
'''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,
)


snake_case_ :  Dict     =					{
    '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  __snake_case     (    _UpperCAmelCase					:       Tuple):
   UpperCamelCase       =  {}
   state_dict.pop('''pixel_mean''',      _UpperCAmelCase)
   state_dict.pop('''pixel_std''',      _UpperCAmelCase)

   UpperCamelCase       =  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       =  key.replace(_UpperCAmelCase,      _UpperCAmelCase)

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

      UpperCamelCase       =  value

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

   return model_state_dict



def  __snake_case     (    _UpperCAmelCase					:       str,      _UpperCAmelCase					:       int,      _UpperCAmelCase					:       str,      _UpperCAmelCase					:       Union[str, Any]="ybelkada/segment-anything"):
   UpperCamelCase       =  hf_hub_download(_UpperCAmelCase,      f'checkpoints/{model_name}.pth')

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

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

      UpperCamelCase       =  SamConfig(
          vision_config=_UpperCAmelCase,      )

   UpperCamelCase       =  torch.load(_UpperCAmelCase,      map_location='''cpu''')
   UpperCamelCase       =  replace_keys(_UpperCAmelCase)

   UpperCamelCase       =  SamImageProcessor()

   UpperCamelCase       =  SamProcessor(image_processor=_UpperCAmelCase)
   UpperCamelCase       =  SamModel(_UpperCAmelCase)

   hf_model.load_state_dict(_UpperCAmelCase)
   UpperCamelCase       =  hf_model.to('''cuda''')

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

   UpperCamelCase       =  [[[400, 650]]]
   UpperCamelCase       =  [[1]]

   UpperCamelCase       =  processor(images=np.array(_UpperCAmelCase),      return_tensors='''pt''').to('''cuda''')

   with torch.no_grad():
      UpperCamelCase       =  hf_model(**_UpperCAmelCase)
   UpperCamelCase       =  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

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

      with torch.no_grad():
         UpperCamelCase       =  hf_model(**_UpperCAmelCase)
      UpperCamelCase       =  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

      UpperCamelCase       =  ((75, 275, 1725, 850),)

      UpperCamelCase       =  processor(images=np.array(_UpperCAmelCase),      input_boxes=_UpperCAmelCase,      return_tensors='''pt''').to('''cuda''')

      with torch.no_grad():
         UpperCamelCase       =  hf_model(**_UpperCAmelCase)
      UpperCamelCase       =  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.
      UpperCamelCase       =  [[[400, 650], [800, 650]]]
      UpperCamelCase       =  [[1, 1]]

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

      with torch.no_grad():
         UpperCamelCase       =  hf_model(**_UpperCAmelCase)
      UpperCamelCase       =  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__":
   snake_case_ :  Tuple     =					argparse.ArgumentParser()
   snake_case_ :  Tuple     =					['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',
   )

   snake_case_ :  Optional[Any]     =					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 os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer


class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   _snake_case		   =							['''image_processor''', '''tokenizer''']
   _snake_case		   =							'''BlipImageProcessor'''
   _snake_case		   =							'''AutoTokenizer'''







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      super().__init__(lowerCamelCase__ ,   lowerCamelCase__   )

      # add QFormer tokenizer
      UpperCamelCase       =  qformer_tokenizer







   def __call__(			self ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = True ,   lowerCamelCase__ = False ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = 0 ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = False ,   lowerCamelCase__ = False ,   lowerCamelCase__ = False ,   lowerCamelCase__ = False ,   lowerCamelCase__ = False ,   lowerCamelCase__ = True ,   lowerCamelCase__ = None ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      if images is None and text is None:
         raise ValueError('''You have to specify at least images or text.'''   )

      UpperCamelCase       =  BatchFeature()

      if text is not None:
         UpperCamelCase       =  self.tokenizer(
             text=lowerCamelCase__ ,   add_special_tokens=lowerCamelCase__ ,   padding=lowerCamelCase__ ,   truncation=lowerCamelCase__ ,   max_length=lowerCamelCase__ ,   stride=lowerCamelCase__ ,   pad_to_multiple_of=lowerCamelCase__ ,   return_attention_mask=lowerCamelCase__ ,   return_overflowing_tokens=lowerCamelCase__ ,   return_special_tokens_mask=lowerCamelCase__ ,   return_offsets_mapping=lowerCamelCase__ ,   return_token_type_ids=lowerCamelCase__ ,   return_length=lowerCamelCase__ ,   verbose=lowerCamelCase__ ,   return_tensors=lowerCamelCase__ ,   **lowerCamelCase__ ,   )
         encoding.update(lowerCamelCase__   )
         UpperCamelCase       =  self.qformer_tokenizer(
             text=lowerCamelCase__ ,   add_special_tokens=lowerCamelCase__ ,   padding=lowerCamelCase__ ,   truncation=lowerCamelCase__ ,   max_length=lowerCamelCase__ ,   stride=lowerCamelCase__ ,   pad_to_multiple_of=lowerCamelCase__ ,   return_attention_mask=lowerCamelCase__ ,   return_overflowing_tokens=lowerCamelCase__ ,   return_special_tokens_mask=lowerCamelCase__ ,   return_offsets_mapping=lowerCamelCase__ ,   return_token_type_ids=lowerCamelCase__ ,   return_length=lowerCamelCase__ ,   verbose=lowerCamelCase__ ,   return_tensors=lowerCamelCase__ ,   **lowerCamelCase__ ,   )
         UpperCamelCase       =  qformer_text_encoding.pop('''input_ids'''   )
         UpperCamelCase       =  qformer_text_encoding.pop('''attention_mask'''   )

      if images is not None:
         UpperCamelCase       =  self.image_processor(lowerCamelCase__ ,   return_tensors=lowerCamelCase__   )
         encoding.update(lowerCamelCase__   )

      return encoding







   def       UpperCAmelCase				(			self ,   *lowerCamelCase__ ,   **lowerCamelCase__   ):

      '''simple docstring'''







      return self.tokenizer.batch_decode(*lowerCamelCase__ ,   **lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   *lowerCamelCase__ ,   **lowerCamelCase__   ):

      '''simple docstring'''







      return self.tokenizer.decode(*lowerCamelCase__ ,   **lowerCamelCase__   )







   @property
   # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.tokenizer.model_input_names
      UpperCamelCase       =  self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   **lowerCamelCase__   ):

      '''simple docstring'''







      if os.path.isfile(lowerCamelCase__   ):
         raise ValueError(f'Provided path ({save_directory}) should be a directory, not a file'   )
      os.makedirs(lowerCamelCase__ ,   exist_ok=lowerCamelCase__   )
      UpperCamelCase       =  os.path.join(lowerCamelCase__ ,   '''qformer_tokenizer'''   )
      self.qformer_tokenizer.save_pretrained(lowerCamelCase__   )
      return super().save_pretrained(lowerCamelCase__ ,   **lowerCamelCase__   )







   @classmethod
   def       UpperCAmelCase				(			cls ,   lowerCamelCase__ ,   **lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  AutoTokenizer.from_pretrained(lowerCamelCase__ ,   subfolder='''qformer_tokenizer'''   )
      UpperCamelCase       =  cls._get_arguments_from_pretrained(lowerCamelCase__ ,   **lowerCamelCase__   )
      args.append(lowerCamelCase__   )
      return cls(*lowerCamelCase__   )





