"""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									=						logging.get_logger(__name__)



@add_end_docstrings(__lowerCamelCase     )
class  _lowerCamelCase			(__lowerCamelCase     ):







				def __init__( self    :       Tuple			,					**lowerCamelCase_    :       List[str]						):

					"""simple docstring"""






					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 							__UpperCAmelCase					( self    :       Union[str, Any]			,					**lowerCamelCase_    :       Optional[int]						):

					"""simple docstring"""






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







				def __call__( self    :       Optional[int]			,					lowerCamelCase_    :       Tuple			,					*lowerCamelCase_    :       Optional[Any]			,					lowerCamelCase_    :       Any=None			,					lowerCamelCase_    :       Optional[int]=None			,					**lowerCamelCase_    :       Any						):

					"""simple docstring"""






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







				def 							__UpperCAmelCase					( self    :       int			,					lowerCamelCase_    :       str			,					lowerCamelCase_    :       List[str]=6_4			,					lowerCamelCase_    :       int = 0			,					lowerCamelCase_    :       float = 5_1_2 / 1_5_0_0			,					lowerCamelCase_    :       Optional[int] = 3_2			,					lowerCamelCase_    :       Optional[int] = 1			,					):

					"""simple docstring"""






					_lowercase :    Any							=							load_image(lowerCamelCase_						)
					_lowercase :    List[Any]							=							self.image_processor.size['longest_edge']
					_lowercase				,      _lowercase				,      _lowercase				,      _lowercase :    Optional[Any]							=							self.image_processor.generate_crop_boxes(
					    lowerCamelCase_			,					lowerCamelCase_			,					lowerCamelCase_			,					lowerCamelCase_			,					lowerCamelCase_			,					lowerCamelCase_						)
					_lowercase :    Optional[int]							=							self.image_processor(images=lowerCamelCase_			,					return_tensors='pt'						)

					with self.device_placement():
						if self.framework == "pt":
							_lowercase :    Any							=							self.get_inference_context()
							with inference_context():
								_lowercase :    Optional[int]							=							self._ensure_tensor_on_device(lowerCamelCase_			,					device=self.device						)
								_lowercase :    Any							=							self.model.get_image_embeddings(model_inputs.pop('pixel_values'						)						)
								_lowercase :    Any							=							image_embeddings

					_lowercase :    Union[str, Any]							=							grid_points.shape[1]
					_lowercase :    Dict							=							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_						):
						_lowercase :    str							=							grid_points[:, i : i + points_per_batch, :, :]
						_lowercase :    Any							=							input_labels[:, i : i + points_per_batch]
						_lowercase :    int							=							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    :       List[Any]			,					lowerCamelCase_    :       List[Any]			,					lowerCamelCase_    :       List[Any]=0.88			,					lowerCamelCase_    :       List[Any]=0.95			,					lowerCamelCase_    :       Optional[Any]=0			,					lowerCamelCase_    :       Any=1			,					):

					"""simple docstring"""






					_lowercase :    Union[str, Any]							=							model_inputs.pop('input_boxes'						)
					_lowercase :    int							=							model_inputs.pop('is_last'						)
					_lowercase :    Tuple							=							model_inputs.pop('original_sizes'						).tolist()
					_lowercase :    List[str]							=							model_inputs.pop('reshaped_input_sizes'						).tolist()

					_lowercase :    str							=							self.model(**lowerCamelCase_						)

					# post processing happens here in order to avoid CPU GPU copies of ALL the masks
					_lowercase :    Optional[Any]							=							model_outputs['pred_masks']
					_lowercase :    Dict							=							self.image_processor.post_process_masks(
					    lowerCamelCase_			,					lowerCamelCase_			,					lowerCamelCase_			,					lowerCamelCase_			,					binarize=lowerCamelCase_						)
					_lowercase :    Dict							=							model_outputs['iou_scores']
					_lowercase				,      _lowercase				,      _lowercase :    Union[str, Any]							=							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 							__UpperCAmelCase					( self    :       Any			,					lowerCamelCase_    :       Optional[int]			,					lowerCamelCase_    :       str=False			,					lowerCamelCase_    :       Optional[int]=False			,					lowerCamelCase_    :       Tuple=0.7			,					):

					"""simple docstring"""






					_lowercase :    Any							=							[]
					_lowercase :    Any							=							[]
					_lowercase :    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'						)						)

					_lowercase :    List[Any]							=							torch.cat(lowerCamelCase_						)
					_lowercase :    List[Any]							=							torch.cat(lowerCamelCase_						)
					_lowercase				,      _lowercase				,      _lowercase				,      _lowercase :    Any							=							self.image_processor.post_process_for_mask_generation(
					    lowerCamelCase_			,					lowerCamelCase_			,					lowerCamelCase_			,					lowerCamelCase_						)

					_lowercase :    Union[str, Any]							=							defaultdict(lowerCamelCase_						)
					for output in model_outputs:
						for k, v in output.items():
							extra[k].append(lowerCamelCase_						)

					_lowercase :    List[str]							=							{}
					if output_rle_mask:
						_lowercase :    Union[str, Any]							=							rle_mask

					if output_bboxes_mask:
						_lowercase :    int							=							bounding_boxes

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




"""simple docstring"""







import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE



class  _lowerCamelCase			(__lowerCamelCase     ):
				_snake_case     =  ""
				_snake_case     =  (
				    None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
				)
				_snake_case     =  None  # compression type in fsspec. ex: "gzip"
				_snake_case     =  None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz







				def __init__( self    :       str			,					lowerCamelCase_    :       str = ""			,					lowerCamelCase_    :       Optional[str] = None			,					lowerCamelCase_    :       Optional[dict] = None			,					**lowerCamelCase_    :       List[str]						):

					"""simple docstring"""






					super().__init__(self			,					**lowerCamelCase_						)
					# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
					_lowercase :    Union[str, Any]							=							fsspec.open(
					    lowerCamelCase_			,					mode='rb'			,					protocol=lowerCamelCase_			,					compression=self.compression			,					client_kwargs={
					        'requote_redirect_url': False,  # see https://github.com/huggingface/datasets/pull/5459
					        'trust_env': True,  # Enable reading proxy env variables.
					        **(target_options or {}).pop('client_kwargs'			,					{}						),  # To avoid issues if it was already passed.
					    }			,					**(target_options or {})			,					)
					_lowercase :    str							=							os.path.basename(self.file.path.split('::'						)[0]						)
					_lowercase :    Optional[int]							=							(
					    self.compressed_name[: self.compressed_name.rindex('.'						)]
					    if '.' in self.compressed_name
					    else self.compressed_name
					)
					_lowercase :    str							=							None







				@classmethod
				def 							__UpperCAmelCase					( cls    :       int			,					lowerCamelCase_    :       List[str]						):

					"""simple docstring"""






					return super()._strip_protocol(lowerCamelCase_						).lstrip('/'						)







				def 							__UpperCAmelCase					( self    :       Optional[Any]						):

					"""simple docstring"""






					if self.dir_cache is None:
						_lowercase :    Tuple							=							{**self.file.fs.info(self.file.path						), 'name': self.uncompressed_name}
						_lowercase :    int							=							{f['name']: f}







				def 							__UpperCAmelCase					( self    :       List[str]			,					lowerCamelCase_    :       str						):

					"""simple docstring"""






					return self.file.open().read()







				def 							__UpperCAmelCase					( self    :       Dict			,					lowerCamelCase_    :       str			,					lowerCamelCase_    :       str = "rb"			,					lowerCamelCase_    :       str=None			,					lowerCamelCase_    :       str=True			,					lowerCamelCase_    :       Union[str, Any]=None			,					**lowerCamelCase_    :       Optional[int]			,					):

					"""simple docstring"""






					_lowercase :    Union[str, Any]							=							self._strip_protocol(lowerCamelCase_						)
					if mode != "rb":
						raise ValueError(F'''Tried to read with mode {mode} on file {self.file.path} opened with mode \'rb\''''						)
					return self.file.open()



class  _lowerCamelCase			(__lowerCamelCase     ):
				_snake_case     =  "bz2"
				_snake_case     =  "bz2"
				_snake_case     =  ".bz2"



class  _lowerCamelCase			(__lowerCamelCase     ):
				_snake_case     =  "gzip"
				_snake_case     =  "gzip"
				_snake_case     =  ".gz"



class  _lowerCamelCase			(__lowerCamelCase     ):
				_snake_case     =  "lz4"
				_snake_case     =  "lz4"
				_snake_case     =  ".lz4"



class  _lowerCamelCase			(__lowerCamelCase     ):
				_snake_case     =  "xz"
				_snake_case     =  "xz"
				_snake_case     =  ".xz"



class  _lowerCamelCase			(__lowerCamelCase     ):
				_snake_case     =  "zstd"
				_snake_case     =  "zstd"
				_snake_case     =  ".zst"







				def __init__( self    :       Union[str, Any]			,					lowerCamelCase_    :       str			,					lowerCamelCase_    :       str = "rb"			,					lowerCamelCase_    :       Optional[str] = None			,					lowerCamelCase_    :       Optional[dict] = None			,					lowerCamelCase_    :       int = DEFAULT_BLOCK_SIZE			,					**lowerCamelCase_    :       int			,					):

					"""simple docstring"""






					super().__init__(
					    fo=lowerCamelCase_			,					mode=lowerCamelCase_			,					target_protocol=lowerCamelCase_			,					target_options=lowerCamelCase_			,					block_size=lowerCamelCase_			,					**lowerCamelCase_			,					)
					# We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
					#
					# File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
					#     out.close = close
					# AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
					#
					# see https://github.com/intake/filesystem_spec/issues/725
					_lowercase :    Any							=							self.file.__enter__



					class  _lowerCamelCase			:







									def __init__( self    :       Any			,					lowerCamelCase_    :       List[Any]						):

										"""simple docstring"""






										_lowercase :    Tuple							=							file_







									def __enter__( self    :       str						):

										"""simple docstring"""






										self._file.__enter__()
										return self







									def __exit__( self    :       Union[str, Any]			,					*lowerCamelCase_    :       Optional[Any]			,					**lowerCamelCase_    :       Union[str, Any]						):

										"""simple docstring"""






										self._file.__exit__(*lowerCamelCase_			,					**lowerCamelCase_						)







									def __iter__( self    :       Optional[int]						):

										"""simple docstring"""






										return iter(self._file						)







									def 							__UpperCAmelCase					( self    :       int						):

										"""simple docstring"""






										return next(self._file						)







									def __getattr__( self    :       List[str]			,					lowerCamelCase_    :       str						):

										"""simple docstring"""






										return getattr(self._file			,					lowerCamelCase_						)

					def fixed_enter(*lowerCamelCase_    :       List[Any]			,					**lowerCamelCase_    :       int						):
						return WrappedFile(_enter(*lowerCamelCase_			,					**lowerCamelCase_						)						)

					_lowercase :    Optional[int]							=							fixed_enter



