






'''simple docstring'''

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

UpperCamelCase							=logging.get_logger(__name__)




@add_end_docstrings(SCREAMING_SNAKE_CASE__						)
class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 def __init__(							self			,     **__lowerCAmelCase ):
       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   _UpperCAmelCase       (							self			,     **__lowerCAmelCase ):
       UpperCamelCase_				:   List[Any]					    =							{}
       UpperCamelCase_				:   List[Any]					    =							{}
       UpperCamelCase_				:   Optional[int]					    =							{}
       # preprocess args
       if "points_per_batch" in kwargs:
             UpperCamelCase_				:   Optional[int]					    =							kwargs["""points_per_batch"""]
       if "points_per_crop" in kwargs:
             UpperCamelCase_				:   str					    =							kwargs["""points_per_crop"""]
       if "crops_n_layers" in kwargs:
             UpperCamelCase_				:   Any					    =							kwargs["""crops_n_layers"""]
       if "crop_overlap_ratio" in kwargs:
             UpperCamelCase_				:   str					    =							kwargs["""crop_overlap_ratio"""]
       if "crop_n_points_downscale_factor" in kwargs:
             UpperCamelCase_				:   Optional[Any]					    =							kwargs["""crop_n_points_downscale_factor"""]
       # postprocess args
       if "pred_iou_thresh" in kwargs:
             UpperCamelCase_				:   int					    =							kwargs["""pred_iou_thresh"""]
       if "stability_score_offset" in kwargs:
             UpperCamelCase_				:   Dict					    =							kwargs["""stability_score_offset"""]
       if "mask_threshold" in kwargs:
             UpperCamelCase_				:   Union[str, Any]					    =							kwargs["""mask_threshold"""]
       if "stability_score_thresh" in kwargs:
             UpperCamelCase_				:   List[Any]					    =							kwargs["""stability_score_thresh"""]
       if "crops_nms_thresh" in kwargs:
             UpperCamelCase_				:   Tuple					    =							kwargs["""crops_nms_thresh"""]
       if "output_rle_mask" in kwargs:
             UpperCamelCase_				:   int					    =							kwargs["""output_rle_mask"""]
       if "output_bboxes_mask" in kwargs:
             UpperCamelCase_				:   Dict					    =							kwargs["""output_bboxes_mask"""]
       return preprocess_kwargs, forward_params, postprocess_kwargs
 def __call__(							self			,     __lowerCAmelCase			,     *__lowerCAmelCase			,     __lowerCAmelCase=None			,     __lowerCAmelCase=None			,     **__lowerCAmelCase ):
       return super().__call__(__lowerCAmelCase			,     *__lowerCAmelCase			,     num_workers=__lowerCAmelCase			,     batch_size=__lowerCAmelCase			,     **__lowerCAmelCase )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase=64			,     __lowerCAmelCase = 0			,     __lowerCAmelCase = 5_12 / 15_00			,     __lowerCAmelCase = 32			,     __lowerCAmelCase = 1			,     ):
       UpperCamelCase_				:   int					    =							load_image(__lowerCAmelCase )
       UpperCamelCase_				:   List[Any]					    =							self.image_processor.size["""longest_edge"""]
       UpperCamelCase_				:   Dict					    =							self.image_processor.generate_crop_boxes(
           __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase )
       UpperCamelCase_				:   Any					    =							self.image_processor(images=__lowerCAmelCase			,     return_tensors="""pt""" )

       with self.device_placement():
             if self.framework == "pt":
                   UpperCamelCase_				:   Optional[int]					    =							self.get_inference_context()
                   with inference_context():
                         UpperCamelCase_				:   Dict					    =							self._ensure_tensor_on_device(__lowerCAmelCase			,     device=self.device )
                         UpperCamelCase_				:   Any					    =							self.model.get_image_embeddings(model_inputs.pop("""pixel_values""" ) )
                         UpperCamelCase_				:   str					    =							image_embeddings

       UpperCamelCase_				:   List[Any]					    =							grid_points.shape[1]
       UpperCamelCase_				:   Any					    =							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 ):
             UpperCamelCase_				:   int					    =							grid_points[:, i : i + points_per_batch, :, :]
             UpperCamelCase_				:   Any					    =							input_labels[:, i : i + points_per_batch]
             UpperCamelCase_				:   Tuple					    =							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   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase=0.88			,     __lowerCAmelCase=0.95			,     __lowerCAmelCase=0			,     __lowerCAmelCase=1			,     ):
       UpperCamelCase_				:   str					    =							model_inputs.pop("""input_boxes""" )
       UpperCamelCase_				:   str					    =							model_inputs.pop("""is_last""" )
       UpperCamelCase_				:   str					    =							model_inputs.pop("""original_sizes""" ).tolist()
       UpperCamelCase_				:   Any					    =							model_inputs.pop("""reshaped_input_sizes""" ).tolist()

       UpperCamelCase_				:   str					    =							self.model(**__lowerCAmelCase )

       # post processing happens here in order to avoid CPU GPU copies of ALL the masks
       UpperCamelCase_				:   Optional[int]					    =							model_outputs["""pred_masks"""]
       UpperCamelCase_				:   Optional[int]					    =							self.image_processor.post_process_masks(
           __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     binarize=__lowerCAmelCase )
       UpperCamelCase_				:   List[Any]					    =							model_outputs["""iou_scores"""]
       UpperCamelCase_				:   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   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase=False			,     __lowerCAmelCase=False			,     __lowerCAmelCase=0.7			,     ):
       UpperCamelCase_				:   Tuple					    =							[]
       UpperCamelCase_				:   Union[str, Any]					    =							[]
       UpperCamelCase_				:   Tuple					    =							[]
       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""" ) )

       UpperCamelCase_				:   Union[str, Any]					    =							torch.cat(__lowerCAmelCase )
       UpperCamelCase_				:   str					    =							torch.cat(__lowerCAmelCase )
       UpperCamelCase_				:   int					    =							self.image_processor.post_process_for_mask_generation(
           __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase )

       UpperCamelCase_				:   Any					    =							defaultdict(__lowerCAmelCase )
       for output in model_outputs:
             for k, v in output.items():
                   extra[k].append(__lowerCAmelCase )

       UpperCamelCase_				:   List[Any]					    =							{}
       if output_rle_mask:
             UpperCamelCase_				:   Any					    =							rle_mask

       if output_bboxes_mask:
             UpperCamelCase_				:   List[Any]					    =							bounding_boxes

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














'''simple docstring'''

import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
      import transformers



def 							snake_case					(     a_							: int	)       ->				Union[str, Any]:





      """simple docstring"""

      random.seed(a_	)
      np.random.seed(a_	)
      torch.manual_seed(a_	)
      torch.cuda.manual_seed_all(a_	)
      # ^^ safe to call this function even if cuda is not available




class     A    :






 """simple docstring"""





 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase = 0.99_99			,     __lowerCAmelCase = 0.0			,     __lowerCAmelCase = 0			,     __lowerCAmelCase = False			,     __lowerCAmelCase = 1.0			,     __lowerCAmelCase = 2 / 3			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     **__lowerCAmelCase			,     ):

       if isinstance(__lowerCAmelCase			,     torch.nn.Module ):
             UpperCamelCase_				:   Dict					    =							(
                 """Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. """
                 """Please pass the parameters of the module instead."""
             )
             deprecate(
                 """passing a `torch.nn.Module` to `ExponentialMovingAverage`"""			,     """1.0.0"""			,     __lowerCAmelCase			,     standard_warn=__lowerCAmelCase			,     )
             UpperCamelCase_				:   str					    =							parameters.parameters()

             # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
             UpperCamelCase_				:   Optional[int]					    =							True

       if kwargs.get("""max_value"""			,     __lowerCAmelCase ) is not None:
             UpperCamelCase_				:   Tuple					    =							"""The `max_value` argument is deprecated. Please use `decay` instead."""
             deprecate("""max_value"""			,     """1.0.0"""			,     __lowerCAmelCase			,     standard_warn=__lowerCAmelCase )
             UpperCamelCase_				:   str					    =							kwargs["""max_value"""]

       if kwargs.get("""min_value"""			,     __lowerCAmelCase ) is not None:
             UpperCamelCase_				:   Dict					    =							"""The `min_value` argument is deprecated. Please use `min_decay` instead."""
             deprecate("""min_value"""			,     """1.0.0"""			,     __lowerCAmelCase			,     standard_warn=__lowerCAmelCase )
             UpperCamelCase_				:   Optional[Any]					    =							kwargs["""min_value"""]

       UpperCamelCase_				:   Optional[Any]					    =							list(__lowerCAmelCase )
       UpperCamelCase_				:   Any					    =							[p.clone().detach() for p in parameters]

       if kwargs.get("""device"""			,     __lowerCAmelCase ) is not None:
             UpperCamelCase_				:   str					    =							"""The `device` argument is deprecated. Please use `to` instead."""
             deprecate("""device"""			,     """1.0.0"""			,     __lowerCAmelCase			,     standard_warn=__lowerCAmelCase )
             self.to(device=kwargs["""device"""] )

       UpperCamelCase_				:   List[Any]					    =							None

       UpperCamelCase_				:   Optional[Any]					    =							decay
       UpperCamelCase_				:   List[str]					    =							min_decay
       UpperCamelCase_				:   int					    =							update_after_step
       UpperCamelCase_				:   Optional[int]					    =							use_ema_warmup
       UpperCamelCase_				:   Optional[int]					    =							inv_gamma
       UpperCamelCase_				:   Any					    =							power
       UpperCamelCase_				:   str					    =							0
       UpperCamelCase_				:   List[str]					    =							None  # set in `step()`

       UpperCamelCase_				:   Union[str, Any]					    =							model_cls
       UpperCamelCase_				:   Any					    =							model_config
 @classmethod
 def   _UpperCAmelCase       (							cls			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       UpperCamelCase_    ,			UpperCamelCase_				:   int					    =							model_cls.load_config(__lowerCAmelCase			,     return_unused_kwargs=__lowerCAmelCase )
       UpperCamelCase_				:   str					    =							model_cls.from_pretrained(__lowerCAmelCase )

       UpperCamelCase_				:   Union[str, Any]					    =							cls(model.parameters()			,     model_cls=__lowerCAmelCase			,     model_config=model.config )

       ema_model.load_state_dict(__lowerCAmelCase )
       return ema_model
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       if self.model_cls is None:
             raise ValueError("""`save_pretrained` can only be used if `model_cls` was defined at __init__.""" )

       if self.model_config is None:
             raise ValueError("""`save_pretrained` can only be used if `model_config` was defined at __init__.""" )

       UpperCamelCase_				:   int					    =							self.model_cls.from_config(self.model_config )
       UpperCamelCase_				:   List[Any]					    =							self.state_dict()
       state_dict.pop("""shadow_params"""			,     __lowerCAmelCase )

       model.register_to_config(**__lowerCAmelCase )
       self.copy_to(model.parameters() )
       model.save_pretrained(__lowerCAmelCase )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Any					    =							max(0			,     optimization_step - self.update_after_step - 1 )

       if step <= 0:
             return 0.0

       if self.use_ema_warmup:
             UpperCamelCase_				:   List[Any]					    =							1 - (1 + step / self.inv_gamma) ** -self.power
       else:
             UpperCamelCase_				:   List[Any]					    =							(1 + step) / (10 + step)

       UpperCamelCase_				:   Optional[Any]					    =							min(__lowerCAmelCase			,     self.decay )
       # make sure decay is not smaller than min_decay
       UpperCamelCase_				:   Optional[Any]					    =							max(__lowerCAmelCase			,     self.min_decay )
       return cur_decay_value
 @torch.no_grad()
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       if isinstance(__lowerCAmelCase			,     torch.nn.Module ):
             UpperCamelCase_				:   str					    =							(
                 """Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. """
                 """Please pass the parameters of the module instead."""
             )
             deprecate(
                 """passing a `torch.nn.Module` to `ExponentialMovingAverage.step`"""			,     """1.0.0"""			,     __lowerCAmelCase			,     standard_warn=__lowerCAmelCase			,     )
             UpperCamelCase_				:   int					    =							parameters.parameters()

       UpperCamelCase_				:   Optional[int]					    =							list(__lowerCAmelCase )

       self.optimization_step += 1

       # Compute the decay factor for the exponential moving average.
       UpperCamelCase_				:   Any					    =							self.get_decay(self.optimization_step )
       UpperCamelCase_				:   List[str]					    =							decay
       UpperCamelCase_				:   Any					    =							1 - decay

       UpperCamelCase_				:   Optional[int]					    =							contextlib.nullcontext
       if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
             import deepspeed

       for s_param, param in zip(self.shadow_params			,     __lowerCAmelCase ):
             if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                   UpperCamelCase_				:   Optional[Any]					    =							deepspeed.zero.GatheredParameters(__lowerCAmelCase			,     modifier_rank=__lowerCAmelCase )

             with context_manager():
                   if param.requires_grad:
                         s_param.sub_(one_minus_decay * (s_param - param) )
                   else:
                         s_param.copy_(__lowerCAmelCase )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   str					    =							list(__lowerCAmelCase )
       for s_param, param in zip(self.shadow_params			,     __lowerCAmelCase ):
             param.data.copy_(s_param.to(param.device ).data )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase=None			,     __lowerCAmelCase=None ):
       UpperCamelCase_				:   Union[str, Any]					    =							[
           p.to(device=__lowerCAmelCase			,     dtype=__lowerCAmelCase ) if p.is_floating_point() else p.to(device=__lowerCAmelCase )
           for p in self.shadow_params
       ]
 def   _UpperCAmelCase       (							self ):
       return {
           "decay": self.decay,
           "min_decay": self.min_decay,
           "optimization_step": self.optimization_step,
           "update_after_step": self.update_after_step,
           "use_ema_warmup": self.use_ema_warmup,
           "inv_gamma": self.inv_gamma,
           "power": self.power,
           "shadow_params": self.shadow_params,
       }
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   List[str]					    =							[param.detach().cpu().clone() for param in parameters]
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       if self.temp_stored_params is None:
             raise RuntimeError("""This ExponentialMovingAverage has no `store()`ed weights """ """to `restore()`""" )
       for c_param, param in zip(self.temp_stored_params			,     __lowerCAmelCase ):
             param.data.copy_(c_param.data )

       # Better memory-wise.
       UpperCamelCase_				:   List[Any]					    =							None





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   List[Any]					    =							copy.deepcopy(__lowerCAmelCase )

       UpperCamelCase_				:   int					    =							state_dict.get("""decay"""			,     self.decay )
       if self.decay < 0.0 or self.decay > 1.0:
             raise ValueError("""Decay must be between 0 and 1""" )

       UpperCamelCase_				:   Dict					    =							state_dict.get("""min_decay"""			,     self.min_decay )
       if not isinstance(self.min_decay			,     __lowerCAmelCase ):
             raise ValueError("""Invalid min_decay""" )

       UpperCamelCase_				:   str					    =							state_dict.get("""optimization_step"""			,     self.optimization_step )
       if not isinstance(self.optimization_step			,     __lowerCAmelCase ):
             raise ValueError("""Invalid optimization_step""" )

       UpperCamelCase_				:   Dict					    =							state_dict.get("""update_after_step"""			,     self.update_after_step )
       if not isinstance(self.update_after_step			,     __lowerCAmelCase ):
             raise ValueError("""Invalid update_after_step""" )

       UpperCamelCase_				:   List[str]					    =							state_dict.get("""use_ema_warmup"""			,     self.use_ema_warmup )
       if not isinstance(self.use_ema_warmup			,     __lowerCAmelCase ):
             raise ValueError("""Invalid use_ema_warmup""" )

       UpperCamelCase_				:   Any					    =							state_dict.get("""inv_gamma"""			,     self.inv_gamma )
       if not isinstance(self.inv_gamma			,     (float, int) ):
             raise ValueError("""Invalid inv_gamma""" )

       UpperCamelCase_				:   str					    =							state_dict.get("""power"""			,     self.power )
       if not isinstance(self.power			,     (float, int) ):
             raise ValueError("""Invalid power""" )

       UpperCamelCase_				:   Dict					    =							state_dict.get("""shadow_params"""			,     __lowerCAmelCase )
       if shadow_params is not None:
             UpperCamelCase_				:   Any					    =							shadow_params
             if not isinstance(self.shadow_params			,     __lowerCAmelCase ):
                   raise ValueError("""shadow_params must be a list""" )
             if not all(isinstance(__lowerCAmelCase			,     torch.Tensor ) for p in self.shadow_params ):
                   raise ValueError("""shadow_params must all be Tensors""" )






