



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_:Dict					=						logging.get_logger(__name__)






@add_end_docstrings(lowerCAmelCase__   )
class SCREAMING_SNAKE_CASE__					(		lowerCAmelCase__   ):




    '''simple docstring'''






    def __init__(	self,		**lowerCamelCase__		):
        super().__init__(**_SCREAMING_SNAKE_CASE		)
        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(_SCREAMING_SNAKE_CASE		)






    def       _lowerCAmelCase (	self,		**lowerCamelCase__		):
        __UpperCamelCase			:	int										=       {}
        __UpperCamelCase			:	Union[str, Any]										=       {}
        __UpperCamelCase			:	Union[str, Any]										=       {}
        # preprocess args
        if "points_per_batch" in kwargs:
            __UpperCamelCase			:	Tuple										=       kwargs["""points_per_batch"""]
        if "points_per_crop" in kwargs:
            __UpperCamelCase			:	Optional[Any]										=       kwargs["""points_per_crop"""]
        if "crops_n_layers" in kwargs:
            __UpperCamelCase			:	Dict										=       kwargs["""crops_n_layers"""]
        if "crop_overlap_ratio" in kwargs:
            __UpperCamelCase			:	List[str]										=       kwargs["""crop_overlap_ratio"""]
        if "crop_n_points_downscale_factor" in kwargs:
            __UpperCamelCase			:	int										=       kwargs["""crop_n_points_downscale_factor"""]
        # postprocess args
        if "pred_iou_thresh" in kwargs:
            __UpperCamelCase			:	List[Any]										=       kwargs["""pred_iou_thresh"""]
        if "stability_score_offset" in kwargs:
            __UpperCamelCase			:	Union[str, Any]										=       kwargs["""stability_score_offset"""]
        if "mask_threshold" in kwargs:
            __UpperCamelCase			:	List[str]										=       kwargs["""mask_threshold"""]
        if "stability_score_thresh" in kwargs:
            __UpperCamelCase			:	Optional[Any]										=       kwargs["""stability_score_thresh"""]
        if "crops_nms_thresh" in kwargs:
            __UpperCamelCase			:	str										=       kwargs["""crops_nms_thresh"""]
        if "output_rle_mask" in kwargs:
            __UpperCamelCase			:	int										=       kwargs["""output_rle_mask"""]
        if "output_bboxes_mask" in kwargs:
            __UpperCamelCase			:	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__(_SCREAMING_SNAKE_CASE,		*_SCREAMING_SNAKE_CASE,		num_workers=_SCREAMING_SNAKE_CASE,		batch_size=_SCREAMING_SNAKE_CASE,		**_SCREAMING_SNAKE_CASE		)






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

        with self.device_placement():
            if self.framework == "pt":
                __UpperCamelCase			:	int										=       self.get_inference_context()
                with inference_context():
                    __UpperCamelCase			:	Tuple										=       self._ensure_tensor_on_device(_SCREAMING_SNAKE_CASE,		device=self.device		)
                    __UpperCamelCase			:	Union[str, Any]										=       self.model.get_image_embeddings(model_inputs.pop("""pixel_values"""		)		)
                    __UpperCamelCase			:	Optional[int]										=       image_embeddings

        __UpperCamelCase			:	Optional[int]										=       grid_points.shape[1]
        __UpperCamelCase			:	Tuple										=       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,		_SCREAMING_SNAKE_CASE,		_SCREAMING_SNAKE_CASE		):
            __UpperCamelCase			:	Dict										=       grid_points[:, i : i + points_per_batch, :, :]
            __UpperCamelCase			:	Tuple										=       input_labels[:, i : i + points_per_batch]
            __UpperCamelCase			:	List[Any]										=       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,		):
        __UpperCamelCase			:	Tuple										=       model_inputs.pop("""input_boxes"""		)
        __UpperCamelCase			:	Dict										=       model_inputs.pop("""is_last"""		)
        __UpperCamelCase			:	Optional[Any]										=       model_inputs.pop("""original_sizes"""		).tolist()
        __UpperCamelCase			:	Union[str, Any]										=       model_inputs.pop("""reshaped_input_sizes"""		).tolist()

        __UpperCamelCase			:	Dict										=       self.model(**_SCREAMING_SNAKE_CASE		)

        # post processing happens here in order to avoid CPU GPU copies of ALL the masks
        __UpperCamelCase			:	Dict										=       model_outputs["""pred_masks"""]
        __UpperCamelCase			:	Optional[int]										=       self.image_processor.post_process_masks(
            _SCREAMING_SNAKE_CASE,		_SCREAMING_SNAKE_CASE,		_SCREAMING_SNAKE_CASE,		_SCREAMING_SNAKE_CASE,		binarize=_SCREAMING_SNAKE_CASE		)
        __UpperCamelCase			:	Any										=       model_outputs["""iou_scores"""]
        __UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase			:	str										=       self.image_processor.filter_masks(
            masks[0],		iou_scores[0],		original_sizes[0],		input_boxes[0],		_SCREAMING_SNAKE_CASE,		_SCREAMING_SNAKE_CASE,		_SCREAMING_SNAKE_CASE,		_SCREAMING_SNAKE_CASE,		)
        return {
            "masks": masks,
            "is_last": is_last,
            "boxes": boxes,
            "iou_scores": iou_scores,
        }






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__=False,		lowerCamelCase__=False,		lowerCamelCase__=0.7,		):
        __UpperCamelCase			:	Optional[int]										=       []
        __UpperCamelCase			:	List[str]										=       []
        __UpperCamelCase			:	Dict										=       []
        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			:	List[str]										=       torch.cat(_SCREAMING_SNAKE_CASE		)
        __UpperCamelCase			:	Optional[int]										=       torch.cat(_SCREAMING_SNAKE_CASE		)
        __UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase			:	Any										=       self.image_processor.post_process_for_mask_generation(
            _SCREAMING_SNAKE_CASE,		_SCREAMING_SNAKE_CASE,		_SCREAMING_SNAKE_CASE,		_SCREAMING_SNAKE_CASE		)

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

        __UpperCamelCase			:	Tuple										=       {}
        if output_rle_mask:
            __UpperCamelCase			:	Optional[int]										=       rle_mask

        if output_bboxes_mask:
            __UpperCamelCase			:	Union[str, Any]										=       bounding_boxes

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







import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
SCREAMING_SNAKE_CASE_:Any					=						"""src/diffusers"""

# Matches is_xxx_available()
SCREAMING_SNAKE_CASE_:Optional[Any]					=						re.compile(R"""is\_([a-z_]*)_available\(\)""")
# Matches from xxx import bla
SCREAMING_SNAKE_CASE_:Union[str, Any]					=						re.compile(R"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")


SCREAMING_SNAKE_CASE_:List[Any]					=						"""
{0} = None
"""

SCREAMING_SNAKE_CASE_:Optional[Any]					=						"""
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, {1})

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, {1})
"""


SCREAMING_SNAKE_CASE_:int					=						"""
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Tuple:



    """simple docstring"""






    A			:	Union[str, Any]										=       _re_backend.findall(_lowerCAmelCase		)
    if len(_lowerCAmelCase		) == 0:
        return None

    return "_and_".join(_lowerCAmelCase		)




def      __UpperCamelCase     (  )	->  str:



    """simple docstring"""






    with open(os.path.join(_lowerCAmelCase				,					"""__init__.py"""		)				,					"""r"""				,					encoding="""utf-8"""				,					newline="""\n"""		) as f:
        A			:	Dict										=       f.readlines()

    # Get to the point we do the actual imports for type checking
    A			:	Dict										=       0
    A			:	List[Any]										=       {}
    # Go through the end of the file
    while line_index < len(_lowerCAmelCase		):
        # If the line contains is_backend_available, we grab all objects associated with the `else` block
        A			:	Optional[int]										=       find_backend(lines[line_index]		)
        if backend is not None:
            while not lines[line_index].startswith("""else:"""		):
                line_index += 1
            line_index += 1
            A			:	str										=       []
            # Until we unindent, add backend objects to the list
            while line_index < len(_lowerCAmelCase		) and len(lines[line_index]		) > 1:
                A			:	Tuple										=       lines[line_index]
                A			:	List[str]										=       _re_single_line_import.search(_lowerCAmelCase		)
                if single_line_import_search is not None:
                    objects.extend(single_line_import_search.groups()[0].split(""", """		)		)
                elif line.startswith(""" """ * 8		):
                    objects.append(line[8:-2]		)
                line_index += 1

            if len(_lowerCAmelCase		) > 0:
                A			:	List[str]										=       objects
        else:
            line_index += 1

    return backend_specific_objects




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  str:



    """simple docstring"""






    if name.isupper():
        return DUMMY_CONSTANT.format(_lowerCAmelCase		)
    elif name.islower():
        return DUMMY_FUNCTION.format(_lowerCAmelCase				,					_lowerCAmelCase		)
    else:
        return DUMMY_CLASS.format(_lowerCAmelCase				,					_lowerCAmelCase		)




def      __UpperCamelCase     (  _lowerCAmelCase=None		)	->  Tuple:



    """simple docstring"""






    if backend_specific_objects is None:
        A			:	Union[str, Any]										=       read_init()
    # For special correspondence backend to module name as used in the function requires_modulename
    A			:	Any										=       {}

    for backend, objects in backend_specific_objects.items():
        A			:	str										=       """[""" + """, """.join(f'''"{b}"''' for b in backend.split("""_and_"""		)		) + """]"""
        A			:	Any										=       """# This file is autogenerated by the command `make fix-copies`, do not edit.\n"""
        dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
        dummy_file += "\n".join([create_dummy_object(_lowerCAmelCase				,					_lowerCAmelCase		) for o in objects]		)
        A			:	Optional[Any]										=       dummy_file

    return dummy_files




def      __UpperCamelCase     (  _lowerCAmelCase=False		)	->  str:



    """simple docstring"""






    A			:	str										=       create_dummy_files()
    # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
    A			:	List[str]										=       {"""torch""": """pt"""}

    # Locate actual dummy modules and read their content.
    A			:	Union[str, Any]										=       os.path.join(_lowerCAmelCase				,					"""utils"""		)
    A			:	Any										=       {
        backend: os.path.join(_lowerCAmelCase				,					f'''dummy_{short_names.get(_lowerCAmelCase				,					_lowerCAmelCase		)}_objects.py'''		)
        for backend in dummy_files.keys()
    }

    A			:	List[Any]										=       {}
    for backend, file_path in dummy_file_paths.items():
        if os.path.isfile(_lowerCAmelCase		):
            with open(_lowerCAmelCase				,					"""r"""				,					encoding="""utf-8"""				,					newline="""\n"""		) as f:
                A			:	Dict										=       f.read()
        else:
            A			:	str										=       """"""

    for backend in dummy_files.keys():
        if dummy_files[backend] != actual_dummies[backend]:
            if overwrite:
                print(
                    f'''Updating diffusers.utils.dummy_{short_names.get(_lowerCAmelCase				,					_lowerCAmelCase		)}_objects.py as the main '''
                    """__init__ has new objects."""		)
                with open(dummy_file_paths[backend]				,					"""w"""				,					encoding="""utf-8"""				,					newline="""\n"""		) as f:
                    f.write(dummy_files[backend]		)
            else:
                raise ValueError(
                    """The main __init__ has objects that are not present in """
                    f'''diffusers.utils.dummy_{short_names.get(_lowerCAmelCase				,					_lowerCAmelCase		)}_objects.py. Run `make fix-copies` '''
                    """to fix this."""		)


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_:List[Any]					=						argparse.ArgumentParser()
     parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
     SCREAMING_SNAKE_CASE_:Any					=						parser.parse_args()

     check_dummies(args.fix_and_overwrite)


