

"""simple docstring"""




import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class      _UpperCAmelCase   (							_lowerCAmelCase       ):
	a__		: List[Any]					=					(EulerDiscreteScheduler,)
	a__		: Dict					=					10



	def        a  (  self     :       List[Any]			,    **_lowercase     :       str						):
		__UpperCAmelCase      		= {
		    '''num_train_timesteps''': 11_00,
		    '''beta_start''': 0.0_001,
		    '''beta_end''': 0.02,
		    '''beta_schedule''': '''linear''',
		}

		config.update(**_lowercase						)
		return config



	def        a  (  self     :       Union[str, Any]						):
		for timesteps in [10, 50, 1_00, 10_00]:
			self.check_over_configs(num_train_timesteps=_lowercase						)



	def        a  (  self     :       Optional[Any]						):
		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=_lowercase			,    beta_end=_lowercase						)



	def        a  (  self     :       Tuple						):
		for schedule in ["linear", "scaled_linear"]:
			self.check_over_configs(beta_schedule=_lowercase						)



	def        a  (  self     :       List[Any]						):
		for prediction_type in ["epsilon", "v_prediction"]:
			self.check_over_configs(prediction_type=_lowercase						)



	def        a  (  self     :       Optional[Any]						):
		__UpperCAmelCase      		= self.scheduler_classes[0]
		__UpperCAmelCase      		= self.get_scheduler_config()
		__UpperCAmelCase      		= scheduler_class(**_lowercase						)

		scheduler.set_timesteps(self.num_inference_steps						)

		__UpperCAmelCase      		= torch.manual_seed(0						)

		__UpperCAmelCase      		= self.dummy_model()
		__UpperCAmelCase      		= self.dummy_sample_deter * scheduler.init_noise_sigma
		__UpperCAmelCase      		= sample.to(_lowercase						)

		for i, t in enumerate(scheduler.timesteps						):
			__UpperCAmelCase      		= scheduler.scale_model_input(_lowercase			,    _lowercase						)

			__UpperCAmelCase      		= model(_lowercase			,    _lowercase						)

			__UpperCAmelCase      		= scheduler.step(_lowercase			,    _lowercase			,    _lowercase			,    generator=_lowercase						)
			__UpperCAmelCase      		= output.prev_sample

		__UpperCAmelCase      		= torch.sum(torch.abs(_lowercase						)						)
		__UpperCAmelCase      		= torch.mean(torch.abs(_lowercase						)						)

		assert abs(result_sum.item() - 10.0_807						) < 1E-2
		assert abs(result_mean.item() - 0.0_131						) < 1E-3



	def        a  (  self     :       Optional[Any]						):
		__UpperCAmelCase      		= self.scheduler_classes[0]
		__UpperCAmelCase      		= self.get_scheduler_config(prediction_type='''v_prediction'''						)
		__UpperCAmelCase      		= scheduler_class(**_lowercase						)

		scheduler.set_timesteps(self.num_inference_steps						)

		__UpperCAmelCase      		= torch.manual_seed(0						)

		__UpperCAmelCase      		= self.dummy_model()
		__UpperCAmelCase      		= self.dummy_sample_deter * scheduler.init_noise_sigma
		__UpperCAmelCase      		= sample.to(_lowercase						)

		for i, t in enumerate(scheduler.timesteps						):
			__UpperCAmelCase      		= scheduler.scale_model_input(_lowercase			,    _lowercase						)

			__UpperCAmelCase      		= model(_lowercase			,    _lowercase						)

			__UpperCAmelCase      		= scheduler.step(_lowercase			,    _lowercase			,    _lowercase			,    generator=_lowercase						)
			__UpperCAmelCase      		= output.prev_sample

		__UpperCAmelCase      		= torch.sum(torch.abs(_lowercase						)						)
		__UpperCAmelCase      		= torch.mean(torch.abs(_lowercase						)						)

		assert abs(result_sum.item() - 0.0_002						) < 1E-2
		assert abs(result_mean.item() - 2.2676E-06						) < 1E-3



	def        a  (  self     :       int						):
		__UpperCAmelCase      		= self.scheduler_classes[0]
		__UpperCAmelCase      		= self.get_scheduler_config()
		__UpperCAmelCase      		= scheduler_class(**_lowercase						)

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

		__UpperCAmelCase      		= torch.manual_seed(0						)

		__UpperCAmelCase      		= self.dummy_model()
		__UpperCAmelCase      		= self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
		__UpperCAmelCase      		= sample.to(_lowercase						)

		for t in scheduler.timesteps:
			__UpperCAmelCase      		= scheduler.scale_model_input(_lowercase			,    _lowercase						)

			__UpperCAmelCase      		= model(_lowercase			,    _lowercase						)

			__UpperCAmelCase      		= scheduler.step(_lowercase			,    _lowercase			,    _lowercase			,    generator=_lowercase						)
			__UpperCAmelCase      		= output.prev_sample

		__UpperCAmelCase      		= torch.sum(torch.abs(_lowercase						)						)
		__UpperCAmelCase      		= torch.mean(torch.abs(_lowercase						)						)

		assert abs(result_sum.item() - 10.0_807						) < 1E-2
		assert abs(result_mean.item() - 0.0_131						) < 1E-3



	def        a  (  self     :       Dict						):
		__UpperCAmelCase      		= self.scheduler_classes[0]
		__UpperCAmelCase      		= self.get_scheduler_config()
		__UpperCAmelCase      		= scheduler_class(**_lowercase			,    use_karras_sigmas=_lowercase						)

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

		__UpperCAmelCase      		= torch.manual_seed(0						)

		__UpperCAmelCase      		= self.dummy_model()
		__UpperCAmelCase      		= self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
		__UpperCAmelCase      		= sample.to(_lowercase						)

		for t in scheduler.timesteps:
			__UpperCAmelCase      		= scheduler.scale_model_input(_lowercase			,    _lowercase						)

			__UpperCAmelCase      		= model(_lowercase			,    _lowercase						)

			__UpperCAmelCase      		= scheduler.step(_lowercase			,    _lowercase			,    _lowercase			,    generator=_lowercase						)
			__UpperCAmelCase      		= output.prev_sample

		__UpperCAmelCase      		= torch.sum(torch.abs(_lowercase						)						)
		__UpperCAmelCase      		= torch.mean(torch.abs(_lowercase						)						)

		assert abs(result_sum.item() - 124.52_299_499_511_719						) < 1E-2
		assert abs(result_mean.item() - 0.16_213_932_633_399_963						) < 1E-3







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

SCREAMING_SNAKE_CASE_   :      Union[str, Any]			=   logging.get_logger(__name__)

@add_end_docstrings(UpperCAmelCase_						)
class 		snake_case_   (							UpperCAmelCase_						):







	'''simple docstring'''




	def __init__(						self							:     Optional[int]					,      *__lowerCamelCase							:     Optional[int]					,      **__lowerCamelCase							:     List[Any]   )       -> Optional[Any]:

			'''simple docstring'''




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




	def    UpperCAmelCase			(						self							:     Optional[int]					,      __lowerCamelCase							:     int=None					,      __lowerCamelCase							:     List[Any]=None					,      __lowerCamelCase							:     Any=None					,      **__lowerCamelCase							:     Optional[int]   )       -> Any:

			'''simple docstring'''




			__lowercase						,      __lowercase											=     {}, {}
			if padding is not None:
					__lowercase											=     padding
			if truncation is not None:
					__lowercase											=     truncation
			if top_k is not None:
					__lowercase											=     top_k
			return preprocess_params, {}, postprocess_params




	def __call__(						self							:     str					,      __lowerCamelCase							:     Union["Image.Image", str]					,      __lowerCamelCase							:     str = None					,      **__lowerCamelCase							:     Dict   )       -> Tuple:

			'''simple docstring'''




			if isinstance(__lowerCamelCase					,      (Image.Image, str)   ) and isinstance(__lowerCamelCase					,      __lowerCamelCase   ):
					__lowercase											=     {'image': image, 'question': question}
			else:
					__lowercase											=     image
			__lowercase											=     super().__call__(__lowerCamelCase					,      **__lowerCamelCase   )
			return results




	def    UpperCAmelCase			(						self							:     List[Any]					,      __lowerCamelCase							:     List[str]					,      __lowerCamelCase							:     Optional[Any]=False					,      __lowerCamelCase							:     Optional[Any]=False   )       -> Optional[int]:

			'''simple docstring'''




			__lowercase											=     load_image(inputs['image']   )
			__lowercase											=     self.tokenizer(
			    inputs['question']					,      return_tensors=self.framework					,      padding=__lowerCamelCase					,      truncation=__lowerCamelCase   )
			__lowercase											=     self.image_processor(images=__lowerCamelCase					,      return_tensors=self.framework   )
			model_inputs.update(__lowerCamelCase   )
			return model_inputs




	def    UpperCAmelCase			(						self							:     str					,      __lowerCamelCase							:     Tuple   )       -> Any:

			'''simple docstring'''




			__lowercase											=     self.model(**__lowerCamelCase   )
			return model_outputs






	def    UpperCAmelCase			(						self							:     int					,      __lowerCamelCase							:     Any					,      __lowerCamelCase							:     int=5   )       -> Tuple:

			'''simple docstring'''




			if top_k > self.model.config.num_labels:
					__lowercase											=     self.model.config.num_labels

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

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




