
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

__magic_name__            =  logging.get_logger(__name__)




@add_end_docstrings(__UpperCamelCase )
class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      def __init__(			self		,	**lowerCamelCase ):
          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		,	lowerCamelCase		,	lowerCamelCase = None		,	**lowerCamelCase		,	):
          if "text_queries" in kwargs:
              snake_case__						=				kwargs.pop("text_queries" )

          if isinstance(lowerCamelCase		,	(str, Image.Image) ):
              snake_case__						=				{"image": image, "candidate_labels": candidate_labels}
          else:
              snake_case__						=				image
          snake_case__						=				super().__call__(lowerCamelCase		,	**lowerCamelCase )
          return results
      def A_			(			self		,	**lowerCamelCase ):
          snake_case__						=				{}
          if "threshold" in kwargs:
              snake_case__						=				kwargs["threshold"]
          if "top_k" in kwargs:
              snake_case__						=				kwargs["top_k"]
          return {}, {}, postprocess_params
      def A_			(			self		,	lowerCamelCase ):
          snake_case__						=				load_image(inputs["image"] )
          snake_case__						=				inputs["candidate_labels"]
          if isinstance(lowerCamelCase		,	lowerCamelCase ):
              snake_case__						=				candidate_labels.split("," )

          snake_case__						=				torch.tensor([[image.height, image.width]]		,	dtype=torch.intaa )
          for i, candidate_label in enumerate(lowerCamelCase ):
              snake_case__						=				self.tokenizer(lowerCamelCase		,	return_tensors=self.framework )
              snake_case__						=				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 A_			(			self		,	lowerCamelCase ):
          snake_case__						=				model_inputs.pop("target_size" )
          snake_case__						=				model_inputs.pop("candidate_label" )
          snake_case__						=				model_inputs.pop("is_last" )

          snake_case__						=				self.model(**lowerCamelCase )

          snake_case__						=				{"target_size": target_size, "candidate_label": candidate_label, "is_last": is_last, **outputs}
          return model_outputs
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase=0.1		,	lowerCamelCase=None ):
          snake_case__						=				[]
          for model_output in model_outputs:
              snake_case__						=				model_output["candidate_label"]
              snake_case__						=				BaseModelOutput(lowerCamelCase )
              snake_case__						=				self.image_processor.post_process_object_detection(
                  outputs=lowerCamelCase		,	threshold=lowerCamelCase		,	target_sizes=model_output["target_size"] )[0]

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

                  snake_case__						=				{"score": score, "label": label, "box": box}
                  results.append(lowerCamelCase )

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

          return results



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





import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		Any    	=     (KDPMaDiscreteScheduler,)
      _A						:		Dict    	=     10
      def A_			(			self		,	**lowerCamelCase ):
          snake_case__						=				{
              "num_train_timesteps": 11_00,
              "beta_start": 0.0_0_0_1,
              "beta_end": 0.0_2,
              "beta_schedule": "linear",
          }

          config.update(**lowerCamelCase )
          return config
      def A_			(			self ):
          for timesteps in [10, 50, 1_00, 10_00]:
              self.check_over_configs(num_train_timesteps=lowerCamelCase )
      def A_			(			self ):
          for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]		,	[0.0_0_0_2, 0.0_0_2, 0.0_2] ):
              self.check_over_configs(beta_start=lowerCamelCase		,	beta_end=lowerCamelCase )
      def A_			(			self ):
          for schedule in ["linear", "scaled_linear"]:
              self.check_over_configs(beta_schedule=lowerCamelCase )
      def A_			(			self ):
          for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=lowerCamelCase )
      def A_			(			self ):
          snake_case__						=				self.scheduler_classes[0]
          snake_case__						=				self.get_scheduler_config(prediction_type="v_prediction" )
          snake_case__						=				scheduler_class(**lowerCamelCase )

          scheduler.set_timesteps(self.num_inference_steps )

          snake_case__						=				self.dummy_model()
          snake_case__						=				self.dummy_sample_deter * scheduler.init_noise_sigma
          snake_case__						=				sample.to(lowerCamelCase )

          for i, t in enumerate(scheduler.timesteps ):
              snake_case__						=				scheduler.scale_model_input(lowerCamelCase		,	lowerCamelCase )

              snake_case__						=				model(lowerCamelCase		,	lowerCamelCase )

              snake_case__						=				scheduler.step(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase )
              snake_case__						=				output.prev_sample

          snake_case__						=				torch.sum(torch.abs(lowerCamelCase ) )
          snake_case__						=				torch.mean(torch.abs(lowerCamelCase ) )

          if torch_device in ["cpu", "mps"]:
              assert abs(result_sum.item() - 4.69_34e-07 ) < 1e-2
              assert abs(result_mean.item() - 6.11_12e-10 ) < 1e-3
          else:
              # CUDA
              assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72e-07 ) < 1e-2
              assert abs(result_mean.item() - 0.0_0_0_2 ) < 1e-3
      def A_			(			self ):
          if torch_device == "mps":
              return
          snake_case__						=				self.scheduler_classes[0]
          snake_case__						=				self.get_scheduler_config()
          snake_case__						=				scheduler_class(**lowerCamelCase )

          scheduler.set_timesteps(self.num_inference_steps )

          snake_case__						=				self.dummy_model()
          snake_case__						=				self.dummy_sample_deter * scheduler.init_noise_sigma
          snake_case__						=				sample.to(lowerCamelCase )

          for i, t in enumerate(scheduler.timesteps ):
              snake_case__						=				scheduler.scale_model_input(lowerCamelCase		,	lowerCamelCase )

              snake_case__						=				model(lowerCamelCase		,	lowerCamelCase )

              snake_case__						=				scheduler.step(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase )
              snake_case__						=				output.prev_sample

          snake_case__						=				torch.sum(torch.abs(lowerCamelCase ) )
          snake_case__						=				torch.mean(torch.abs(lowerCamelCase ) )

          if torch_device in ["cpu", "mps"]:
              assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
              assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3
          else:
              # CUDA
              assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
              assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3



      def A_			(			self ):
          if torch_device == "mps":
              return
          snake_case__						=				self.scheduler_classes[0]
          snake_case__						=				self.get_scheduler_config()
          snake_case__						=				scheduler_class(**lowerCamelCase )

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

          snake_case__						=				self.dummy_model()
          snake_case__						=				self.dummy_sample_deter.to(lowerCamelCase ) * scheduler.init_noise_sigma

          for t in scheduler.timesteps:
              snake_case__						=				scheduler.scale_model_input(lowerCamelCase		,	lowerCamelCase )

              snake_case__						=				model(lowerCamelCase		,	lowerCamelCase )

              snake_case__						=				scheduler.step(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase )
              snake_case__						=				output.prev_sample

          snake_case__						=				torch.sum(torch.abs(lowerCamelCase ) )
          snake_case__						=				torch.mean(torch.abs(lowerCamelCase ) )

          if str(lowerCamelCase ).startswith("cpu" ):
              # The following sum varies between 148 and 156 on mps. Why?
              assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
              assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3
          else:
              # CUDA
              assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
              assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3



