


import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest


class        lowerCAmelCase_			(							SCREAMING_SNAKE_CASE_):
						lowerCamelCase_    =  (CMStochasticIterativeScheduler,)
						lowerCamelCase_    =  10




						def 			_snake_case       (						self    : int ,  **__A    : str    )   ->str:






									"""simple docstring"""






									a__			:str    	=     {
									    "num_train_timesteps": 201,
									    "sigma_min": 0.002,
									    "sigma_max": 80.0,
									}

									config.update(**lowercase_    )
									return config




						def 			_snake_case       (						self    : Union[str, Any]    )   ->List[Any]:






									"""simple docstring"""






									a__			:str    	=     10

									a__			:List[str]    	=     self.get_scheduler_config()
									a__			:Union[str, Any]    	=     self.scheduler_classes[0](**lowercase_    )

									scheduler.set_timesteps(lowercase_    )

									a__			:Optional[Any]    	=     scheduler.timesteps[0]
									a__			:int    	=     scheduler.timesteps[1]

									a__			:Tuple    	=     self.dummy_sample
									a__			:Tuple    	=     0.1 * sample

									a__			:Tuple    	=     scheduler.step(lowercase_ ,  lowercase_ ,  lowercase_    ).prev_sample
									a__			:Optional[Any]    	=     scheduler.step(lowercase_ ,  lowercase_ ,  lowercase_    ).prev_sample

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




						def 			_snake_case       (						self    : List[Any]    )   ->int:






									"""simple docstring"""






									for timesteps in [10, 50, 100, 1000]:
												self.check_over_configs(num_train_timesteps=lowercase_    )




						def 			_snake_case       (						self    : int    )   ->Optional[int]:






									"""simple docstring"""






									for clip_denoised in [True, False]:
												self.check_over_configs(clip_denoised=lowercase_    )




						def 			_snake_case       (						self    : List[Any]    )   ->Tuple:






									"""simple docstring"""






									a__			:Dict    	=     self.scheduler_classes[0]
									a__			:Dict    	=     self.get_scheduler_config()
									a__			:Tuple    	=     scheduler_class(**lowercase_    )

									a__			:List[Any]    	=     1
									scheduler.set_timesteps(lowercase_    )
									a__			:int    	=     scheduler.timesteps

									a__			:Any    	=     torch.manual_seed(0    )

									a__			:Tuple    	=     self.dummy_model()
									a__			:Any    	=     self.dummy_sample_deter * scheduler.init_noise_sigma

									for i, t in enumerate(lowercase_    ):
												# 1. scale model input
												a__			:Optional[Any]    	=     scheduler.scale_model_input(lowercase_ ,  lowercase_    )

												# 2. predict noise residual
												a__			:Optional[Any]    	=     model(lowercase_ ,  lowercase_    )

												# 3. predict previous sample x_t-1
												a__			:str    	=     scheduler.step(lowercase_ ,  lowercase_ ,  lowercase_ ,  generator=lowercase_    ).prev_sample

												a__			:Optional[int]    	=     pred_prev_sample

									a__			:List[Any]    	=     torch.sum(torch.abs(lowercase_    )    )
									a__			:Optional[Any]    	=     torch.mean(torch.abs(lowercase_    )    )

									assert abs(result_sum.item() - 192.7614    ) < 1E-2
									assert abs(result_mean.item() - 0.2_510    ) < 1E-3




						def 			_snake_case       (						self    : List[Any]    )   ->str:






									"""simple docstring"""






									a__			:str    	=     self.scheduler_classes[0]
									a__			:Any    	=     self.get_scheduler_config()
									a__			:List[str]    	=     scheduler_class(**lowercase_    )

									a__			:str    	=     [106, 0]
									scheduler.set_timesteps(timesteps=lowercase_    )
									a__			:List[str]    	=     scheduler.timesteps

									a__			:List[str]    	=     torch.manual_seed(0    )

									a__			:Tuple    	=     self.dummy_model()
									a__			:str    	=     self.dummy_sample_deter * scheduler.init_noise_sigma

									for t in timesteps:
												# 1. scale model input
												a__			:Optional[Any]    	=     scheduler.scale_model_input(lowercase_ ,  lowercase_    )

												# 2. predict noise residual
												a__			:Any    	=     model(lowercase_ ,  lowercase_    )

												# 3. predict previous sample x_t-1
												a__			:Optional[int]    	=     scheduler.step(lowercase_ ,  lowercase_ ,  lowercase_ ,  generator=lowercase_    ).prev_sample

												a__			:Optional[int]    	=     pred_prev_sample

									a__			:Any    	=     torch.sum(torch.abs(lowercase_    )    )
									a__			:Dict    	=     torch.mean(torch.abs(lowercase_    )    )

									assert abs(result_sum.item() - 347.6357    ) < 1E-2
									assert abs(result_mean.item() - 0.4_527    ) < 1E-3




						def 			_snake_case       (						self    : Dict    )   ->Optional[Any]:






									"""simple docstring"""






									a__			:str    	=     self.scheduler_classes[0]
									a__			:Tuple    	=     self.get_scheduler_config()
									a__			:Optional[Any]    	=     scheduler_class(**lowercase_    )

									a__			:Any    	=     [39, 30, 12, 15, 0]

									with self.assertRaises(lowercase_ ,  msg="`timesteps` must be in descending order."    ):
												scheduler.set_timesteps(timesteps=lowercase_    )




						def 			_snake_case       (						self    : List[str]    )   ->Optional[Any]:






									"""simple docstring"""






									a__			:Any    	=     self.scheduler_classes[0]
									a__			:Union[str, Any]    	=     self.get_scheduler_config()
									a__			:Dict    	=     scheduler_class(**lowercase_    )

									a__			:str    	=     [39, 30, 12, 1, 0]
									a__			:Optional[Any]    	=     len(lowercase_    )

									with self.assertRaises(lowercase_ ,  msg="Can only pass one of `num_inference_steps` or `timesteps`."    ):
												scheduler.set_timesteps(num_inference_steps=lowercase_ ,  timesteps=lowercase_    )






						def 			_snake_case       (						self    : Dict    )   ->Union[str, Any]:






									"""simple docstring"""






									a__			:Any    	=     self.scheduler_classes[0]
									a__			:List[Any]    	=     self.get_scheduler_config()
									a__			:Any    	=     scheduler_class(**lowercase_    )

									a__			:List[str]    	=     [scheduler.config.num_train_timesteps]

									with self.assertRaises(
									    lowercase_ ,  msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}" ,  ):
												scheduler.set_timesteps(timesteps=lowercase_    )




"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE_            =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            =    {
    '''kssteven/ibert-roberta-base''': '''https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json''',
    '''kssteven/ibert-roberta-large''': '''https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json''',
    '''kssteven/ibert-roberta-large-mnli''': (
        '''https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'''
    ),
}
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):
      __SCREAMING_SNAKE_CASE :				Tuple				   =						"ibert"




      def __init__(				self ,							lowercase_=3_0_5_2_2 ,							lowercase_=7_6_8 ,							lowercase_=1_2 ,							lowercase_=1_2 ,							lowercase_=3_0_7_2 ,							lowercase_="gelu" ,							lowercase_=0.1 ,							lowercase_=0.1 ,							lowercase_=5_1_2 ,							lowercase_=2 ,							lowercase_=0.0_2 ,							lowercase_=1E-12 ,							lowercase_=1 ,							lowercase_=0 ,							lowercase_=2 ,							lowercase_="absolute" ,							lowercase_=False ,							lowercase_="none" ,							**lowercase_ ,							)    ->       Optional[int]:
          super().__init__(pad_token_id=lowercase_ ,							bos_token_id=lowercase_ ,							eos_token_id=lowercase_ ,							**lowercase_				)

          UpperCAmelCase          =       vocab_size
          UpperCAmelCase          =       hidden_size
          UpperCAmelCase          =       num_hidden_layers
          UpperCAmelCase          =       num_attention_heads
          UpperCAmelCase          =       hidden_act
          UpperCAmelCase          =       intermediate_size
          UpperCAmelCase          =       hidden_dropout_prob
          UpperCAmelCase          =       attention_probs_dropout_prob
          UpperCAmelCase          =       max_position_embeddings
          UpperCAmelCase          =       type_vocab_size
          UpperCAmelCase          =       initializer_range
          UpperCAmelCase          =       layer_norm_eps
          UpperCAmelCase          =       position_embedding_type
          UpperCAmelCase          =       quant_mode
          UpperCAmelCase          =       force_dequant

class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):




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



