
import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest





class   UpperCamelCase_      (      UpperCAmelCase_		):







						'''simple docstring'''



						UpperCamelCase      :  int				   =					(DDPMScheduler,)





						def        SCREAMING_SNAKE_CASE(			self							:List[str]		,    **lowerCAmelCase__							:Optional[int]     )  ->Dict:
										lowercase         =      {
										    """num_train_timesteps""": 1000,
										    """beta_start""": 0.00_01,
										    """beta_end""": 0.02,
										    """beta_schedule""": """linear""",
										    """variance_type""": """fixed_small""",
										    """clip_sample""": True,
										}

										config.update(**SCREAMING_SNAKE_CASE_     )
										return config





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->List[str]:




										for timesteps in [1, 5, 100, 1000]:
														self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_     )





						def        SCREAMING_SNAKE_CASE(			self							:Dict     )  ->Any:




										for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]		,    [0.0_02, 0.02, 0.2, 2]     ):
														self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_		,    beta_end=SCREAMING_SNAKE_CASE_     )





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




										for schedule in ["linear", "squaredcos_cap_v2"]:
														self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_     )





						def        SCREAMING_SNAKE_CASE(			self							:Tuple     )  ->Union[str, Any]:




										for variance in ["fixed_small", "fixed_large", "other"]:
														self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE_     )





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




										for clip_sample in [True, False]:
														self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE_     )





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]     )  ->List[str]:
										self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE_     )
										for threshold in [0.5, 1.0, 2.0]:
														for prediction_type in ["epsilon", "sample", "v_prediction"]:
																		self.check_over_configs(
																		    thresholding=SCREAMING_SNAKE_CASE_		,    prediction_type=SCREAMING_SNAKE_CASE_		,    sample_max_value=SCREAMING_SNAKE_CASE_		,    )





						def        SCREAMING_SNAKE_CASE(			self							:Dict     )  ->Dict:




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





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]     )  ->List[Any]:




										for t in [0, 500, 999]:
														self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_     )





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]     )  ->int:
										lowercase         =      self.scheduler_classes[0]
										lowercase         =      self.get_scheduler_config()
										lowercase         =      scheduler_class(**SCREAMING_SNAKE_CASE_     )

										assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 0.0     )     ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(487     ) - 0.0_09_79     )     ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(999     ) - 0.02     )     ) < 1E-5





						def        SCREAMING_SNAKE_CASE(			self							:str     )  ->str:
										lowercase         =      self.scheduler_classes[0]
										lowercase         =      self.get_scheduler_config()
										lowercase         =      scheduler_class(**SCREAMING_SNAKE_CASE_     )

										lowercase         =      len(SCREAMING_SNAKE_CASE_     )

										lowercase         =      self.dummy_model()
										lowercase         =      self.dummy_sample_deter
										lowercase         =      torch.manual_seed(0     )

										for t in reversed(range(SCREAMING_SNAKE_CASE_     )     ):
														# 1. predict noise residual
														lowercase         =      model(SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_     )

														# 2. predict previous mean of sample x_t-1
														lowercase         =      scheduler.step(SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    generator=SCREAMING_SNAKE_CASE_     ).prev_sample

														# if t > 0:
														#     noise = self.dummy_sample_deter
														#     variance = scheduler.get_variance(t) ** (0.5) * noise
														#
														# sample = pred_prev_sample + variance
														lowercase         =      pred_prev_sample

										lowercase         =      torch.sum(torch.abs(SCREAMING_SNAKE_CASE_     )     )
										lowercase         =      torch.mean(torch.abs(SCREAMING_SNAKE_CASE_     )     )

										assert abs(result_sum.item() - 258.9606     ) < 1E-2
										assert abs(result_mean.item() - 0.33_72     ) < 1E-3





						def        SCREAMING_SNAKE_CASE(			self							:Tuple     )  ->List[str]:
										lowercase         =      self.scheduler_classes[0]
										lowercase         =      self.get_scheduler_config(prediction_type="v_prediction"     )
										lowercase         =      scheduler_class(**SCREAMING_SNAKE_CASE_     )

										lowercase         =      len(SCREAMING_SNAKE_CASE_     )

										lowercase         =      self.dummy_model()
										lowercase         =      self.dummy_sample_deter
										lowercase         =      torch.manual_seed(0     )

										for t in reversed(range(SCREAMING_SNAKE_CASE_     )     ):
														# 1. predict noise residual
														lowercase         =      model(SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_     )

														# 2. predict previous mean of sample x_t-1
														lowercase         =      scheduler.step(SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    generator=SCREAMING_SNAKE_CASE_     ).prev_sample

														# if t > 0:
														#     noise = self.dummy_sample_deter
														#     variance = scheduler.get_variance(t) ** (0.5) * noise
														#
														# sample = pred_prev_sample + variance
														lowercase         =      pred_prev_sample

										lowercase         =      torch.sum(torch.abs(SCREAMING_SNAKE_CASE_     )     )
										lowercase         =      torch.mean(torch.abs(SCREAMING_SNAKE_CASE_     )     )

										assert abs(result_sum.item() - 202.0296     ) < 1E-2
										assert abs(result_mean.item() - 0.26_31     ) < 1E-3





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]     )  ->Dict:
										lowercase         =      self.scheduler_classes[0]
										lowercase         =      self.get_scheduler_config()
										lowercase         =      scheduler_class(**SCREAMING_SNAKE_CASE_     )

										lowercase         =      [100, 87, 50, 1, 0]

										scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_     )

										lowercase         =      scheduler.timesteps

										for i, timestep in enumerate(SCREAMING_SNAKE_CASE_     ):
														if i == len(SCREAMING_SNAKE_CASE_     ) - 1:
																		lowercase         =      -1
														else:
																		lowercase         =      timesteps[i + 1]

														lowercase         =      scheduler.previous_timestep(SCREAMING_SNAKE_CASE_     )
														lowercase         =      prev_t.item()

														self.assertEqual(SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_     )





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]     )  ->str:
										lowercase         =      self.scheduler_classes[0]
										lowercase         =      self.get_scheduler_config()
										lowercase         =      scheduler_class(**SCREAMING_SNAKE_CASE_     )

										lowercase         =      [100, 87, 50, 51, 0]

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





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]     )  ->Dict:
										lowercase         =      self.scheduler_classes[0]
										lowercase         =      self.get_scheduler_config()
										lowercase         =      scheduler_class(**SCREAMING_SNAKE_CASE_     )

										lowercase         =      [100, 87, 50, 1, 0]
										lowercase         =      len(SCREAMING_SNAKE_CASE_     )

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





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->str:
										lowercase         =      self.scheduler_classes[0]
										lowercase         =      self.get_scheduler_config()
										lowercase         =      scheduler_class(**SCREAMING_SNAKE_CASE_     )

										lowercase         =      [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_     )


from collections import OrderedDict
from typing import Mapping

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


lowerCamelCase__     :		List[Any]        = logging.get_logger(__name__)

lowerCamelCase__     :		Union[str, Any]        = {
    """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    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  Union[str, Any]	   =      'convbert'


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_=3_05_22       ,	SCREAMING_SNAKE_CASE_=7_68       ,	SCREAMING_SNAKE_CASE_=12       ,	SCREAMING_SNAKE_CASE_=12       ,	SCREAMING_SNAKE_CASE_=30_72       ,	SCREAMING_SNAKE_CASE_="gelu"       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=5_12       ,	SCREAMING_SNAKE_CASE_=2       ,	SCREAMING_SNAKE_CASE_=0.0_2       ,	SCREAMING_SNAKE_CASE_=1E-12       ,	SCREAMING_SNAKE_CASE_=1       ,	SCREAMING_SNAKE_CASE_=0       ,	SCREAMING_SNAKE_CASE_=2       ,	SCREAMING_SNAKE_CASE_=7_68       ,	SCREAMING_SNAKE_CASE_=2       ,	SCREAMING_SNAKE_CASE_=9       ,	SCREAMING_SNAKE_CASE_=1       ,	SCREAMING_SNAKE_CASE_=None       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   super().__init__(
       pad_token_id=SCREAMING_SNAKE_CASE_       ,	bos_token_id=SCREAMING_SNAKE_CASE_       ,	eos_token_id=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_       ,	)

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



class    _snake_case (						UpperCAmelCase_				):


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   if self.task == "multiple-choice":
     lowercase__	: Union[str, Any]     		=   {0: """batch""", 1: """choice""", 2: """sequence"""}
   else:
     lowercase__	: str     		=   {0: """batch""", 1: """sequence"""}
   return OrderedDict(
       [
           ("""input_ids""", dynamic_axis),
           ("""attention_mask""", dynamic_axis),
           ("""token_type_ids""", dynamic_axis),
       ])






