






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

__magic_name__   :  int        =							logging.get_logger(__name__)





@add_end_docstrings(__snake_case )
class 					A__     (     __snake_case ):

     '''simple docstring'''






     def __init__( self      :    List[str]   ,						**_SCREAMING_SNAKE_CASE      :    List[Any] ):






           """simple docstring"""




           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   _SCREAMING_SNAKE_CASE     ( self      :    Tuple   ,						**_SCREAMING_SNAKE_CASE      :    Union[str, 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      :    Union[str, Any]   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any]   ,						*_SCREAMING_SNAKE_CASE      :    List[Any]   ,						_SCREAMING_SNAKE_CASE      :    Union[str, Any]=None   ,						_SCREAMING_SNAKE_CASE      :    Tuple=None   ,						**_SCREAMING_SNAKE_CASE      :    List[Any] ):






           """simple docstring"""




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







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[str]   ,						_SCREAMING_SNAKE_CASE      :    List[Any]   ,						_SCREAMING_SNAKE_CASE      :    Union[str, Any]=64   ,						_SCREAMING_SNAKE_CASE      :    int = 0   ,						_SCREAMING_SNAKE_CASE      :    float = 512 / 1500   ,						_SCREAMING_SNAKE_CASE      :    Optional[int] = 32   ,						_SCREAMING_SNAKE_CASE      :    Optional[int] = 1   ,						):






           """simple docstring"""




           UpperCamelCase					       =  load_image(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  self.image_processor.size['longest_edge']
           UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  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					       =  self.image_processor(images=_SCREAMING_SNAKE_CASE   ,						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(_SCREAMING_SNAKE_CASE   ,						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   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ):
                 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   _SCREAMING_SNAKE_CASE     ( self      :    List[Any]   ,						_SCREAMING_SNAKE_CASE      :    str   ,						_SCREAMING_SNAKE_CASE      :    str=0.8_8   ,						_SCREAMING_SNAKE_CASE      :    Optional[int]=0.9_5   ,						_SCREAMING_SNAKE_CASE      :    Dict=0   ,						_SCREAMING_SNAKE_CASE      :    Tuple=1   ,						):






           """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(**_SCREAMING_SNAKE_CASE )

           # 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(
               _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						binarize=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  model_outputs['iou_scores']
           UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  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   _SCREAMING_SNAKE_CASE     ( self      :    Dict   ,						_SCREAMING_SNAKE_CASE      :    Union[str, Any]   ,						_SCREAMING_SNAKE_CASE      :    List[Any]=False   ,						_SCREAMING_SNAKE_CASE      :    int=False   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any]=0.7   ,						):






           """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(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  torch.cat(_SCREAMING_SNAKE_CASE )
           UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  self.image_processor.post_process_for_mask_generation(
               _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

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

           UpperCamelCase					       =  {}
           if output_rle_mask:
                 UpperCamelCase					       =  rle_mask

           if output_bboxes_mask:
                 UpperCamelCase					       =  bounding_boxes

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











import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
__magic_name__   :  str        =							logging.get_logger(__name__)
def 		lowercase__  (				_UpperCamelCase) ->			int:
      """simple docstring"""

      UpperCamelCase					       =  torch.load(_UpperCamelCase       ,							map_location='cpu')
      if "model" in sd.keys():
            UpperCamelCase					       =  torch.load(_UpperCamelCase       ,							map_location='cpu')['model']

      # pop unnecessary weights
      UpperCamelCase					       =  [
          'decoder.version',
          'decoder.output_projection.weight',
      ]
      for key in keys_to_delete:
            if key in sd:
                  sd.pop(_UpperCamelCase)

      UpperCamelCase					       =  {
          'decoder.project_in_dim.weight': 'decoder.project_in.weight',
          'decoder.project_out_dim.weight': 'decoder.project_out.weight',
          'decoder.layer_norm.weight': 'decoder.final_layer_norm.weight',
          'decoder.layer_norm.bias': 'decoder.final_layer_norm.bias',
      }
      for old_key, new_key in keys_to_rename.items():
            if old_key in sd:
                  UpperCamelCase					       =  sd.pop(_UpperCamelCase)

      UpperCamelCase					       =  list(sd.keys())
      for key in keys:
            if ".qkv_proj." in key:
                  UpperCamelCase					       =  sd[key]
                  # We split QKV in separate Q,K,V

                  UpperCamelCase					       =  key.replace('.qkv_proj.'       ,							'.q_proj.')
                  UpperCamelCase					       =  key.replace('.qkv_proj.'       ,							'.k_proj.')
                  UpperCamelCase					       =  key.replace('.qkv_proj.'       ,							'.v_proj.')

                  UpperCamelCase					       =  value.shape[0]
                  assert depth % 3 == 0
                  # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
                  # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
                  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  torch.split(_UpperCamelCase       ,							depth // 3       ,							dim=0)

                  UpperCamelCase					       =  q
                  UpperCamelCase					       =  k
                  UpperCamelCase					       =  v
                  del sd[key]

      return sd
@torch.no_grad()
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase=None) ->			Optional[int]:
      """simple docstring"""

      UpperCamelCase					       =  load_checkpoint(_UpperCamelCase)

      if config is not None:
            UpperCamelCase					       =  OPTConfig.from_pretrained(_UpperCamelCase)
      else:
            UpperCamelCase					       =  OPTConfig()

      UpperCamelCase					       =  OPTModel(_UpperCamelCase).half().eval()
      model.load_state_dict(_UpperCamelCase)

      # Check results
      Path(_UpperCamelCase).mkdir(exist_ok=_UpperCamelCase)
      model.save_pretrained(_UpperCamelCase)


if __name__ == "__main__":
       __magic_name__   :  Any        =							argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--fairseq_path''',
           type=str,
           help=(
               '''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'''
               ''' https://huggingface.co/models?other=opt_metasq'''
           ),
       )
       parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
       parser.add_argument('''--hf_config''', default=None, type=str, help='''Define HF config.''')
       __magic_name__   :  str        =							parser.parse_args()
       convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)



