





'''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

a__							:							Tuple =logging.get_logger(__name__)

@add_end_docstrings(__lowerCamelCase       )
class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 def __init__(       self       :							Optional[Any]							,	**__A       :							Dict	):
        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        _lowerCamelCase					(       self       :							int							,	**__A       :							Dict	):
        __UpperCamelCase       =  {}
        __UpperCamelCase       =  {}
        __UpperCamelCase       =  {}
        # preprocess args
        if "points_per_batch" in kwargs:
               __UpperCamelCase       =  kwargs['points_per_batch']
        if "points_per_crop" in kwargs:
               __UpperCamelCase       =  kwargs['points_per_crop']
        if "crops_n_layers" in kwargs:
               __UpperCamelCase       =  kwargs['crops_n_layers']
        if "crop_overlap_ratio" in kwargs:
               __UpperCamelCase       =  kwargs['crop_overlap_ratio']
        if "crop_n_points_downscale_factor" in kwargs:
               __UpperCamelCase       =  kwargs['crop_n_points_downscale_factor']
        # postprocess args
        if "pred_iou_thresh" in kwargs:
               __UpperCamelCase       =  kwargs['pred_iou_thresh']
        if "stability_score_offset" in kwargs:
               __UpperCamelCase       =  kwargs['stability_score_offset']
        if "mask_threshold" in kwargs:
               __UpperCamelCase       =  kwargs['mask_threshold']
        if "stability_score_thresh" in kwargs:
               __UpperCamelCase       =  kwargs['stability_score_thresh']
        if "crops_nms_thresh" in kwargs:
               __UpperCamelCase       =  kwargs['crops_nms_thresh']
        if "output_rle_mask" in kwargs:
               __UpperCamelCase       =  kwargs['output_rle_mask']
        if "output_bboxes_mask" in kwargs:
               __UpperCamelCase       =  kwargs['output_bboxes_mask']
        return preprocess_kwargs, forward_params, postprocess_kwargs






 def __call__(       self       :							Union[str, Any]							,	__A       :							List[str]							,	*__A       :							List[Any]							,	__A       :							int=None							,	__A       :							Optional[int]=None							,	**__A       :							Any	):
        return super().__call__(__A							,	*__A							,	num_workers=__A							,	batch_size=__A							,	**__A	)






 def        _lowerCamelCase					(       self       :							Optional[int]							,	__A       :							Optional[int]							,	__A       :							Union[str, Any]=6_4							,	__A       :							int = 0							,	__A       :							float = 5_1_2 / 1_5_0_0							,	__A       :							Optional[int] = 3_2							,	__A       :							Optional[int] = 1							,	):
        __UpperCamelCase       =  load_image(__A	)
        __UpperCamelCase       =  self.image_processor.size['longest_edge']
        __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  self.image_processor.generate_crop_boxes(
            __A							,	__A							,	__A							,	__A							,	__A							,	__A	)
        __UpperCamelCase       =  self.image_processor(images=__A							,	return_tensors='pt'	)

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

        __UpperCamelCase       =  grid_points.shape[1]
        __UpperCamelCase       =  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	):
               __UpperCamelCase       =  grid_points[:, i : i + points_per_batch, :, :]
               __UpperCamelCase       =  input_labels[:, i : i + points_per_batch]
               __UpperCamelCase       =  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       :							Dict							,	__A       :							Union[str, Any]							,	__A       :							Any=0.88							,	__A       :							Any=0.95							,	__A       :							Tuple=0							,	__A       :							str=1							,	):
        __UpperCamelCase       =  model_inputs.pop('input_boxes'	)
        __UpperCamelCase       =  model_inputs.pop('is_last'	)
        __UpperCamelCase       =  model_inputs.pop('original_sizes'	).tolist()
        __UpperCamelCase       =  model_inputs.pop('reshaped_input_sizes'	).tolist()

        __UpperCamelCase       =  self.model(**__A	)

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

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

        __UpperCamelCase       =  {}
        if output_rle_mask:
               __UpperCamelCase       =  rle_mask

        if output_bboxes_mask:
               __UpperCamelCase       =  bounding_boxes

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





'''simple docstring'''

import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
							import sqlitea

							import sqlalchemy

class 	__magic_name__						(    _UpperCamelCase   ):







							def __init__(							self					:       Optional[int]	,_UpperCAmelCase					:       Union[str, "sqlalchemy.sql.Selectable"]	,_UpperCAmelCase					:       Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]	,_UpperCAmelCase					:       Optional[Features] = None	,_UpperCAmelCase					:       str = None	,_UpperCAmelCase					:       bool = False	,**_UpperCAmelCase					:       Dict	,):
									super().__init__(features=_UpperCAmelCase	,cache_dir=_UpperCAmelCase	,keep_in_memory=_UpperCAmelCase	,**_UpperCAmelCase		)
									_a     :      Tuple        =							Sql(
									    cache_dir=_UpperCAmelCase	,features=_UpperCAmelCase	,sql=_UpperCAmelCase	,con=_UpperCAmelCase	,**_UpperCAmelCase	,)







							def 							__lowercase			(							self					:       Dict		):
									_a     :      Optional[Any]        =							None
									_a     :      Dict        =							None
									_a     :      Dict        =							None
									_a     :      Optional[int]        =							None

									self.builder.download_and_prepare(
									    download_config=_UpperCAmelCase	,download_mode=_UpperCAmelCase	,verification_mode=_UpperCAmelCase	,base_path=_UpperCAmelCase	,)

									# Build dataset for splits
									_a     :      List[str]        =							self.builder.as_dataset(
									    split='train'	,verification_mode=_UpperCAmelCase	,in_memory=self.keep_in_memory		)
									return dataset

class 	__magic_name__						:







							def __init__(							self					:       Optional[int]	,_UpperCAmelCase					:       Dataset	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]	,_UpperCAmelCase					:       Optional[int] = None	,_UpperCAmelCase					:       Optional[int] = None	,**_UpperCAmelCase					:       Dict	,):
									if num_proc is not None and num_proc <= 0:
											raise ValueError(F"""num_proc {num_proc} must be an integer > 0."""		)

									_a     :      Dict        =							dataset
									_a     :      List[Any]        =							name
									_a     :      Tuple        =							con
									_a     :      Union[str, Any]        =							batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
									_a     :      List[Any]        =							num_proc
									_a     :      Tuple        =							to_sql_kwargs







							def 							__lowercase			(							self					:       List[Any]		):
									_a     :      Tuple        =							self.to_sql_kwargs.pop('sql'	,_UpperCAmelCase		)
									_a     :      str        =							self.to_sql_kwargs.pop('con'	,_UpperCAmelCase		)
									_a     :      Optional[Any]        =							self.to_sql_kwargs.pop('index'	,_UpperCAmelCase		)

									_a     :      Any        =							self._write(index=_UpperCAmelCase	,**self.to_sql_kwargs		)
									return written







							def 							__lowercase			(							self					:       Optional[int]	,_UpperCAmelCase					:       Dict		):
									_a       ,					_a       ,					_a     :      Any        =							args
									_a     :      Tuple        =							{**to_sql_kwargs, 'if_exists': 'append'} if offset > 0 else to_sql_kwargs
									_a     :      Dict        =							query_table(
									    table=self.dataset.data	,key=slice(_UpperCAmelCase	,offset + self.batch_size		)	,indices=self.dataset._indices	,)
									_a     :      Tuple        =							batch.to_pandas()
									_a     :      Dict        =							df.to_sql(self.name	,self.con	,index=_UpperCAmelCase	,**_UpperCAmelCase		)
									return num_rows or len(_UpperCAmelCase		)







							def 							__lowercase			(							self					:       int	,_UpperCAmelCase					:       Optional[int]	,**_UpperCAmelCase					:       List[Any]		):
									_a     :      Union[str, Any]        =							0

									if self.num_proc is None or self.num_proc == 1:
											for offset in logging.tqdm(
											    range(0	,len(self.dataset		)	,self.batch_size		)	,unit='ba'	,disable=not logging.is_progress_bar_enabled()	,desc='Creating SQL from Arrow format'	,):
													written += self._batch_sql((offset, index, to_sql_kwargs)		)
									else:
											_a       ,					_a     :      List[Any]        =							len(self.dataset		), self.batch_size
											with multiprocessing.Pool(self.num_proc		) as pool:
													for num_rows in logging.tqdm(
													    pool.imap(
													        self._batch_sql	,[(offset, index, to_sql_kwargs) for offset in range(0	,_UpperCAmelCase	,_UpperCAmelCase		)]	,)	,total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size	,unit='ba'	,disable=not logging.is_progress_bar_enabled()	,desc='Creating SQL from Arrow format'	,):
															written += num_rows

									return written




