




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     :   Any            = {
    "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_			(  snake_case_  ):
      _A							:  int    =		{}
      state_dict.pop("""pixel_mean""",snake_case_  )
      state_dict.pop("""pixel_std""",snake_case_  )

      _A							:  Tuple    =		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							:  int    =		key.replace(snake_case_,snake_case_  )

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

            _A							:  Union[str, Any]    =		value

      _A							:  Tuple    =		model_state_dict[
          """prompt_encoder.shared_embedding.positional_embedding"""
      ]

      return model_state_dict







def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_,snake_case_="ybelkada/segment-anything"  ):
      _A							:  Union[str, Any]    =		hf_hub_download(snake_case_,f'''checkpoints/{model_name}.pth'''  )

      if "sam_vit_b" in model_name:
            _A							:  Dict    =		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							:  str    =		SamConfig(
                vision_config=snake_case_,)
      elif "sam_vit_h" in model_name:
            _A							:  int    =		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=snake_case_,)

      _A							:  Union[str, Any]    =		torch.load(snake_case_,map_location="""cpu"""  )
      _A							:  Optional[Any]    =		replace_keys(snake_case_  )

      _A							:  Union[str, Any]    =		SamImageProcessor()

      _A							:  Any    =		SamProcessor(image_processor=snake_case_  )
      _A							:  Optional[Any]    =		SamModel(snake_case_  )

      hf_model.load_state_dict(snake_case_  )
      _A							:  List[Any]    =		hf_model.to("""cuda"""  )

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

      _A							:  Dict    =		[[[400, 650]]]
      _A							:  List[str]    =		[[1]]

      _A							:  Dict    =		processor(images=np.array(snake_case_  ),return_tensors="""pt"""  ).to("""cuda"""  )

      with torch.no_grad():
            _A							:  Optional[int]    =		hf_model(**snake_case_  )
      _A							:  int    =		output.iou_scores.squeeze()

      if model_name == "sam_vit_h_4b8939":
            assert scores[-1].item() == 0.5_79_89_02_51_15_96_68

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

            with torch.no_grad():
                  _A							:  Optional[Any]    =		hf_model(**snake_case_  )
            _A							:  Any    =		output.iou_scores.squeeze()

            assert scores[-1].item() == 0.97_12_60_30_92_19_36_04

            _A							:  Optional[int]    =		((75, 275, 1725, 850),)

            _A							:  int    =		processor(images=np.array(snake_case_  ),input_boxes=snake_case_,return_tensors="""pt"""  ).to("""cuda"""  )

            with torch.no_grad():
                  _A							:  List[str]    =		hf_model(**snake_case_  )
            _A							:  Any    =		output.iou_scores.squeeze()

            assert scores[-1].item() == 0.86_86_01_56_05_92_65_14

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

            _A							:  List[str]    =		processor(
                images=np.array(snake_case_  ),input_points=snake_case_,input_labels=snake_case_,return_tensors="""pt"""  ).to("""cuda"""  )

            with torch.no_grad():
                  _A							:  Dict    =		hf_model(**snake_case_  )
            _A							:  List[str]    =		output.iou_scores.squeeze()

            assert scores[-1].item() == 0.99_36_04_77_92_43_46_92


if __name__ == "__main__":
  _snake_case     :   Any            = argparse.ArgumentParser()
  _snake_case     :   Optional[int]            = ["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     :   Any            = parser.parse_args()

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







import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer







class 		lowercase							(     tf.keras.layers.Layer	):

     def __init__(						self	,  _a	,  _a	,  _a = None	,  _a = None       )		->							Any:
           super().__init__()
           _A							:  Dict    =		pad_token_id
           _A							:  List[Any]    =		max_length
           _A							:  Optional[int]    =		vocab
           _A							:  Optional[int]    =		merges
           _A							:  Optional[int]    =		BytePairTokenizer(_a	,  _a	,  sequence_length=_a       )

     @classmethod
     def       a__			(						cls	,  _a	,  *_a	,  **_a       )		->							str:
           _A							:  Any    =		[""" """.join(_a       ) for m in tokenizer.bpe_ranks.keys()]
           _A							:  str    =		tokenizer.get_vocab()
           return cls(_a	,  _a	,  *_a	,  **_a       )

     @classmethod
     def       a__			(						cls	,  _a	,  *_a	,  **_a       )		->							List[Any]:
           _A							:  Union[str, Any]    =		GPTaTokenizer.from_pretrained(_a	,  *_a	,  **_a       )
           return cls.from_tokenizer(_a	,  *_a	,  **_a       )

     @classmethod
     def       a__			(						cls	,  _a       )		->							Union[str, Any]:
           return cls(**_a       )

     def       a__			(						self       )		->							Union[str, Any]:
           return {
               "vocab": self.vocab,
               "merges": self.merges,
               "max_length": self.max_length,
               "pad_token_id": self.pad_token_id,
           }



     def       a__			(						self	,  _a	,  _a = None       )		->							int:
           _A							:  Optional[int]    =		self.tf_tokenizer(_a       )
           _A							:  Tuple    =		tf.ones_like(_a       )

           if self.pad_token_id is not None:
                 # pad the tokens up to max length
                 _A							:  Dict    =		max_length if max_length is not None else self.max_length

                 if max_length is not None:
                       _A    ,     _A							:  Dict    =		pad_model_inputs(
                           _a	,  max_seq_length=_a	,  pad_value=self.pad_token_id       )

           return {"attention_mask": attention_mask, "input_ids": input_ids}

