

"""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"""



from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
  from ...onnx.config import PatchingSpec
  from ...tokenization_utils_base import PreTrainedTokenizerBase


__snake_case       =       logging.get_logger(__name__)

__snake_case       =       {
    """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096""": """https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096-finetuned-triviaqa""": (
        """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"""
    ),
    """allenai/longformer-base-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
    """allenai/longformer-large-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
}







class _lowerCAmelCase							( snake_case_      ):
   __UpperCAmelCase					:			Tuple				    =    '''longformer'''







   def __init__(      self		,      UpperCamelCase__ = 512		,      UpperCamelCase__ = 2		,      UpperCamelCase__ = 1		,      UpperCamelCase__ = 0		,      UpperCamelCase__ = 2		,      UpperCamelCase__ = 3_0522		,      UpperCamelCase__ = 768		,      UpperCamelCase__ = 12		,      UpperCamelCase__ = 12		,      UpperCamelCase__ = 3072		,      UpperCamelCase__ = "gelu"		,      UpperCamelCase__ = 0.1		,      UpperCamelCase__ = 0.1		,      UpperCamelCase__ = 512		,      UpperCamelCase__ = 2		,      UpperCamelCase__ = 0.02		,      UpperCamelCase__ = 1e-12		,      UpperCamelCase__ = False		,      **UpperCamelCase__		,      )      ->						str:

       '''simple docstring'''




       super().__init__(pad_token_id=UpperCamelCase__		,      **UpperCamelCase__		)

       snake_case       : int    =       attention_window
       snake_case       : Any    =       sep_token_id
       snake_case       : Dict    =       bos_token_id
       snake_case       : int    =       eos_token_id
       snake_case       : List[str]    =       vocab_size
       snake_case       : Dict    =       hidden_size
       snake_case       : Optional[int]    =       num_hidden_layers
       snake_case       : List[str]    =       num_attention_heads
       snake_case       : Dict    =       hidden_act
       snake_case       : Union[str, Any]    =       intermediate_size
       snake_case       : Optional[int]    =       hidden_dropout_prob
       snake_case       : List[Any]    =       attention_probs_dropout_prob
       snake_case       : Any    =       max_position_embeddings
       snake_case       : Union[str, Any]    =       type_vocab_size
       snake_case       : Union[str, Any]    =       initializer_range
       snake_case       : int    =       layer_norm_eps
       snake_case       : str    =       onnx_export







class _lowerCAmelCase							( snake_case_      ):







   def __init__(      self		,      UpperCamelCase__		,      UpperCamelCase__ = "default"		,      UpperCamelCase__ = None		)      ->						List[str]:

       '''simple docstring'''




       super().__init__(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)
       snake_case       : int    =       True







   @property
   def 					lowerCamelCase						(      self		)      ->						Mapping[str, Mapping[int, str]]:

       '''simple docstring'''




       if self.task == "multiple-choice":
           snake_case       : str    =       {0: "batch", 1: "choice", 2: "sequence"}
       else:
           snake_case       : List[str]    =       {0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
               ("global_attention_mask", dynamic_axis),
           ]		)







   @property
   def 					lowerCamelCase						(      self		)      ->						Mapping[str, Mapping[int, str]]:

       '''simple docstring'''




       snake_case       : str    =       super().outputs
       if self.task == "default":
           snake_case       : str    =       {0: "batch"}
       return outputs







   @property
   def 					lowerCamelCase						(      self		)      ->						float:

       '''simple docstring'''




       return 1e-4







   @property
   def 					lowerCamelCase						(      self		)      ->						int:

       '''simple docstring'''




       return max(super().default_onnx_opset		,      14		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__ = -1		,      UpperCamelCase__ = -1		,      UpperCamelCase__ = False		,      UpperCamelCase__ = None		,      )      ->						Mapping[str, Any]:

       '''simple docstring'''




       snake_case       : Optional[int]    =       super().generate_dummy_inputs(
           preprocessor=UpperCamelCase__		,      batch_size=UpperCamelCase__		,      seq_length=UpperCamelCase__		,      is_pair=UpperCamelCase__		,      framework=UpperCamelCase__		)
       import torch

       # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
       # makes the export fail randomly
       snake_case       : Any    =       torch.zeros_like(inputs["input_ids"]		)
       # make every second token global
       snake_case       : Any    =       1

       return inputs





