

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

__A     :					Optional[int]     = logging.get_logger(__name__)


@add_end_docstrings(_UpperCAmelCase)
class        _SCREAMING_SNAKE_CASE	(   _UpperCAmelCase):







						def __init__(       self			,		**_SCREAMING_SNAKE_CASE				)->				Tuple:
													super().__init__(**lowercase_				)
													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(lowercase_				)







						def     _snake_case							(       self			,		**_SCREAMING_SNAKE_CASE				)->				List[Any]:
													lowerCamelCase_				     ={}
													lowerCamelCase_				     ={}
													lowerCamelCase_				     ={}
													# preprocess args
													if "points_per_batch" in kwargs:
																				lowerCamelCase_				     =kwargs["""points_per_batch"""]
													if "points_per_crop" in kwargs:
																				lowerCamelCase_				     =kwargs["""points_per_crop"""]
													if "crops_n_layers" in kwargs:
																				lowerCamelCase_				     =kwargs["""crops_n_layers"""]
													if "crop_overlap_ratio" in kwargs:
																				lowerCamelCase_				     =kwargs["""crop_overlap_ratio"""]
													if "crop_n_points_downscale_factor" in kwargs:
																				lowerCamelCase_				     =kwargs["""crop_n_points_downscale_factor"""]
													# postprocess args
													if "pred_iou_thresh" in kwargs:
																				lowerCamelCase_				     =kwargs["""pred_iou_thresh"""]
													if "stability_score_offset" in kwargs:
																				lowerCamelCase_				     =kwargs["""stability_score_offset"""]
													if "mask_threshold" in kwargs:
																				lowerCamelCase_				     =kwargs["""mask_threshold"""]
													if "stability_score_thresh" in kwargs:
																				lowerCamelCase_				     =kwargs["""stability_score_thresh"""]
													if "crops_nms_thresh" in kwargs:
																				lowerCamelCase_				     =kwargs["""crops_nms_thresh"""]
													if "output_rle_mask" in kwargs:
																				lowerCamelCase_				     =kwargs["""output_rle_mask"""]
													if "output_bboxes_mask" in kwargs:
																				lowerCamelCase_				     =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				)->				List[Any]:
													return super().__call__(lowercase_			,		*lowercase_			,		num_workers=lowercase_			,		batch_size=lowercase_			,		**lowercase_				)







						def     _snake_case							(       self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE=64			,		_SCREAMING_SNAKE_CASE = 0			,		_SCREAMING_SNAKE_CASE = 512 / 1500			,		_SCREAMING_SNAKE_CASE = 32			,		_SCREAMING_SNAKE_CASE = 1			,		)->				Optional[Any]:
													lowerCamelCase_				     =load_image(lowercase_				)
													lowerCamelCase_				     =self.image_processor.size["""longest_edge"""]
													lowerCamelCase_				     =self.image_processor.generate_crop_boxes(
													    lowercase_			,		lowercase_			,		lowercase_			,		lowercase_			,		lowercase_			,		lowercase_				)
													lowerCamelCase_				     =self.image_processor(images=lowercase_			,		return_tensors="""pt"""				)

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

													lowerCamelCase_				     =grid_points.shape[1]
													lowerCamelCase_				     =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			,		lowercase_			,		lowercase_				):
																				lowerCamelCase_				     =grid_points[:, i : i + points_per_batch, :, :]
																				lowerCamelCase_				     =input_labels[:, i : i + points_per_batch]
																				lowerCamelCase_				     =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     _snake_case							(       self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE=0.8_8			,		_SCREAMING_SNAKE_CASE=0.9_5			,		_SCREAMING_SNAKE_CASE=0			,		_SCREAMING_SNAKE_CASE=1			,		)->				Optional[int]:
													lowerCamelCase_				     =model_inputs.pop("""input_boxes"""				)
													lowerCamelCase_				     =model_inputs.pop("""is_last"""				)
													lowerCamelCase_				     =model_inputs.pop("""original_sizes"""				).tolist()
													lowerCamelCase_				     =model_inputs.pop("""reshaped_input_sizes"""				).tolist()

													lowerCamelCase_				     =self.model(**lowercase_				)

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







						def     _snake_case							(       self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE=False			,		_SCREAMING_SNAKE_CASE=False			,		_SCREAMING_SNAKE_CASE=0.7			,		)->				Tuple:
													lowerCamelCase_				     =[]
													lowerCamelCase_				     =[]
													lowerCamelCase_				     =[]
													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"""				)				)

													lowerCamelCase_				     =torch.cat(lowercase_				)
													lowerCamelCase_				     =torch.cat(lowercase_				)
													lowerCamelCase_				     =self.image_processor.post_process_for_mask_generation(
													    lowercase_			,		lowercase_			,		lowercase_			,		lowercase_				)

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

													lowerCamelCase_				     ={}
													if output_rle_mask:
																				lowerCamelCase_				     =rle_mask

													if output_bboxes_mask:
																				lowerCamelCase_				     =bounding_boxes

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

'''simple docstring'''




import os




def 							lowerCamelCase	(      UpperCAmelCase__       :					str = "input.txt" )			->      int:

							with open(os.path.join(os.path.dirname(UpperCAmelCase__ )      ,      UpperCAmelCase__ ) ) as input_file:
														lowercase_					:			str										=       [
														    [int(UpperCAmelCase__ ) for element in line.split(""",""" )]
														    for line in input_file.readlines()
														]

							lowercase_					:			Optional[Any]										=       len(UpperCAmelCase__ )
							lowercase_					:			Any										=       len(matrix[0] )

							lowercase_					:			Union[str, Any]										=       [[-1 for _ in range(UpperCAmelCase__ )] for _ in range(UpperCAmelCase__ )]
							for i in range(UpperCAmelCase__ ):
														lowercase_					:			int										=       matrix[i][0]

							for j in range(1      ,      UpperCAmelCase__ ):
														for i in range(UpperCAmelCase__ ):
																					lowercase_					:			Union[str, Any]										=       minimal_path_sums[i][j - 1] + matrix[i][j]

														for i in range(1      ,      UpperCAmelCase__ ):
																					lowercase_					:			Tuple										=       min(
																					    minimal_path_sums[i][j]      ,      minimal_path_sums[i - 1][j] + matrix[i][j] )

														for i in range(rows - 2      ,      -1      ,      -1 ):
																					lowercase_					:			Dict										=       min(
																					    minimal_path_sums[i][j]      ,      minimal_path_sums[i + 1][j] + matrix[i][j] )

							return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums )


if __name__ == "__main__":
						print(f"""{solution() = }""")





