
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					:	List[str]							=		logging.get_logger(__name__)




@add_end_docstrings(__lowerCamelCase	)
class 		a_						( __lowerCamelCase	):







    def __init__(			self			:       str     ,					**__UpperCamelCase			:       Optional[Any]     )		->Optional[Any]:






        '''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      _snake_case					(			self			:       List[str]     ,					**__UpperCamelCase			:       int     )		->List[Any]:






        '''simple docstring'''






        _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			:       Optional[Any]     ,					__UpperCamelCase			:       List[str]     ,					*__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       List[str]=None     ,					__UpperCamelCase			:       Optional[Any]=None     ,					**__UpperCamelCase			:       Tuple     )		->Union[str, Any]:






        '''simple docstring'''






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







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






        '''simple docstring'''






        _UpperCAmelCase          =   load_image(a_     )
        _UpperCAmelCase          =   self.image_processor.size["longest_edge"]
        _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      _snake_case					(			self			:       List[Any]     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       str=0.8_8     ,					__UpperCamelCase			:       Optional[int]=0.9_5     ,					__UpperCamelCase			:       Optional[Any]=0     ,					__UpperCamelCase			:       str=1     ,					)		->Dict:






        '''simple docstring'''






        _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          =   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      _snake_case					(			self			:       Dict     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       str=False     ,					__UpperCamelCase			:       Optional[int]=False     ,					__UpperCamelCase			:       List[str]=0.7     ,					)		->Optional[int]:






        '''simple docstring'''






        _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          =   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 argparse
import os
import re

import packaging.version


a					:	str							=		'''examples/'''
a					:	List[str]							=		{
    '''examples''': (re.compile(r'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
    '''init''': (re.compile(r'''^__version__\s+=\s+"([^"]+)"\s*$''', re.MULTILINE), '''__version__ = "VERSION"\n'''),
    '''setup''': (re.compile(r'''^(\s*)version\s*=\s*"[^"]+",''', re.MULTILINE), r'''\1version="VERSION",'''),
    '''doc''': (re.compile(r'''^(\s*)release\s*=\s*"[^"]+"$''', re.MULTILINE), '''release = "VERSION"\n'''),
}
a					:	Tuple							=		{
    '''init''': '''src/diffusers/__init__.py''',
    '''setup''': '''setup.py''',
}
a					:	List[str]							=		'''README.md'''


def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  Dict:





    """simple docstring"""

    with open(_A					,      """r"""					,      encoding="""utf-8"""					,      newline="""\n"""      ) as f:
        _UpperCAmelCase          =   f.read()
    _UpperCAmelCase    ,_UpperCAmelCase          =   REPLACE_PATTERNS[pattern]
    _UpperCAmelCase          =   replace.replace("""VERSION"""					,      _A      )
    _UpperCAmelCase          =   re_pattern.sub(_A					,      _A      )
    with open(_A					,      """w"""					,      encoding="""utf-8"""					,      newline="""\n"""      ) as f:
        f.write(_A      )


def  _UpperCamelCase	(						_A      )    ->  Union[str, Any]:





    """simple docstring"""

    for folder, directories, fnames in os.walk(_A      ):
        # Removing some of the folders with non-actively maintained examples from the walk
        if "research_projects" in directories:
            directories.remove("""research_projects"""      )
        if "legacy" in directories:
            directories.remove("""legacy"""      )
        for fname in fnames:
            if fname.endswith(""".py"""      ):
                update_version_in_file(os.path.join(_A					,      _A      )					,      _A					,      pattern="""examples"""      )


def  _UpperCamelCase	(						_A					,      _A=False      )    ->  int:





    """simple docstring"""

    for pattern, fname in REPLACE_FILES.items():
        update_version_in_file(_A					,      _A					,      _A      )
    if not patch:
        update_version_in_examples(_A      )


def  _UpperCamelCase	(						)    ->  Any:





    """simple docstring"""

    _UpperCAmelCase          =   """🤗 Transformers currently provides the following architectures"""
    _UpperCAmelCase          =   """1. Want to contribute a new model?"""
    with open(_A					,      """r"""					,      encoding="""utf-8"""					,      newline="""\n"""      ) as f:
        _UpperCAmelCase          =   f.readlines()

    # Find the start of the list.
    _UpperCAmelCase          =   0
    while not lines[start_index].startswith(_start_prompt      ):
        start_index += 1
    start_index += 1

    _UpperCAmelCase          =   start_index
    # Update the lines in the model list.
    while not lines[index].startswith(_end_prompt      ):
        if lines[index].startswith("""1."""      ):
            _UpperCAmelCase          =   lines[index].replace(
                """https://huggingface.co/docs/diffusers/main/model_doc"""					,      """https://huggingface.co/docs/diffusers/model_doc"""					,      )
        index += 1

    with open(_A					,      """w"""					,      encoding="""utf-8"""					,      newline="""\n"""      ) as f:
        f.writelines(_A      )


def  _UpperCamelCase	(						)    ->  Optional[int]:





    """simple docstring"""

    with open(REPLACE_FILES["""init"""]					,      """r"""      ) as f:
        _UpperCAmelCase          =   f.read()
    _UpperCAmelCase          =   REPLACE_PATTERNS["""init"""][0].search(_A      ).groups()[0]
    return packaging.version.parse(_A      )


def  _UpperCamelCase	(						_A=False      )    ->  List[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   get_version()
    if patch and default_version.is_devrelease:
        raise ValueError("""Can't create a patch version from the dev branch, checkout a released version!"""      )
    if default_version.is_devrelease:
        _UpperCAmelCase          =   default_version.base_version
    elif patch:
        _UpperCAmelCase          =   F"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
    else:
        _UpperCAmelCase          =   F"""{default_version.major}.{default_version.minor + 1}.0"""

    # Now let's ask nicely if that's the right one.
    _UpperCAmelCase          =   input(F"""Which version are you releasing? [{default_version}]"""      )
    if len(_A      ) == 0:
        _UpperCAmelCase          =   default_version

    print(F"""Updating version to {version}."""      )
    global_version_update(_A					,      patch=_A      )


def  _UpperCamelCase	(						)    ->  List[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   get_version()
    _UpperCAmelCase          =   F"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
    _UpperCAmelCase          =   current_version.base_version

    # Check with the user we got that right.
    _UpperCAmelCase          =   input(F"""Which version are we developing now? [{dev_version}]"""      )
    if len(_A      ) == 0:
        _UpperCAmelCase          =   dev_version

    print(F"""Updating version to {version}."""      )
    global_version_update(_A      )


#    print("Cleaning main README, don't forget to run `make fix-copies`.")
#    clean_main_ref_in_model_list()


if __name__ == "__main__":
 a					:	Dict							=		argparse.ArgumentParser()
 parser.add_argument('''--post_release''', action='''store_true''', help='''Whether this is pre or post release.''')
 parser.add_argument('''--patch''', action='''store_true''', help='''Whether or not this is a patch release.''')
 a					:	Tuple							=		parser.parse_args()
 if not args.post_release:
  pre_release_work(patch=args.patch)
 elif args.patch:
  print('''Nothing to do after a patch :-)''')
 else:
  post_release_work()