





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	:       Optional[Any]    	=							logging.get_logger(__name__)







@add_end_docstrings(__lowerCamelCase      )
class    __snake_case	(							__lowerCamelCase      ):





       '''simple docstring'''







       def __init__(  self			:   Any							,    **A			:   int		):
         super().__init__(**A		)
         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(A		)




       def 							UpperCAmelCase__			(  self			:   List[str]							,    **A			:   Union[str, Any]		):
         __snake_case:						Optional[Any]			  =						{}
         __snake_case:						Optional[int]			  =						{}
         __snake_case:						Any			  =						{}
         # preprocess args
         if "points_per_batch" in kwargs:
           __snake_case:						Dict			  =						kwargs["""points_per_batch"""]
         if "points_per_crop" in kwargs:
           __snake_case:						int			  =						kwargs["""points_per_crop"""]
         if "crops_n_layers" in kwargs:
           __snake_case:						Any			  =						kwargs["""crops_n_layers"""]
         if "crop_overlap_ratio" in kwargs:
           __snake_case:						Dict			  =						kwargs["""crop_overlap_ratio"""]
         if "crop_n_points_downscale_factor" in kwargs:
           __snake_case:						Optional[int]			  =						kwargs["""crop_n_points_downscale_factor"""]
         # postprocess args
         if "pred_iou_thresh" in kwargs:
           __snake_case:						Dict			  =						kwargs["""pred_iou_thresh"""]
         if "stability_score_offset" in kwargs:
           __snake_case:						Optional[Any]			  =						kwargs["""stability_score_offset"""]
         if "mask_threshold" in kwargs:
           __snake_case:						str			  =						kwargs["""mask_threshold"""]
         if "stability_score_thresh" in kwargs:
           __snake_case:						Dict			  =						kwargs["""stability_score_thresh"""]
         if "crops_nms_thresh" in kwargs:
           __snake_case:						Union[str, Any]			  =						kwargs["""crops_nms_thresh"""]
         if "output_rle_mask" in kwargs:
           __snake_case:						Any			  =						kwargs["""output_rle_mask"""]
         if "output_bboxes_mask" in kwargs:
           __snake_case:						Union[str, Any]			  =						kwargs["""output_bboxes_mask"""]
         return preprocess_kwargs, forward_params, postprocess_kwargs




       def __call__(  self			:   Optional[int]							,    A			:   Dict							,    *A			:   str							,    A			:   int=None							,    A			:   Optional[Any]=None							,    **A			:   Dict		):
         return super().__call__(A							,    *A							,    num_workers=A							,    batch_size=A							,    **A		)




       def 							UpperCAmelCase__			(  self			:   Tuple							,    A			:   Optional[Any]							,    A			:   Union[str, Any]=64							,    A			:   int = 0							,    A			:   float = 512 / 1_500							,    A			:   Optional[int] = 32							,    A			:   Optional[int] = 1							,    ):
         __snake_case:						Dict			  =						load_image(A		)
         __snake_case:						Union[str, Any]			  =						self.image_processor.size["""longest_edge"""]
         __snake_case  ,       __snake_case  ,       __snake_case  ,       __snake_case:						List[str]			  =						self.image_processor.generate_crop_boxes(
             A							,    A							,    A							,    A							,    A							,    A		)
         __snake_case:						Any			  =						self.image_processor(images=A							,    return_tensors="""pt"""		)

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

         __snake_case:						Dict			  =						grid_points.shape[1]
         __snake_case:						Optional[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							,    A							,    A		):
           __snake_case:						Optional[Any]			  =						grid_points[:, i : i + points_per_batch, :, :]
           __snake_case:						int			  =						input_labels[:, i : i + points_per_batch]
           __snake_case:						List[str]			  =						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			:   Any							,    A			:   Optional[Any]							,    A			:   Optional[int]=0.88							,    A			:   int=0.95							,    A			:   int=0							,    A			:   Union[str, Any]=1							,    ):
         __snake_case:						str			  =						model_inputs.pop("""input_boxes"""		)
         __snake_case:						Any			  =						model_inputs.pop("""is_last"""		)
         __snake_case:						List[str]			  =						model_inputs.pop("""original_sizes"""		).tolist()
         __snake_case:						int			  =						model_inputs.pop("""reshaped_input_sizes"""		).tolist()

         __snake_case:						Any			  =						self.model(**A		)

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






       def 							UpperCAmelCase__			(  self			:   Optional[Any]							,    A			:   Optional[int]							,    A			:   Dict=False							,    A			:   str=False							,    A			:   Optional[int]=0.7							,    ):
         __snake_case:						Dict			  =						[]
         __snake_case:						List[str]			  =						[]
         __snake_case:						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"""		)		)

         __snake_case:						int			  =						torch.cat(A		)
         __snake_case:						List[str]			  =						torch.cat(A		)
         __snake_case  ,       __snake_case  ,       __snake_case  ,       __snake_case:						Union[str, Any]			  =						self.image_processor.post_process_for_mask_generation(
             A							,    A							,    A							,    A		)

         __snake_case:						Union[str, Any]			  =						defaultdict(A		)
         for output in model_outputs:
           for k, v in output.items():
             extra[k].append(A		)

         __snake_case:						str			  =						{}
         if output_rle_mask:
           __snake_case:						Any			  =						rle_mask

         if output_bboxes_mask:
           __snake_case:						Optional[int]			  =						bounding_boxes

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













import math
import time

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
      import torch_xla.core.xla_model as xm
      import torch_xla.debug.metrics as met







class    __snake_case	(							__lowerCamelCase      ):





       '''simple docstring'''







       def __init__(  self			:   str							,    *A			:   Dict							,    A			:   Optional[int]=None							,    A			:   Tuple=None							,    **A			:   Optional[int]		):
         super().__init__(*A							,    **A		)
         __snake_case:						List[Any]			  =						eval_examples
         __snake_case:						str			  =						post_process_function




       def 							UpperCAmelCase__			(  self			:   List[Any]							,    A			:   Dict=None							,    A			:   int=None							,    A			:   List[Any]=None							,    A			:   str = "eval"		):
         __snake_case:						int			  =						self.eval_dataset if eval_dataset is None else eval_dataset
         __snake_case:						Any			  =						self.get_eval_dataloader(A		)
         __snake_case:						Optional[Any]			  =						self.eval_examples if eval_examples is None else eval_examples

         # Temporarily disable metric computation, we will do it in the loop here.
         __snake_case:						Union[str, Any]			  =						self.compute_metrics
         __snake_case:						List[str]			  =						None
         __snake_case:						Tuple			  =						self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
         __snake_case:						Tuple			  =						time.time()
         try:
           __snake_case:						Any			  =						eval_loop(
               A							,    description="""Evaluation"""							,    prediction_loss_only=True if compute_metrics is None else None							,    ignore_keys=A							,    metric_key_prefix=A							,    )
         finally:
           __snake_case:						Optional[int]			  =						compute_metrics
         __snake_case:						Union[str, Any]			  =						self.args.eval_batch_size * self.args.world_size
         if f'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
           start_time += output.metrics[f'''{metric_key_prefix}_jit_compilation_time''']
         output.metrics.update(
             speed_metrics(
                 A							,    A							,    num_samples=output.num_samples							,    num_steps=math.ceil(output.num_samples / total_batch_size		)							,    )		)
         if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
           # Only the main node write the results by default
           __snake_case:						List[str]			  =						self.post_process_function(A							,    A							,    output.predictions		)
           __snake_case:						List[Any]			  =						self.compute_metrics(A		)

           # Prefix all keys with metric_key_prefix + '_'
           for key in list(metrics.keys()		):
             if not key.startswith(f'''{metric_key_prefix}_'''		):
               __snake_case:						str			  =						metrics.pop(A		)
           metrics.update(output.metrics		)
         else:
           __snake_case:						List[Any]			  =						output.metrics

         if self.args.should_log:
           # Only the main node log the results by default
           self.log(A		)

         if self.args.tpu_metrics_debug or self.args.debug:
           # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
           xm.master_print(met.metrics_report()		)

         __snake_case:						str			  =						self.callback_handler.on_evaluate(self.args							,    self.state							,    self.control							,    A		)
         return metrics






       def 							UpperCAmelCase__			(  self			:   Optional[Any]							,    A			:   List[Any]							,    A			:   List[str]							,    A			:   str=None							,    A			:   str = "test"		):
         __snake_case:						Optional[Any]			  =						self.get_test_dataloader(A		)

         # Temporarily disable metric computation, we will do it in the loop here.
         __snake_case:						Optional[int]			  =						self.compute_metrics
         __snake_case:						List[Any]			  =						None
         __snake_case:						str			  =						self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
         __snake_case:						Dict			  =						time.time()
         try:
           __snake_case:						str			  =						eval_loop(
               A							,    description="""Prediction"""							,    prediction_loss_only=True if compute_metrics is None else None							,    ignore_keys=A							,    metric_key_prefix=A							,    )
         finally:
           __snake_case:						List[Any]			  =						compute_metrics
         __snake_case:						Dict			  =						self.args.eval_batch_size * self.args.world_size
         if f'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
           start_time += output.metrics[f'''{metric_key_prefix}_jit_compilation_time''']
         output.metrics.update(
             speed_metrics(
                 A							,    A							,    num_samples=output.num_samples							,    num_steps=math.ceil(output.num_samples / total_batch_size		)							,    )		)

         if self.post_process_function is None or self.compute_metrics is None:
           return output

         __snake_case:						Union[str, Any]			  =						self.post_process_function(A							,    A							,    output.predictions							,    """predict"""		)
         __snake_case:						str			  =						self.compute_metrics(A		)

         # Prefix all keys with metric_key_prefix + '_'
         for key in list(metrics.keys()		):
           if not key.startswith(f'''{metric_key_prefix}_'''		):
             __snake_case:						List[str]			  =						metrics.pop(A		)
         metrics.update(output.metrics		)
         return PredictionOutput(predictions=predictions.predictions							,    label_ids=predictions.label_ids							,    metrics=A		)






