






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce





def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	return (torch.arange(state.num_processes       ) + 1.0 + (state.num_processes * state.process_index)).to(state.device       )





def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	__lowercase   							=	create_tensor(lowerCamelCase       )
	__lowercase   							=	gather(lowerCamelCase       )
	assert gathered_tensor.tolist() == list(range(1					,	state.num_processes**2 + 1       )       )





def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	__lowercase   							=	[state.process_index]
	__lowercase   							=	gather_object(lowerCamelCase       )
	assert len(lowerCamelCase       ) == state.num_processes, F'{gathered_obj}, {len(lowerCamelCase       )} != {state.num_processes}'
	assert gathered_obj == list(range(state.num_processes       )       ), F'{gathered_obj} != {list(range(state.num_processes       )       )}'





def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	__lowercase   							=	create_tensor(lowerCamelCase       )
	__lowercase   							=	broadcast(lowerCamelCase       )
	assert broadcasted_tensor.shape == torch.Size([state.num_processes]       )
	assert broadcasted_tensor.tolist() == list(range(1					,	state.num_processes + 1       )       )





def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	if state.is_main_process:
		__lowercase   							=	torch.arange(state.num_processes + 1       ).to(state.device       )
	else:
		__lowercase   							=	torch.arange(state.num_processes       ).to(state.device       )
	__lowercase   							=	pad_across_processes(lowerCamelCase       )
	assert padded_tensor.shape == torch.Size([state.num_processes + 1]       )
	if not state.is_main_process:
		assert padded_tensor.tolist() == list(range(0					,	state.num_processes       )       ) + [0]





def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	if state.num_processes != 2:
		return
	__lowercase   							=	create_tensor(lowerCamelCase       )
	__lowercase   							=	reduce(lowerCamelCase					,	"""sum"""       )
	__lowercase   							=	torch.tensor([4.0, 6]       ).to(state.device       )
	assert torch.allclose(lowerCamelCase					,	lowerCamelCase       ), F'{reduced_tensor} != {truth_tensor}'





def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	if state.num_processes != 2:
		return
	__lowercase   							=	create_tensor(lowerCamelCase       )
	__lowercase   							=	reduce(lowerCamelCase					,	"""mean"""       )
	__lowercase   							=	torch.tensor([2.0, 3]       ).to(state.device       )
	assert torch.allclose(lowerCamelCase					,	lowerCamelCase       ), F'{reduced_tensor} != {truth_tensor}'





def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	main()





def 					snake_case	(  ):




	'''simple docstring'''







	__lowercase   							=	PartialState()
	state.print(F'State: {state}'       )
	state.print("""testing gather"""       )
	test_gather(lowerCamelCase       )
	state.print("""testing gather_object"""       )
	test_gather_object(lowerCamelCase       )
	state.print("""testing broadcast"""       )
	test_broadcast(lowerCamelCase       )
	state.print("""testing pad_across_processes"""       )
	test_pad_across_processes(lowerCamelCase       )
	state.print("""testing reduce_sum"""       )
	test_reduce_sum(lowerCamelCase       )
	state.print("""testing reduce_mean"""       )
	test_reduce_mean(lowerCamelCase       )


if __name__ == "__main__":
						main()









'''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_					: int            =				{}
       UpperCamelCase_					: int            =				{}
       UpperCamelCase_					: Any            =				{}
       # preprocess args
       if "points_per_batch" in kwargs:
             UpperCamelCase_					: Dict            =				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_					: Tuple            =				kwargs["""crop_n_points_downscale_factor"""]
       # postprocess args
       if "pred_iou_thresh" in kwargs:
             UpperCamelCase_					: Any            =				kwargs["""pred_iou_thresh"""]
       if "stability_score_offset" in kwargs:
             UpperCamelCase_					: List[str]            =				kwargs["""stability_score_offset"""]
       if "mask_threshold" in kwargs:
             UpperCamelCase_					: Tuple            =				kwargs["""mask_threshold"""]
       if "stability_score_thresh" in kwargs:
             UpperCamelCase_					: Optional[Any]            =				kwargs["""stability_score_thresh"""]
       if "crops_nms_thresh" in kwargs:
             UpperCamelCase_					: Dict            =				kwargs["""crops_nms_thresh"""]
       if "output_rle_mask" in kwargs:
             UpperCamelCase_					: Optional[int]            =				kwargs["""output_rle_mask"""]
       if "output_bboxes_mask" in kwargs:
             UpperCamelCase_					: List[Any]            =				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_					: Tuple            =				load_image(__lowerCAmelCase			)
       UpperCamelCase_					: Tuple            =				self.image_processor.size["""longest_edge"""]
       UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_     ,      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_					: str            =				self.get_inference_context()
                   with inference_context():
                         UpperCamelCase_					: Optional[int]            =				self._ensure_tensor_on_device(__lowerCAmelCase      ,  device=self.device			)
                         UpperCamelCase_					: Tuple            =				self.model.get_image_embeddings(model_inputs.pop("""pixel_values"""			)			)
                         UpperCamelCase_					: List[Any]            =				image_embeddings

       UpperCamelCase_					: Any            =				grid_points.shape[1]
       UpperCamelCase_					: List[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      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
             UpperCamelCase_					: Dict            =				grid_points[:, i : i + points_per_batch, :, :]
             UpperCamelCase_					: List[Any]            =				input_labels[:, i : i + points_per_batch]
             UpperCamelCase_					: Dict            =				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_					: Union[str, Any]            =				model_inputs.pop("""input_boxes"""			)
       UpperCamelCase_					: Tuple            =				model_inputs.pop("""is_last"""			)
       UpperCamelCase_					: Dict            =				model_inputs.pop("""original_sizes"""			).tolist()
       UpperCamelCase_					: int            =				model_inputs.pop("""reshaped_input_sizes"""			).tolist()

       UpperCamelCase_					: Optional[Any]            =				self.model(**__lowerCAmelCase			)

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

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

       UpperCamelCase_					: Optional[Any]            =				{}
       if output_rle_mask:
             UpperCamelCase_					: int            =				rle_mask

       if output_bboxes_mask:
             UpperCamelCase_					: int            =				bounding_boxes

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






