
"""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

lowerCAmelCase__				=					logging.get_logger(__name__)





@add_end_docstrings(__lowercase )
class 			snake_case  (				__lowercase ):

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

									"""simple docstring"""







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

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

									"""simple docstring"""







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

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

									"""simple docstring"""







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

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

									"""simple docstring"""







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

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.model(**SCREAMING_SNAKE_CASE_     )
									return model_outputs







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

									"""simple docstring"""







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

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

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

"""simple docstring"""




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest





class 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   (UnCLIPScheduler,)

							def _lowercase       (self							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			{
									    '''num_train_timesteps''': 10_00,
									    '''variance_type''': '''fixed_small_log''',
									    '''clip_sample''': True,
									    '''clip_sample_range''': 1.0,
									    '''prediction_type''': '''epsilon''',
									}

									config.update(**SCREAMING_SNAKE_CASE_     )
									return config

							def _lowercase       (self     ):

									"""simple docstring"""







									for timesteps in [1, 5, 1_00, 10_00]:
											self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									for variance in ["fixed_small_log", "learned_range"]:
											self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







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

							def _lowercase       (self     ):

									"""simple docstring"""







									for clip_sample_range in [1, 5, 10, 20]:
											self.check_over_configs(clip_sample_range=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







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

							def _lowercase       (self     ):

									"""simple docstring"""







									for time_step in [0, 5_00, 9_99]:
											for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
													if prev_timestep is not None and prev_timestep >= time_step:
															continue

													self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_							,							prev_timestep=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config(variance_type='''fixed_small_log'''     )
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 1.0_0_0_0e-1_0     )     ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(4_87     ) - 0.0_54_96_25     )     ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(9_99     ) - 0.9_99_49_87     )     ) < 1e-5

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config(variance_type='''learned_range'''     )
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			0.5

									assert scheduler._get_variance(1							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -10.1_71_27_90 < 1e-5
									assert scheduler._get_variance(4_87							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -5.7_99_80_52 < 1e-5
									assert scheduler._get_variance(9_99							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -0.0_01_00_11 < 1e-5

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			scheduler.timesteps

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

									for i, t in enumerate(SCREAMING_SNAKE_CASE_     ):
											# 1. predict noise residual
											SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

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

											SCREAMING_SNAKE_CASE_    			=			pred_prev_sample

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

									assert abs(result_sum.item() - 2_52.2_68_24_95     ) < 1e-2
									assert abs(result_mean.item() - 0.3_28_47_43     ) < 1e-3

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									scheduler.set_timesteps(25     )

									SCREAMING_SNAKE_CASE_    			=			scheduler.timesteps

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

									for i, t in enumerate(SCREAMING_SNAKE_CASE_     ):
											# 1. predict noise residual
											SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

											if i + 1 == timesteps.shape[0]:
													SCREAMING_SNAKE_CASE_    			=			None
											else:
													SCREAMING_SNAKE_CASE_    			=			timesteps[i + 1]

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

											SCREAMING_SNAKE_CASE_    			=			pred_prev_sample

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

									assert abs(result_sum.item() - 2_58.2_04_49_83     ) < 1e-2
									assert abs(result_mean.item() - 0.3_36_20_38     ) < 1e-3

							def _lowercase       (self     ):

									"""simple docstring"""







									pass







							def _lowercase       (self     ):

									"""simple docstring"""







									pass