       snake_case__						= torch.mean(torch.abs(_a   )   )

       assert abs(result_mean.item() - 0.2464   ) < 1e-3

       snake_case__						= DPMSolverSinglestepScheduler.from_config(scheduler.config   )
       snake_case__						= DEISMultistepScheduler.from_config(scheduler.config   )
       snake_case__						= DPMSolverMultistepScheduler.from_config(scheduler.config   )
       snake_case__						= UniPCMultistepScheduler.from_config(scheduler.config   )

       snake_case__						= self.full_loop(scheduler=_a   )
       snake_case__						= torch.mean(torch.abs(_a   )   )

       assert abs(result_mean.item() - 0.2464   ) < 1e-3

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       for timesteps in [25, 50, 1_00, 9_99, 10_00]:
         self.check_over_configs(num_train_timesteps=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       self.check_over_configs(thresholding=_a   )
       for order in [1, 2, 3]:
         for solver_type in ["bh1", "bh2"]:
           for threshold in [0.5, 1.0, 2.0]:
             for prediction_type in ["epsilon", "sample"]:
               self.check_over_configs(
                   thresholding=_a     ,		prediction_type=_a     ,		sample_max_value=_a     ,		solver_order=_a     ,		solver_type=_a     ,		)

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

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       for solver_type in ["bh1", "bh2"]:
         for order in [1, 2, 3]:
           for prediction_type in ["epsilon", "sample"]:
             self.check_over_configs(
                 solver_order=_a     ,		solver_type=_a     ,		prediction_type=_a     ,		)
             snake_case__						= self.full_loop(
                 solver_order=_a     ,		solver_type=_a     ,		prediction_type=_a     ,		)
             assert not torch.isnan(_a   ).any(), "Samples have nan numbers"

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       self.check_over_configs(lower_order_final=_a   )
       self.check_over_configs(lower_order_final=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
         self.check_over_forward(num_inference_steps=_a     ,		time_step=0   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       snake_case__						= self.full_loop()
       snake_case__						= torch.mean(torch.abs(_a   )   )

       assert abs(result_mean.item() - 0.2464   ) < 1e-3

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       snake_case__						= self.full_loop(prediction_type='''v_prediction'''   )
       snake_case__						= torch.mean(torch.abs(_a   )   )

       assert abs(result_mean.item() - 0.1014   ) < 1e-3

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config(thresholding=_a     ,		dynamic_thresholding_ratio=0   )
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= 10
       snake_case__						= self.dummy_model()
       snake_case__						= self.dummy_sample_deter.half()
       scheduler.set_timesteps(_a   )

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

       assert sample.dtype == torch.floataa







     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]     ,		**_a:List[str]   ):
       for scheduler_class in self.scheduler_classes:
         snake_case__						= self.get_scheduler_config(**_a   )
         snake_case__						= scheduler_class(**_a   )

         scheduler.set_timesteps(scheduler.config.num_train_timesteps   )
         assert len(scheduler.timesteps.unique()   ) == scheduler.num_inference_steps





import os
import sys

lowerCamelCase__							:						Optional[int]							     =   os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


lowerCamelCase__							:						Optional[int]							     =   [
    """torch""",
    """numpy""",
    """tokenizers""",
    """filelock""",
    """requests""",
    """tqdm""",
    """regex""",
    """sentencepiece""",
    """sacremoses""",
    """importlib_metadata""",
    """huggingface_hub""",
]







@add_start_docstrings(AutoConfig.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				Any:
  return AutoConfig.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoTokenizer.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				List[str]:
  return AutoTokenizer.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoModel.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				Tuple:
  return AutoModel.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoModelForCausalLM.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				Union[str, Any]:
  return AutoModelForCausalLM.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoModelForMaskedLM.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				List[Any]:
  return AutoModelForMaskedLM.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoModelForSequenceClassification.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				List[str]:
  return AutoModelForSequenceClassification.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoModelForQuestionAnswering.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				Union[str, Any]:
  return AutoModelForQuestionAnswering.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)

