


'''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,
)


__magic_name__			=      {
    '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					(      lowerCamelCase  :      Optional[Any]):
      A_						:  Optional[Any]          =				{}
      state_dict.pop("""pixel_mean"""							,						lowerCamelCase)
      state_dict.pop("""pixel_std"""							,						lowerCamelCase)

      A_						:  List[Any]          =				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:
                        A_						:  Dict          =				key.replace(lowerCamelCase							,						lowerCamelCase)

            if re.match(lowerCamelCase							,						lowerCamelCase):
                  A_						:  Union[str, Any]          =				int(re.match(lowerCamelCase							,						lowerCamelCase).group(2))
                  if layer_nb == 0:
                        A_						:  List[str]          =				key.replace("""layers.0"""							,						"""proj_in""")
                  elif layer_nb == 1:
                        A_						:  Optional[Any]          =				key.replace("""layers.1"""							,						"""layers.0""")
                  elif layer_nb == 2:
                        A_						:  int          =				key.replace("""layers.2"""							,						"""proj_out""")

            A_						:  int          =				value

      A_						:  str          =				model_state_dict[
          """prompt_encoder.shared_embedding.positional_embedding"""
      ]

      return model_state_dict




def      lowerCamelCase					(      lowerCamelCase  :      Any							,						lowerCamelCase  :      Optional[Any]							,						lowerCamelCase  :      str							,						lowerCamelCase  :      Union[str, Any]="ybelkada/segment-anything"):
      A_						:  List[Any]          =				hf_hub_download(lowerCamelCase							,						F'checkpoints/{model_name}.pth')

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

            A_						:  int          =				SamConfig(
                vision_config=lowerCamelCase							,						)
      elif "sam_vit_h" in model_name:
            A_						:  str          =				SamVisionConfig(
                hidden_size=1280							,						num_hidden_layers=32							,						num_attention_heads=16							,						global_attn_indexes=[7, 15, 23, 31]							,						)

            A_						:  Optional[int]          =				SamConfig(
                vision_config=lowerCamelCase							,						)

      A_						:  int          =				torch.load(lowerCamelCase							,						map_location="""cpu""")
      A_						:  List[str]          =				replace_keys(lowerCamelCase)

      A_						:  Dict          =				SamImageProcessor()

      A_						:  Union[str, Any]          =				SamProcessor(image_processor=lowerCamelCase)
      A_						:  List[Any]          =				SamModel(lowerCamelCase)

      hf_model.load_state_dict(lowerCamelCase)
      A_						:  List[str]          =				hf_model.to("""cuda""")

      A_						:  int          =				"""https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"""
      A_						:  Dict          =				Image.open(requests.get(lowerCamelCase							,						stream=lowerCamelCase).raw).convert("""RGB""")

      A_						:  List[Any]          =				[[[400, 650]]]
      A_						:  Union[str, Any]          =				[[1]]

      A_						:  List[Any]          =				processor(images=np.array(lowerCamelCase)							,						return_tensors="""pt""").to("""cuda""")

      with torch.no_grad():
            A_						:  Union[str, Any]          =				hf_model(**lowerCamelCase)
      A_						:  Tuple          =				output.iou_scores.squeeze()

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

            A_						:  Tuple          =				processor(
                images=np.array(lowerCamelCase)							,						input_points=lowerCamelCase							,						input_labels=lowerCamelCase							,						return_tensors="""pt""").to("""cuda""")

            with torch.no_grad():
                  A_						:  Tuple          =				hf_model(**lowerCamelCase)
            A_						:  Tuple          =				output.iou_scores.squeeze()

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

            A_						:  List[Any]          =				((75, 275, 1725, 850),)

            A_						:  List[str]          =				processor(images=np.array(lowerCamelCase)							,						input_boxes=lowerCamelCase							,						return_tensors="""pt""").to("""cuda""")

            with torch.no_grad():
                  A_						:  Union[str, Any]          =				hf_model(**lowerCamelCase)
            A_						:  Dict          =				output.iou_scores.squeeze()

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

            # Test with 2 points and 1 image.
            A_						:  Any          =				[[[400, 650], [800, 650]]]
            A_						:  Union[str, Any]          =				[[1, 1]]

            A_						:  int          =				processor(
                images=np.array(lowerCamelCase)							,						input_points=lowerCamelCase							,						input_labels=lowerCamelCase							,						return_tensors="""pt""").to("""cuda""")

            with torch.no_grad():
                  A_						:  int          =				hf_model(**lowerCamelCase)
            A_						:  Tuple          =				output.iou_scores.squeeze()

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


if __name__ == "__main__":
     __magic_name__			=      argparse.ArgumentParser()
     __magic_name__			=      ['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',
     )

     __magic_name__			=      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 typing import TYPE_CHECKING

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


__magic_name__			=      {}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      ['NllbTokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      ['NllbTokenizerFast']


if TYPE_CHECKING:
     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_nllb import NllbTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_nllb_fast import NllbTokenizerFast

else:
     import sys

     __magic_name__			=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
