


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest





class  _a							(    lowerCamelCase__):
	_a  :  Any				   =							(UniPCMultistepScheduler,)
	_a  :  int				   =							(('''num_inference_steps''', 25),)




	def 							UpperCAmelCase__( self						:		Any		, **_SCREAMING_SNAKE_CASE						:		Tuple		)->	Dict:
			lowerCAmelCase__       :      Optional[int]   					= {
			    "num_train_timesteps": 1000,
			    "beta_start": 0.0001,
			    "beta_end": 0.02,
			    "beta_schedule": "linear",
			    "solver_order": 2,
			    "solver_type": "bh2",
			}

			config.update(**_SCREAMING_SNAKE_CASE		)
			return config




	def 							UpperCAmelCase__( self						:		Tuple		, _SCREAMING_SNAKE_CASE						:		int=0		, **_SCREAMING_SNAKE_CASE						:		str		)->	int:
			lowerCAmelCase__       :      List[str]   					= dict(self.forward_default_kwargs		)
			lowerCAmelCase__       :      Optional[int]   					= kwargs.pop('''num_inference_steps'''		, _SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Optional[Any]   					= self.dummy_sample
			lowerCAmelCase__       :      int   					= 0.1 * sample
			lowerCAmelCase__       :      str   					= [residual + 0.2, residual + 0.15, residual + 0.10]

			for scheduler_class in self.scheduler_classes:
					lowerCAmelCase__       :      int   					= self.get_scheduler_config(**_SCREAMING_SNAKE_CASE		)
					lowerCAmelCase__       :      Dict   					= scheduler_class(**_SCREAMING_SNAKE_CASE		)
					scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)
					# copy over dummy past residuals
					lowerCAmelCase__       :      Any   					= dummy_past_residuals[: scheduler.config.solver_order]

					with tempfile.TemporaryDirectory() as tmpdirname:
							scheduler.save_config(_SCREAMING_SNAKE_CASE		)
							lowerCAmelCase__       :      Union[str, Any]   					= scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE		)
							new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)
							# copy over dummy past residuals
							lowerCAmelCase__       :      Optional[int]   					= dummy_past_residuals[: new_scheduler.config.solver_order]

					lowerCAmelCase__       :      int   					= sample, sample
					for t in range(_SCREAMING_SNAKE_CASE		, time_step + scheduler.config.solver_order + 1		):
							lowerCAmelCase__       :      Any   					= scheduler.step(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		).prev_sample
							lowerCAmelCase__       :      int   					= new_scheduler.step(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		).prev_sample

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




	def 							UpperCAmelCase__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=0		, **_SCREAMING_SNAKE_CASE						:		Tuple		)->	str:
			lowerCAmelCase__       :      Optional[int]   					= dict(self.forward_default_kwargs		)
			lowerCAmelCase__       :      str   					= kwargs.pop('''num_inference_steps'''		, _SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Any   					= self.dummy_sample
			lowerCAmelCase__       :      List[str]   					= 0.1 * sample
			lowerCAmelCase__       :      Dict   					= [residual + 0.2, residual + 0.15, residual + 0.10]

			for scheduler_class in self.scheduler_classes:
					lowerCAmelCase__       :      Any   					= self.get_scheduler_config()
					lowerCAmelCase__       :      Dict   					= scheduler_class(**_SCREAMING_SNAKE_CASE		)
					scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)

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

					with tempfile.TemporaryDirectory() as tmpdirname:
							scheduler.save_config(_SCREAMING_SNAKE_CASE		)
							lowerCAmelCase__       :      Union[str, Any]   					= scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE		)
							# copy over dummy past residuals
							new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)

							# copy over dummy past residual (must be after setting timesteps)
							lowerCAmelCase__       :      Optional[int]   					= dummy_past_residuals[: new_scheduler.config.solver_order]

					lowerCAmelCase__       :      str   					= scheduler.step(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		).prev_sample
					lowerCAmelCase__       :      List[Any]   					= new_scheduler.step(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		).prev_sample

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




	def 							UpperCAmelCase__( self						:		Tuple		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=None		, **_SCREAMING_SNAKE_CASE						:		Tuple		)->	int:




			if scheduler is None:
					lowerCAmelCase__       :      Any   					= self.scheduler_classes[0]
					lowerCAmelCase__       :      Optional[int]   					= self.get_scheduler_config(**_SCREAMING_SNAKE_CASE		)
					lowerCAmelCase__       :      Tuple   					= scheduler_class(**_SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      List[Any]   					= self.scheduler_classes[0]
			lowerCAmelCase__       :      int   					= self.get_scheduler_config(**_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      List[str]   					= scheduler_class(**_SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      Tuple   					= 10
			lowerCAmelCase__       :      int   					= self.dummy_model()
			lowerCAmelCase__       :      int   					= self.dummy_sample_deter
			scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)

			for i, t in enumerate(scheduler.timesteps		):
					lowerCAmelCase__       :      Any   					= model(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)
					lowerCAmelCase__       :      Tuple   					= scheduler.step(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		).prev_sample

			return sample




	def 							UpperCAmelCase__( self						:		Union[str, Any]		)->	int:
			lowerCAmelCase__       :      Optional[Any]   					= dict(self.forward_default_kwargs		)

			lowerCAmelCase__       :      List[Any]   					= kwargs.pop('''num_inference_steps'''		, _SCREAMING_SNAKE_CASE		)

			for scheduler_class in self.scheduler_classes:
					lowerCAmelCase__       :      Union[str, Any]   					= self.get_scheduler_config()
					lowerCAmelCase__       :      List[Any]   					= scheduler_class(**_SCREAMING_SNAKE_CASE		)

					lowerCAmelCase__       :      Tuple   					= self.dummy_sample
					lowerCAmelCase__       :      Optional[int]   					= 0.1 * sample

					if num_inference_steps is not None and hasattr(_SCREAMING_SNAKE_CASE		, '''set_timesteps'''		):
							scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)
					elif num_inference_steps is not None and not hasattr(_SCREAMING_SNAKE_CASE		, '''set_timesteps'''		):
							lowerCAmelCase__       :      Union[str, Any]   					= num_inference_steps

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

					lowerCAmelCase__       :      Dict   					= scheduler.timesteps[5]
					lowerCAmelCase__       :      str   					= scheduler.timesteps[6]

					lowerCAmelCase__       :      Union[str, Any]   					= scheduler.step(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		).prev_sample
					lowerCAmelCase__       :      Optional[Any]   					= scheduler.step(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		).prev_sample

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




	def 							UpperCAmelCase__( self						:		Any		)->	Any:
			lowerCAmelCase__       :      int   					= UniPCMultistepScheduler(**self.get_scheduler_config()		)
			lowerCAmelCase__       :      Union[str, Any]   					= self.full_loop(scheduler=_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Optional[Any]   					= torch.mean(torch.abs(_SCREAMING_SNAKE_CASE		)		)

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

			lowerCAmelCase__       :      List[str]   					= DPMSolverSinglestepScheduler.from_config(scheduler.config		)
			lowerCAmelCase__       :      str   					= DEISMultistepScheduler.from_config(scheduler.config		)
			lowerCAmelCase__       :      List[str]   					= DPMSolverMultistepScheduler.from_config(scheduler.config		)
			lowerCAmelCase__       :      Dict   					= UniPCMultistepScheduler.from_config(scheduler.config		)

			lowerCAmelCase__       :      Optional[int]   					= self.full_loop(scheduler=_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Optional[int]   					= torch.mean(torch.abs(_SCREAMING_SNAKE_CASE		)		)

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




	def 							UpperCAmelCase__( self						:		Tuple		)->	Dict:




			for timesteps in [25, 50, 100, 999, 1000]:
					self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		Tuple		)->	Tuple:
			self.check_over_configs(thresholding=_SCREAMING_SNAKE_CASE		)
			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=_SCREAMING_SNAKE_CASE		, prediction_type=_SCREAMING_SNAKE_CASE		, sample_max_value=_SCREAMING_SNAKE_CASE		, solver_order=_SCREAMING_SNAKE_CASE		, solver_type=_SCREAMING_SNAKE_CASE		, )




	def 							UpperCAmelCase__( self						:		Any		)->	Dict:




			for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		Optional[int]		)->	Tuple:




			for solver_type in ["bh1", "bh2"]:
					for order in [1, 2, 3]:
							for prediction_type in ["epsilon", "sample"]:
									self.check_over_configs(
									    solver_order=_SCREAMING_SNAKE_CASE		, solver_type=_SCREAMING_SNAKE_CASE		, prediction_type=_SCREAMING_SNAKE_CASE		, )
									lowerCAmelCase__       :      Dict   					= self.full_loop(
									    solver_order=_SCREAMING_SNAKE_CASE		, solver_type=_SCREAMING_SNAKE_CASE		, prediction_type=_SCREAMING_SNAKE_CASE		, )
									assert not torch.isnan(_SCREAMING_SNAKE_CASE		).any(), "Samples have nan numbers"




	def 							UpperCAmelCase__( self						:		List[Any]		)->	Optional[int]:
			self.check_over_configs(lower_order_final=_SCREAMING_SNAKE_CASE		)
			self.check_over_configs(lower_order_final=_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		Tuple		)->	str:




			for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
					self.check_over_forward(num_inference_steps=_SCREAMING_SNAKE_CASE		, time_step=0		)




	def 							UpperCAmelCase__( self						:		Tuple		)->	Optional[Any]:
			lowerCAmelCase__       :      Union[str, Any]   					= self.full_loop()
			lowerCAmelCase__       :      List[str]   					= torch.mean(torch.abs(_SCREAMING_SNAKE_CASE		)		)

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




	def 							UpperCAmelCase__( self						:		Any		)->	Tuple:
			lowerCAmelCase__       :      Optional[Any]   					= self.full_loop(prediction_type='''v_prediction'''		)
			lowerCAmelCase__       :      str   					= torch.mean(torch.abs(_SCREAMING_SNAKE_CASE		)		)

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




	def 							UpperCAmelCase__( self						:		str		)->	Optional[Any]:
			lowerCAmelCase__       :      Optional[int]   					= self.scheduler_classes[0]
			lowerCAmelCase__       :      Union[str, Any]   					= self.get_scheduler_config(thresholding=_SCREAMING_SNAKE_CASE		, dynamic_thresholding_ratio=0		)
			lowerCAmelCase__       :      Any   					= scheduler_class(**_SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      Optional[Any]   					= 10
			lowerCAmelCase__       :      Tuple   					= self.dummy_model()
			lowerCAmelCase__       :      Optional[int]   					= self.dummy_sample_deter.half()
			scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)

			for i, t in enumerate(scheduler.timesteps		):
					lowerCAmelCase__       :      Tuple   					= model(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)
					lowerCAmelCase__       :      List[str]   					= scheduler.step(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		).prev_sample

			assert sample.dtype == torch.floataa




	def 							UpperCAmelCase__( self						:		Dict		, **_SCREAMING_SNAKE_CASE						:		Optional[Any]		)->	Tuple:




			for scheduler_class in self.scheduler_classes:
					lowerCAmelCase__       :      List[str]   					= self.get_scheduler_config(**_SCREAMING_SNAKE_CASE		)
					lowerCAmelCase__       :      List[Any]   					= scheduler_class(**_SCREAMING_SNAKE_CASE		)

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








from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase						=		logging.get_logger(__name__)

lowerCamelCase						=		{
    '''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json''',
    '''YituTech/conv-bert-medium-small''': (
        '''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'''
    ),
    '''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json''',
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}





class  _a							(    _lowercase):
	_a  :  Dict				   =							'''convbert'''




	def __init__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		str=3_0522		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=768		, _SCREAMING_SNAKE_CASE						:		Tuple=12		, _SCREAMING_SNAKE_CASE						:		str=12		, _SCREAMING_SNAKE_CASE						:		Any=3072		, _SCREAMING_SNAKE_CASE						:		List[Any]="gelu"		, _SCREAMING_SNAKE_CASE						:		Dict=0.1		, _SCREAMING_SNAKE_CASE						:		List[str]=0.1		, _SCREAMING_SNAKE_CASE						:		int=512		, _SCREAMING_SNAKE_CASE						:		Any=2		, _SCREAMING_SNAKE_CASE						:		Optional[int]=0.02		, _SCREAMING_SNAKE_CASE						:		Optional[int]=1E-12		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=1		, _SCREAMING_SNAKE_CASE						:		Optional[int]=0		, _SCREAMING_SNAKE_CASE						:		Dict=2		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=768		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=2		, _SCREAMING_SNAKE_CASE						:		Tuple=9		, _SCREAMING_SNAKE_CASE						:		int=1		, _SCREAMING_SNAKE_CASE						:		Tuple=None		, **_SCREAMING_SNAKE_CASE						:		List[str]		, )->	List[str]:
			super().__init__(
			    pad_token_id=_SCREAMING_SNAKE_CASE		, bos_token_id=_SCREAMING_SNAKE_CASE		, eos_token_id=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		, )

			lowerCAmelCase__       :      Union[str, Any]   					= vocab_size
			lowerCAmelCase__       :      List[Any]   					= hidden_size
			lowerCAmelCase__       :      Tuple   					= num_hidden_layers
			lowerCAmelCase__       :      Union[str, Any]   					= num_attention_heads
			lowerCAmelCase__       :      Optional[Any]   					= intermediate_size
			lowerCAmelCase__       :      Tuple   					= hidden_act
			lowerCAmelCase__       :      Dict   					= hidden_dropout_prob
			lowerCAmelCase__       :      str   					= attention_probs_dropout_prob
			lowerCAmelCase__       :      str   					= max_position_embeddings
			lowerCAmelCase__       :      Tuple   					= type_vocab_size
			lowerCAmelCase__       :      Optional[Any]   					= initializer_range
			lowerCAmelCase__       :      List[Any]   					= layer_norm_eps
			lowerCAmelCase__       :      int   					= embedding_size
			lowerCAmelCase__       :      Union[str, Any]   					= head_ratio
			lowerCAmelCase__       :      Optional[int]   					= conv_kernel_size
			lowerCAmelCase__       :      List[str]   					= num_groups
			lowerCAmelCase__       :      Dict   					= classifier_dropout





class  _a							(    _lowercase):




	@property
	def 							UpperCAmelCase__( self						:		Tuple		)->	Mapping[str, Mapping[int, str]]:
			if self.task == "multiple-choice":
					lowerCAmelCase__       :      Optional[Any]   					= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
			else:
					lowerCAmelCase__       :      List[str]   					= {0: '''batch''', 1: '''sequence'''}
			return OrderedDict(
			    [
			        ('''input_ids''', dynamic_axis),
			        ('''attention_mask''', dynamic_axis),
			        ('''token_type_ids''', dynamic_axis),
			    ]		)




