


"""simple docstring"""







import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 __lowerCAmelCase			 =  (DDPMScheduler,)


 def 				SCREAMING_SNAKE_CASE							(		self			,   **__A						) ->					Any:
     a					={
         '''num_train_timesteps''': 1000,
         '''beta_start''': 0.0_001,
         '''beta_end''': 0.02,
         '''beta_schedule''': '''linear''',
         '''variance_type''': '''fixed_small''',
         '''clip_sample''': True,
     }

     config.update(**__A						)
     return config


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
     for timesteps in [1, 5, 100, 1000]:
         self.check_over_configs(num_train_timesteps=__A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
     for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]			,   [0.002, 0.02, 0.2, 2]						):
         self.check_over_configs(beta_start=__A			,   beta_end=__A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
     for schedule in ["linear", "squaredcos_cap_v2"]:
         self.check_over_configs(beta_schedule=__A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
     for variance in ["fixed_small", "fixed_large", "other"]:
         self.check_over_configs(variance_type=__A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     for clip_sample in [True, False]:
         self.check_over_configs(clip_sample=__A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Union[str, Any]:
     self.check_over_configs(thresholding=__A						)
     for threshold in [0.5, 1.0, 2.0]:
         for prediction_type in ["epsilon", "sample", "v_prediction"]:
             self.check_over_configs(
                 thresholding=__A			,   prediction_type=__A			,   sample_max_value=__A			,   )


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Any:
     for prediction_type in ["epsilon", "sample", "v_prediction"]:
         self.check_over_configs(prediction_type=__A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[Any]:
     for t in [0, 500, 999]:
         self.check_over_forward(time_step=__A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
     a					=self.scheduler_classes[0]
     a					=self.get_scheduler_config()
     a					=scheduler_class(**__A						)

     assert torch.sum(torch.abs(scheduler._get_variance(0						) - 0.0						)						) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(487						) - 0.00_979						)						) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(999						) - 0.02						)						) < 1E-5


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
     a					=self.scheduler_classes[0]
     a					=self.get_scheduler_config()
     a					=scheduler_class(**__A						)

     a					=len(__A						)

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

     for t in reversed(range(__A						)						):
         # 1. predict noise residual
         a					=model(__A			,   __A						)

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

         # if t > 0:
         #     noise = self.dummy_sample_deter
         #     variance = scheduler.get_variance(t) ** (0.5) * noise
         #
         # sample = pred_prev_sample + variance
         a					=pred_prev_sample

     a					=torch.sum(torch.abs(__A						)						)
     a					=torch.mean(torch.abs(__A						)						)

     assert abs(result_sum.item() - 258.9_606						) < 1E-2
     assert abs(result_mean.item() - 0.3_372						) < 1E-3


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     a					=self.scheduler_classes[0]
     a					=self.get_scheduler_config(prediction_type='''v_prediction'''						)
     a					=scheduler_class(**__A						)

     a					=len(__A						)

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

     for t in reversed(range(__A						)						):
         # 1. predict noise residual
         a					=model(__A			,   __A						)

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

         # if t > 0:
         #     noise = self.dummy_sample_deter
         #     variance = scheduler.get_variance(t) ** (0.5) * noise
         #
         # sample = pred_prev_sample + variance
         a					=pred_prev_sample

     a					=torch.sum(torch.abs(__A						)						)
     a					=torch.mean(torch.abs(__A						)						)

     assert abs(result_sum.item() - 202.0_296						) < 1E-2
     assert abs(result_mean.item() - 0.2_631						) < 1E-3


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Any:
     a					=self.scheduler_classes[0]
     a					=self.get_scheduler_config()
     a					=scheduler_class(**__A						)

     a					=[100, 87, 50, 1, 0]

     scheduler.set_timesteps(timesteps=__A						)

     a					=scheduler.timesteps

     for i, timestep in enumerate(__A						):
         if i == len(__A						) - 1:
             a					=-1
         else:
             a					=timesteps[i + 1]

         a					=scheduler.previous_timestep(__A						)
         a					=prev_t.item()

         self.assertEqual(__A			,   __A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
     a					=self.scheduler_classes[0]
     a					=self.get_scheduler_config()
     a					=scheduler_class(**__A						)

     a					=[100, 87, 50, 51, 0]

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


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
     a					=self.scheduler_classes[0]
     a					=self.get_scheduler_config()
     a					=scheduler_class(**__A						)

     a					=[100, 87, 50, 1, 0]
     a					=len(__A						)

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





 def 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
     a					=self.scheduler_classes[0]
     a					=self.get_scheduler_config()
     a					=scheduler_class(**__A						)

     a					=[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						)



"""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

lowerCamelCase_       :       Dict      						=  logging.get_logger(__name__)






@add_end_docstrings(_SCREAMING_SNAKE_CASE      )
class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 def __init__(		self			,   **__A						) ->					Dict:
     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			,   __A			,   __A = None			,   **__A			,   ) ->					List[str]:
     if "text_queries" in kwargs:
         a					=kwargs.pop('''text_queries'''						)

     if isinstance(__A			,   (str, Image.Image)						):
         a					={'''image''': image, '''candidate_labels''': candidate_labels}
     else:
         a					=image
     a					=super().__call__(__A			,   **__A						)
     return results


 def 				SCREAMING_SNAKE_CASE							(		self			,   **__A						) ->					Optional[Any]:
     a					={}
     if "threshold" in kwargs:
         a					=kwargs['''threshold''']
     if "top_k" in kwargs:
         a					=kwargs['''top_k''']
     return {}, {}, postprocess_params


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					str:
     a					=load_image(inputs['''image''']						)
     a					=inputs['''candidate_labels''']
     if isinstance(__A			,   __A						):
         a					=candidate_labels.split(''','''						)

     a					=torch.tensor([[image.height, image.width]]			,   dtype=torch.intaa						)
     for i, candidate_label in enumerate(__A						):
         a					=self.tokenizer(__A			,   return_tensors=self.framework						)
         a					=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 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					List[Any]:
     a					=model_inputs.pop('''target_size'''						)
     a					=model_inputs.pop('''candidate_label'''						)
     a					=model_inputs.pop('''is_last'''						)

     a					=self.model(**__A						)

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


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A=0.1			,   __A=None						) ->					List[str]:
     a					=[]
     for model_output in model_outputs:
         a					=model_output['''candidate_label''']
         a					=BaseModelOutput(__A						)
         a					=self.image_processor.post_process_object_detection(
             outputs=__A			,   threshold=__A			,   target_sizes=model_output['''target_size''']						)[0]

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

             a					={'''score''': score, '''label''': label, '''box''': box}
             results.append(__A						)

     a					=sorted(__A			,   key=lambda __A						: x["score"]			,   reverse=__A						)
     if top_k:
         a					=results[:top_k]

     return results





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					Dict[str, int]:
     if self.framework != "pt":
         raise ValueError('''The ZeroShotObjectDetectionPipeline is only available in PyTorch.'''						)
     a		,				a		,				a		,				a					=box.int().tolist()
     a					={
         '''xmin''': xmin,
         '''ymin''': ymin,
         '''xmax''': xmax,
         '''ymax''': ymax,
     }
     return bbox