

"""simple docstring"""




import argparse
import re

import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SamConfig,
    SamImageProcessor,
    SamModel,
    SamProcessor,
    SamVisionConfig,
)


__UpperCAmelCase			=					{
    'iou_prediction_head.layers.0': 'iou_prediction_head.proj_in',
    'iou_prediction_head.layers.1': 'iou_prediction_head.layers.0',
    'iou_prediction_head.layers.2': 'iou_prediction_head.proj_out',
    'mask_decoder.output_upscaling.0': 'mask_decoder.upscale_conv1',
    'mask_decoder.output_upscaling.1': 'mask_decoder.upscale_layer_norm',
    'mask_decoder.output_upscaling.3': 'mask_decoder.upscale_conv2',
    'mask_downscaling.0': 'mask_embed.conv1',
    'mask_downscaling.1': 'mask_embed.layer_norm1',
    'mask_downscaling.3': 'mask_embed.conv2',
    'mask_downscaling.4': 'mask_embed.layer_norm2',
    'mask_downscaling.6': 'mask_embed.conv3',
    'point_embeddings': 'point_embed',
    'pe_layer.positional_encoding_gaussian_matrix': 'shared_embedding.positional_embedding',
    'image_encoder': 'vision_encoder',
    'neck.0': 'neck.conv1',
    'neck.1': 'neck.layer_norm1',
    'neck.2': 'neck.conv2',
    'neck.3': 'neck.layer_norm2',
    'patch_embed.proj': 'patch_embed.projection',
    '.norm': '.layer_norm',
    'blocks': 'layers',
}
def 	lowerCAmelCase    (       __UpperCamelCase   ):
 '''simple docstring'''





 UpperCAmelCase__       :	Optional[int]        =       {}
 state_dict.pop("""pixel_mean"""				,     __UpperCamelCase   )
 state_dict.pop("""pixel_std"""				,     __UpperCamelCase   )

 UpperCAmelCase__       :	int        =       r""".*.output_hypernetworks_mlps.(\d+).layers.(\d+).*"""

 for key, value in state_dict.items():
  for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
   if key_to_modify in key:
    UpperCAmelCase__       :	List[str]        =       key.replace(__UpperCamelCase				,     __UpperCamelCase   )

  if re.match(__UpperCamelCase				,     __UpperCamelCase   ):
   UpperCAmelCase__       :	Tuple        =       int(re.match(__UpperCamelCase				,     __UpperCamelCase   ).group(2   )   )
   if layer_nb == 0:
    UpperCAmelCase__       :	Optional[Any]        =       key.replace("""layers.0"""				,     """proj_in"""   )
   elif layer_nb == 1:
    UpperCAmelCase__       :	Any        =       key.replace("""layers.1"""				,     """layers.0"""   )
   elif layer_nb == 2:
    UpperCAmelCase__       :	Any        =       key.replace("""layers.2"""				,     """proj_out"""   )

  UpperCAmelCase__       :	Union[str, Any]        =       value

 UpperCAmelCase__       :	Dict        =       model_state_dict[
     """prompt_encoder.shared_embedding.positional_embedding"""
 ]

 return model_state_dict
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase="ybelkada/segment-anything"   ):
 '''simple docstring'''





 UpperCAmelCase__       :	str        =       hf_hub_download(__UpperCamelCase				,     F"checkpoints/{model_name}.pth"   )

 if "sam_vit_b" in model_name:
  UpperCAmelCase__       :	Any        =       SamConfig()
 elif "sam_vit_l" in model_name:
  UpperCAmelCase__       :	Union[str, Any]        =       SamVisionConfig(
      hidden_size=1024				,     num_hidden_layers=24				,     num_attention_heads=16				,     global_attn_indexes=[5, 11, 17, 23]				,     )

  UpperCAmelCase__       :	str        =       SamConfig(
      vision_config=__UpperCamelCase				,     )
 elif "sam_vit_h" in model_name:
  UpperCAmelCase__       :	List[Any]        =       SamVisionConfig(
      hidden_size=1280				,     num_hidden_layers=32				,     num_attention_heads=16				,     global_attn_indexes=[7, 15, 23, 31]				,     )

  UpperCAmelCase__       :	Optional[int]        =       SamConfig(
      vision_config=__UpperCamelCase				,     )

 UpperCAmelCase__       :	List[str]        =       torch.load(__UpperCamelCase				,     map_location="""cpu"""   )
 UpperCAmelCase__       :	Tuple        =       replace_keys(__UpperCamelCase   )

 UpperCAmelCase__       :	Optional[int]        =       SamImageProcessor()

 UpperCAmelCase__       :	Dict        =       SamProcessor(image_processor=__UpperCamelCase   )
 UpperCAmelCase__       :	int        =       SamModel(__UpperCamelCase   )

 hf_model.load_state_dict(__UpperCamelCase   )
 UpperCAmelCase__       :	List[Any]        =       hf_model.to("""cuda"""   )

 UpperCAmelCase__       :	Optional[Any]        =       """https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"""
 UpperCAmelCase__       :	Dict        =       Image.open(requests.get(__UpperCamelCase				,     stream=__UpperCamelCase   ).raw   ).convert("""RGB"""   )

 UpperCAmelCase__       :	Tuple        =       [[[400, 650]]]
 UpperCAmelCase__       :	Tuple        =       [[1]]

 UpperCAmelCase__       :	int        =       processor(images=np.array(__UpperCamelCase   )				,     return_tensors="""pt"""   ).to("""cuda"""   )

 with torch.no_grad():
  UpperCAmelCase__       :	Optional[int]        =       hf_model(**__UpperCamelCase   )
 UpperCAmelCase__       :	Optional[Any]        =       output.iou_scores.squeeze()

 if model_name == "sam_vit_h_4b8939":
  assert scores[-1].item() == 0.579_8902_5115_9668

  UpperCAmelCase__       :	Union[str, Any]        =       processor(
      images=np.array(__UpperCamelCase   )				,     input_points=__UpperCamelCase				,     input_labels=__UpperCamelCase				,     return_tensors="""pt"""   ).to("""cuda"""   )

  with torch.no_grad():
   UpperCAmelCase__       :	Optional[Any]        =       hf_model(**__UpperCamelCase   )
  UpperCAmelCase__       :	str        =       output.iou_scores.squeeze()

  assert scores[-1].item() == 0.9712_6030_9219_3604

  UpperCAmelCase__       :	str        =       ((75, 275, 1725, 850),)

  UpperCAmelCase__       :	List[Any]        =       processor(images=np.array(__UpperCamelCase   )				,     input_boxes=__UpperCamelCase				,     return_tensors="""pt"""   ).to("""cuda"""   )

  with torch.no_grad():
   UpperCAmelCase__       :	Optional[int]        =       hf_model(**__UpperCamelCase   )
  UpperCAmelCase__       :	Union[str, Any]        =       output.iou_scores.squeeze()

  assert scores[-1].item() == 0.8686_0156_0592_6514

  # Test with 2 points and 1 image.
  UpperCAmelCase__       :	Any        =       [[[400, 650], [800, 650]]]
  UpperCAmelCase__       :	int        =       [[1, 1]]

  UpperCAmelCase__       :	str        =       processor(
      images=np.array(__UpperCamelCase   )				,     input_points=__UpperCamelCase				,     input_labels=__UpperCamelCase				,     return_tensors="""pt"""   ).to("""cuda"""   )

  with torch.no_grad():
   UpperCAmelCase__       :	Tuple        =       hf_model(**__UpperCamelCase   )
  UpperCAmelCase__       :	Optional[Any]        =       output.iou_scores.squeeze()

  assert scores[-1].item() == 0.9936_0477_9243_4692


if __name__ == "__main__":
      __UpperCAmelCase			=					argparse.ArgumentParser()
      __UpperCAmelCase			=					['sam_vit_b_01ec64', 'sam_vit_h_4b8939', 'sam_vit_l_0b3195']
      parser.add_argument(
          '--model_name',
          default='sam_vit_h_4b8939',
          choices=choices,
          type=str,
          help='Path to hf config.json of model to convert',
      )
      parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
      parser.add_argument(
          '--push_to_hub',
          action='store_true',
          help='Whether to push the model and processor to the hub after converting',
      )
      parser.add_argument(
          '--model_hub_id',
          default='ybelkada/segment-anything',
          choices=choices,
          type=str,
          help='Path to hf config.json of model to convert',
      )

      __UpperCAmelCase			=					parser.parse_args()

      convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)











'''simple docstring'''






import logging
import os
from typing import List, Tuple

import numpy as np
import psutil
import torch
import torch.distributed as dist

from transformers import RagRetriever


A_			      =		logging.getLogger(__name__)






class        lowercase_      (						lowerCAmelCase_							):





     def __init__(     self     :					List[Any]     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					str     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					int=None			):
           super().__init__(
               __lowerCamelCase     ,							question_encoder_tokenizer=__lowerCamelCase     ,							generator_tokenizer=__lowerCamelCase     ,							index=__lowerCamelCase     ,							init_retrieval=__lowerCamelCase     ,							)
           snake_case__       :   Optional[int]	      =    None





     def 			_lowerCAmelCase      (     self     :					List[str]     ,							__lowerCamelCase     :					int			):
           logger.info('initializing retrieval'			)

           # initializing a separate process group for retrieval as the default
           # nccl backend doesn't support gather/scatter operations while gloo
           # is too slow to replace nccl for the core gpu communication
           if dist.is_initialized():
                 logger.info('dist initialized'			)
                 # needs to be set manually
                 snake_case__       :   Any	      =    self._infer_socket_ifname()
                 # avoid clash with the NCCL port
                 snake_case__       :   int	      =    str(distributed_port + 1			)
                 snake_case__       :   Any	      =    dist.new_group(ranks=__lowerCamelCase     ,							backend='gloo'			)

           # initialize retriever only on the main worker
           if not dist.is_initialized() or self._is_main():
                 logger.info('dist not initialized / main'			)
                 self.index.init_index()

           # all processes wait untill the retriever is initialized by the main process
           if dist.is_initialized():
                 torch.distributed.barrier(group=self.process_group			)





     def 			_lowerCAmelCase      (     self     :					List[Any]			):
           return dist.get_rank(group=self.process_group			) == 0





     def 			_lowerCAmelCase      (     self     :					Any     ,							__lowerCamelCase     :					Dict     ,							__lowerCamelCase     :					str     ,							__lowerCamelCase     :					Union[str, Any]=torch.floataa			):
           snake_case__       :   Optional[int]	      =    torch.empty(__lowerCamelCase     ,							dtype=__lowerCamelCase			)
           dist.scatter(__lowerCamelCase     ,							src=0     ,							scatter_list=__lowerCamelCase     ,							group=self.process_group			)
           return target_tensor





     def 			_lowerCAmelCase      (     self     :					Dict			):
           snake_case__       :   Union[str, Any]	      =    psutil.net_if_addrs()
           # a hacky way to deal with varying network interface names
           snake_case__       :   Any	      =    next((addr for addr in addrs if addr.startswith('e'			))     ,							__lowerCamelCase			)
           return ifname





     def 			_lowerCAmelCase      (     self     :					Tuple     ,							__lowerCamelCase     :					np.ndarray     ,							__lowerCamelCase     :					int			):

           # single GPU training
           if not dist.is_initialized():
                 snake_case__      ,							snake_case__       :   Optional[int]	      =    self._main_retrieve(__lowerCamelCase     ,							__lowerCamelCase			)
                 return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__lowerCamelCase			)

           # distributed training
           snake_case__       :   Optional[Any]	      =    dist.get_world_size(group=self.process_group			)

           # gather logic
           snake_case__       :   Optional[int]	      =    None
           if self._is_main():
                 snake_case__       :   Optional[int]	      =    [torch.empty(question_hidden_states.shape     ,							dtype=torch.floataa			) for _ in range(__lowerCamelCase			)]
           dist.gather(torch.tensor(__lowerCamelCase			)     ,							dst=0     ,							gather_list=__lowerCamelCase     ,							group=self.process_group			)

           # scatter logic
           snake_case__       :   Union[str, Any]	      =    question_hidden_states.shape[0]
           snake_case__       :   Tuple	      =    []
           snake_case__       :   List[Any]	      =    []
           if self._is_main():
                 assert len(__lowerCamelCase			) == world_size
                 snake_case__      ,							snake_case__       :   Optional[int]	      =    self._main_retrieve(torch.cat(__lowerCamelCase			).numpy()     ,							__lowerCamelCase			)
                 snake_case__      ,							snake_case__       :   Optional[Any]	      =    torch.tensor(__lowerCamelCase			), torch.tensor(__lowerCamelCase			)
                 snake_case__       :   str	      =    self._chunk_tensor(__lowerCamelCase     ,							__lowerCamelCase			)
                 snake_case__       :   Dict	      =    self._chunk_tensor(__lowerCamelCase     ,							__lowerCamelCase			)
           snake_case__       :   Tuple	      =    self._scattered(__lowerCamelCase     ,							[n_queries, n_docs]     ,							target_type=torch.intaa			)
           snake_case__       :   Dict	      =    self._scattered(__lowerCamelCase     ,							[n_queries, n_docs, question_hidden_states.shape[1]]			)

           return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(__lowerCamelCase			)
