

"""simple docstring"""

import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest






@require_torchsde
class 			__a					(						__magic_name__       ):





       """simple docstring"""


       __UpperCamelCase					:      List[Any]    =						(DPMSolverSDEScheduler,)
       __UpperCamelCase					:      Optional[Any]    =						10






       def 			SCREAMING_SNAKE_CASE_	(   self			,					**snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				Dict 			=				{
            "num_train_timesteps": 1_100,
            "beta_start": 0.0_001,
            "beta_end": 0.02,
            "beta_schedule": "linear",
            "noise_sampler_seed": 0,
        }

        config.update(**snake_case   )
        return config






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        for timesteps in [10, 50, 100, 1_000]:
         self.check_over_configs(num_train_timesteps=snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]			,					[0.0_002, 0.002, 0.02]   ):
         self.check_over_configs(beta_start=snake_case			,					beta_end=snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[Any] 			=				self.scheduler_classes[0]
        lowerCAmelCase__   :				Any 			=				self.get_scheduler_config()
        lowerCAmelCase__   :				Tuple 			=				scheduler_class(**snake_case   )

        scheduler.set_timesteps(self.num_inference_steps   )

        lowerCAmelCase__   :				Dict 			=				self.dummy_model()
        lowerCAmelCase__   :				Optional[int] 			=				self.dummy_sample_deter * scheduler.init_noise_sigma
        lowerCAmelCase__   :				List[str] 			=				sample.to(snake_case   )

        for i, t in enumerate(scheduler.timesteps   ):
         lowerCAmelCase__   :				str 			=				scheduler.scale_model_input(snake_case			,					snake_case   )

         lowerCAmelCase__   :				str 			=				model(snake_case			,					snake_case   )

         lowerCAmelCase__   :				str 			=				scheduler.step(snake_case			,					snake_case			,					snake_case   )
         lowerCAmelCase__   :				Any 			=				output.prev_sample

        lowerCAmelCase__   :				Any 			=				torch.sum(torch.abs(snake_case   )   )
        lowerCAmelCase__   :				int 			=				torch.mean(torch.abs(snake_case   )   )

        if torch_device in ["mps"]:
         assert abs(result_sum.item() - 167.47_821_044_921_875   ) < 1e-2
         assert abs(result_mean.item() - 0.2_178_705_964_565_277   ) < 1e-3
        elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 171.59_352_111_816_406   ) < 1e-2
         assert abs(result_mean.item() - 0.22_342_906_892_299_652   ) < 1e-3
        else:
         assert abs(result_sum.item() - 162.52_383_422_851_562   ) < 1e-2
         assert abs(result_mean.item() - 0.211_619_570_851_326   ) < 1e-3






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				self.scheduler_classes[0]
        lowerCAmelCase__   :				List[str] 			=				self.get_scheduler_config(prediction_type="v_prediction"   )
        lowerCAmelCase__   :				str 			=				scheduler_class(**snake_case   )

        scheduler.set_timesteps(self.num_inference_steps   )

        lowerCAmelCase__   :				Union[str, Any] 			=				self.dummy_model()
        lowerCAmelCase__   :				List[Any] 			=				self.dummy_sample_deter * scheduler.init_noise_sigma
        lowerCAmelCase__   :				List[Any] 			=				sample.to(snake_case   )

        for i, t in enumerate(scheduler.timesteps   ):
         lowerCAmelCase__   :				List[Any] 			=				scheduler.scale_model_input(snake_case			,					snake_case   )

         lowerCAmelCase__   :				Optional[int] 			=				model(snake_case			,					snake_case   )

         lowerCAmelCase__   :				Union[str, Any] 			=				scheduler.step(snake_case			,					snake_case			,					snake_case   )
         lowerCAmelCase__   :				Dict 			=				output.prev_sample

        lowerCAmelCase__   :				str 			=				torch.sum(torch.abs(snake_case   )   )
        lowerCAmelCase__   :				Optional[int] 			=				torch.mean(torch.abs(snake_case   )   )

        if torch_device in ["mps"]:
         assert abs(result_sum.item() - 124.77_149_200_439_453   ) < 1e-2
         assert abs(result_mean.item() - 0.16_226_289_014_816_284   ) < 1e-3
        elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 128.1_663_360_595_703   ) < 1e-2
         assert abs(result_mean.item() - 0.16_688_326_001_167_297   ) < 1e-3
        else:
         assert abs(result_sum.item() - 119.8_487_548_828_125   ) < 1e-2
         assert abs(result_mean.item() - 0.1_560_530_662_536_621   ) < 1e-3






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Union[str, Any] 			=				self.scheduler_classes[0]
        lowerCAmelCase__   :				Optional[Any] 			=				self.get_scheduler_config()
        lowerCAmelCase__   :				Optional[int] 			=				scheduler_class(**snake_case   )

        scheduler.set_timesteps(self.num_inference_steps			,					device=snake_case   )

        lowerCAmelCase__   :				Optional[int] 			=				self.dummy_model()
        lowerCAmelCase__   :				Dict 			=				self.dummy_sample_deter.to(snake_case   ) * scheduler.init_noise_sigma

        for t in scheduler.timesteps:
         lowerCAmelCase__   :				Dict 			=				scheduler.scale_model_input(snake_case			,					snake_case   )

         lowerCAmelCase__   :				Optional[Any] 			=				model(snake_case			,					snake_case   )

         lowerCAmelCase__   :				int 			=				scheduler.step(snake_case			,					snake_case			,					snake_case   )
         lowerCAmelCase__   :				Dict 			=				output.prev_sample

        lowerCAmelCase__   :				int 			=				torch.sum(torch.abs(snake_case   )   )
        lowerCAmelCase__   :				Union[str, Any] 			=				torch.mean(torch.abs(snake_case   )   )

        if torch_device in ["mps"]:
         assert abs(result_sum.item() - 167.46_957_397_460_938   ) < 1e-2
         assert abs(result_mean.item() - 0.21_805_934_607_982_635   ) < 1e-3
        elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 171.59_353_637_695_312   ) < 1e-2
         assert abs(result_mean.item() - 0.22_342_908_382_415_771   ) < 1e-3
        else:
         assert abs(result_sum.item() - 162.52_383_422_851_562   ) < 1e-2
         assert abs(result_mean.item() - 0.211_619_570_851_326   ) < 1e-3






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				int 			=				self.scheduler_classes[0]
        lowerCAmelCase__   :				Any 			=				self.get_scheduler_config()
        lowerCAmelCase__   :				List[Any] 			=				scheduler_class(**snake_case			,					use_karras_sigmas=snake_case   )

        scheduler.set_timesteps(self.num_inference_steps			,					device=snake_case   )

        lowerCAmelCase__   :				List[str] 			=				self.dummy_model()
        lowerCAmelCase__   :				List[Any] 			=				self.dummy_sample_deter.to(snake_case   ) * scheduler.init_noise_sigma
        lowerCAmelCase__   :				List[Any] 			=				sample.to(snake_case   )

        for t in scheduler.timesteps:
         lowerCAmelCase__   :				int 			=				scheduler.scale_model_input(snake_case			,					snake_case   )

         lowerCAmelCase__   :				int 			=				model(snake_case			,					snake_case   )

         lowerCAmelCase__   :				Any 			=				scheduler.step(snake_case			,					snake_case			,					snake_case   )
         lowerCAmelCase__   :				Optional[Any] 			=				output.prev_sample

        lowerCAmelCase__   :				Any 			=				torch.sum(torch.abs(snake_case   )   )
        lowerCAmelCase__   :				int 			=				torch.mean(torch.abs(snake_case   )   )

        if torch_device in ["mps"]:
         assert abs(result_sum.item() - 176.66_974_135_742_188   ) < 1e-2
         assert abs(result_mean.item() - 0.23_003_872_730_981_811   ) < 1e-2
        elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 177.63_653_564_453_125   ) < 1e-2
         assert abs(result_mean.item() - 0.23_003_872_730_981_811   ) < 1e-2
        else:
         assert abs(result_sum.item() - 170.3_135_223_388_672   ) < 1e-2
         assert abs(result_mean.item() - 0.23_003_872_730_981_811   ) < 1e-2









"""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

_UpperCamelCase		     =    logging.get_logger(__name__)






@add_end_docstrings(__magic_name__       )
class 			__a					(						__magic_name__       ):





       """simple docstring"""


       def __init__(   self			,					*snake_case			,					**snake_case   ):





        """simple docstring"""

        super().__init__(*snake_case			,					**snake_case   )
        self.check_model_type(snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case=None			,					snake_case=None			,					snake_case=None			,					**snake_case   ):





        """simple docstring"""

        lowerCAmelCase__			,						lowerCAmelCase__   :				List[Any] 			=				{}, {}
        if padding is not None:
         lowerCAmelCase__   :				Dict 			=				padding
        if truncation is not None:
         lowerCAmelCase__   :				Tuple 			=				truncation
        if top_k is not None:
         lowerCAmelCase__   :				str 			=				top_k
        return preprocess_params, {}, postprocess_params






       def __call__(   self			,					snake_case			,					snake_case = None			,					**snake_case   ):





        """simple docstring"""

        if isinstance(snake_case			,					(Image.Image, str)   ) and isinstance(snake_case			,					snake_case   ):
         lowerCAmelCase__   :				int 			=				{"image": image, "question": question}
        else:
         lowerCAmelCase__   :				Optional[int] 			=				image
        lowerCAmelCase__   :				Optional[Any] 			=				super().__call__(snake_case			,					**snake_case   )
        return results






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case=False			,					snake_case=False   ):





        """simple docstring"""

        lowerCAmelCase__   :				Union[str, Any] 			=				load_image(inputs["image"]   )
        lowerCAmelCase__   :				str 			=				self.tokenizer(
            inputs["question"]			,					return_tensors=self.framework			,					padding=snake_case			,					truncation=snake_case   )
        lowerCAmelCase__   :				int 			=				self.image_processor(images=snake_case			,					return_tensors=self.framework   )
        model_inputs.update(snake_case   )
        return model_inputs






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[int] 			=				self.model(**snake_case   )
        return model_outputs






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case=5   ):





        """simple docstring"""

        if top_k > self.model.config.num_labels:
         lowerCAmelCase__   :				List[Any] 			=				self.model.config.num_labels

        if self.framework == "pt":
         lowerCAmelCase__   :				Optional[int] 			=				model_outputs.logits.sigmoid()[0]
         lowerCAmelCase__			,						lowerCAmelCase__   :				Optional[int] 			=				probs.topk(snake_case   )
        else:
         raise ValueError(F"""Unsupported framework: {self.framework}"""   )

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






