


'''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

__SCREAMING_SNAKE_CASE					:				List[str]									=		logging.get_logger(__name__)






@add_end_docstrings(lowercase						)
class 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






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






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






						def 				_UpperCAmelCase       (  self							:      Any ,  lowerCAmelCase							:      str ,  lowerCAmelCase							:      List[Any]=64 ,  lowerCAmelCase							:      int = 0 ,  lowerCAmelCase							:      float = 5_12 / 15_00 ,  lowerCAmelCase							:      Optional[int] = 32 ,  lowerCAmelCase							:      Optional[int] = 1 ,  ):
													A_			    =		load_image(lowerCAmelCase	)
													A_			    =		self.image_processor.size["longest_edge"]
													A_    ,					A_    ,					A_    ,					A_			    =		self.image_processor.generate_crop_boxes(
													    lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase	)
													A_			    =		self.image_processor(images=lowerCAmelCase ,  return_tensors="pt"	)

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

													A_			    =		grid_points.shape[1]
													A_			    =		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	):
																				A_			    =		grid_points[:, i : i + points_per_batch, :, :]
																				A_			    =		input_labels[:, i : i + points_per_batch]
																				A_			    =		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 ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      Any=0.8_8 ,  lowerCAmelCase							:      Union[str, Any]=0.9_5 ,  lowerCAmelCase							:      List[Any]=0 ,  lowerCAmelCase							:      int=1 ,  ):
													A_			    =		model_inputs.pop("input_boxes"	)
													A_			    =		model_inputs.pop("is_last"	)
													A_			    =		model_inputs.pop("original_sizes"	).tolist()
													A_			    =		model_inputs.pop("reshaped_input_sizes"	).tolist()

													A_			    =		self.model(**lowerCAmelCase	)

													# post processing happens here in order to avoid CPU GPU copies of ALL the masks
													A_			    =		model_outputs["pred_masks"]
													A_			    =		self.image_processor.post_process_masks(
													    lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase ,  binarize=lowerCAmelCase	)
													A_			    =		model_outputs["iou_scores"]
													A_    ,					A_    ,					A_			    =		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							:      Any ,  lowerCAmelCase							:      List[str] ,  lowerCAmelCase							:      Dict=False ,  lowerCAmelCase							:      Union[str, Any]=False ,  lowerCAmelCase							:      List[str]=0.7 ,  ):
													A_			    =		[]
													A_			    =		[]
													A_			    =		[]
													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_			    =		torch.cat(lowerCAmelCase	)
													A_			    =		torch.cat(lowerCAmelCase	)
													A_    ,					A_    ,					A_    ,					A_			    =		self.image_processor.post_process_for_mask_generation(
													    lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase	)

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

													A_			    =		{}
													if output_rle_mask:
																				A_			    =		rle_mask

													if output_bboxes_mask:
																				A_			    =		bounding_boxes

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









'''simple docstring'''
import string
from math import logaa







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		document.translate(
							    str.maketrans(""						,  ""						,  string.punctuation ) ).replace("\n"						,  "" )
							A_			    =		document_without_punctuation.split(" " )  # word tokenization
							return len([word for word in tokenize_document if word.lower() == term.lower()] )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		corpus.lower().translate(
							    str.maketrans(""						,  ""						,  string.punctuation ) )  # strip all punctuation and replace it with ''
							A_			    =		corpus_without_punctuation.split("\n" )
							A_			    =		term.lower()
							return (len([doc for doc in docs if term in doc] ), len(UpperCamelCase_ ))







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_=False ):
							if smoothing:
														if n == 0:
																					raise ValueError("log10(0) is undefined." )
														return round(1 + logaa(n / (1 + df) )						,  3 )

							if df == 0:
														raise ZeroDivisionError("df must be > 0" )
							elif n == 0:
														raise ValueError("log10(0) is undefined." )
							return round(logaa(n / df )						,  3 )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							return round(tf * idf						,  3 )





