


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,
)


__a	 =      {
    """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					(    lowerCAmelCase_ )    ->Union[str, Any]:
   UpperCAmelCase      					=  {}
   state_dict.pop("""pixel_mean"""	,    lowerCAmelCase_ )
   state_dict.pop("""pixel_std"""	,    lowerCAmelCase_ )

   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(lowerCAmelCase_	,    lowerCAmelCase_ )

      if re.match(lowerCAmelCase_	,    lowerCAmelCase_ ):
         UpperCAmelCase      					=  int(re.match(lowerCAmelCase_	,    lowerCAmelCase_ ).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 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_="ybelkada/segment-anything" )    ->Any:
   UpperCAmelCase      					=  hf_hub_download(lowerCAmelCase_	,    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=1_0_2_4	,    num_hidden_layers=2_4	,    num_attention_heads=1_6	,    global_attn_indexes=[5, 1_1, 1_7, 2_3]	,    )

      UpperCAmelCase      					=  SamConfig(
          vision_config=lowerCAmelCase_	,    )
   elif "sam_vit_h" in model_name:
      UpperCAmelCase      					=  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]	,    )

      UpperCAmelCase      					=  SamConfig(
          vision_config=lowerCAmelCase_	,    )

   UpperCAmelCase      					=  torch.load(lowerCAmelCase_	,    map_location="""cpu""" )
   UpperCAmelCase      					=  replace_keys(lowerCAmelCase_ )

   UpperCAmelCase      					=  SamImageProcessor()

   UpperCAmelCase      					=  SamProcessor(image_processor=lowerCAmelCase_ )
   UpperCAmelCase      					=  SamModel(lowerCAmelCase_ )

   hf_model.load_state_dict(lowerCAmelCase_ )
   UpperCAmelCase      					=  hf_model.to("""cuda""" )

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

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

   UpperCAmelCase      					=  processor(images=np.array(lowerCAmelCase_ )	,    return_tensors="""pt""" ).to("""cuda""" )

   with torch.no_grad():
      UpperCAmelCase      					=  hf_model(**lowerCAmelCase_ )
   UpperCAmelCase      					=  output.iou_scores.squeeze()

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

      UpperCAmelCase      					=  processor(
          images=np.array(lowerCAmelCase_ )	,    input_points=lowerCAmelCase_	,    input_labels=lowerCAmelCase_	,    return_tensors="""pt""" ).to("""cuda""" )

      with torch.no_grad():
         UpperCAmelCase      					=  hf_model(**lowerCAmelCase_ )
      UpperCAmelCase      					=  output.iou_scores.squeeze()

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

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

      UpperCAmelCase      					=  processor(images=np.array(lowerCAmelCase_ )	,    input_boxes=lowerCAmelCase_	,    return_tensors="""pt""" ).to("""cuda""" )

      with torch.no_grad():
         UpperCAmelCase      					=  hf_model(**lowerCAmelCase_ )
      UpperCAmelCase      					=  output.iou_scores.squeeze()

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

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

      UpperCAmelCase      					=  processor(
          images=np.array(lowerCAmelCase_ )	,    input_points=lowerCAmelCase_	,    input_labels=lowerCAmelCase_	,    return_tensors="""pt""" ).to("""cuda""" )

      with torch.no_grad():
         UpperCAmelCase      					=  hf_model(**lowerCAmelCase_ )
      UpperCAmelCase      					=  output.iou_scores.squeeze()

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


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

 __a	 =      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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__a	 =      {"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __a	 =      ["""ReformerTokenizer"""]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __a	 =      ["""ReformerTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __a	 =      [
     """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """ReformerAttention""",
     """ReformerForMaskedLM""",
     """ReformerForQuestionAnswering""",
     """ReformerForSequenceClassification""",
     """ReformerLayer""",
     """ReformerModel""",
     """ReformerModelWithLMHead""",
     """ReformerPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_reformer import ReformerTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_reformer_fast import ReformerTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_reformer import (
      REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      ReformerAttention,
      ReformerForMaskedLM,
      ReformerForQuestionAnswering,
      ReformerForSequenceClassification,
      ReformerLayer,
      ReformerModel,
      ReformerModelWithLMHead,
      ReformerPreTrainedModel,
  )

else:
 import sys

 __a	 =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



