



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(SCREAMING_SNAKE_CASE__   )
class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    def __init__(	self,		**lowerCamelCase__		):
        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       _lowerCAmelCase (	self,		**lowerCamelCase__		):
        A			:	Tuple										=       {}
        A			:	Optional[int]										=       {}
        A			:	List[str]										=       {}
        # preprocess args
        if "points_per_batch" in kwargs:
            A			:	Optional[int]										=       kwargs["""points_per_batch"""]
        if "points_per_crop" in kwargs:
            A			:	int										=       kwargs["""points_per_crop"""]
        if "crops_n_layers" in kwargs:
            A			:	str										=       kwargs["""crops_n_layers"""]
        if "crop_overlap_ratio" in kwargs:
            A			:	List[Any]										=       kwargs["""crop_overlap_ratio"""]
        if "crop_n_points_downscale_factor" in kwargs:
            A			:	List[str]										=       kwargs["""crop_n_points_downscale_factor"""]
        # postprocess args
        if "pred_iou_thresh" in kwargs:
            A			:	Tuple										=       kwargs["""pred_iou_thresh"""]
        if "stability_score_offset" in kwargs:
            A			:	int										=       kwargs["""stability_score_offset"""]
        if "mask_threshold" in kwargs:
            A			:	Any										=       kwargs["""mask_threshold"""]
        if "stability_score_thresh" in kwargs:
            A			:	Union[str, Any]										=       kwargs["""stability_score_thresh"""]
        if "crops_nms_thresh" in kwargs:
            A			:	Optional[Any]										=       kwargs["""crops_nms_thresh"""]
        if "output_rle_mask" in kwargs:
            A			:	Any										=       kwargs["""output_rle_mask"""]
        if "output_bboxes_mask" in kwargs:
            A			:	Union[str, Any]										=       kwargs["""output_bboxes_mask"""]
        return preprocess_kwargs, forward_params, postprocess_kwargs






    def __call__(	self,		lowerCamelCase__,		*lowerCamelCase__,		lowerCamelCase__=None,		lowerCamelCase__=None,		**lowerCamelCase__		):
        return super().__call__(lowerCamelCase__,		*lowerCamelCase__,		num_workers=lowerCamelCase__,		batch_size=lowerCamelCase__,		**lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__=64,		lowerCamelCase__ = 0,		lowerCamelCase__ = 512 / 1500,		lowerCamelCase__ = 32,		lowerCamelCase__ = 1,		):
        A			:	str										=       load_image(lowerCamelCase__		)
        A			:	Any										=       self.image_processor.size["""longest_edge"""]
        A			:	str										=       self.image_processor.generate_crop_boxes(
            lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)
        A			:	str										=       self.image_processor(images=lowerCamelCase__,		return_tensors="""pt"""		)

        with self.device_placement():
            if self.framework == "pt":
                A			:	str										=       self.get_inference_context()
                with inference_context():
                    A			:	Dict										=       self._ensure_tensor_on_device(lowerCamelCase__,		device=self.device		)
                    A			:	Dict										=       self.model.get_image_embeddings(model_inputs.pop("""pixel_values"""		)		)
                    A			:	Optional[int]										=       image_embeddings

        A			:	Dict										=       grid_points.shape[1]
        A			:	Optional[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,		lowerCamelCase__,		lowerCamelCase__		):
            A			:	Optional[Any]										=       grid_points[:, i : i + points_per_batch, :, :]
            A			:	str										=       input_labels[:, i : i + points_per_batch]
            A			:	Optional[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       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__=0.88,		lowerCamelCase__=0.95,		lowerCamelCase__=0,		lowerCamelCase__=1,		):
        A			:	Tuple										=       model_inputs.pop("""input_boxes"""		)
        A			:	List[Any]										=       model_inputs.pop("""is_last"""		)
        A			:	List[str]										=       model_inputs.pop("""original_sizes"""		).tolist()
        A			:	int										=       model_inputs.pop("""reshaped_input_sizes"""		).tolist()

        A			:	int										=       self.model(**lowerCamelCase__		)

        # post processing happens here in order to avoid CPU GPU copies of ALL the masks
        A			:	Union[str, Any]										=       model_outputs["""pred_masks"""]
        A			:	int										=       self.image_processor.post_process_masks(
            lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		binarize=lowerCamelCase__		)
        A			:	List[Any]										=       model_outputs["""iou_scores"""]
        A			:	List[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       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__=False,		lowerCamelCase__=False,		lowerCamelCase__=0.7,		):
        A			:	Optional[Any]										=       []
        A			:	Tuple										=       []
        A			:	Union[str, Any]										=       []
        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			:	Optional[Any]										=       torch.cat(lowerCamelCase__		)
        A			:	Optional[Any]										=       torch.cat(lowerCamelCase__		)
        A			:	int										=       self.image_processor.post_process_for_mask_generation(
            lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)

        A			:	List[str]										=       defaultdict(lowerCamelCase__		)
        for output in model_outputs:
            for k, v in output.items():
                extra[k].append(lowerCamelCase__		)

        A			:	List[Any]										=       {}
        if output_rle_mask:
            A			:	List[str]										=       rle_mask

        if output_bboxes_mask:
            A			:	Tuple										=       bounding_boxes

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







import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  str:



    """simple docstring"""






    A			:	int										=       []
    for line in lines:
        A			:	int										=       re.sub(R"""#.*"""				,					""""""				,					_lowerCAmelCase		)  # remove comments
        if line:
            filtered_lines.append(_lowerCAmelCase		)
    A			:	Tuple										=       """\n""".join(_lowerCAmelCase		)

    # Make a hash from all this code
    A			:	Union[str, Any]										=       full_str.encode("""utf-8"""		)
    return shaaaa(_lowerCAmelCase		).hexdigest()


# get importable module names and hash for caching
SCREAMING_SNAKE_CASE_:List[Any]					=						{
    """csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    """json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    """pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    """parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    """arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    """text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    """imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    """audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
SCREAMING_SNAKE_CASE_:Optional[Any]					=						{
    """.csv""": ("""csv""", {}),
    """.tsv""": ("""csv""", {"""sep""": """\t"""}),
    """.json""": ("""json""", {}),
    """.jsonl""": ("""json""", {}),
    """.parquet""": ("""parquet""", {}),
    """.arrow""": ("""arrow""", {}),
    """.txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
SCREAMING_SNAKE_CASE_:Optional[int]					=						{"""imagefolder""", """audiofolder"""}

# Used to filter data files based on extensions given a module name
SCREAMING_SNAKE_CASE_:Dict[str, List[str]]					=						{}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
     _MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")




_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")


