



"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin


@flax.struct.dataclass
class    SCREAMING_SNAKE_CASE__ :
    # setable values
    lowercase__      =   None
    lowercase__      =   None
    lowercase__      =   None  # sigma(t_i)

    @classmethod
    def 					_UpperCAmelCase	(				cls			:				Any):







      """simple docstring"""







      return cls()







@dataclass
class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):
    lowercase__      =   42
    lowercase__      =   42
    lowercase__      =   42







class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase      ,  __UpperCAmelCase   ):

    @property
    def 					_UpperCAmelCase	(				self			:				int):







      """simple docstring"""







      return True

    @register_to_config
    def __init__(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				float = 0.02 ,  lowerCAmelCase_			:				float = 1_0_0 ,  lowerCAmelCase_			:				float = 1.007 ,  lowerCAmelCase_			:				float = 8_0 ,  lowerCAmelCase_			:				float = 0.05 ,  lowerCAmelCase_			:				float = 5_0 ,  ):







      """simple docstring"""







      pass

    def 					_UpperCAmelCase	(				self			:				Optional[int]):







      """simple docstring"""







      return KarrasVeSchedulerState.create()

    def 					_UpperCAmelCase	(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				KarrasVeSchedulerState ,  lowerCAmelCase_			:				int ,  lowerCAmelCase_			:				Tuple = ()):







      """simple docstring"""







      lowercase_								= jnp.arange(0 ,  lowerCAmelCase_)[::-1].copy()
      lowercase_								= [
          (
              self.config.sigma_max**2
              * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
          )
          for i in timesteps
      ]

      return state.replace(
          num_inference_steps=lowerCAmelCase_ ,  schedule=jnp.array(lowerCAmelCase_ ,  dtype=jnp.floataa) ,  timesteps=lowerCAmelCase_ ,  )

    def 					_UpperCAmelCase	(				self			:				Optional[int] ,  lowerCAmelCase_			:				KarrasVeSchedulerState ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				float ,  lowerCAmelCase_			:				random.KeyArray ,  ):







      """simple docstring"""







      if self.config.s_min <= sigma <= self.config.s_max:
        lowercase_								= min(self.config.s_churn / state.num_inference_steps ,  2**0.5 - 1)
      else:
        lowercase_								= 0

      # sample eps ~ N(0, S_noise^2 * I)
      lowercase_								= random.split(lowerCAmelCase_ ,  num=1)
      lowercase_								= self.config.s_noise * random.normal(key=lowerCAmelCase_ ,  shape=sample.shape)
      lowercase_								= sigma + gamma * sigma
      lowercase_								= sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

      return sample_hat, sigma_hat

    def 					_UpperCAmelCase	(				self			:				Tuple ,  lowerCAmelCase_			:				KarrasVeSchedulerState ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				float ,  lowerCAmelCase_			:				float ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				bool = True ,  ):







      """simple docstring"""







      lowercase_								= sample_hat + sigma_hat * model_output
      lowercase_								= (sample_hat - pred_original_sample) / sigma_hat
      lowercase_								= sample_hat + (sigma_prev - sigma_hat) * derivative

      if not return_dict:
        return (sample_prev, derivative, state)

      return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase_ ,  derivative=lowerCAmelCase_ ,  state=lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				Dict ,  lowerCAmelCase_			:				KarrasVeSchedulerState ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				float ,  lowerCAmelCase_			:				float ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				bool = True ,  ):







      """simple docstring"""







      lowercase_								= sample_prev + sigma_prev * model_output
      lowercase_								= (sample_prev - pred_original_sample) / sigma_prev
      lowercase_								= sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

      if not return_dict:
        return (sample_prev, derivative, state)

      return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase_ ,  derivative=lowerCAmelCase_ ,  state=lowerCAmelCase_)




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







      """simple docstring"""







      raise NotImplementedError()






"""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

