
'''simple docstring'''
import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class  __A				( _SCREAMING_SNAKE_CASE			):
 lowerCamelCase				       =(DDIMParallelScheduler,)
 lowerCamelCase				       =(('''eta''', 0.0), ('''num_inference_steps''', 50))
 def 		lowercase_(   self			:    Any				,   **lowerCamelCase			:    int       ):



   """simple docstring"""





   __A		:     int												=		{
       """num_train_timesteps""": 10_00,
       """beta_start""": 0.0_001,
       """beta_end""": 0.02,
       """beta_schedule""": """linear""",
       """clip_sample""": True,
   }

   config.update(**lowerCamelCase       )
   return config
 def 		lowercase_(   self			:    List[Any]				,   **lowerCamelCase			:    Tuple       ):



   """simple docstring"""





   __A		:     int												=		self.scheduler_classes[0]
   __A		:     int												=		self.get_scheduler_config(**lowerCamelCase       )
   __A		:     Any												=		scheduler_class(**lowerCamelCase       )

   __A							,					__A		:     List[str]												=		10, 0.0

   __A		:     Dict												=		self.dummy_model()
   __A		:     List[Any]												=		self.dummy_sample_deter

   scheduler.set_timesteps(lowerCamelCase       )

   for t in scheduler.timesteps:
     __A		:     Union[str, Any]												=		model(lowerCamelCase				,   lowerCamelCase       )
     __A		:     Optional[int]												=		scheduler.step(lowerCamelCase				,   lowerCamelCase				,   lowerCamelCase				,   lowerCamelCase       ).prev_sample

   return sample
 def 		lowercase_(   self			:    int       ):



   """simple docstring"""





   for timesteps in [1_00, 5_00, 10_00]:
     self.check_over_configs(num_train_timesteps=lowerCamelCase       )
 def 		lowercase_(   self			:    int       ):



   """simple docstring"""





   for steps_offset in [0, 1]:
     self.check_over_configs(steps_offset=lowerCamelCase       )

   __A		:     Any												=		self.scheduler_classes[0]
   __A		:     List[str]												=		self.get_scheduler_config(steps_offset=1       )
   __A		:     Dict												=		scheduler_class(**lowerCamelCase       )
   scheduler.set_timesteps(5       )
   assert torch.equal(scheduler.timesteps				,   torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]       )       )
 def 		lowercase_(   self			:    Dict       ):



   """simple docstring"""





   for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]				,   [0.002, 0.02, 0.2, 2]       ):
     self.check_over_configs(beta_start=lowerCamelCase				,   beta_end=lowerCamelCase       )
 def 		lowercase_(   self			:    Dict       ):



   """simple docstring"""





   for schedule in ["linear", "squaredcos_cap_v2"]:
     self.check_over_configs(beta_schedule=lowerCamelCase       )
 def 		lowercase_(   self			:    Any       ):



   """simple docstring"""





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



   """simple docstring"""





   for clip_sample in [True, False]:
     self.check_over_configs(clip_sample=lowerCamelCase       )
 def 		lowercase_(   self			:    Any       ):



   """simple docstring"""





   for timestep_spacing in ["trailing", "leading"]:
     self.check_over_configs(timestep_spacing=lowerCamelCase       )
 def 		lowercase_(   self			:    List[str]       ):



   """simple docstring"""





   for rescale_betas_zero_snr in [True, False]:
     self.check_over_configs(rescale_betas_zero_snr=lowerCamelCase       )
 def 		lowercase_(   self			:    str       ):



   """simple docstring"""





   self.check_over_configs(thresholding=lowerCamelCase       )
   for threshold in [0.5, 1.0, 2.0]:
     for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(
           thresholding=lowerCamelCase				,   prediction_type=lowerCamelCase				,   sample_max_value=lowerCamelCase				,   )
 def 		lowercase_(   self			:    int       ):



   """simple docstring"""





   for t in [1, 10, 49]:
     self.check_over_forward(time_step=lowerCamelCase       )
 def 		lowercase_(   self			:    List[str]       ):



   """simple docstring"""





   for t, num_inference_steps in zip([1, 10, 50]				,   [10, 50, 5_00]       ):
     self.check_over_forward(time_step=lowerCamelCase				,   num_inference_steps=lowerCamelCase       )
 def 		lowercase_(   self			:    Any       ):



   """simple docstring"""





   for t, eta in zip([1, 10, 49]				,   [0.0, 0.5, 1.0]       ):
     self.check_over_forward(time_step=lowerCamelCase				,   eta=lowerCamelCase       )
 def 		lowercase_(   self			:    int       ):



   """simple docstring"""





   __A		:     Dict												=		self.scheduler_classes[0]
   __A		:     Optional[int]												=		self.get_scheduler_config()
   __A		:     List[Any]												=		scheduler_class(**lowerCamelCase       )

   assert torch.sum(torch.abs(scheduler._get_variance(0				,   0       ) - 0.0       )       ) < 1e-5
   assert torch.sum(torch.abs(scheduler._get_variance(4_20				,   4_00       ) - 0.14_771       )       ) < 1e-5
   assert torch.sum(torch.abs(scheduler._get_variance(9_80				,   9_60       ) - 0.32_460       )       ) < 1e-5
   assert torch.sum(torch.abs(scheduler._get_variance(0				,   0       ) - 0.0       )       ) < 1e-5
   assert torch.sum(torch.abs(scheduler._get_variance(4_87				,   4_86       ) - 0.00_979       )       ) < 1e-5
   assert torch.sum(torch.abs(scheduler._get_variance(9_99				,   9_98       ) - 0.02       )       ) < 1e-5
 def 		lowercase_(   self			:    Optional[Any]       ):



   """simple docstring"""





   __A		:     int												=		self.scheduler_classes[0]
   __A		:     Dict												=		self.get_scheduler_config()
   __A		:     List[str]												=		scheduler_class(**lowerCamelCase       )

   __A							,					__A		:     List[Any]												=		10, 0.0
   scheduler.set_timesteps(lowerCamelCase       )

   __A		:     Optional[int]												=		self.dummy_model()
   __A		:     Optional[int]												=		self.dummy_sample_deter
   __A		:     Union[str, Any]												=		self.dummy_sample_deter + 0.1
   __A		:     Optional[Any]												=		self.dummy_sample_deter - 0.1

   __A		:     List[Any]												=		samplea.shape[0]
   __A		:     Dict												=		torch.stack([samplea, samplea, samplea]				,   dim=0       )
   __A		:     Union[str, Any]												=		torch.arange(lowerCamelCase       )[0:3, None].repeat(1				,   lowerCamelCase       )

   __A		:     List[str]												=		model(samples.flatten(0				,   1       )				,   timesteps.flatten(0				,   1       )       )
   __A		:     Tuple												=		scheduler.batch_step_no_noise(lowerCamelCase				,   timesteps.flatten(0				,   1       )				,   samples.flatten(0				,   1       )				,   lowerCamelCase       )

   __A		:     Dict												=		torch.sum(torch.abs(lowerCamelCase       )       )
   __A		:     int												=		torch.mean(torch.abs(lowerCamelCase       )       )

   assert abs(result_sum.item() - 1_147.7_904       ) < 1e-2
   assert abs(result_mean.item() - 0.4_982       ) < 1e-3
 def 		lowercase_(   self			:    str       ):



   """simple docstring"""





   __A		:     Optional[Any]												=		self.full_loop()

   __A		:     Optional[Any]												=		torch.sum(torch.abs(lowerCamelCase       )       )
   __A		:     Dict												=		torch.mean(torch.abs(lowerCamelCase       )       )

   assert abs(result_sum.item() - 172.0_067       ) < 1e-2
   assert abs(result_mean.item() - 0.223_967       ) < 1e-3
 def 		lowercase_(   self			:    Union[str, Any]       ):



   """simple docstring"""





   __A		:     str												=		self.full_loop(prediction_type="""v_prediction"""       )

   __A		:     Tuple												=		torch.sum(torch.abs(lowerCamelCase       )       )
   __A		:     Tuple												=		torch.mean(torch.abs(lowerCamelCase       )       )

   assert abs(result_sum.item() - 52.5_302       ) < 1e-2
   assert abs(result_mean.item() - 0.0_684       ) < 1e-3
 def 		lowercase_(   self			:    Any       ):



   """simple docstring"""





   __A		:     Optional[Any]												=		self.full_loop(set_alpha_to_one=lowerCamelCase				,   beta_start=0.01       )
   __A		:     Union[str, Any]												=		torch.sum(torch.abs(lowerCamelCase       )       )
   __A		:     Optional[Any]												=		torch.mean(torch.abs(lowerCamelCase       )       )

   assert abs(result_sum.item() - 149.8_295       ) < 1e-2
   assert abs(result_mean.item() - 0.1_951       ) < 1e-3






 def 		lowercase_(   self			:    Dict       ):



   """simple docstring"""





   __A		:     int												=		self.full_loop(set_alpha_to_one=lowerCamelCase				,   beta_start=0.01       )
   __A		:     Union[str, Any]												=		torch.sum(torch.abs(lowerCamelCase       )       )
   __A		:     Optional[int]												=		torch.mean(torch.abs(lowerCamelCase       )       )

   assert abs(result_sum.item() - 149.0_784       ) < 1e-2
   assert abs(result_mean.item() - 0.1_941       ) < 1e-3





'''simple docstring'''
from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

A__   :		Any 		=logging.get_logger(__name__)







@add_end_docstrings(_SCREAMING_SNAKE_CASE			)
class  __A				( _SCREAMING_SNAKE_CASE			):
 def __init__(   self			:    Union[str, Any]				,   *lowerCamelCase			:    int				,   **lowerCamelCase			:    Optional[int]       ):



   """simple docstring"""





   super().__init__(*lowerCamelCase				,   **lowerCamelCase       )
   self.check_model_type(lowerCamelCase       )
 def 		lowercase_(   self			:    Any				,   lowerCamelCase			:    Dict=None				,   lowerCamelCase			:    Union[str, Any]=None				,   lowerCamelCase			:    int=None				,   **lowerCamelCase			:    int       ):



   """simple docstring"""





   __A							,					__A		:     Tuple												=		{}, {}
   if padding is not None:
     __A		:     Any												=		padding
   if truncation is not None:
     __A		:     Optional[Any]												=		truncation
   if top_k is not None:
     __A		:     List[str]												=		top_k
   return preprocess_params, {}, postprocess_params
 def __call__(   self			:    Any				,   lowerCamelCase			:    Union["Image.Image", str]				,   lowerCamelCase			:    str = None				,   **lowerCamelCase			:    Tuple       ):



   """simple docstring"""





   if isinstance(lowerCamelCase				,   (Image.Image, str)       ) and isinstance(lowerCamelCase				,   lowerCamelCase       ):
     __A		:     Tuple												=		{"""image""": image, """question""": question}
   else:
     __A		:     List[Any]												=		image
   __A		:     Any												=		super().__call__(lowerCamelCase				,   **lowerCamelCase       )
   return results
 def 		lowercase_(   self			:    int				,   lowerCamelCase			:    Optional[int]				,   lowerCamelCase			:    Dict=False				,   lowerCamelCase			:    str=False       ):



   """simple docstring"""





   __A		:     List[str]												=		load_image(inputs["""image"""]       )
   __A		:     Optional[Any]												=		self.tokenizer(
       inputs["""question"""]				,   return_tensors=self.framework				,   padding=lowerCamelCase				,   truncation=lowerCamelCase       )
   __A		:     Union[str, Any]												=		self.image_processor(images=lowerCamelCase				,   return_tensors=self.framework       )
   model_inputs.update(lowerCamelCase       )
   return model_inputs
 def 		lowercase_(   self			:    List[str]				,   lowerCamelCase			:    Tuple       ):



   """simple docstring"""





   __A		:     List[str]												=		self.model(**lowerCamelCase       )
   return model_outputs






 def 		lowercase_(   self			:    str				,   lowerCamelCase			:    Optional[Any]				,   lowerCamelCase			:    List[Any]=5       ):



   """simple docstring"""





   if top_k > self.model.config.num_labels:
     __A		:     str												=		self.model.config.num_labels

   if self.framework == "pt":
     __A		:     Optional[Any]												=		model_outputs.logits.sigmoid()[0]
     __A							,					__A		:     int												=		probs.topk(lowerCamelCase       )
   else:
     raise ValueError(f"Unsupported framework: {self.framework}"       )

   __A		:     Optional[Any]												=		scores.tolist()
   __A		:     Dict												=		ids.tolist()
   return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase				,   lowerCamelCase       )]



