



'''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

snake_case_ :	Optional[Any]         =  logging.get_logger(__name__)






@add_end_docstrings(lowercase   )
class 							lowercase__	(							lowercase   ):





	def __init__( self					:				List[Any] ,*lowerCamelCase__					:				List[str] ,**lowerCamelCase__					:				Tuple ):







							'''simple docstring'''
							super().__init__(*lowerCamelCase__ ,**lowerCamelCase__ )
							self.check_model_type(lowerCamelCase__ )





	def 	UpperCamelCase_  ( self					:				List[str] ,lowerCamelCase__					:				int=None ,lowerCamelCase__					:				int=None ,lowerCamelCase__					:				int=None ,**lowerCamelCase__					:				Dict ):







							'''simple docstring'''
							_UpperCamelCase				,			_UpperCamelCase					:		str 				=   {}, {}
							if padding is not None:
													_UpperCamelCase					:		List[str] 				=   padding
							if truncation is not None:
													_UpperCamelCase					:		Optional[int] 				=   truncation
							if top_k is not None:
													_UpperCamelCase					:		List[Any] 				=   top_k
							return preprocess_params, {}, postprocess_params





	def __call__( self					:				int ,lowerCamelCase__					:				Union["Image.Image", str] ,lowerCamelCase__					:				str = None ,**lowerCamelCase__					:				List[Any] ):







							'''simple docstring'''
							if isinstance(lowerCamelCase__ ,(Image.Image, str) ) and isinstance(lowerCamelCase__ ,lowerCamelCase__ ):
													_UpperCamelCase					:		int 				=   {'image': image, 'question': question}
							else:
													_UpperCamelCase					:		List[Any] 				=   image
							_UpperCamelCase					:		Union[str, Any] 				=   super().__call__(lowerCamelCase__ ,**lowerCamelCase__ )
							return results





	def 	UpperCamelCase_  ( self					:				Union[str, Any] ,lowerCamelCase__					:				str ,lowerCamelCase__					:				Any=False ,lowerCamelCase__					:				int=False ):







							'''simple docstring'''
							_UpperCamelCase					:		str 				=   load_image(inputs['image'] )
							_UpperCamelCase					:		Optional[int] 				=   self.tokenizer(
							    inputs['question'] ,return_tensors=self.framework ,padding=lowerCamelCase__ ,truncation=lowerCamelCase__ )
							_UpperCamelCase					:		Any 				=   self.image_processor(images=lowerCamelCase__ ,return_tensors=self.framework )
							model_inputs.update(lowerCamelCase__ )
							return model_inputs





	def 	UpperCamelCase_  ( self					:				Dict ,lowerCamelCase__					:				Optional[Any] ):







							'''simple docstring'''
							_UpperCamelCase					:		Tuple 				=   self.model(**lowerCamelCase__ )
							return model_outputs





	def 	UpperCamelCase_  ( self					:				List[Any] ,lowerCamelCase__					:				Dict ,lowerCamelCase__					:				Any=5 ):







							'''simple docstring'''
							if top_k > self.model.config.num_labels:
													_UpperCamelCase					:		List[str] 				=   self.model.config.num_labels

							if self.framework == "pt":
													_UpperCamelCase					:		List[str] 				=   model_outputs.logits.sigmoid()[0]
													_UpperCamelCase				,			_UpperCamelCase					:		Union[str, Any] 				=   probs.topk(lowerCamelCase__ )
							else:
													raise ValueError(F'Unsupported framework: {self.framework}' )

							_UpperCamelCase					:		Optional[int] 				=   scores.tolist()
							_UpperCamelCase					:		int 				=   ids.tolist()
							return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase__ ,lowerCamelCase__ )]




"""simple docstring"""





import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Optional[int]       =		(DDIMParallelScheduler,)
  SCREAMING_SNAKE_CASE__			:						Optional[Any]       =		(("""eta""", 0.0), ("""num_inference_steps""", 50))







  def        UpperCamelCase__					(		self				,   **lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    {
         "num_train_timesteps": 1000,
         "beta_start": 0.00_01,
         "beta_end": 0.02,
         "beta_schedule": "linear",
         "clip_sample": True,
     }

     config.update(**lowercase_  )
     return config







  def        UpperCamelCase__					(		self				,   **lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					Dict			     =    self.scheduler_classes[0]
     UpperCAmelCase_					:					Union[str, Any]			     =    self.get_scheduler_config(**lowercase_  )
     UpperCAmelCase_					:					int			     =    scheduler_class(**lowercase_  )

     UpperCAmelCase_     ,   UpperCAmelCase_					:					str			     =    10, 0.0

     UpperCAmelCase_					:					Optional[int]			     =    self.dummy_model()
     UpperCAmelCase_					:					str			     =    self.dummy_sample_deter

     scheduler.set_timesteps(lowercase_  )

     for t in scheduler.timesteps:
        UpperCAmelCase_					:					Dict			     =    model(lowercase_				,   lowercase_  )
        UpperCAmelCase_					:					Dict			     =    scheduler.step(lowercase_				,   lowercase_				,   lowercase_				,   lowercase_  ).prev_sample

     return sample







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for timesteps in [100, 500, 1000]:
        self.check_over_configs(num_train_timesteps=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for steps_offset in [0, 1]:
        self.check_over_configs(steps_offset=lowercase_  )

     UpperCAmelCase_					:					str			     =    self.scheduler_classes[0]
     UpperCAmelCase_					:					List[str]			     =    self.get_scheduler_config(steps_offset=1  )
     UpperCAmelCase_					:					List[str]			     =    scheduler_class(**lowercase_  )
     scheduler.set_timesteps(5  )
     assert torch.equal(scheduler.timesteps				,   torch.LongTensor([801, 601, 401, 201, 1]  )  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     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=lowercase_				,   beta_end=lowercase_  )







  def        UpperCamelCase__					(		self  ):

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







  def        UpperCamelCase__					(		self  ):

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







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for clip_sample in [True, False]:
        self.check_over_configs(clip_sample=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for timestep_spacing in ["trailing", "leading"]:
        self.check_over_configs(timestep_spacing=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for rescale_betas_zero_snr in [True, False]:
        self.check_over_configs(rescale_betas_zero_snr=lowercase_  )







  def        UpperCamelCase__					(		self  ):

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







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for t in [1, 10, 49]:
        self.check_over_forward(time_step=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for t, num_inference_steps in zip([1, 10, 50]				,   [10, 50, 500]  ):
        self.check_over_forward(time_step=lowercase_				,   num_inference_steps=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for t, eta in zip([1, 10, 49]				,   [0.0, 0.5, 1.0]  ):
        self.check_over_forward(time_step=lowercase_				,   eta=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    self.scheduler_classes[0]
     UpperCAmelCase_					:					List[str]			     =    self.get_scheduler_config()
     UpperCAmelCase_					:					List[str]			     =    scheduler_class(**lowercase_  )

     assert torch.sum(torch.abs(scheduler._get_variance(0				,   0  ) - 0.0  )  ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(420				,   400  ) - 0.1_47_71  )  ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(980				,   960  ) - 0.3_24_60  )  ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(0				,   0  ) - 0.0  )  ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(487				,   486  ) - 0.0_09_79  )  ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(999				,   998  ) - 0.02  )  ) < 1E-5







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    self.scheduler_classes[0]
     UpperCAmelCase_					:					Optional[int]			     =    self.get_scheduler_config()
     UpperCAmelCase_					:					List[str]			     =    scheduler_class(**lowercase_  )

     UpperCAmelCase_     ,   UpperCAmelCase_					:					Tuple			     =    10, 0.0
     scheduler.set_timesteps(lowercase_  )

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

     UpperCAmelCase_					:					List[Any]			     =    samplea.shape[0]
     UpperCAmelCase_					:					int			     =    torch.stack([samplea, samplea, samplea]				,   dim=0  )
     UpperCAmelCase_					:					int			     =    torch.arange(lowercase_  )[0:3, None].repeat(1				,   lowercase_  )

     UpperCAmelCase_					:					int			     =    model(samples.flatten(0				,   1  )				,   timesteps.flatten(0				,   1  )  )
     UpperCAmelCase_					:					Optional[Any]			     =    scheduler.batch_step_no_noise(lowercase_				,   timesteps.flatten(0				,   1  )				,   samples.flatten(0				,   1  )				,   lowercase_  )

     UpperCAmelCase_					:					List[Any]			     =    torch.sum(torch.abs(lowercase_  )  )
     UpperCAmelCase_					:					str			     =    torch.mean(torch.abs(lowercase_  )  )

     assert abs(result_sum.item() - 11_47.79_04  ) < 1E-2
     assert abs(result_mean.item() - 0.49_82  ) < 1E-3







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    self.full_loop()

     UpperCAmelCase_					:					int			     =    torch.sum(torch.abs(lowercase_  )  )
     UpperCAmelCase_					:					List[str]			     =    torch.mean(torch.abs(lowercase_  )  )

     assert abs(result_sum.item() - 1_72.00_67  ) < 1E-2
     assert abs(result_mean.item() - 0.22_39_67  ) < 1E-3







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    self.full_loop(prediction_type="v_prediction"  )

     UpperCAmelCase_					:					str			     =    torch.sum(torch.abs(lowercase_  )  )
     UpperCAmelCase_					:					Dict			     =    torch.mean(torch.abs(lowercase_  )  )

     assert abs(result_sum.item() - 52.53_02  ) < 1E-2
     assert abs(result_mean.item() - 0.06_84  ) < 1E-3







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     # We specify different beta, so that the first alpha is 0.99
     UpperCAmelCase_					:					List[str]			     =    self.full_loop(set_alpha_to_one=lowercase_				,   beta_start=0.01  )
     UpperCAmelCase_					:					Dict			     =    torch.sum(torch.abs(lowercase_  )  )
     UpperCAmelCase_					:					Tuple			     =    torch.mean(torch.abs(lowercase_  )  )

     assert abs(result_sum.item() - 1_49.82_95  ) < 1E-2
     assert abs(result_mean.item() - 0.19_51  ) < 1E-3







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     # We specify different beta, so that the first alpha is 0.99
     UpperCAmelCase_					:					int			     =    self.full_loop(set_alpha_to_one=lowercase_				,   beta_start=0.01  )
     UpperCAmelCase_					:					List[Any]			     =    torch.sum(torch.abs(lowercase_  )  )
     UpperCAmelCase_					:					Dict			     =    torch.mean(torch.abs(lowercase_  )  )

     assert abs(result_sum.item() - 1_49.07_84  ) < 1E-2
     assert abs(result_mean.item() - 0.19_41  ) < 1E-3


