



"""simple docstring"""
import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest


class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):
    lowercase__      =   (CMStochasticIterativeScheduler,)
    lowercase__      =   10

    def 					_UpperCAmelCase	(				self			:				Optional[Any] ,  **lowerCAmelCase_			:				Dict):
      lowercase_								= {
          """num_train_timesteps""": 2_0_1,
          """sigma_min""": 0.002,
          """sigma_max""": 8_0.0,
      }

      config.update(**_SCREAMING_SNAKE_CASE)
      return config

    def 					_UpperCAmelCase	(				self			:				Union[str, Any]):
      lowercase_								= 1_0

      lowercase_								= self.get_scheduler_config()
      lowercase_								= self.scheduler_classes[0](**_SCREAMING_SNAKE_CASE)

      scheduler.set_timesteps(_SCREAMING_SNAKE_CASE)

      lowercase_								= scheduler.timesteps[0]
      lowercase_								= scheduler.timesteps[1]

      lowercase_								= self.dummy_sample
      lowercase_								= 0.1 * sample

      lowercase_								= scheduler.step(_SCREAMING_SNAKE_CASE ,  _SCREAMING_SNAKE_CASE ,  _SCREAMING_SNAKE_CASE).prev_sample
      lowercase_								= scheduler.step(_SCREAMING_SNAKE_CASE ,  _SCREAMING_SNAKE_CASE ,  _SCREAMING_SNAKE_CASE).prev_sample

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

    def 					_UpperCAmelCase	(				self			:				List[Any]):






      for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
        self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE)

    def 					_UpperCAmelCase	(				self			:				Tuple):






      for clip_denoised in [True, False]:
        self.check_over_configs(clip_denoised=_SCREAMING_SNAKE_CASE)

    def 					_UpperCAmelCase	(				self			:				str):
      lowercase_								= self.scheduler_classes[0]
      lowercase_								= self.get_scheduler_config()
      lowercase_								= scheduler_class(**_SCREAMING_SNAKE_CASE)

      lowercase_								= 1
      scheduler.set_timesteps(_SCREAMING_SNAKE_CASE)
      lowercase_								= scheduler.timesteps

      lowercase_								= torch.manual_seed(0)

      lowercase_								= self.dummy_model()
      lowercase_								= self.dummy_sample_deter * scheduler.init_noise_sigma

      for i, t in enumerate(_SCREAMING_SNAKE_CASE):
        # 1. scale model input
        lowercase_								= scheduler.scale_model_input(_SCREAMING_SNAKE_CASE ,  _SCREAMING_SNAKE_CASE)

        # 2. predict noise residual
        lowercase_								= model(_SCREAMING_SNAKE_CASE ,  _SCREAMING_SNAKE_CASE)

        # 3. predict previous sample x_t-1
        lowercase_								= scheduler.step(_SCREAMING_SNAKE_CASE ,  _SCREAMING_SNAKE_CASE ,  _SCREAMING_SNAKE_CASE ,  generator=_SCREAMING_SNAKE_CASE).prev_sample

        lowercase_								= pred_prev_sample

      lowercase_								= torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
      lowercase_								= torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

      assert abs(result_sum.item() - 1_9_2.7_6_1_4) < 1E-2
      assert abs(result_mean.item() - 0.2_510) < 1E-3

    def 					_UpperCAmelCase	(				self			:				Optional[int]):
      lowercase_								= self.scheduler_classes[0]
      lowercase_								= self.get_scheduler_config()
      lowercase_								= scheduler_class(**_SCREAMING_SNAKE_CASE)

      lowercase_								= [1_0_6, 0]
      scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE)
      lowercase_								= scheduler.timesteps

      lowercase_								= torch.manual_seed(0)

      lowercase_								= self.dummy_model()
      lowercase_								= self.dummy_sample_deter * scheduler.init_noise_sigma

      for t in timesteps:
        # 1. scale model input
        lowercase_								= scheduler.scale_model_input(_SCREAMING_SNAKE_CASE ,  _SCREAMING_SNAKE_CASE)

        # 2. predict noise residual
        lowercase_								= model(_SCREAMING_SNAKE_CASE ,  _SCREAMING_SNAKE_CASE)

        # 3. predict previous sample x_t-1
        lowercase_								= scheduler.step(_SCREAMING_SNAKE_CASE ,  _SCREAMING_SNAKE_CASE ,  _SCREAMING_SNAKE_CASE ,  generator=_SCREAMING_SNAKE_CASE).prev_sample

        lowercase_								= pred_prev_sample

      lowercase_								= torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
      lowercase_								= torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

      assert abs(result_sum.item() - 3_4_7.6_3_5_7) < 1E-2
      assert abs(result_mean.item() - 0.4_527) < 1E-3

    def 					_UpperCAmelCase	(				self			:				Dict):
      lowercase_								= self.scheduler_classes[0]
      lowercase_								= self.get_scheduler_config()
      lowercase_								= scheduler_class(**_SCREAMING_SNAKE_CASE)

      lowercase_								= [3_9, 3_0, 1_2, 1_5, 0]

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

    def 					_UpperCAmelCase	(				self			:				Dict):
      lowercase_								= self.scheduler_classes[0]
      lowercase_								= self.get_scheduler_config()
      lowercase_								= scheduler_class(**_SCREAMING_SNAKE_CASE)

      lowercase_								= [3_9, 3_0, 1_2, 1, 0]
      lowercase_								= len(_SCREAMING_SNAKE_CASE)

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




    def 					_UpperCAmelCase	(				self			:				Any):
      lowercase_								= self.scheduler_classes[0]
      lowercase_								= self.get_scheduler_config()
      lowercase_								= scheduler_class(**_SCREAMING_SNAKE_CASE)

      lowercase_								= [scheduler.config.num_train_timesteps]

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






"""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

UpperCAmelCase						:		Optional[Any]			= logging.get_logger(__name__)


@add_end_docstrings(__UpperCAmelCase   )
class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):

    def __init__(				self			:				Optional[int] ,  **lowerCAmelCase_			:				List[Any]):







      """simple docstring"""







      super().__init__(**lowerCAmelCase_)

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

      requires_backends(self ,  """vision""")
      self.check_model_type(lowerCAmelCase_)

    def __call__(				self			:				Dict ,  lowerCAmelCase_			:				Union[str, "Image.Image", List[Dict[str, Any]]] ,  lowerCAmelCase_			:				Union[str, List[str]] = None ,  **lowerCAmelCase_			:				Dict ,  ):







      """simple docstring"""







      if "text_queries" in kwargs:
        lowercase_								= kwargs.pop("""text_queries""")

      if isinstance(lowerCAmelCase_ ,  (str, Image.Image)):
        lowercase_								= {"""image""": image, """candidate_labels""": candidate_labels}
      else:
        lowercase_								= image
      lowercase_								= super().__call__(lowerCAmelCase_ ,  **lowerCAmelCase_)
      return results

    def 					_UpperCAmelCase	(				self			:				Optional[Any] ,  **lowerCAmelCase_			:				Optional[Any]):







      """simple docstring"""







      lowercase_								= {}
      if "threshold" in kwargs:
        lowercase_								= kwargs["""threshold"""]
      if "top_k" in kwargs:
        lowercase_								= kwargs["""top_k"""]
      return {}, {}, postprocess_params

    def 					_UpperCAmelCase	(				self			:				int ,  lowerCAmelCase_			:				List[Any]):







      """simple docstring"""







      lowercase_								= load_image(inputs["""image"""])
      lowercase_								= inputs["""candidate_labels"""]
      if isinstance(lowerCAmelCase_ ,  lowerCAmelCase_):
        lowercase_								= candidate_labels.split(""",""")

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

    def 					_UpperCAmelCase	(				self			:				List[Any] ,  lowerCAmelCase_			:				List[Any]):







      """simple docstring"""







      lowercase_								= model_inputs.pop("""target_size""")
      lowercase_								= model_inputs.pop("""candidate_label""")
      lowercase_								= model_inputs.pop("""is_last""")

      lowercase_								= self.model(**lowerCAmelCase_)

      lowercase_								= {"""target_size""": target_size, """candidate_label""": candidate_label, """is_last""": is_last, **outputs}
      return model_outputs

    def 					_UpperCAmelCase	(				self			:				int ,  lowerCAmelCase_			:				Optional[int] ,  lowerCAmelCase_			:				Tuple=0.1 ,  lowerCAmelCase_			:				List[str]=None):







      """simple docstring"""







      lowercase_								= []
      for model_output in model_outputs:
        lowercase_								= model_output["""candidate_label"""]
        lowercase_								= BaseModelOutput(lowerCAmelCase_)
        lowercase_								= self.image_processor.post_process_object_detection(
            outputs=lowerCAmelCase_ ,  threshold=lowerCAmelCase_ ,  target_sizes=model_output["""target_size"""])[0]

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

          lowercase_								= {"""score""": score, """label""": label, """box""": box}
          results.append(lowerCAmelCase_)

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

      return results




    def 					_UpperCAmelCase	(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				"torch.Tensor"):







      """simple docstring"""







      if self.framework != "pt":
        raise ValueError("""The ZeroShotObjectDetectionPipeline is only available in PyTorch.""")
      lowercase_					,    lowercase_					,    lowercase_					,    lowercase_								= box.int().tolist()
      lowercase_								= {
          """xmin""": xmin,
          """ymin""": ymin,
          """xmax""": xmax,
          """ymax""": ymax,
      }
      return bbox

