





'''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,
)


A_		:  List[str]		 =						{
    "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__		(						__magic_name__  :   Tuple      )				->			Optional[Any]:
 '''simple docstring'''






 snake_case__     :					List[str]    =  {}
 state_dict.pop("""pixel_mean"""		,       __magic_name__      )
 state_dict.pop("""pixel_std"""		,       __magic_name__      )

 snake_case__     :					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:
    snake_case__     :					Tuple    =  key.replace(__magic_name__		,       __magic_name__      )

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

  snake_case__     :					Any    =  value

 snake_case__     :					Any    =  model_state_dict[
     """prompt_encoder.shared_embedding.positional_embedding"""
 ]

 return model_state_dict



def        UpperCamelCase__		(						__magic_name__  :   int		,       __magic_name__  :   Optional[int]		,       __magic_name__  :   Tuple		,       __magic_name__  :   Optional[int]="ybelkada/segment-anything"      )				->			Optional[Any]:
 '''simple docstring'''






 snake_case__     :					str    =  hf_hub_download(__magic_name__		,       f"checkpoints/{model_name}.pth"      )

 if "sam_vit_b" in model_name:
  snake_case__     :					Union[str, Any]    =  SamConfig()
 elif "sam_vit_l" in model_name:
  snake_case__     :					Optional[int]    =  SamVisionConfig(
      hidden_size=10_24		,       num_hidden_layers=24		,       num_attention_heads=16		,       global_attn_indexes=[5, 11, 17, 23]		,       )

  snake_case__     :					Union[str, Any]    =  SamConfig(
      vision_config=__magic_name__		,       )
 elif "sam_vit_h" in model_name:
  snake_case__     :					Union[str, Any]    =  SamVisionConfig(
      hidden_size=12_80		,       num_hidden_layers=32		,       num_attention_heads=16		,       global_attn_indexes=[7, 15, 23, 31]		,       )

  snake_case__     :					int    =  SamConfig(
      vision_config=__magic_name__		,       )

 snake_case__     :					List[Any]    =  torch.load(__magic_name__		,       map_location="""cpu"""      )
 snake_case__     :					List[str]    =  replace_keys(__magic_name__      )

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

 snake_case__     :					str    =  SamProcessor(image_processor=__magic_name__      )
 snake_case__     :					int    =  SamModel(__magic_name__      )

 hf_model.load_state_dict(__magic_name__      )
 snake_case__     :					List[str]    =  hf_model.to("""cuda"""      )

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

 snake_case__     :					List[str]    =  [[[4_00, 6_50]]]
 snake_case__     :					int    =  [[1]]

 snake_case__     :					Optional[Any]    =  processor(images=np.array(__magic_name__      )		,       return_tensors="""pt"""      ).to("""cuda"""      )

 with torch.no_grad():
  snake_case__     :					Tuple    =  hf_model(**__magic_name__      )
 snake_case__     :					Optional[Any]    =  output.iou_scores.squeeze()

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

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

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

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

  snake_case__     :					Any    =  ((75, 2_75, 17_25, 8_50),)

  snake_case__     :					List[Any]    =  processor(images=np.array(__magic_name__      )		,       input_boxes=__magic_name__		,       return_tensors="""pt"""      ).to("""cuda"""      )

  with torch.no_grad():
   snake_case__     :					List[Any]    =  hf_model(**__magic_name__      )
  snake_case__     :					Union[str, Any]    =  output.iou_scores.squeeze()

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

  # Test with 2 points and 1 image.
  snake_case__     :					List[Any]    =  [[[4_00, 6_50], [8_00, 6_50]]]
  snake_case__     :					Dict    =  [[1, 1]]

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

  with torch.no_grad():
   snake_case__     :					Optional[Any]    =  hf_model(**__magic_name__      )
  snake_case__     :					List[str]    =  output.iou_scores.squeeze()

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


if __name__ == "__main__":
    A_		:  str		 =						argparse.ArgumentParser()
    A_		:  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",
    )

    A_		:  str		 =						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 shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_a					:			Tuple							=    logging.get_logger(__name__)

_a					:			Optional[int]							=    '▁'

_a					:			str							=    {'vocab_file': 'sentencepiece.bpe.model', 'monolingual_vocab_file': 'dict.txt'}

_a					:			Optional[Any]							=    {
    'vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model',
    },
    'monolingual_vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt',
    },
}

_a					:			Union[str, Any]							=    {'vinai/bartpho-syllable': 1_024}



class 							lowercase_						(		a			):

  '''simple docstring'''





  __lowerCAmelCase       :			Dict     	= VOCAB_FILES_NAMES
  __lowerCAmelCase       :			str     	= PRETRAINED_VOCAB_FILES_MAP
  __lowerCAmelCase       :			Any     	= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCAmelCase       :			int     	= ["input_ids", "attention_mask"]
  def __init__(  self ,			a_ ,			a_ ,			a_="<s>" ,			a_="</s>" ,			a_="</s>" ,			a_="<s>" ,			a_="<unk>" ,			a_="<pad>" ,			a_="<mask>" ,			a_ = None ,			**a_ ,			)				->					None:


    """simple docstring"""
    # Mask token behave like a normal word, i.e. include the space before it
    UpperCAmelCase    				=						AddedToken(a_ ,			lstrip=a_ ,			rstrip=a_  ) if isinstance(a_ ,			a_  ) else mask_token

    UpperCAmelCase    				=						{} if sp_model_kwargs is None else sp_model_kwargs

    super().__init__(
        bos_token=a_ ,			eos_token=a_ ,			unk_token=a_ ,			sep_token=a_ ,			cls_token=a_ ,			pad_token=a_ ,			mask_token=a_ ,			sp_model_kwargs=self.sp_model_kwargs ,			**a_ ,			)

    UpperCAmelCase    				=						vocab_file
    UpperCAmelCase    				=						monolingual_vocab_file
    UpperCAmelCase    				=						spm.SentencePieceProcessor(**self.sp_model_kwargs  )
    self.sp_model.Load(str(a_  )  )

    # Load the reduced vocab

    # Keep order of special tokens for backward compatibility
    UpperCAmelCase    				=						{}
    UpperCAmelCase    				=						0
    for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
      if str(a_  ) not in self.fairseq_tokens_to_ids:
        UpperCAmelCase    				=						cnt
        cnt += 1
    with open(a_ ,			'r' ,			encoding='utf-8'  ) as f:
      for line in f.readlines():
        UpperCAmelCase    				=						line.strip().split()[0]
        UpperCAmelCase    				=						len(self.fairseq_tokens_to_ids  )
    if str(a_  ) not in self.fairseq_tokens_to_ids:
      UpperCAmelCase    				=						len(self.fairseq_tokens_to_ids  )

    UpperCAmelCase    				=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}
  def __getstate__(  self  )				->					Any:


    """simple docstring"""
    UpperCAmelCase    				=						self.__dict__.copy()
    UpperCAmelCase    				=						None
    UpperCAmelCase    				=						self.sp_model.serialized_model_proto()
    return state
  def __setstate__(  self ,			a_  )				->					int:


    """simple docstring"""
    UpperCAmelCase    				=						d

    # for backward compatibility
    if not hasattr(self ,			'sp_model_kwargs'  ):
      UpperCAmelCase    				=						{}

    UpperCAmelCase    				=						spm.SentencePieceProcessor(**self.sp_model_kwargs  )
    self.sp_model.LoadFromSerializedProto(self.sp_model_proto  )
  def     snake_case_      (  self ,			a_ ,			a_ = None  )				->					List[int]:


    """simple docstring"""

    if token_ids_a is None:
      return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    UpperCAmelCase    				=						[self.cls_token_id]
    UpperCAmelCase    				=						[self.sep_token_id]
    return cls + token_ids_a + sep + sep + token_ids_a + sep
  def     snake_case_      (  self ,			a_ ,			a_ = None ,			a_ = False  )				->					List[int]:


    """simple docstring"""

    if already_has_special_tokens:
      return super().get_special_tokens_mask(
          token_ids_a=a_ ,			token_ids_a=a_ ,			already_has_special_tokens=a_  )

    if token_ids_a is None:
      return [1] + ([0] * len(a_  )) + [1]
    return [1] + ([0] * len(a_  )) + [1, 1] + ([0] * len(a_  )) + [1]
  def     snake_case_      (  self ,			a_ ,			a_ = None  )				->					List[int]:


    """simple docstring"""
    UpperCAmelCase    				=						[self.sep_token_id]
    UpperCAmelCase    				=						[self.cls_token_id]

    if token_ids_a is None:
      return len(cls + token_ids_a + sep  ) * [0]
    return len(cls + token_ids_a + sep + sep + token_ids_a + sep  ) * [0]
  @property
  def     snake_case_      (  self  )				->					Union[str, Any]:


    """simple docstring"""
    return len(self.fairseq_ids_to_tokens  )
  def     snake_case_      (  self  )				->					int:


    """simple docstring"""
    UpperCAmelCase    				=						{self.convert_ids_to_tokens(a_  ): i for i in range(self.vocab_size  )}
    vocab.update(self.added_tokens_encoder  )
    return vocab
  def     snake_case_      (  self ,			a_  )				->					List[str]:


    """simple docstring"""
    return self.sp_model.encode(a_ ,			out_type=a_  )
  def     snake_case_      (  self ,			a_  )				->					Any:


    """simple docstring"""
    if token in self.fairseq_tokens_to_ids:
      return self.fairseq_tokens_to_ids[token]
    else:
      return self.unk_token_id
  def     snake_case_      (  self ,			a_  )				->					Dict:


    """simple docstring"""
    return self.fairseq_ids_to_tokens[index]
  def     snake_case_      (  self ,			a_  )				->					Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase    				=						''.join(a_  ).replace(a_ ,			' '  ).strip()
    return out_string





  def     snake_case_      (  self ,			a_ ,			a_ = None  )				->					Tuple[str]:


    """simple docstring"""
    if not os.path.isdir(a_  ):
      logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
      return
    UpperCAmelCase    				=						os.path.join(
        a_ ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )
    UpperCAmelCase    				=						os.path.join(
        a_ ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['monolingual_vocab_file'] ,			)

    if os.path.abspath(self.vocab_file  ) != os.path.abspath(a_  ) and os.path.isfile(self.vocab_file  ):
      copyfile(self.vocab_file ,			a_  )
    elif not os.path.isfile(self.vocab_file  ):
      with open(a_ ,			'wb'  ) as fi:
        UpperCAmelCase    				=						self.sp_model.serialized_model_proto()
        fi.write(a_  )

    if os.path.abspath(self.monolingual_vocab_file  ) != os.path.abspath(
        a_  ) and os.path.isfile(self.monolingual_vocab_file  ):
      copyfile(self.monolingual_vocab_file ,			a_  )
    elif not os.path.isfile(self.monolingual_vocab_file  ):
      with open(a_ ,			'w' ,			encoding='utf-8'  ) as fp:
        for token in self.fairseq_tokens_to_ids:
          if token not in self.all_special_tokens:
            fp.write(F'''{str(a_  )} \n'''  )

    return out_vocab_file, out_monolingual_vocab_file


