

'''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  :		List[str]  =		{
    '''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 	UpperCamelCase   (       lowercase_						:    Any	)		->	int:




  '''simple docstring'''



  lowercase									={}
  state_dict.pop('''pixel_mean'''     ,					lowercase_	)
  state_dict.pop('''pixel_std'''     ,					lowercase_	)

  lowercase									=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:
        lowercase									=key.replace(lowercase_     ,					lowercase_	)

    if re.match(lowercase_     ,					lowercase_	):
      lowercase									=int(re.match(lowercase_     ,					lowercase_	).group(2	)	)
      if layer_nb == 0:
        lowercase									=key.replace('''layers.0'''     ,					'''proj_in'''	)
      elif layer_nb == 1:
        lowercase									=key.replace('''layers.1'''     ,					'''layers.0'''	)
      elif layer_nb == 2:
        lowercase									=key.replace('''layers.2'''     ,					'''proj_out'''	)

    lowercase									=value

  lowercase									=model_state_dict[
      '''prompt_encoder.shared_embedding.positional_embedding'''
  ]

  return model_state_dict




def 	UpperCamelCase   (       lowercase_						:    List[Any]     ,					lowercase_						:    int     ,					lowercase_						:    List[Any]     ,					lowercase_						:    Any="ybelkada/segment-anything"	)		->	Tuple:




  '''simple docstring'''



  lowercase									=hf_hub_download(lowercase_     ,					f'checkpoints/{model_name}.pth'	)

  if "sam_vit_b" in model_name:
    lowercase									=SamConfig()
  elif "sam_vit_l" in model_name:
    lowercase									=SamVisionConfig(
        hidden_size=1_0_2_4     ,					num_hidden_layers=2_4     ,					num_attention_heads=1_6     ,					global_attn_indexes=[5, 1_1, 1_7, 2_3]     ,					)

    lowercase									=SamConfig(
        vision_config=lowercase_     ,					)
  elif "sam_vit_h" in model_name:
    lowercase									=SamVisionConfig(
        hidden_size=1_2_8_0     ,					num_hidden_layers=3_2     ,					num_attention_heads=1_6     ,					global_attn_indexes=[7, 1_5, 2_3, 3_1]     ,					)

    lowercase									=SamConfig(
        vision_config=lowercase_     ,					)

  lowercase									=torch.load(lowercase_     ,					map_location='''cpu'''	)
  lowercase									=replace_keys(lowercase_	)

  lowercase									=SamImageProcessor()

  lowercase									=SamProcessor(image_processor=lowercase_	)
  lowercase									=SamModel(lowercase_	)

  hf_model.load_state_dict(lowercase_	)
  lowercase									=hf_model.to('''cuda'''	)

  lowercase									='''https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'''
  lowercase									=Image.open(requests.get(lowercase_     ,					stream=lowercase_	).raw	).convert('''RGB'''	)

  lowercase									=[[[4_0_0, 6_5_0]]]
  lowercase									=[[1]]

  lowercase									=processor(images=np.array(lowercase_	)     ,					return_tensors='''pt'''	).to('''cuda'''	)

  with torch.no_grad():
    lowercase									=hf_model(**lowercase_	)
  lowercase									=output.iou_scores.squeeze()

  if model_name == "sam_vit_h_4b8939":
    assert scores[-1].item() == 0.5_7_9_8_9_0_2_5_1_1_5_9_6_6_8

    lowercase									=processor(
        images=np.array(lowercase_	)     ,					input_points=lowercase_     ,					input_labels=lowercase_     ,					return_tensors='''pt'''	).to('''cuda'''	)

    with torch.no_grad():
      lowercase									=hf_model(**lowercase_	)
    lowercase									=output.iou_scores.squeeze()

    assert scores[-1].item() == 0.9_7_1_2_6_0_3_0_9_2_1_9_3_6_0_4

    lowercase									=((7_5, 2_7_5, 1_7_2_5, 8_5_0),)

    lowercase									=processor(images=np.array(lowercase_	)     ,					input_boxes=lowercase_     ,					return_tensors='''pt'''	).to('''cuda'''	)

    with torch.no_grad():
      lowercase									=hf_model(**lowercase_	)
    lowercase									=output.iou_scores.squeeze()

    assert scores[-1].item() == 0.8_6_8_6_0_1_5_6_0_5_9_2_6_5_1_4

    # Test with 2 points and 1 image.
    lowercase									=[[[4_0_0, 6_5_0], [8_0_0, 6_5_0]]]
    lowercase									=[[1, 1]]

    lowercase									=processor(
        images=np.array(lowercase_	)     ,					input_points=lowercase_     ,					input_labels=lowercase_     ,					return_tensors='''pt'''	).to('''cuda'''	)

    with torch.no_grad():
      lowercase									=hf_model(**lowercase_	)
    lowercase									=output.iou_scores.squeeze()

    assert scores[-1].item() == 0.9_9_3_6_0_4_7_7_9_2_4_3_4_6_9_2


if __name__ == "__main__":
     _UpperCAmelCase  :		Tuple  =		argparse.ArgumentParser()
     _UpperCAmelCase  :		Optional[Any]  =		['''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  :		List[str]  =		parser.parse_args()

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







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


a__: str							   =						{
    'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__: Union[str, Any]							   =						[
           'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST',
           'MegaForCausalLM',
           'MegaForMaskedLM',
           'MegaForMultipleChoice',
           'MegaForQuestionAnswering',
           'MegaForSequenceClassification',
           'MegaForTokenClassification',
           'MegaModel',
           'MegaPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mega import (
                  MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MegaForCausalLM,
                  MegaForMaskedLM,
                  MegaForMultipleChoice,
                  MegaForQuestionAnswering,
                  MegaForSequenceClassification,
                  MegaForTokenClassification,
                  MegaModel,
                  MegaPreTrainedModel,
              )

else:
       import sys

       a__: Union[str, Any]							   =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





