





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,
)


__lowercase	: 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      a__			(       lowercase__	):






   '''simple docstring'''






   UpperCAmelCase_           ={}
   state_dict.pop("pixel_mean"	,     lowercase__	)
   state_dict.pop("pixel_std"	,     lowercase__	)

   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(lowercase__	,     lowercase__	)

      if re.match(lowercase__	,     lowercase__	):
         UpperCAmelCase_           =int(re.match(lowercase__	,     lowercase__	).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      a__			(       lowercase__	,     lowercase__	,     lowercase__	,     lowercase__="ybelkada/segment-anything"	):






   '''simple docstring'''






   UpperCAmelCase_           =hf_hub_download(lowercase__	,     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=lowercase__	,     )
   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=lowercase__	,     )

   UpperCAmelCase_           =torch.load(lowercase__	,     map_location="cpu"	)
   UpperCAmelCase_           =replace_keys(lowercase__	)

   UpperCAmelCase_           =SamImageProcessor()

   UpperCAmelCase_           =SamProcessor(image_processor=lowercase__	)
   UpperCAmelCase_           =SamModel(lowercase__	)

   hf_model.load_state_dict(lowercase__	)
   UpperCAmelCase_           =hf_model.to("cuda"	)

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

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

   UpperCAmelCase_           =processor(images=np.array(lowercase__	)	,     return_tensors="pt"	).to("cuda"	)

   with torch.no_grad():
      UpperCAmelCase_           =hf_model(**lowercase__	)
   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(lowercase__	)	,     input_points=lowercase__	,     input_labels=lowercase__	,     return_tensors="pt"	).to("cuda"	)

      with torch.no_grad():
         UpperCAmelCase_           =hf_model(**lowercase__	)
      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(lowercase__	)	,     input_boxes=lowercase__	,     return_tensors="pt"	).to("cuda"	)

      with torch.no_grad():
         UpperCAmelCase_           =hf_model(**lowercase__	)
      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(lowercase__	)	,     input_points=lowercase__	,     input_labels=lowercase__	,     return_tensors="pt"	).to("cuda"	)

      with torch.no_grad():
         UpperCAmelCase_           =hf_model(**lowercase__	)
      UpperCAmelCase_           =output.iou_scores.squeeze()

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


if __name__ == "__main__":
     __lowercase	: Optional[Any]       =argparse.ArgumentParser()
     __lowercase	: List[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""",
     )

     __lowercase	: List[Any]       =parser.parse_args()

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











from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
    import numpy as np
    import tensorflow as tf

    from transformers import TFCamembertModel

@require_tf
@require_sentencepiece
@require_tokenizers
class   lowerCAmelCase				(   unittest.TestCase     ):
 @slow
 def 	A_      ( self    : Union[str, Any]      )     ->      List[Any]:
     lowerCamelCase__       : Tuple   					=						TFCamembertModel.from_pretrained('jplu/tf-camembert-base'      )

     lowerCamelCase__       : Any   					=						tf.convert_to_tensor(
         [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]     ,	dtype=tf.intaa     ,	)  # J'aime le camembert !"

     lowerCamelCase__       : List[str]   					=						model(UpperCAmelCase      )['last_hidden_state']
     lowerCamelCase__       : Tuple   					=						tf.TensorShape((1, 10, 768)      )
     self.assertEqual(output.shape     ,	UpperCAmelCase      )
     # compare the actual values for a slice.
     lowerCamelCase__       : Optional[Any]   					=						tf.convert_to_tensor(
         [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]     ,	dtype=tf.floataa     ,	)
     # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
     # camembert.eval()
     # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

     self.assertTrue(np.allclose(output[:, :3, :3].numpy()     ,	expected_slice.numpy()     ,	atol=1e-4      )      )





