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     :  str					     =       logging.get_logger(__name__)


@add_end_docstrings(_snake_case					)
class   lowerCamelCase					(_snake_case					):
  """simple docstring"""







  def __init__(							self :       Any  ,     **__magic_name__ :       Any   )  ->	Dict:
        super().__init__(**__magic_name__   )

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

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







  def __call__(							self :       List[str]  ,     __magic_name__ :       Union[str, "Image.Image", List[Dict[str, Any]]]  ,     __magic_name__ :       Union[str, List[str]] = None  ,     **__magic_name__ :       Optional[Any]  ,     )  ->	int:

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

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







  def    __A     (							self :       Any  ,     **__magic_name__ :       Any   )  ->	Union[str, Any]:
        SCREAMING_SNAKE_CASE_          =  {}
        if "threshold" in kwargs:
              SCREAMING_SNAKE_CASE_          =  kwargs["threshold"]
        if "top_k" in kwargs:
              SCREAMING_SNAKE_CASE_          =  kwargs["top_k"]
        return {}, {}, postprocess_params







  def    __A     (							self :       Tuple  ,     __magic_name__ :       List[Any]   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  load_image(inputs["image"]   )
        SCREAMING_SNAKE_CASE_          =  inputs["candidate_labels"]
        if isinstance(__magic_name__  ,     __magic_name__   ):
              SCREAMING_SNAKE_CASE_          =  candidate_labels.split(","   )

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







  def    __A     (							self :       Optional[Any]  ,     __magic_name__ :       Union[str, Any]   )  ->	Tuple:
        SCREAMING_SNAKE_CASE_          =  model_inputs.pop("target_size"   )
        SCREAMING_SNAKE_CASE_          =  model_inputs.pop("candidate_label"   )
        SCREAMING_SNAKE_CASE_          =  model_inputs.pop("is_last"   )

        SCREAMING_SNAKE_CASE_          =  self.model(**__magic_name__   )

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







  def    __A     (							self :       Any  ,     __magic_name__ :       Tuple  ,     __magic_name__ :       Any=0.1  ,     __magic_name__ :       Optional[int]=None   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  []
        for model_output in model_outputs:
              SCREAMING_SNAKE_CASE_          =  model_output["candidate_label"]
              SCREAMING_SNAKE_CASE_          =  BaseModelOutput(__magic_name__   )
              SCREAMING_SNAKE_CASE_          =  self.image_processor.post_process_object_detection(
                  outputs=__magic_name__  ,     threshold=__magic_name__  ,     target_sizes=model_output["target_size"]   )[0]

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

                    SCREAMING_SNAKE_CASE_          =  {"score": score, "label": label, "box": box}
                    results.append(__magic_name__   )

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

        return results







  def    __A     (							self :       Dict  ,     __magic_name__ :       "torch.Tensor"   )  ->	Optional[int]:

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



# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

A     :  Dict					     =       TypeVar("T")


class   lowerCamelCase					(Generic[T]					):
  """simple docstring"""







  def __init__(							self :       Optional[int]  ,     __magic_name__ :       bool = True   )  ->	None:
        SCREAMING_SNAKE_CASE_          =  {}  # dictionary of lists
        SCREAMING_SNAKE_CASE_          =  directed







  def    __A     (							self :       Dict  ,     __magic_name__ :       T  ,     __magic_name__ :       T   )  ->	GraphAdjacencyList[T]:

        if not self.directed:  # For undirected graphs
              # if both source vertex and destination vertex are both present in the
              # adjacency list, add destination vertex to source vertex list of adjacent
              # vertices and add source vertex to destination vertex list of adjacent
              # vertices.
              if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                    self.adj_list[source_vertex].append(__magic_name__   )
                    self.adj_list[destination_vertex].append(__magic_name__   )
              # if only source vertex is present in adjacency list, add destination vertex
              # to source vertex list of adjacent vertices, then create a new vertex with
              # destination vertex as key and assign a list containing the source vertex
              # as it's first adjacent vertex.
              elif source_vertex in self.adj_list:
                    self.adj_list[source_vertex].append(__magic_name__   )
                    SCREAMING_SNAKE_CASE_          =  [source_vertex]
              # if only destination vertex is present in adjacency list, add source vertex
              # to destination vertex list of adjacent vertices, then create a new vertex
              # with source vertex as key and assign a list containing the source vertex
              # as it's first adjacent vertex.
              elif destination_vertex in self.adj_list:
                    self.adj_list[destination_vertex].append(__magic_name__   )
                    SCREAMING_SNAKE_CASE_          =  [destination_vertex]
              # if both source vertex and destination vertex are not present in adjacency
              # list, create a new vertex with source vertex as key and assign a list
              # containing the destination vertex as it's first adjacent vertex also
              # create a new vertex with destination vertex as key and assign a list
              # containing the source vertex as it's first adjacent vertex.
              else:
                    SCREAMING_SNAKE_CASE_          =  [destination_vertex]
                    SCREAMING_SNAKE_CASE_          =  [source_vertex]
        else:  # For directed graphs
              # if both source vertex and destination vertex are present in adjacency
              # list, add destination vertex to source vertex list of adjacent vertices.
              if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                    self.adj_list[source_vertex].append(__magic_name__   )
              # if only source vertex is present in adjacency list, add destination
              # vertex to source vertex list of adjacent vertices and create a new vertex
              # with destination vertex as key, which has no adjacent vertex
              elif source_vertex in self.adj_list:
                    self.adj_list[source_vertex].append(__magic_name__   )
                    SCREAMING_SNAKE_CASE_          =  []
              # if only destination vertex is present in adjacency list, create a new
              # vertex with source vertex as key and assign a list containing destination
              # vertex as first adjacent vertex
              elif destination_vertex in self.adj_list:
                    SCREAMING_SNAKE_CASE_          =  [destination_vertex]
              # if both source vertex and destination vertex are not present in adjacency
              # list, create a new vertex with source vertex as key and a list containing
              # destination vertex as it's first adjacent vertex. Then create a new vertex
              # with destination vertex as key, which has no adjacent vertex
              else:
                    SCREAMING_SNAKE_CASE_          =  [destination_vertex]
                    SCREAMING_SNAKE_CASE_          =  []

        return self







  def __repr__(							self :       int   )  ->	str:
        return pformat(self.adj_list   )


