
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

lowercase__          =logging.get_logger(__name__)



@add_end_docstrings(__lowercase   )
class 		UpperCamelCase__  (	__lowercase   ):
   def __init__(self			: str ,    **snake_case_			: List[str]       ):
    super().__init__(**snake_case_       )
    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(snake_case_       )
   def 				lowerCAmelCase      (self			: int ,    **snake_case_			: int       ):
    __a			:  Optional[Any]		    =  {}
    __a			:  int		    =  {}
    __a			:  Any		    =  {}
    # preprocess args
    if "points_per_batch" in kwargs:
     __a			:  Union[str, Any]		    =  kwargs['''points_per_batch''']
    if "points_per_crop" in kwargs:
     __a			:  List[str]		    =  kwargs['''points_per_crop''']
    if "crops_n_layers" in kwargs:
     __a			:  List[Any]		    =  kwargs['''crops_n_layers''']
    if "crop_overlap_ratio" in kwargs:
     __a			:  Optional[int]		    =  kwargs['''crop_overlap_ratio''']
    if "crop_n_points_downscale_factor" in kwargs:
     __a			:  Dict		    =  kwargs['''crop_n_points_downscale_factor''']
    # postprocess args
    if "pred_iou_thresh" in kwargs:
     __a			:  List[Any]		    =  kwargs['''pred_iou_thresh''']
    if "stability_score_offset" in kwargs:
     __a			:  Any		    =  kwargs['''stability_score_offset''']
    if "mask_threshold" in kwargs:
     __a			:  int		    =  kwargs['''mask_threshold''']
    if "stability_score_thresh" in kwargs:
     __a			:  Union[str, Any]		    =  kwargs['''stability_score_thresh''']
    if "crops_nms_thresh" in kwargs:
     __a			:  Any		    =  kwargs['''crops_nms_thresh''']
    if "output_rle_mask" in kwargs:
     __a			:  Tuple		    =  kwargs['''output_rle_mask''']
    if "output_bboxes_mask" in kwargs:
     __a			:  int		    =  kwargs['''output_bboxes_mask''']
    return preprocess_kwargs, forward_params, postprocess_kwargs
   def __call__(self			: List[Any] ,    snake_case_			: int ,    *snake_case_			: List[Any] ,    snake_case_			: Optional[Any]=None ,    snake_case_			: Optional[int]=None ,    **snake_case_			: Any       ):
    return super().__call__(snake_case_ ,    *snake_case_ ,    num_workers=snake_case_ ,    batch_size=snake_case_ ,    **snake_case_       )
   def 				lowerCAmelCase      (self			: Optional[Any] ,    snake_case_			: str ,    snake_case_			: List[str]=6_4 ,    snake_case_			: int = 0 ,    snake_case_			: float = 5_1_2 / 1_5_0_0 ,    snake_case_			: Optional[int] = 3_2 ,    snake_case_			: Optional[int] = 1 ,    ):
    __a			:  Optional[Any]		    =  load_image(snake_case_       )
    __a			:  Optional[int]		    =  self.image_processor.size['''longest_edge''']
    __a    ,	__a    ,	__a    ,	__a			:  List[str]		    =  self.image_processor.generate_crop_boxes(
        snake_case_ ,    snake_case_ ,    snake_case_ ,    snake_case_ ,    snake_case_ ,    snake_case_       )
    __a			:  Any		    =  self.image_processor(images=snake_case_ ,    return_tensors='''pt'''       )

    with self.device_placement():
     if self.framework == "pt":
      __a			:  int		    =  self.get_inference_context()
      with inference_context():
       __a			:  Any		    =  self._ensure_tensor_on_device(snake_case_ ,    device=self.device       )
       __a			:  int		    =  self.model.get_image_embeddings(model_inputs.pop('''pixel_values'''       )       )
       __a			:  Dict		    =  image_embeddings

    __a			:  Optional[Any]		    =  grid_points.shape[1]
    __a			:  str		    =  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 ,    snake_case_ ,    snake_case_       ):
     __a			:  Optional[int]		    =  grid_points[:, i : i + points_per_batch, :, :]
     __a			:  Tuple		    =  input_labels[:, i : i + points_per_batch]
     __a			:  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 				lowerCAmelCase      (self			: Optional[Any] ,    snake_case_			: str ,    snake_case_			: str=0.88 ,    snake_case_			: Tuple=0.95 ,    snake_case_			: Dict=0 ,    snake_case_			: List[str]=1 ,    ):
    __a			:  List[Any]		    =  model_inputs.pop('''input_boxes'''       )
    __a			:  str		    =  model_inputs.pop('''is_last'''       )
    __a			:  Optional[Any]		    =  model_inputs.pop('''original_sizes'''       ).tolist()
    __a			:  List[Any]		    =  model_inputs.pop('''reshaped_input_sizes'''       ).tolist()

    __a			:  Optional[Any]		    =  self.model(**snake_case_       )

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







   def 				lowerCAmelCase      (self			: int ,    snake_case_			: Any ,    snake_case_			: str=False ,    snake_case_			: Tuple=False ,    snake_case_			: Any=0.7 ,    ):
    __a			:  int		    =  []
    __a			:  Dict		    =  []
    __a			:  Optional[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'''       )       )

    __a			:  List[Any]		    =  torch.cat(snake_case_       )
    __a			:  List[Any]		    =  torch.cat(snake_case_       )
    __a    ,	__a    ,	__a    ,	__a			:  int		    =  self.image_processor.post_process_for_mask_generation(
        snake_case_ ,    snake_case_ ,    snake_case_ ,    snake_case_       )

    __a			:  Dict		    =  defaultdict(snake_case_       )
    for output in model_outputs:
     for k, v in output.items():
      extra[k].append(snake_case_       )

    __a			:  List[Any]		    =  {}
    if output_rle_mask:
     __a			:  Tuple		    =  rle_mask

    if output_bboxes_mask:
     __a			:  Optional[Any]		    =  bounding_boxes

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


import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


lowercase__          =50000
lowercase__          =5000

lowercase__       ,      lowercase__          =os.path.split(__file__)
lowercase__          =os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))




@get_duration
def        __UpperCamelCase					(    lowerCAmelCase__							:    datasets.Dataset  ,       lowerCAmelCase__							:    List[str]  ):
 for i in range(lowerCAmelCase__  ):
  __a			:  str		    =  dataset[i]




@get_duration
def        __UpperCamelCase					(    lowerCAmelCase__							:    datasets.Dataset  ,       lowerCAmelCase__							:    Optional[Any]  ,       lowerCAmelCase__							:    Tuple  ):
 for i in range(0  ,       len(lowerCAmelCase__  )  ,       lowerCAmelCase__  ):
  __a			:  Optional[int]		    =  dataset[i : i + batch_size]




@get_duration
def        __UpperCamelCase					(    lowerCAmelCase__							:    datasets.Dataset  ,       lowerCAmelCase__							:    Union[str, Any]  ,       lowerCAmelCase__							:    str  ):
 with dataset.formatted_as(type=lowerCAmelCase__  ):
  for i in range(lowerCAmelCase__  ):
   __a			:  Dict		    =  dataset[i]




@get_duration
def        __UpperCamelCase					(    lowerCAmelCase__							:    datasets.Dataset  ,       lowerCAmelCase__							:    List[str]  ,       lowerCAmelCase__							:    Optional[Any]  ,       lowerCAmelCase__							:    int  ):
 with dataset.formatted_as(type=lowerCAmelCase__  ):
  for i in range(0  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ):
   __a			:  int		    =  dataset[i : i + batch_size]




def        __UpperCamelCase					(    ):
 __a			:  Any		    =  {'''num examples''': SPEED_TEST_N_EXAMPLES}
 __a			:  List[Any]		    =  [
     (read, {'''length''': SMALL_TEST}),
     (read, {'''length''': SPEED_TEST_N_EXAMPLES}),
     (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_0}),
     (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_0_0}),
     (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_0_0_0}),
     (read_formatted, {'''type''': '''numpy''', '''length''': SMALL_TEST}),
     (read_formatted, {'''type''': '''pandas''', '''length''': SMALL_TEST}),
     (read_formatted, {'''type''': '''torch''', '''length''': SMALL_TEST}),
     (read_formatted, {'''type''': '''tensorflow''', '''length''': SMALL_TEST}),
     (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1_0}),
     (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1_0_0_0}),
 ]

 __a			:  Union[str, Any]		    =  [
     (read, {'''length''': SMALL_TEST}),
     (read, {'''length''': SPEED_TEST_N_EXAMPLES}),
     (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_0}),
     (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_0_0}),
     (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_0_0_0}),
     (read_formatted, {'''type''': '''numpy''', '''length''': SMALL_TEST}),
     (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1_0}),
     (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1_0_0_0}),
 ]
 with tempfile.TemporaryDirectory() as tmp_dir:
  print('''generating dataset'''  )
  __a			:  Optional[Any]		    =  datasets.Features(
      {'''list''': datasets.Sequence(datasets.Value('''float32'''  )  ), '''numbers''': datasets.Value('''float32'''  )}  )
  __a			:  Optional[int]		    =  generate_example_dataset(
      os.path.join(lowerCAmelCase__  ,       '''dataset.arrow'''  )  ,       lowerCAmelCase__  ,       num_examples=lowerCAmelCase__  ,       seq_shapes={'''list''': (1_0_0,)}  ,       )
  print('''first set of iterations'''  )
  for func, kwargs in functions:
   print(func.__name__  ,       str(lowerCAmelCase__  )  )
   __a			:  str		    =  func(lowerCAmelCase__  ,       **lowerCAmelCase__  )

  print('''shuffling dataset'''  )
  __a			:  int		    =  dataset.shuffle()
  print('''Second set of iterations (after shuffling'''  )
  for func, kwargs in functions_shuffled:
   print('''shuffled '''  ,       func.__name__  ,       str(lowerCAmelCase__  )  )
   __a			:  List[Any]		    =  func(
       lowerCAmelCase__  ,       **lowerCAmelCase__  )

 with open(lowerCAmelCase__  ,       '''wb'''  ) as f:
  f.write(json.dumps(lowerCAmelCase__  ).encode('''utf-8'''  )  )


if __name__ == "__main__":  # useful to run the profiler
      benchmark_iterating()
