



import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest



class 							lowerCAmelCase_						(    A_				):
       UpperCAmelCase__							:   Dict  	=    (CMStochasticIterativeScheduler,)
       UpperCAmelCase__							:   Optional[Any]  	=    10
       def    snake_case_			(     self,     **SCREAMING_SNAKE_CASE_			)	->						Dict:
          UpperCamelCase       :    Tuple		      =      {
              """num_train_timesteps""": 201,
              """sigma_min""": 0.0_02,
              """sigma_max""": 80.0,
          }

          config.update(**SCREAMING_SNAKE_CASE_			)
          return config
       def    snake_case_			(     self			)	->						Tuple:
          UpperCamelCase       :    List[str]		      =      10

          UpperCamelCase       :    Tuple		      =      self.get_scheduler_config()
          UpperCamelCase       :    int		      =      self.scheduler_classes[0](**SCREAMING_SNAKE_CASE_			)

          scheduler.set_timesteps(SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    Optional[Any]		      =      scheduler.timesteps[0]
          UpperCamelCase       :    Union[str, Any]		      =      scheduler.timesteps[1]

          UpperCamelCase       :    int		      =      self.dummy_sample
          UpperCamelCase       :    Optional[Any]		      =      0.1 * sample

          UpperCamelCase       :    str		      =      scheduler.step(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			).prev_sample
          UpperCamelCase       :    List[str]		      =      scheduler.step(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    snake_case_			(     self			)	->						Union[str, Any]:
          for timesteps in [10, 50, 100, 1000]:
             self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self			)	->						Union[str, Any]:
          for clip_denoised in [True, False]:
             self.check_over_configs(clip_denoised=SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self			)	->						List[str]:
          UpperCamelCase       :    Optional[int]		      =      self.scheduler_classes[0]
          UpperCamelCase       :    Dict		      =      self.get_scheduler_config()
          UpperCamelCase       :    Optional[int]		      =      scheduler_class(**SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    Any		      =      1
          scheduler.set_timesteps(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    int		      =      scheduler.timesteps

          UpperCamelCase       :    Optional[int]		      =      torch.manual_seed(0			)

          UpperCamelCase       :    Optional[Any]		      =      self.dummy_model()
          UpperCamelCase       :    int		      =      self.dummy_sample_deter * scheduler.init_noise_sigma

          for i, t in enumerate(SCREAMING_SNAKE_CASE_			):
             # 1. scale model input
             UpperCamelCase       :    Tuple		      =      scheduler.scale_model_input(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)

             # 2. predict noise residual
             UpperCamelCase       :    Optional[int]		      =      model(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)

             # 3. predict previous sample x_t-1
             UpperCamelCase       :    Optional[Any]		      =      scheduler.step(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     generator=SCREAMING_SNAKE_CASE_			).prev_sample

             UpperCamelCase       :    List[Any]		      =      pred_prev_sample

          UpperCamelCase       :    List[str]		      =      torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
          UpperCamelCase       :    Optional[int]		      =      torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

          assert abs(result_sum.item() - 192.7614			) < 1e-2
          assert abs(result_mean.item() - 0.25_10			) < 1e-3
       def    snake_case_			(     self			)	->						Tuple:
          UpperCamelCase       :    Any		      =      self.scheduler_classes[0]
          UpperCamelCase       :    Optional[Any]		      =      self.get_scheduler_config()
          UpperCamelCase       :    str		      =      scheduler_class(**SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    Dict		      =      [106, 0]
          scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Dict		      =      scheduler.timesteps

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

          UpperCamelCase       :    List[Any]		      =      self.dummy_model()
          UpperCamelCase       :    List[Any]		      =      self.dummy_sample_deter * scheduler.init_noise_sigma

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

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

             # 3. predict previous sample x_t-1
             UpperCamelCase       :    Dict		      =      scheduler.step(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     generator=SCREAMING_SNAKE_CASE_			).prev_sample

             UpperCamelCase       :    Union[str, Any]		      =      pred_prev_sample

          UpperCamelCase       :    str		      =      torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
          UpperCamelCase       :    Optional[int]		      =      torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

          assert abs(result_sum.item() - 347.6357			) < 1e-2
          assert abs(result_mean.item() - 0.45_27			) < 1e-3
       def    snake_case_			(     self			)	->						List[Any]:
          UpperCamelCase       :    List[str]		      =      self.scheduler_classes[0]
          UpperCamelCase       :    List[str]		      =      self.get_scheduler_config()
          UpperCamelCase       :    Optional[Any]		      =      scheduler_class(**SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    int		      =      [39, 30, 12, 15, 0]

          with self.assertRaises(SCREAMING_SNAKE_CASE_,     msg='`timesteps` must be in descending order.'			):
             scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self			)	->						Union[str, Any]:
          UpperCamelCase       :    Any		      =      self.scheduler_classes[0]
          UpperCamelCase       :    Optional[int]		      =      self.get_scheduler_config()
          UpperCamelCase       :    List[str]		      =      scheduler_class(**SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    List[Any]		      =      [39, 30, 12, 1, 0]
          UpperCamelCase       :    Any		      =      len(SCREAMING_SNAKE_CASE_			)

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






       def    snake_case_			(     self			)	->						Tuple:
          UpperCamelCase       :    Union[str, Any]		      =      self.scheduler_classes[0]
          UpperCamelCase       :    Optional[int]		      =      self.get_scheduler_config()
          UpperCamelCase       :    Tuple		      =      scheduler_class(**SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    int		      =      [scheduler.config.num_train_timesteps]

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







"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase				=						logging.get_logger(__name__)

UpperCAmelCase				=						{
    """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 	lowercase__				(					A_				):
   __UpperCAmelCase							       =					'''ibert'''





   def __init__(							self      ,   SCREAMING_SNAKE_CASE=3_0522      ,   SCREAMING_SNAKE_CASE=768      ,   SCREAMING_SNAKE_CASE=12      ,   SCREAMING_SNAKE_CASE=12      ,   SCREAMING_SNAKE_CASE=3072      ,   SCREAMING_SNAKE_CASE="gelu"      ,   SCREAMING_SNAKE_CASE=0.1      ,   SCREAMING_SNAKE_CASE=0.1      ,   SCREAMING_SNAKE_CASE=512      ,   SCREAMING_SNAKE_CASE=2      ,   SCREAMING_SNAKE_CASE=0.02      ,   SCREAMING_SNAKE_CASE=1e-1_2      ,   SCREAMING_SNAKE_CASE=1      ,   SCREAMING_SNAKE_CASE=0      ,   SCREAMING_SNAKE_CASE=2      ,   SCREAMING_SNAKE_CASE="absolute"      ,   SCREAMING_SNAKE_CASE=False      ,   SCREAMING_SNAKE_CASE="none"      ,   **SCREAMING_SNAKE_CASE      ,   )  ->  Any:
      super().__init__(pad_token_id=SCREAMING_SNAKE_CASE      ,   bos_token_id=SCREAMING_SNAKE_CASE      ,   eos_token_id=SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Optional[int]   =		vocab_size
      _lowerCamelCase	:					Dict   =		hidden_size
      _lowerCamelCase	:					List[str]   =		num_hidden_layers
      _lowerCamelCase	:					int   =		num_attention_heads
      _lowerCamelCase	:					Tuple   =		hidden_act
      _lowerCamelCase	:					str   =		intermediate_size
      _lowerCamelCase	:					Union[str, Any]   =		hidden_dropout_prob
      _lowerCamelCase	:					Tuple   =		attention_probs_dropout_prob
      _lowerCamelCase	:					Any   =		max_position_embeddings
      _lowerCamelCase	:					Dict   =		type_vocab_size
      _lowerCamelCase	:					List[Any]   =		initializer_range
      _lowerCamelCase	:					Dict   =		layer_norm_eps
      _lowerCamelCase	:					List[Any]   =		position_embedding_type
      _lowerCamelCase	:					Any   =		quant_mode
      _lowerCamelCase	:					List[str]   =		force_dequant







class 	lowercase__				(					A_				):





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

