



'''simple docstring'''

from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
	from PIL import Image

	from ..image_utils import load_image

if is_torch_available():
	import torch

	from transformers.modeling_outputs import BaseModelOutput

	from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

a							   =				logging.get_logger(__name__)






@add_end_docstrings(snake_case    )
class 						a_		(     snake_case    ):


							def __init__(				self   :						Tuple      ,						**a_   :						str			)			->					Optional[int]:
										super().__init__(**a_			)

										if self.framework == "tf":
													raise ValueError(F'''The {self.__class__} is only available in PyTorch.'''			)

										requires_backends(self      ,						'vision'			)
										self.check_model_type(a_			)


							def __call__(				self   :						Union[str, Any]      ,						a_   :						Union[str, "Image.Image", List[Dict[str, Any]]]      ,						a_   :						Union[str, List[str]] = None      ,						**a_   :						Dict      ,						)			->					str:
										if "text_queries" in kwargs:
													snake_case:    List[Any]      							=kwargs.pop('text_queries'			)

										if isinstance(a_      ,						(str, Image.Image)			):
													snake_case:    int      							={'image': image, 'candidate_labels': candidate_labels}
										else:
													snake_case:    int      							=image
										snake_case:    List[Any]      							=super().__call__(a_      ,						**a_			)
										return results


							def    UpperCamelCase     (				self   :						int      ,						**a_   :						Dict			)			->					Union[str, Any]:
										snake_case:    Tuple      							={}
										if "threshold" in kwargs:
													snake_case:    Tuple      							=kwargs['threshold']
										if "top_k" in kwargs:
													snake_case:    int      							=kwargs['top_k']
										return {}, {}, postprocess_params


							def    UpperCamelCase     (				self   :						List[Any]      ,						a_   :						str			)			->					List[Any]:
										snake_case:    Any      							=load_image(inputs['image']			)
										snake_case:    Dict      							=inputs['candidate_labels']
										if isinstance(a_      ,						a_			):
													snake_case:    Any      							=candidate_labels.split(','			)

										snake_case:    List[str]      							=torch.tensor([[image.height, image.width]]      ,						dtype=torch.intaa			)
										for i, candidate_label in enumerate(a_			):
													snake_case:    Optional[Any]      							=self.tokenizer(a_      ,						return_tensors=self.framework			)
													snake_case:    int      							=self.image_processor(a_      ,						return_tensors=self.framework			)
													yield {
													    "is_last": i == len(a_			) - 1,
													    "target_size": target_size,
													    "candidate_label": candidate_label,
													    **text_inputs,
													    **image_features,
													}


							def    UpperCamelCase     (				self   :						Optional[int]      ,						a_   :						int			)			->					Optional[int]:
										snake_case:    str      							=model_inputs.pop('target_size'			)
										snake_case:    List[Any]      							=model_inputs.pop('candidate_label'			)
										snake_case:    List[str]      							=model_inputs.pop('is_last'			)

										snake_case:    Union[str, Any]      							=self.model(**a_			)

										snake_case:    Any      							={'target_size': target_size, 'candidate_label': candidate_label, 'is_last': is_last, **outputs}
										return model_outputs


							def    UpperCamelCase     (				self   :						str      ,						a_   :						Optional[Any]      ,						a_   :						Union[str, Any]=0.1      ,						a_   :						Dict=None			)			->					Union[str, Any]:
										snake_case:    List[str]      							=[]
										for model_output in model_outputs:
													snake_case:    Any      							=model_output['candidate_label']
													snake_case:    Dict      							=BaseModelOutput(a_			)
													snake_case:    Optional[Any]      							=self.image_processor.post_process_object_detection(
													    outputs=a_      ,						threshold=a_      ,						target_sizes=model_output['target_size']			)[0]

													for index in outputs["scores"].nonzero():
																snake_case:    Optional[Any]      							=outputs['scores'][index].item()
																snake_case:    Tuple      							=self._get_bounding_box(outputs['boxes'][index][0]			)

																snake_case:    str      							={'score': score, 'label': label, 'box': box}
																results.append(a_			)

										snake_case:    Optional[Any]      							=sorted(a_      ,						key=lambda a_			: x["score"]      ,						reverse=a_			)
										if top_k:
													snake_case:    str      							=results[:top_k]

										return results




							def    UpperCamelCase     (				self   :						Optional[int]      ,						a_   :						"torch.Tensor"			)			->					Dict[str, int]:
										if self.framework != "pt":
													raise ValueError('The ZeroShotObjectDetectionPipeline is only available in PyTorch.'			)
										snake_case							,  snake_case							,  snake_case							,  snake_case:    Union[str, Any]      							=box.int().tolist()
										snake_case:    Any      							={
										    'xmin': xmin,
										    'ymin': ymin,
										    'xmax': xmax,
										    'ymax': ymax,
										}
										return bbox









'''simple docstring'''

import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class 						a_		(     snake_case    ):
							UpperCAmelCase			:		str        =				(CMStochasticIterativeScheduler,)
							UpperCAmelCase			:		int        =				10


							def    UpperCamelCase     (				self   :						Dict      ,						**a_   :						List[str]			)			->					Any:
										snake_case:    Any      							={
										    'num_train_timesteps': 2_0_1,
										    'sigma_min': 0.0_0_2,
										    'sigma_max': 8_0.0,
										}

										config.update(**a_			)
										return config


							def    UpperCamelCase     (				self   :						List[Any]			)			->					List[Any]:
										snake_case:    Any      							=1_0

										snake_case:    List[str]      							=self.get_scheduler_config()
										snake_case:    List[Any]      							=self.scheduler_classes[0](**a_			)

										scheduler.set_timesteps(a_			)

										snake_case:    Dict      							=scheduler.timesteps[0]
										snake_case:    Union[str, Any]      							=scheduler.timesteps[1]

										snake_case:    List[str]      							=self.dummy_sample
										snake_case:    List[str]      							=0.1 * sample

										snake_case:    int      							=scheduler.step(a_      ,						a_      ,						a_			).prev_sample
										snake_case:    Optional[Any]      							=scheduler.step(a_      ,						a_      ,						a_			).prev_sample

										self.assertEqual(output_a.shape      ,						sample.shape			)
										self.assertEqual(output_a.shape      ,						output_a.shape			)


							def    UpperCamelCase     (				self   :						int			)			->					int:
										for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
													self.check_over_configs(num_train_timesteps=a_			)


							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Dict:
										for clip_denoised in [True, False]:
													self.check_over_configs(clip_denoised=a_			)


							def    UpperCamelCase     (				self   :						Tuple			)			->					List[str]:
										snake_case:    List[Any]      							=self.scheduler_classes[0]
										snake_case:    List[Any]      							=self.get_scheduler_config()
										snake_case:    Any      							=scheduler_class(**a_			)

										snake_case:    Dict      							=1
										scheduler.set_timesteps(a_			)
										snake_case:    List[Any]      							=scheduler.timesteps

										snake_case:    Optional[Any]      							=torch.manual_seed(0			)

										snake_case:    Optional[Any]      							=self.dummy_model()
										snake_case:    List[Any]      							=self.dummy_sample_deter * scheduler.init_noise_sigma

										for i, t in enumerate(a_			):
													# 1. scale model input
													snake_case:    Any      							=scheduler.scale_model_input(a_      ,						a_			)

													# 2. predict noise residual
													snake_case:    List[str]      							=model(a_      ,						a_			)

													# 3. predict previous sample x_t-1
													snake_case:    Dict      							=scheduler.step(a_      ,						a_      ,						a_      ,						generator=a_			).prev_sample

													snake_case:    List[Any]      							=pred_prev_sample

										snake_case:    Optional[Any]      							=torch.sum(torch.abs(a_			)			)
										snake_case:    Optional[Any]      							=torch.mean(torch.abs(a_			)			)

										assert abs(result_sum.item() - 1_9_2.7_6_1_4			) < 1E-2
										assert abs(result_mean.item() - 0.2_5_1_0			) < 1E-3


							def    UpperCamelCase     (				self   :						Dict			)			->					Union[str, Any]:
										snake_case:    Dict      							=self.scheduler_classes[0]
										snake_case:    Tuple      							=self.get_scheduler_config()
										snake_case:    str      							=scheduler_class(**a_			)

										snake_case:    List[Any]      							=[1_0_6, 0]
										scheduler.set_timesteps(timesteps=a_			)
										snake_case:    Optional[Any]      							=scheduler.timesteps

										snake_case:    Dict      							=torch.manual_seed(0			)

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

										for t in timesteps:
													# 1. scale model input
													snake_case:    List[Any]      							=scheduler.scale_model_input(a_      ,						a_			)

													# 2. predict noise residual
													snake_case:    Any      							=model(a_      ,						a_			)

													# 3. predict previous sample x_t-1
													snake_case:    List[str]      							=scheduler.step(a_      ,						a_      ,						a_      ,						generator=a_			).prev_sample

													snake_case:    Optional[Any]      							=pred_prev_sample

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

										assert abs(result_sum.item() - 3_4_7.6_3_5_7			) < 1E-2
										assert abs(result_mean.item() - 0.4_5_2_7			) < 1E-3


							def    UpperCamelCase     (				self   :						int			)			->					Tuple:
										snake_case:    List[Any]      							=self.scheduler_classes[0]
										snake_case:    Union[str, Any]      							=self.get_scheduler_config()
										snake_case:    str      							=scheduler_class(**a_			)

										snake_case:    str      							=[3_9, 3_0, 1_2, 1_5, 0]

										with self.assertRaises(a_      ,						msg='`timesteps` must be in descending order.'			):
													scheduler.set_timesteps(timesteps=a_			)


							def    UpperCamelCase     (				self   :						Dict			)			->					Optional[int]:
										snake_case:    Optional[Any]      							=self.scheduler_classes[0]
										snake_case:    Dict      							=self.get_scheduler_config()
										snake_case:    str      							=scheduler_class(**a_			)

										snake_case:    Any      							=[3_9, 3_0, 1_2, 1, 0]
										snake_case:    List[Any]      							=len(a_			)

										with self.assertRaises(a_      ,						msg='Can only pass one of `num_inference_steps` or `timesteps`.'			):
													scheduler.set_timesteps(num_inference_steps=a_      ,						timesteps=a_			)




							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Tuple:
										snake_case:    Any      							=self.scheduler_classes[0]
										snake_case:    int      							=self.get_scheduler_config()
										snake_case:    Optional[Any]      							=scheduler_class(**a_			)

										snake_case:    List[Any]      							=[scheduler.config.num_train_timesteps]

										with self.assertRaises(
										    a_      ,						msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'      ,						):
													scheduler.set_timesteps(timesteps=a_			)




