
import warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType




class      __A(    _lowerCamelCase			):

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    data







  def __iter__(self						):


       for element in self.data:
            yield element






def 						__magic_name__			(   __a      :	List[Any]=True						):



     '''simple docstring'''




     UpperCamelCase__				=    Accelerator(even_batches=UpperCAmelCase_						)
     assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
     return accelerator






def 						__magic_name__			(   __a      :	Accelerator	,		__a      :	int	,		__a      :	int	,		__a      :	bool = False						):



     '''simple docstring'''




     if iterable:
          UpperCamelCase__				=    DummyIterableDataset(torch.as_tensor(range(UpperCAmelCase_						)						)						)
     else:
          UpperCamelCase__				=    TensorDataset(torch.as_tensor(range(UpperCAmelCase_						)						)						)

     UpperCamelCase__				=    DataLoader(UpperCAmelCase_	,		batch_size=UpperCAmelCase_						)
     UpperCamelCase__				=    accelerator.prepare(UpperCAmelCase_						)

     return dl






def 						__magic_name__			(   __a      :	Accelerator	,		__a      :	int	,		__a      :	int	,		__a      :	List[int]	,		__a      :	List[int]	,		):



     '''simple docstring'''




     UpperCamelCase__				=    create_dataloader(accelerator=UpperCAmelCase_	,		dataset_size=UpperCAmelCase_	,		batch_size=UpperCAmelCase_						)

     UpperCamelCase__				=    [len(batch[0]						) for batch in dl]

     if accelerator.process_index == 0:
          assert batch_sizes == process_0_expected_batch_sizes
     elif accelerator.process_index == 1:
          assert batch_sizes == process_1_expected_batch_sizes






def 						__magic_name__			(   ):



     '''simple docstring'''




     UpperCamelCase__				=    create_accelerator()

     # without padding, we would expect a different number of batches
     verify_dataloader_batch_sizes(
         UpperCAmelCase_	,		dataset_size=3	,		batch_size=1	,		process_0_expected_batch_sizes=[1, 1]	,		process_1_expected_batch_sizes=[1, 1]	,		)

     # without padding, we would expect the same number of batches, but different sizes
     verify_dataloader_batch_sizes(
         UpperCAmelCase_	,		dataset_size=7	,		batch_size=2	,		process_0_expected_batch_sizes=[2, 2]	,		process_1_expected_batch_sizes=[2, 2]	,		)






def 						__magic_name__			(   ):



     '''simple docstring'''




     UpperCamelCase__				=    create_accelerator(even_batches=UpperCAmelCase_						)

     verify_dataloader_batch_sizes(
         UpperCAmelCase_	,		dataset_size=3	,		batch_size=1	,		process_0_expected_batch_sizes=[1, 1]	,		process_1_expected_batch_sizes=[1]	,		)

     verify_dataloader_batch_sizes(
         UpperCAmelCase_	,		dataset_size=7	,		batch_size=2	,		process_0_expected_batch_sizes=[2, 2]	,		process_1_expected_batch_sizes=[2, 1]	,		)






def 						__magic_name__			(   ):



     '''simple docstring'''




     UpperCamelCase__				=    create_accelerator(even_batches=UpperCAmelCase_						)

     UpperCamelCase__				=    torch.nn.Linear(1	,		1						)
     UpperCamelCase__				=    accelerator.prepare(UpperCAmelCase_						)

     UpperCamelCase__				=    create_dataloader(UpperCAmelCase_	,		dataset_size=3	,		batch_size=1						)

     UpperCamelCase__				=    []
     with accelerator.join_uneven_inputs([ddp_model]						):
          for batch_idx, batch in enumerate(UpperCAmelCase_						):
               UpperCamelCase__				=    ddp_model(batch[0].float()						)
               UpperCamelCase__				=    output.sum()
               loss.backward()
               batch_idxs.append(UpperCAmelCase_						)

     accelerator.wait_for_everyone()

     if accelerator.process_index == 0:
          assert batch_idxs == [0, 1]
     elif accelerator.process_index == 1:
          assert batch_idxs == [0]






def 						__magic_name__			(   __a      :	Dict						):



     '''simple docstring'''




     with warnings.catch_warnings(record=UpperCAmelCase_						) as w:
          with accelerator.join_uneven_inputs([Mock()]						):
               pass

          assert issubclass(w[-1].category	,		UpperCAmelCase_						)
          assert "only supported for multi-GPU" in str(w[-1].message						)






def 						__magic_name__			(   ):



     '''simple docstring'''




     UpperCamelCase__				=    True
     UpperCamelCase__				=    False
     UpperCamelCase__				=    create_accelerator(even_batches=UpperCAmelCase_						)
     UpperCamelCase__				=    torch.nn.Linear(1	,		1						)
     UpperCamelCase__				=    accelerator.prepare(UpperCAmelCase_						)
     UpperCamelCase__				=    create_dataloader(UpperCAmelCase_	,		dataset_size=3	,		batch_size=1						)
     UpperCamelCase__				=    create_dataloader(UpperCAmelCase_	,		dataset_size=3	,		batch_size=1						)

     with accelerator.join_uneven_inputs([ddp_model]	,		even_batches=UpperCAmelCase_						):
          UpperCamelCase__				=    train_dl.batch_sampler.even_batches
          UpperCamelCase__				=    valid_dl.batch_sampler.even_batches

     assert train_dl_overridden_value == overridden_even_batches
     assert valid_dl_overridden_value == overridden_even_batches
     assert train_dl.batch_sampler.even_batches == default_even_batches
     assert valid_dl.batch_sampler.even_batches == default_even_batches






def 						__magic_name__			(   ):



     '''simple docstring'''




     UpperCamelCase__				=    True
     UpperCamelCase__				=    False
     UpperCamelCase__				=    create_accelerator(even_batches=UpperCAmelCase_						)
     UpperCamelCase__				=    torch.nn.Linear(1	,		1						)
     UpperCamelCase__				=    accelerator.prepare(UpperCAmelCase_						)
     create_dataloader(UpperCAmelCase_	,		dataset_size=3	,		batch_size=1	,		iterable=UpperCAmelCase_						)
     UpperCamelCase__				=    create_dataloader(UpperCAmelCase_	,		dataset_size=3	,		batch_size=1						)

     with warnings.catch_warnings():
          warnings.filterwarnings("""ignore"""						)
          try:
               with accelerator.join_uneven_inputs([ddp_model]	,		even_batches=UpperCAmelCase_						):
                    UpperCamelCase__				=    batch_dl.batch_sampler.even_batches
          except AttributeError:
               # ensure attribute error is not raised when processing iterable dl
               raise AssertionError

     assert batch_dl_overridden_value == overridden_even_batches
     assert batch_dl.batch_sampler.even_batches == default_even_batches






def 						__magic_name__			(   ):



     '''simple docstring'''




     UpperCamelCase__				=    create_accelerator()
     UpperCamelCase__				=    torch.nn.Linear(1	,		1						)
     UpperCamelCase__				=    accelerator.prepare(UpperCAmelCase_						)
     create_dataloader(UpperCAmelCase_	,		dataset_size=3	,		batch_size=1	,		iterable=UpperCAmelCase_						)

     with warnings.catch_warnings(record=UpperCAmelCase_						) as w:
          with accelerator.join_uneven_inputs([ddp_model]	,		even_batches=UpperCAmelCase_						):
               pass

          assert issubclass(w[-1].category	,		UpperCAmelCase_						)
          assert "only supported for map-style datasets" in str(w[-1].message						)






def 						__magic_name__			(   ):



     '''simple docstring'''




     UpperCamelCase__				=    create_accelerator()

     accelerator.print("""Test that even_batches variable ensures uniform batches across processes"""						)
     test_default_ensures_even_batch_sizes()

     accelerator.print("""Run tests with even_batches disabled"""						)
     test_can_disable_even_batches()

     accelerator.print("""Test joining uneven inputs"""						)
     test_can_join_uneven_inputs()

     accelerator.print("""Test overriding even_batches when joining uneven inputs"""						)
     test_join_can_override_even_batches()

     accelerator.print("""Test overriding even_batches for mixed dataloader types"""						)
     test_join_can_override_for_mixed_type_dataloaders()

     accelerator.print("""Test overriding even_batches raises a warning for iterable dataloaders"""						)
     test_join_raises_warning_for_iterable_when_overriding_even_batches()

     accelerator.print("""Test join with non DDP distributed raises warning"""						)
     UpperCamelCase__				=    accelerator.state.distributed_type
     UpperCamelCase__				=    DistributedType.FSDP
     test_join_raises_warning_for_non_ddp_distributed(UpperCAmelCase_						)
     UpperCamelCase__				=    original_state


if __name__ == "__main__":
    main()







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

lowerCamelCase_						     =					logging.get_logger(__name__)




@add_end_docstrings(__lowerCamelCase			)
class      __A(    __lowerCamelCase			):

  """simple docstring"""







  def __init__(self	,      **SCREAMING_SNAKE_CASE_						):
       super().__init__(**SCREAMING_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(SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self	,      **SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    {}
       UpperCamelCase__				=    {}
       UpperCamelCase__				=    {}
       # preprocess args
       if "points_per_batch" in kwargs:
            UpperCamelCase__				=    kwargs["""points_per_batch"""]
       if "points_per_crop" in kwargs:
            UpperCamelCase__				=    kwargs["""points_per_crop"""]
       if "crops_n_layers" in kwargs:
            UpperCamelCase__				=    kwargs["""crops_n_layers"""]
       if "crop_overlap_ratio" in kwargs:
            UpperCamelCase__				=    kwargs["""crop_overlap_ratio"""]
       if "crop_n_points_downscale_factor" in kwargs:
            UpperCamelCase__				=    kwargs["""crop_n_points_downscale_factor"""]
       # postprocess args
       if "pred_iou_thresh" in kwargs:
            UpperCamelCase__				=    kwargs["""pred_iou_thresh"""]
       if "stability_score_offset" in kwargs:
            UpperCamelCase__				=    kwargs["""stability_score_offset"""]
       if "mask_threshold" in kwargs:
            UpperCamelCase__				=    kwargs["""mask_threshold"""]
       if "stability_score_thresh" in kwargs:
            UpperCamelCase__				=    kwargs["""stability_score_thresh"""]
       if "crops_nms_thresh" in kwargs:
            UpperCamelCase__				=    kwargs["""crops_nms_thresh"""]
       if "output_rle_mask" in kwargs:
            UpperCamelCase__				=    kwargs["""output_rle_mask"""]
       if "output_bboxes_mask" in kwargs:
            UpperCamelCase__				=    kwargs["""output_bboxes_mask"""]
       return preprocess_kwargs, forward_params, postprocess_kwargs







  def __call__(self	,      SCREAMING_SNAKE_CASE_	,      *SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=None	,      **SCREAMING_SNAKE_CASE_						):
       return super().__call__(SCREAMING_SNAKE_CASE_	,      *SCREAMING_SNAKE_CASE_	,      num_workers=SCREAMING_SNAKE_CASE_	,      batch_size=SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=64	,      SCREAMING_SNAKE_CASE_ = 0	,      SCREAMING_SNAKE_CASE_ = 5_12 / 15_00	,      SCREAMING_SNAKE_CASE_ = 32	,      SCREAMING_SNAKE_CASE_ = 1	,      ):
       UpperCamelCase__				=    load_image(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    self.image_processor.size["""longest_edge"""]
       UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    self.image_processor.generate_crop_boxes(
           SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    self.image_processor(images=SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""						)

       with self.device_placement():
            if self.framework == "pt":
                 UpperCamelCase__				=    self.get_inference_context()
                 with inference_context():
                      UpperCamelCase__				=    self._ensure_tensor_on_device(SCREAMING_SNAKE_CASE_	,      device=self.device						)
                      UpperCamelCase__				=    self.model.get_image_embeddings(model_inputs.pop("""pixel_values"""						)						)
                      UpperCamelCase__				=    image_embeddings

       UpperCamelCase__				=    grid_points.shape[1]
       UpperCamelCase__				=    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	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
            UpperCamelCase__				=    grid_points[:, i : i + points_per_batch, :, :]
            UpperCamelCase__				=    input_labels[:, i : i + points_per_batch]
            UpperCamelCase__				=    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	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=0.88	,      SCREAMING_SNAKE_CASE_=0.95	,      SCREAMING_SNAKE_CASE_=0	,      SCREAMING_SNAKE_CASE_=1	,      ):
       UpperCamelCase__				=    model_inputs.pop("""input_boxes"""						)
       UpperCamelCase__				=    model_inputs.pop("""is_last"""						)
       UpperCamelCase__				=    model_inputs.pop("""original_sizes"""						).tolist()
       UpperCamelCase__				=    model_inputs.pop("""reshaped_input_sizes"""						).tolist()

       UpperCamelCase__				=    self.model(**SCREAMING_SNAKE_CASE_						)

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







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=False	,      SCREAMING_SNAKE_CASE_=False	,      SCREAMING_SNAKE_CASE_=0.7	,      ):
       UpperCamelCase__				=    []
       UpperCamelCase__				=    []
       UpperCamelCase__				=    []
       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__				=    torch.cat(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    torch.cat(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    self.image_processor.post_process_for_mask_generation(
           SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    defaultdict(SCREAMING_SNAKE_CASE_						)
       for output in model_outputs:
            for k, v in output.items():
                 extra[k].append(SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    {}
       if output_rle_mask:
            UpperCamelCase__				=    rle_mask

       if output_bboxes_mask:
            UpperCamelCase__				=    bounding_boxes

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





