






"""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						:						Union[str, Any]					   =       logging.get_logger(__name__)





@add_end_docstrings(_lowerCamelCase       )
class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	def __init__(      self  ,					**__a						):


					"""simple docstring"""

					super().__init__(**__a						)
					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(__a						)


	def       _UpperCAmelCase						(      self  ,					**__a						):


					"""simple docstring"""

					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  ,					__a  ,					*__a  ,					__a=None  ,					__a=None  ,					**__a						):


					"""simple docstring"""

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


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a=64  ,					__a = 0  ,					__a = 512 / 1500  ,					__a = 32  ,					__a = 1  ,					):


					"""simple docstring"""

					A__							 =       load_image(__a						)
					A__							 =       self.image_processor.size['longest_edge']
					A__ ,			A__ ,			A__ ,			A__							 =       self.image_processor.generate_crop_boxes(
					    __a  ,					__a  ,					__a  ,					__a  ,					__a  ,					__a						)
					A__							 =       self.image_processor(images=__a  ,					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(__a  ,					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  ,					__a  ,					__a						):
									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  ,					__a  ,					__a=0.88  ,					__a=0.95  ,					__a=0  ,					__a=1  ,					):


					"""simple docstring"""

					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(**__a						)

					# 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(
					    __a  ,					__a  ,					__a  ,					__a  ,					binarize=__a						)
					A__							 =       model_outputs['iou_scores']
					A__ ,			A__ ,			A__							 =       self.image_processor.filter_masks(
					    masks[0]  ,					iou_scores[0]  ,					original_sizes[0]  ,					input_boxes[0]  ,					__a  ,					__a  ,					__a  ,					__a  ,					)
					return {
					    "masks": masks,
					    "is_last": is_last,
					    "boxes": boxes,
					    "iou_scores": iou_scores,
					}






	def       _UpperCAmelCase						(      self  ,					__a  ,					__a=False  ,					__a=False  ,					__a=0.7  ,					):


					"""simple docstring"""

					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(__a						)
					A__							 =       torch.cat(__a						)
					A__ ,			A__ ,			A__ ,			A__							 =       self.image_processor.post_process_for_mask_generation(
					    __a  ,					__a  ,					__a  ,					__a						)

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

					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 argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
SCREAMING_SNAKE_CASE						:						Optional[int]					   =       logging.getLogger(__name__)



def  __lowerCamelCase					(					):
				A__							 =       argparse.ArgumentParser(
				    description='Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'				)
				parser.add_argument('--file_path'       ,type=lowerCAmelCase__       ,default='data/dump.txt'       ,help='The path to the data.'				)
				parser.add_argument('--tokenizer_type'       ,type=lowerCAmelCase__       ,default='bert'       ,choices=['bert', 'roberta', 'gpt2']				)
				parser.add_argument('--tokenizer_name'       ,type=lowerCAmelCase__       ,default='bert-base-uncased'       ,help='The tokenizer to use.'				)
				parser.add_argument('--dump_file'       ,type=lowerCAmelCase__       ,default='data/dump'       ,help='The dump file prefix.'				)
				A__							 =       parser.parse_args()

				logger.info(f'''Loading Tokenizer ({args.tokenizer_name})'''				)
				if args.tokenizer_type == "bert":
								A__							 =       BertTokenizer.from_pretrained(args.tokenizer_name				)
								A__							 =       tokenizer.special_tokens_map['cls_token']  # `[CLS]`
								A__							 =       tokenizer.special_tokens_map['sep_token']  # `[SEP]`
				elif args.tokenizer_type == "roberta":
								A__							 =       RobertaTokenizer.from_pretrained(args.tokenizer_name				)
								A__							 =       tokenizer.special_tokens_map['cls_token']  # `<s>`
								A__							 =       tokenizer.special_tokens_map['sep_token']  # `</s>`
				elif args.tokenizer_type == "gpt2":
								A__							 =       GPTaTokenizer.from_pretrained(args.tokenizer_name				)
								A__							 =       tokenizer.special_tokens_map['bos_token']  # `<|endoftext|>`
								A__							 =       tokenizer.special_tokens_map['eos_token']  # `<|endoftext|>`

				logger.info(f'''Loading text from {args.file_path}'''				)
				with open(args.file_path       ,'r'       ,encoding='utf8'				) as fp:
								A__							 =       fp.readlines()

				logger.info('Start encoding'				)
				logger.info(f'''{len(lowerCAmelCase__				)} examples to process.'''				)

				A__							 =       []
				A__							 =       0
				A__							 =       1_0000
				A__							 =       time.time()
				for text in data:
								A__							 =       f'''{bos} {text.strip()} {sep}'''
								A__							 =       tokenizer.encode(lowerCAmelCase__       ,add_special_tokens=lowerCAmelCase__				)
								rslt.append(lowerCAmelCase__				)

								iter += 1
								if iter % interval == 0:
												A__							 =       time.time()
												logger.info(f'''{iter} examples processed. - {(end-start):.2f}s/{interval}expl'''				)
												A__							 =       time.time()
				logger.info('Finished binarization'				)
				logger.info(f'''{len(lowerCAmelCase__				)} examples processed.'''				)

				A__							 =       f'''{args.dump_file}.{args.tokenizer_name}.pickle'''
				A__							 =       tokenizer.vocab_size
				if vocab_size < (1 << 16):
								A__							 =       [np.uintaa(lowerCAmelCase__				) for d in rslt]
				else:
								A__							 =       [np.intaa(lowerCAmelCase__				) for d in rslt]
				random.shuffle(rslt_				)
				logger.info(f'''Dump to {dp_file}'''				)
				with open(lowerCAmelCase__       ,'wb'				) as handle:
								pickle.dump(rslt_       ,lowerCAmelCase__       ,protocol=pickle.HIGHEST_PROTOCOL				)


if __name__ == "__main__":
					main()


