






"""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

_lowerCAmelCase         =							logging.get_logger(__name__)



@add_end_docstrings(UpperCAmelCase__						)
class      SCREAMING_SNAKE_CASE  (				UpperCAmelCase__						):





	"""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    lowerCamelCase(self     ,     **lowerCAmelCase_							):
		A_		:						Union[str, Any]	    =   {}
		A_		:						Union[str, Any]	    =   {}
		A_		:						Union[str, Any]	    =   {}
		# preprocess args
		if "points_per_batch" in kwargs:
			A_		:						Tuple	    =   kwargs["""points_per_batch"""]
		if "points_per_crop" in kwargs:
			A_		:						Optional[Any]	    =   kwargs["""points_per_crop"""]
		if "crops_n_layers" in kwargs:
			A_		:						Union[str, Any]	    =   kwargs["""crops_n_layers"""]
		if "crop_overlap_ratio" in kwargs:
			A_		:						Optional[Any]	    =   kwargs["""crop_overlap_ratio"""]
		if "crop_n_points_downscale_factor" in kwargs:
			A_		:						Any	    =   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_		:						Tuple	    =   kwargs["""stability_score_offset"""]
		if "mask_threshold" in kwargs:
			A_		:						List[str]	    =   kwargs["""mask_threshold"""]
		if "stability_score_thresh" in kwargs:
			A_		:						Optional[Any]	    =   kwargs["""stability_score_thresh"""]
		if "crops_nms_thresh" in kwargs:
			A_		:						List[str]	    =   kwargs["""crops_nms_thresh"""]
		if "output_rle_mask" in kwargs:
			A_		:						Dict	    =   kwargs["""output_rle_mask"""]
		if "output_bboxes_mask" in kwargs:
			A_		:						Union[str, 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    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_=64     ,     lowerCAmelCase_ = 0     ,     lowerCAmelCase_ = 512 / 1500     ,     lowerCAmelCase_ = 32     ,     lowerCAmelCase_ = 1     ,     ):
		A_		:						str	    =   load_image(lowerCAmelCase_							)
		A_		:						Optional[int]	    =   self.image_processor.size["""longest_edge"""]
		A_		:						int	    =   self.image_processor.generate_crop_boxes(
		    lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							)
		A_		:						Tuple	    =   self.image_processor(images=lowerCAmelCase_     ,     return_tensors="""pt"""							)

		with self.device_placement():
			if self.framework == "pt":
				A_		:						List[Any]	    =   self.get_inference_context()
				with inference_context():
					A_		:						Union[str, Any]	    =   self._ensure_tensor_on_device(lowerCAmelCase_     ,     device=self.device							)
					A_		:						Union[str, Any]	    =   self.model.get_image_embeddings(model_inputs.pop("""pixel_values"""							)							)
					A_		:						List[Any]	    =   image_embeddings

		A_		:						Any	    =   grid_points.shape[1]
		A_		:						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_							):
			A_		:						Union[str, Any]	    =   grid_points[:, i : i + points_per_batch, :, :]
			A_		:						Dict	    =   input_labels[:, i : i + points_per_batch]
			A_		:						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    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_=0.88     ,     lowerCAmelCase_=0.95     ,     lowerCAmelCase_=0     ,     lowerCAmelCase_=1     ,     ):
		A_		:						List[Any]	    =   model_inputs.pop("""input_boxes"""							)
		A_		:						List[str]	    =   model_inputs.pop("""is_last"""							)
		A_		:						Tuple	    =   model_inputs.pop("""original_sizes"""							).tolist()
		A_		:						Tuple	    =   model_inputs.pop("""reshaped_input_sizes"""							).tolist()

		A_		:						List[str]	    =   self.model(**lowerCAmelCase_							)

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

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

		A_		:						List[str]	    =   {}
		if output_rle_mask:
			A_		:						Dict	    =   rle_mask

		if output_bboxes_mask:
			A_		:						Union[str, Any]	    =   bounding_boxes

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









"""simple docstring"""
import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
							print("Googling.....")
							_lowerCAmelCase         =							"https://www.google.com/search?q=" + " ".join(sys.argv[1:])
							_lowerCAmelCase         =							requests.get(url, headers={"UserAgent": UserAgent().random})
							# res.raise_for_status()
							with open("project1a.html", "wb") as out_file:  # only for knowing the class
														for data in res.iter_content(10_000):
																					out_file.write(data)
							_lowerCAmelCase         =							BeautifulSoup(res.text, "html.parser")
							_lowerCAmelCase         =							list(soup.select(".eZt8xd"))[:5]

							print(len(links))
							for link in links:
														if link.text == "Maps":
																					webbrowser.open(link.get("href"))
														else:
																					webbrowser.open(F'https://google.com{link.get("href")}')

