


'''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

__snake_case							   =  logging.get_logger(__name__)
@add_end_docstrings(A__      )
class      lowercase			(							A__      ):

		"""simple docstring"""

		def __init__(	self					,     **UpperCamelCase_							):

							'''simple docstring'''


							super().__init__(**UpperCamelCase_							)
							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(UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				{}
							UpperCamelCase__     :Dict							      =				{}
							UpperCamelCase__     :Optional[int]							      =				{}
							# preprocess args
							if "points_per_batch" in kwargs:
												UpperCamelCase__     :int							      =				kwargs['''points_per_batch''']
							if "points_per_crop" in kwargs:
												UpperCamelCase__     :Any							      =				kwargs['''points_per_crop''']
							if "crops_n_layers" in kwargs:
												UpperCamelCase__     :Dict							      =				kwargs['''crops_n_layers''']
							if "crop_overlap_ratio" in kwargs:
												UpperCamelCase__     :Union[str, Any]							      =				kwargs['''crop_overlap_ratio''']
							if "crop_n_points_downscale_factor" in kwargs:
												UpperCamelCase__     :Optional[Any]							      =				kwargs['''crop_n_points_downscale_factor''']
							# postprocess args
							if "pred_iou_thresh" in kwargs:
												UpperCamelCase__     :int							      =				kwargs['''pred_iou_thresh''']
							if "stability_score_offset" in kwargs:
												UpperCamelCase__     :List[str]							      =				kwargs['''stability_score_offset''']
							if "mask_threshold" in kwargs:
												UpperCamelCase__     :Optional[int]							      =				kwargs['''mask_threshold''']
							if "stability_score_thresh" in kwargs:
												UpperCamelCase__     :Dict							      =				kwargs['''stability_score_thresh''']
							if "crops_nms_thresh" in kwargs:
												UpperCamelCase__     :Dict							      =				kwargs['''crops_nms_thresh''']
							if "output_rle_mask" in kwargs:
												UpperCamelCase__     :Dict							      =				kwargs['''output_rle_mask''']
							if "output_bboxes_mask" in kwargs:
												UpperCamelCase__     :List[str]							      =				kwargs['''output_bboxes_mask''']
							return preprocess_kwargs, forward_params, postprocess_kwargs




		def __call__(	self					,     UpperCamelCase_					,     *UpperCamelCase_					,     UpperCamelCase_=None					,     UpperCamelCase_=None					,     **UpperCamelCase_							):

							'''simple docstring'''


							return super().__call__(UpperCamelCase_					,     *UpperCamelCase_					,     num_workers=UpperCamelCase_					,     batch_size=UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_=64					,     UpperCamelCase_ = 0					,     UpperCamelCase_ = 512 / 1500					,     UpperCamelCase_ = 32					,     UpperCamelCase_ = 1					,     ):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				load_image(UpperCamelCase_							)
							UpperCamelCase__     :Optional[Any]							      =				self.image_processor.size['''longest_edge''']
							UpperCamelCase__     :int							      =				self.image_processor.generate_crop_boxes(
							    UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							)
							UpperCamelCase__     :Union[str, Any]							      =				self.image_processor(images=UpperCamelCase_					,     return_tensors='''pt'''							)

							with self.device_placement():
												if self.framework == "pt":
																	UpperCamelCase__     :List[str]							      =				self.get_inference_context()
																	with inference_context():
																						UpperCamelCase__     :str							      =				self._ensure_tensor_on_device(UpperCamelCase_					,     device=self.device							)
																						UpperCamelCase__     :Tuple							      =				self.model.get_image_embeddings(model_inputs.pop('''pixel_values'''							)							)
																						UpperCamelCase__     :Optional[int]							      =				image_embeddings

							UpperCamelCase__     :Optional[Any]							      =				grid_points.shape[1]
							UpperCamelCase__     :int							      =				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					,     UpperCamelCase_					,     UpperCamelCase_							):
												UpperCamelCase__     :Optional[Any]							      =				grid_points[:, i : i + points_per_batch, :, :]
												UpperCamelCase__     :Dict							      =				input_labels[:, i : i + points_per_batch]
												UpperCamelCase__     :Optional[Any]							      =				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					,     UpperCamelCase_					,     UpperCamelCase_=0.88					,     UpperCamelCase_=0.95					,     UpperCamelCase_=0					,     UpperCamelCase_=1					,     ):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				model_inputs.pop('''input_boxes'''							)
							UpperCamelCase__     :Optional[int]							      =				model_inputs.pop('''is_last'''							)
							UpperCamelCase__     :int							      =				model_inputs.pop('''original_sizes'''							).tolist()
							UpperCamelCase__     :str							      =				model_inputs.pop('''reshaped_input_sizes'''							).tolist()

							UpperCamelCase__     :Dict							      =				self.model(**UpperCamelCase_							)

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




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_=False					,     UpperCamelCase_=False					,     UpperCamelCase_=0.7					,     ):

							'''simple docstring'''


							UpperCamelCase__     :List[str]							      =				[]
							UpperCamelCase__     :List[str]							      =				[]
							UpperCamelCase__     :Dict							      =				[]
							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__     :Optional[Any]							      =				torch.cat(UpperCamelCase_							)
							UpperCamelCase__     :Optional[Any]							      =				torch.cat(UpperCamelCase_							)
							UpperCamelCase__     :List[Any]							      =				self.image_processor.post_process_for_mask_generation(
							    UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							)

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

							UpperCamelCase__     :List[str]							      =				{}
							if output_rle_mask:
												UpperCamelCase__     :Tuple							      =				rle_mask

							if output_bboxes_mask:
												UpperCamelCase__     :Optional[Any]							      =				bounding_boxes

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



'''simple docstring'''
from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup


def     a   (    __a = ""     )						->			dict[str, float]:



					'''simple docstring'''






					UpperCamelCase__     :Optional[Any]							      =				url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
					UpperCamelCase__     :Optional[int]							      =				BeautifulSoup(requests.get(__a     ).text				,      '''html.parser'''     )
					UpperCamelCase__     :Tuple							      =				soup.find_all('''td'''				,      attrs='''titleColumn'''     )
					UpperCamelCase__     :Union[str, Any]							      =				soup.find_all('''td'''				,      class_='''ratingColumn imdbRating'''     )
					return {
					    title.a.text: float(rating.strong.text     )
					    for title, rating in zip(__a				,      __a     )
					}


def     a   (    __a = "IMDb_Top_250_Movies.csv"     )						->			None:



					'''simple docstring'''






					UpperCamelCase__     :Optional[Any]							      =				get_imdb_top_aaa_movies()
					with open(__a				,      '''w'''				,      newline=''''''     ) as out_file:
										UpperCamelCase__     :int							      =				csv.writer(__a     )
										writer.writerow(['''Movie title''', '''IMDb rating''']     )
										for title, rating in movies.items():
															writer.writerow([title, rating]     )


if __name__ == "__main__":
		write_movies()