



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__       =      logging.get_logger(__name__)


@add_end_docstrings(UpperCamelCase_      )
class      __lowerCAmelCase     (		UpperCamelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       int				,					**_snake_case					:       Optional[int]					):






								"""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     _a							(     self					:       List[str]				,					**_snake_case					:       Any					):






								"""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					:       Optional[int]				,					_snake_case					:       Any				,					*_snake_case					:       List[str]				,					_snake_case					:       str=None				,					_snake_case					:       Dict=None				,					**_snake_case					:       Tuple					):






								"""simple docstring"""


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



					def     _a							(     self					:       int				,					_snake_case					:       List[str]				,					_snake_case					:       List[Any]=64				,					_snake_case					:       List[str] = 0				,					_snake_case					:       Optional[Any] = 5_12 / 15_00				,					_snake_case					:       str = 32				,					_snake_case					:       Union[str, Any] = 1				,					):






								"""simple docstring"""


								A__       =    load_image(__a					)
								A__       =    self.image_processor.size['longest_edge']
								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     _a							(     self					:       Union[str, Any]				,					_snake_case					:       Any				,					_snake_case					:       Union[str, Any]=0.88				,					_snake_case					:       int=0.95				,					_snake_case					:       Any=0				,					_snake_case					:       Any=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__       =    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     _a							(     self					:       List[str]				,					_snake_case					:       Union[str, Any]				,					_snake_case					:       List[Any]=False				,					_snake_case					:       Dict=False				,					_snake_case					:       List[Any]=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__       =    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}









import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_rembert import RemBertTokenizer
else:
							SCREAMING_SNAKE_CASE__       =      None

SCREAMING_SNAKE_CASE__       =      logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__       =      {'''vocab_file''': '''sentencepiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

SCREAMING_SNAKE_CASE__       =      {
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
    '''tokenizer_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/tokenizer.json''',
    },
}

SCREAMING_SNAKE_CASE__       =      {
    '''google/rembert''': 2_5_6,
}

SCREAMING_SNAKE_CASE__       =      '''▁'''


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		Any			       =    VOCAB_FILES_NAMES
					A__				:		str			       =    PRETRAINED_VOCAB_FILES_MAP
					A__				:		str			       =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A__				:		int			       =    RemBertTokenizer



					def __init__(     self					:       Union[str, Any]				,					_snake_case					:       Any=None				,					_snake_case					:       Optional[Any]=None				,					_snake_case					:       Any=True				,					_snake_case					:       Optional[int]=True				,					_snake_case					:       Dict=False				,					_snake_case					:       Dict="[CLS]"				,					_snake_case					:       List[Any]="[SEP]"				,					_snake_case					:       Union[str, Any]="<unk>"				,					_snake_case					:       List[str]="[SEP]"				,					_snake_case					:       List[str]="<pad>"				,					_snake_case					:       str="[CLS]"				,					_snake_case					:       Any="[MASK]"				,					**_snake_case					:       Any				,					):






								"""simple docstring"""


								A__       =    AddedToken(_snake_case				,					lstrip=_snake_case				,					rstrip=_snake_case					) if isinstance(_snake_case				,					_snake_case					) else mask_token

								super().__init__(
								    _snake_case				,					tokenizer_file=_snake_case				,					do_lower_case=_snake_case				,					remove_space=_snake_case				,					keep_accents=_snake_case				,					bos_token=_snake_case				,					eos_token=_snake_case				,					unk_token=_snake_case				,					sep_token=_snake_case				,					pad_token=_snake_case				,					cls_token=_snake_case				,					mask_token=_snake_case				,					**_snake_case				,					)

								A__       =    do_lower_case
								A__       =    remove_space
								A__       =    keep_accents
								A__       =    vocab_file
								A__       =    False if not self.vocab_file else True



					def     _a							(     self					:       Any				,					_snake_case					:       List[int]				,					_snake_case					:       Optional[List[int]] = None					):






								"""simple docstring"""


								A__       =    [self.sep_token_id]
								A__       =    [self.cls_token_id]
								if token_ids_a is None:
											return cls + token_ids_a + sep
								return cls + token_ids_a + sep + token_ids_a + sep



					def     _a							(     self					:       Tuple				,					_snake_case					:       List[int]				,					_snake_case					:       Optional[List[int]] = None				,					_snake_case					:       bool = False					):






								"""simple docstring"""


								if already_has_special_tokens:
											if token_ids_a is not None:
														raise ValueError(
														    'You should not supply a second sequence if the provided sequence of '
														    'ids is already formatted with special tokens for the model.'					)
											return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

								if token_ids_a is not None:
											return [1] + ([0] * len(_snake_case					)) + [1] + ([0] * len(_snake_case					)) + [1]
								return [1] + ([0] * len(_snake_case					)) + [1]



					def     _a							(     self					:       Dict				,					_snake_case					:       List[int]				,					_snake_case					:       Optional[List[int]] = None					):






								"""simple docstring"""


								A__       =    [self.sep_token_id]
								A__       =    [self.cls_token_id]

								if token_ids_a is None:
											return len(cls + token_ids_a + sep					) * [0]
								return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]



					def     _a							(     self					:       Any				,					_snake_case					:       str				,					_snake_case					:       Optional[str] = None					):






								"""simple docstring"""


								if not os.path.isdir(_snake_case					):
											logger.error('Vocabulary path ({}) should be a directory'.format(_snake_case					)					)
											return
								A__       =    os.path.join(
								    _snake_case				,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)

								if os.path.abspath(self.vocab_file					) != os.path.abspath(_snake_case					):
											copyfile(self.vocab_file				,					_snake_case					)

								return (out_vocab_file,)




