






'''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

UpperCamelCase							=logging.get_logger(__name__)




@add_end_docstrings(SCREAMING_SNAKE_CASE__						)
class     A    (      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   _UpperCAmelCase       (							self			,     **__lowerCAmelCase ):
       UpperCamelCase_				:   List[Any]					    =							{}
       UpperCamelCase_				:   List[Any]					    =							{}
       UpperCamelCase_				:   Optional[int]					    =							{}
       # preprocess args
       if "points_per_batch" in kwargs:
             UpperCamelCase_				:   Optional[int]					    =							kwargs["""points_per_batch"""]
       if "points_per_crop" in kwargs:
             UpperCamelCase_				:   str					    =							kwargs["""points_per_crop"""]
       if "crops_n_layers" in kwargs:
             UpperCamelCase_				:   Any					    =							kwargs["""crops_n_layers"""]
       if "crop_overlap_ratio" in kwargs:
             UpperCamelCase_				:   str					    =							kwargs["""crop_overlap_ratio"""]
       if "crop_n_points_downscale_factor" in kwargs:
             UpperCamelCase_				:   Optional[Any]					    =							kwargs["""crop_n_points_downscale_factor"""]
       # postprocess args
       if "pred_iou_thresh" in kwargs:
             UpperCamelCase_				:   int					    =							kwargs["""pred_iou_thresh"""]
       if "stability_score_offset" in kwargs:
             UpperCamelCase_				:   Dict					    =							kwargs["""stability_score_offset"""]
       if "mask_threshold" in kwargs:
             UpperCamelCase_				:   Union[str, Any]					    =							kwargs["""mask_threshold"""]
       if "stability_score_thresh" in kwargs:
             UpperCamelCase_				:   List[Any]					    =							kwargs["""stability_score_thresh"""]
       if "crops_nms_thresh" in kwargs:
             UpperCamelCase_				:   Tuple					    =							kwargs["""crops_nms_thresh"""]
       if "output_rle_mask" in kwargs:
             UpperCamelCase_				:   int					    =							kwargs["""output_rle_mask"""]
       if "output_bboxes_mask" in kwargs:
             UpperCamelCase_				:   Dict					    =							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   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase=64			,     __lowerCAmelCase = 0			,     __lowerCAmelCase = 5_12 / 15_00			,     __lowerCAmelCase = 32			,     __lowerCAmelCase = 1			,     ):
       UpperCamelCase_				:   int					    =							load_image(__lowerCAmelCase )
       UpperCamelCase_				:   List[Any]					    =							self.image_processor.size["""longest_edge"""]
       UpperCamelCase_    ,			UpperCamelCase_    ,			UpperCamelCase_    ,			UpperCamelCase_				:   Dict					    =							self.image_processor.generate_crop_boxes(
           __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase )
       UpperCamelCase_				:   Any					    =							self.image_processor(images=__lowerCAmelCase			,     return_tensors="""pt""" )

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

       UpperCamelCase_				:   List[Any]					    =							grid_points.shape[1]
       UpperCamelCase_				:   Any					    =							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 ):
             UpperCamelCase_				:   int					    =							grid_points[:, i : i + points_per_batch, :, :]
             UpperCamelCase_				:   Any					    =							input_labels[:, i : i + points_per_batch]
             UpperCamelCase_				:   Tuple					    =							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			,     __lowerCAmelCase			,     __lowerCAmelCase=0.88			,     __lowerCAmelCase=0.95			,     __lowerCAmelCase=0			,     __lowerCAmelCase=1			,     ):
       UpperCamelCase_				:   str					    =							model_inputs.pop("""input_boxes""" )
       UpperCamelCase_				:   str					    =							model_inputs.pop("""is_last""" )
       UpperCamelCase_				:   str					    =							model_inputs.pop("""original_sizes""" ).tolist()
       UpperCamelCase_				:   Any					    =							model_inputs.pop("""reshaped_input_sizes""" ).tolist()

       UpperCamelCase_				:   str					    =							self.model(**__lowerCAmelCase )

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

       UpperCamelCase_				:   Any					    =							defaultdict(__lowerCAmelCase )
       for output in model_outputs:
             for k, v in output.items():
                   extra[k].append(__lowerCAmelCase )

       UpperCamelCase_				:   List[Any]					    =							{}
       if output_rle_mask:
             UpperCamelCase_				:   Any					    =							rle_mask

       if output_bboxes_mask:
             UpperCamelCase_				:   List[Any]					    =							bounding_boxes

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














'''simple docstring'''

from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


UpperCamelCase							=logging.get_logger(__name__)  # pylint: disable=invalid-name



def 							snake_case					(     a_							: str	)       ->				int:





      """simple docstring"""

      if not path:
            return "pipe"

      for ext in PipelineDataFormat.SUPPORTED_FORMATS:
            if path.endswith(a_	):
                  return ext

      raise Exception(
          f"Unable to determine file format from file extension {path}. "
          f"Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}"	)



def 							snake_case					(     a_							: List[Any]	)       ->				Tuple:





      """simple docstring"""

      UpperCamelCase_				:   Any					    =							pipeline(
          task=args.task    ,   model=args.model if args.model else None    ,   config=args.config    ,   tokenizer=args.tokenizer    ,   device=args.device    ,   )
      UpperCamelCase_				:   List[Any]					    =							try_infer_format_from_ext(args.input	) if args.format == """infer""" else args.format
      UpperCamelCase_				:   Tuple					    =							PipelineDataFormat.from_str(
          format=a_    ,   output_path=args.output    ,   input_path=args.input    ,   column=args.column if args.column else nlp.default_input_names    ,   overwrite=args.overwrite    ,   )
      return RunCommand(a_    ,   a_	)




class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Union[str, Any]					    =							nlp
       UpperCamelCase_				:   Optional[Any]					    =							reader
 @staticmethod
 def   _UpperCAmelCase       (							__lowerCAmelCase ):
       UpperCamelCase_				:   List[str]					    =							parser.add_parser("""run"""			,     help="""Run a pipeline through the CLI""" )
       run_parser.add_argument("""--task"""			,     choices=get_supported_tasks()			,     help="""Task to run""" )
       run_parser.add_argument("""--input"""			,     type=__lowerCAmelCase			,     help="""Path to the file to use for inference""" )
       run_parser.add_argument("""--output"""			,     type=__lowerCAmelCase			,     help="""Path to the file that will be used post to write results.""" )
       run_parser.add_argument("""--model"""			,     type=__lowerCAmelCase			,     help="""Name or path to the model to instantiate.""" )
       run_parser.add_argument("""--config"""			,     type=__lowerCAmelCase			,     help="""Name or path to the model's config to instantiate.""" )
       run_parser.add_argument(
           """--tokenizer"""			,     type=__lowerCAmelCase			,     help="""Name of the tokenizer to use. (default: same as the model name)""" )
       run_parser.add_argument(
           """--column"""			,     type=__lowerCAmelCase			,     help="""Name of the column to use as input. (For multi columns input as QA use column1,columns2)"""			,     )
       run_parser.add_argument(
           """--format"""			,     type=__lowerCAmelCase			,     default="""infer"""			,     choices=PipelineDataFormat.SUPPORTED_FORMATS			,     help="""Input format to read from"""			,     )
       run_parser.add_argument(
           """--device"""			,     type=__lowerCAmelCase			,     default=-1			,     help="""Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"""			,     )
       run_parser.add_argument("""--overwrite"""			,     action="""store_true"""			,     help="""Allow overwriting the output file.""" )
       run_parser.set_defaults(func=__lowerCAmelCase )





 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_    ,			UpperCamelCase_				:   str					    =							self._nlp, []

       for entry in self._reader:
             UpperCamelCase_				:   List[Any]					    =							nlp(**__lowerCAmelCase ) if self._reader.is_multi_columns else nlp(__lowerCAmelCase )
             if isinstance(__lowerCAmelCase			,     __lowerCAmelCase ):
                   outputs.append(__lowerCAmelCase )
             else:
                   outputs += output

        # Saving data
       if self._nlp.binary_output:
             UpperCamelCase_				:   int					    =							self._reader.save_binary(__lowerCAmelCase )
             logger.warning(F"Current pipeline requires output to be in binary format, saving at {binary_path}" )
       else:
             self._reader.save(__lowerCAmelCase )






