


from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
    import torch

    from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

A   :					str	=      logging.get_logger(__name__)







@add_end_docstrings(A_)
class     _lowercase				(							A_):


       """simple docstring"""




       def __init__(     self							:    Tuple      ,   **__lowerCamelCase							:    Optional[Any] ):


             '''simple docstring'''



             super().__init__(**__lowerCamelCase )
             requires_backends(self      ,   "vision" )
             requires_backends(self      ,   "torch" )

             if self.framework != "pt":
                   raise ValueError(f"The {self.__class__} is only available in PyTorch." )

             self.check_model_type(__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Optional[Any]      ,   **__lowerCamelCase							:    List[str] ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[Any]					=						{}
             lowerCamelCase__	:       Tuple					=						{}
             lowerCamelCase__	:       Dict					=						{}
             # preprocess args
             if "points_per_batch" in kwargs:
                   lowerCamelCase__	:       str					=						kwargs["""points_per_batch"""]
             if "points_per_crop" in kwargs:
                   lowerCamelCase__	:       int					=						kwargs["""points_per_crop"""]
             if "crops_n_layers" in kwargs:
                   lowerCamelCase__	:       Optional[Any]					=						kwargs["""crops_n_layers"""]
             if "crop_overlap_ratio" in kwargs:
                   lowerCamelCase__	:       Dict					=						kwargs["""crop_overlap_ratio"""]
             if "crop_n_points_downscale_factor" in kwargs:
                   lowerCamelCase__	:       Dict					=						kwargs["""crop_n_points_downscale_factor"""]
             # postprocess args
             if "pred_iou_thresh" in kwargs:
                   lowerCamelCase__	:       List[Any]					=						kwargs["""pred_iou_thresh"""]
             if "stability_score_offset" in kwargs:
                   lowerCamelCase__	:       Optional[int]					=						kwargs["""stability_score_offset"""]
             if "mask_threshold" in kwargs:
                   lowerCamelCase__	:       Optional[int]					=						kwargs["""mask_threshold"""]
             if "stability_score_thresh" in kwargs:
                   lowerCamelCase__	:       List[str]					=						kwargs["""stability_score_thresh"""]
             if "crops_nms_thresh" in kwargs:
                   lowerCamelCase__	:       List[Any]					=						kwargs["""crops_nms_thresh"""]
             if "output_rle_mask" in kwargs:
                   lowerCamelCase__	:       int					=						kwargs["""output_rle_mask"""]
             if "output_bboxes_mask" in kwargs:
                   lowerCamelCase__	:       Tuple					=						kwargs["""output_bboxes_mask"""]
             return preprocess_kwargs, forward_params, postprocess_kwargs





       def __call__(     self							:    List[Any]      ,   __lowerCamelCase							:    Any      ,   *__lowerCamelCase							:    Any      ,   __lowerCamelCase							:    int=None      ,   __lowerCamelCase							:    str=None      ,   **__lowerCamelCase							:    Optional[int] ):


             '''simple docstring'''



             return super().__call__(__lowerCamelCase      ,   *__lowerCamelCase      ,   num_workers=__lowerCamelCase      ,   batch_size=__lowerCamelCase      ,   **__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    str      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    Union[str, Any]=64      ,   __lowerCamelCase							:    Any = 0      ,   __lowerCamelCase							:    List[str] = 512 / 1500      ,   __lowerCamelCase							:    List[str] = 32      ,   __lowerCamelCase							:    Dict = 1      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						load_image(__lowerCamelCase )
             lowerCamelCase__	:       Optional[int]					=						self.image_processor.size["""longest_edge"""]
             lowerCamelCase__	:       List[str]					=						self.image_processor.generate_crop_boxes(
                 __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase )
             lowerCamelCase__	:       List[str]					=						self.image_processor(images=__lowerCamelCase      ,   return_tensors="pt" )

             with self.device_placement():
                   if self.framework == "pt":
                         lowerCamelCase__	:       str					=						self.get_inference_context()
                         with inference_context():
                               lowerCamelCase__	:       int					=						self._ensure_tensor_on_device(__lowerCamelCase      ,   device=self.device )
                               lowerCamelCase__	:       List[str]					=						self.model.get_image_embeddings(model_inputs.pop("pixel_values" ) )
                               lowerCamelCase__	:       int					=						image_embeddings

             lowerCamelCase__	:       str					=						grid_points.shape[1]
             lowerCamelCase__	:       Optional[int]					=						points_per_batch if points_per_batch is not None else n_points

             if points_per_batch <= 0:
                   raise ValueError(
                       "Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. "
                       "To return all points at once, set points_per_batch to None" )

             for i in range(0      ,   __lowerCamelCase      ,   __lowerCamelCase ):
                   lowerCamelCase__	:       Any					=						grid_points[:, i : i + points_per_batch, :, :]
                   lowerCamelCase__	:       List[str]					=						input_labels[:, i : i + points_per_batch]
                   lowerCamelCase__	:       Any					=						i == n_points - points_per_batch
                   yield {
                       "input_points": batched_points,
                       "input_labels": labels,
                       "input_boxes": crop_boxes,
                       "is_last": is_last,
                       **model_inputs,
                   }





       def 		lowerCAmelCase  (     self							:    Dict      ,   __lowerCamelCase							:    List[Any]      ,   __lowerCamelCase							:    Tuple=0.8_8      ,   __lowerCamelCase							:    Tuple=0.9_5      ,   __lowerCamelCase							:    Optional[Any]=0      ,   __lowerCamelCase							:    Any=1      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       Tuple					=						model_inputs.pop("input_boxes" )
             lowerCamelCase__	:       List[str]					=						model_inputs.pop("is_last" )
             lowerCamelCase__	:       Union[str, Any]					=						model_inputs.pop("original_sizes" ).tolist()
             lowerCamelCase__	:       List[str]					=						model_inputs.pop("reshaped_input_sizes" ).tolist()

             lowerCamelCase__	:       Any					=						self.model(**__lowerCamelCase )

             # post processing happens here in order to avoid CPU GPU copies of ALL the masks
             lowerCamelCase__	:       Optional[int]					=						model_outputs["""pred_masks"""]
             lowerCamelCase__	:       int					=						self.image_processor.post_process_masks(
                 __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   binarize=__lowerCamelCase )
             lowerCamelCase__	:       Dict					=						model_outputs["""iou_scores"""]
             lowerCamelCase__	:       List[str]					=						self.image_processor.filter_masks(
                 masks[0]      ,   iou_scores[0]      ,   original_sizes[0]      ,   input_boxes[0]      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   )
             return {
                 "masks": masks,
                 "is_last": is_last,
                 "boxes": boxes,
                 "iou_scores": iou_scores,
             }





       def 		lowerCAmelCase  (     self							:    Dict      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    Tuple=False      ,   __lowerCamelCase							:    Tuple=False      ,   __lowerCamelCase							:    List[Any]=0.7      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						[]
             lowerCamelCase__	:       Optional[int]					=						[]
             lowerCamelCase__	:       List[Any]					=						[]
             for model_output in model_outputs:
                   all_scores.append(model_output.pop("iou_scores" ) )
                   all_masks.extend(model_output.pop("masks" ) )
                   all_boxes.append(model_output.pop("boxes" ) )

             lowerCamelCase__	:       Union[str, Any]					=						torch.cat(__lowerCamelCase )
             lowerCamelCase__	:       Any					=						torch.cat(__lowerCamelCase )
             lowerCamelCase__	:       Dict					=						self.image_processor.post_process_for_mask_generation(
                 __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase )

             lowerCamelCase__	:       Optional[int]					=						defaultdict(__lowerCamelCase )
             for output in model_outputs:
                   for k, v in output.items():
                         extra[k].append(__lowerCamelCase )

             lowerCamelCase__	:       Optional[Any]					=						{}
             if output_rle_mask:
                   lowerCamelCase__	:       Dict					=						rle_mask

             if output_bboxes_mask:
                   lowerCamelCase__	:       str					=						bounding_boxes

             return {"masks": output_masks, "scores": iou_scores, **optional, **extra}






from __future__ import annotations

import time

import numpy as np

A   :					Dict	=      [8, 5, 9, 7]
A   :					Optional[Any]	=      [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
A   :					Any	=      [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]







class     _lowercase				:


       """simple docstring"""




       def __init__(     self							:    str      ,   __lowerCamelCase							:    list[int]      ,   __lowerCamelCase							:    list[list[int]]      ,   __lowerCamelCase							:    list[list[int]]      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						claim_vector
             lowerCamelCase__	:       str					=						allocated_resources_table
             lowerCamelCase__	:       int					=						maximum_claim_table





       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             return [
                 sum(p_item[i] for p_item in self.__allocated_resources_table )
                 for i in range(len(self.__allocated_resources_table[0] ) )
             ]





       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             return np.array(self.__claim_vector ) - np.array(
                 self.__processes_resource_summation() )





       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             return [
                 list(np.array(self.__maximum_claim_table[i] ) - np.array(__lowerCamelCase ) )
                 for i, allocated_resource in enumerate(self.__allocated_resources_table )
             ]





       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             return {self.__need().index(__lowerCamelCase ): i for i in self.__need()}





       def 		lowerCAmelCase  (     self							:    List[str]      ,   **__lowerCamelCase							:    Union[str, Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[Any]					=						self.__need()
             lowerCamelCase__	:       str					=						self.__allocated_resources_table
             lowerCamelCase__	:       List[Any]					=						self.__available_resources()
             lowerCamelCase__	:       str					=						self.__need_index_manager()
             for kw, val in kwargs.items():
                   if kw and val is True:
                         self.__pretty_data()
             print("_" * 50 + "\n" )
             while need_list:
                   lowerCamelCase__	:       int					=						False
                   for each_need in need_list:
                         lowerCamelCase__	:       Dict					=						True
                         for index, need in enumerate(__lowerCamelCase ):
                               if need > available_resources[index]:
                                     lowerCamelCase__	:       str					=						False
                                     break
                         if execution:
                               lowerCamelCase__	:       Tuple					=						True
                               # get the original index of the process from ind_ctrl db
                               for original_need_index, need_clone in need_index_manager.items():
                                     if each_need == need_clone:
                                           lowerCamelCase__	:       Any					=						original_need_index
                               print(f"Process {process_number + 1} is executing." )
                               # remove the process run from stack
                               need_list.remove(__lowerCamelCase )
                               # update available/freed resources stack
                               lowerCamelCase__	:       Union[str, Any]					=						np.array(__lowerCamelCase ) + np.array(
                                   alloc_resources_table[process_number] )
                               print(
                                   "Updated available resource stack for processes: "
                                   + " ".join([str(__lowerCamelCase ) for x in available_resources] ) )
                               break
                   if safe:
                         print("The process is in a safe state.\n" )
                   else:
                         print("System in unsafe state. Aborting...\n" )
                         break





       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             print(" " * 9 + "Allocated Resource Table" )
             for item in self.__allocated_resources_table:
                   print(
                       f"P{self.__allocated_resources_table.index(__lowerCamelCase ) + 1}"
                       + " ".join(f"{it:>8}" for it in item )
                       + "\n" )
             print(" " * 9 + "System Resource Table" )
             for item in self.__maximum_claim_table:
                   print(
                       f"P{self.__maximum_claim_table.index(__lowerCamelCase ) + 1}"
                       + " ".join(f"{it:>8}" for it in item )
                       + "\n" )
             print(
                 "Current Usage by Active Processes: "
                 + " ".join(str(__lowerCamelCase ) for x in self.__claim_vector ) )
             print(
                 "Initial Available Resources:       "
                 + " ".join(str(__lowerCamelCase ) for x in self.__available_resources() ) )
             time.sleep(1 )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


