



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,
)


__lowerCamelCase         =			{
    '''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 (       __snake_case    )     ->     Tuple:






      '''simple docstring'''







      UpperCAmelCase_  :	Any				      =		{}
      state_dict.pop("pixel_mean"					,  __snake_case    )
      state_dict.pop("pixel_std"					,  __snake_case    )

      UpperCAmelCase_  :	Optional[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:
                        UpperCAmelCase_  :	Optional[Any]				      =		key.replace(__snake_case					,  __snake_case    )

            if re.match(__snake_case					,  __snake_case    ):
                  UpperCAmelCase_  :	Any				      =		int(re.match(__snake_case					,  __snake_case    ).group(2    )    )
                  if layer_nb == 0:
                        UpperCAmelCase_  :	Tuple				      =		key.replace("layers.0"					,  "proj_in"    )
                  elif layer_nb == 1:
                        UpperCAmelCase_  :	str				      =		key.replace("layers.1"					,  "layers.0"    )
                  elif layer_nb == 2:
                        UpperCAmelCase_  :	Union[str, Any]				      =		key.replace("layers.2"					,  "proj_out"    )

            UpperCAmelCase_  :	Union[str, Any]				      =		value

      UpperCAmelCase_  :	Optional[int]				      =		model_state_dict[
          "prompt_encoder.shared_embedding.positional_embedding"
      ]

      return model_state_dict







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case					,  __snake_case="ybelkada/segment-anything"    )     ->     Union[str, Any]:






      '''simple docstring'''







      UpperCAmelCase_  :	Dict				      =		hf_hub_download(__snake_case					,  F"""checkpoints/{model_name}.pth"""    )

      if "sam_vit_b" in model_name:
            UpperCAmelCase_  :	Tuple				      =		SamConfig()
      elif "sam_vit_l" in model_name:
            UpperCAmelCase_  :	List[Any]				      =		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_  :	Any				      =		SamConfig(
                vision_config=__snake_case					,  )
      elif "sam_vit_h" in model_name:
            UpperCAmelCase_  :	Any				      =		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_  :	Tuple				      =		SamConfig(
                vision_config=__snake_case					,  )

      UpperCAmelCase_  :	Optional[int]				      =		torch.load(__snake_case					,  map_location="cpu"    )
      UpperCAmelCase_  :	List[Any]				      =		replace_keys(__snake_case    )

      UpperCAmelCase_  :	Any				      =		SamImageProcessor()

      UpperCAmelCase_  :	Optional[int]				      =		SamProcessor(image_processor=__snake_case    )
      UpperCAmelCase_  :	Optional[int]				      =		SamModel(__snake_case    )

      hf_model.load_state_dict(__snake_case    )
      UpperCAmelCase_  :	Optional[int]				      =		hf_model.to("cuda"    )

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

      UpperCAmelCase_  :	str				      =		[[[4_0_0, 6_5_0]]]
      UpperCAmelCase_  :	List[str]				      =		[[1]]

      UpperCAmelCase_  :	Any				      =		processor(images=np.array(__snake_case    )					,  return_tensors="pt"    ).to("cuda"    )

      with torch.no_grad():
            UpperCAmelCase_  :	Union[str, Any]				      =		hf_model(**__snake_case    )
      UpperCAmelCase_  :	Optional[int]				      =		output.iou_scores.squeeze()

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

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

            with torch.no_grad():
                  UpperCAmelCase_  :	List[Any]				      =		hf_model(**__snake_case    )
            UpperCAmelCase_  :	Optional[Any]				      =		output.iou_scores.squeeze()

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

            UpperCAmelCase_  :	int				      =		((7_5, 2_7_5, 1_7_2_5, 8_5_0),)

            UpperCAmelCase_  :	Union[str, Any]				      =		processor(images=np.array(__snake_case    )					,  input_boxes=__snake_case					,  return_tensors="pt"    ).to("cuda"    )

            with torch.no_grad():
                  UpperCAmelCase_  :	Optional[int]				      =		hf_model(**__snake_case    )
            UpperCAmelCase_  :	Dict				      =		output.iou_scores.squeeze()

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

            # Test with 2 points and 1 image.
            UpperCAmelCase_  :	Any				      =		[[[4_0_0, 6_5_0], [8_0_0, 6_5_0]]]
            UpperCAmelCase_  :	List[Any]				      =		[[1, 1]]

            UpperCAmelCase_  :	Optional[Any]				      =		processor(
                images=np.array(__snake_case    )					,  input_points=__snake_case					,  input_labels=__snake_case					,  return_tensors="pt"    ).to("cuda"    )

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

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


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

   __lowerCamelCase         =			parser.parse_args()

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





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase         =			logging.get_logger(__name__)

__lowerCamelCase         =			{
    '''unc-nlp/lxmert-base-uncased''': '''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json''',
}


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				"""lxmert"""
       _lowerCamelCase			    =				{}



       def __init__(     self     ,lowercase=30522     ,lowercase=768     ,lowercase=12     ,lowercase=9500     ,lowercase=1600     ,lowercase=400     ,lowercase=3072     ,lowercase="gelu"     ,lowercase=0.1     ,lowercase=0.1     ,lowercase=512     ,lowercase=2     ,lowercase=0.02     ,lowercase=1E-12     ,lowercase=9     ,lowercase=5     ,lowercase=5     ,lowercase=2048     ,lowercase=4     ,lowercase=6.67     ,lowercase=True     ,lowercase=True     ,lowercase=True     ,lowercase=True     ,lowercase=True     ,lowercase=True     ,lowercase=True     ,**lowercase     ,):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		vocab_size
             UpperCAmelCase_  :	str				      =		hidden_size
             UpperCAmelCase_  :	List[str]				      =		num_attention_heads
             UpperCAmelCase_  :	Dict				      =		hidden_act
             UpperCAmelCase_  :	List[Any]				      =		intermediate_size
             UpperCAmelCase_  :	Tuple				      =		hidden_dropout_prob
             UpperCAmelCase_  :	List[Any]				      =		attention_probs_dropout_prob
             UpperCAmelCase_  :	Any				      =		max_position_embeddings
             UpperCAmelCase_  :	str				      =		type_vocab_size
             UpperCAmelCase_  :	Any				      =		initializer_range
             UpperCAmelCase_  :	Optional[Any]				      =		layer_norm_eps
             UpperCAmelCase_  :	Any				      =		num_qa_labels
             UpperCAmelCase_  :	str				      =		num_object_labels
             UpperCAmelCase_  :	Dict				      =		num_attr_labels
             UpperCAmelCase_  :	Tuple				      =		l_layers
             UpperCAmelCase_  :	Tuple				      =		x_layers
             UpperCAmelCase_  :	int				      =		r_layers
             UpperCAmelCase_  :	Optional[Any]				      =		visual_feat_dim
             UpperCAmelCase_  :	List[Any]				      =		visual_pos_dim
             UpperCAmelCase_  :	int				      =		visual_loss_normalizer
             UpperCAmelCase_  :	str				      =		task_matched
             UpperCAmelCase_  :	str				      =		task_mask_lm
             UpperCAmelCase_  :	int				      =		task_obj_predict
             UpperCAmelCase_  :	List[str]				      =		task_qa
             UpperCAmelCase_  :	Optional[int]				      =		visual_obj_loss
             UpperCAmelCase_  :	List[str]				      =		visual_attr_loss
             UpperCAmelCase_  :	str				      =		visual_feat_loss
             UpperCAmelCase_  :	List[Any]				      =		{"vision": r_layers, "cross_encoder": x_layers, "language": l_layers}
             super().__init__(**lowercase)
