






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						:			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__ (       __lowerCamelCase     :	str						):
  __UpperCAmelCase				:      int	 =    {}
  state_dict.pop("""pixel_mean"""    ,						__lowerCamelCase						)
  state_dict.pop("""pixel_std"""    ,						__lowerCamelCase						)

  __UpperCAmelCase				:      int	 =    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[int]	 =    key.replace(__lowerCamelCase    ,						__lowerCamelCase						)

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

    __UpperCAmelCase				:      Tuple	 =    value

  __UpperCAmelCase				:      List[Any]	 =    model_state_dict[
      """prompt_encoder.shared_embedding.positional_embedding"""
  ]

  return model_state_dict






def 							lowerCamelCase__ (       __lowerCamelCase     :	Optional[Any]    ,						__lowerCamelCase     :	Optional[int]    ,						__lowerCamelCase     :	Optional[int]    ,						__lowerCamelCase     :	int="ybelkada/segment-anything"						):
  __UpperCAmelCase				:      Union[str, Any]	 =    hf_hub_download(__lowerCamelCase    ,						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=1024    ,						num_hidden_layers=24    ,						num_attention_heads=16    ,						global_attn_indexes=[5, 11, 17, 23]    ,						)

    __UpperCAmelCase				:      int	 =    SamConfig(
        vision_config=__lowerCamelCase    ,						)
  elif "sam_vit_h" in model_name:
    __UpperCAmelCase				:      Optional[Any]	 =    SamVisionConfig(
        hidden_size=1280    ,						num_hidden_layers=32    ,						num_attention_heads=16    ,						global_attn_indexes=[7, 15, 23, 31]    ,						)

    __UpperCAmelCase				:      Optional[Any]	 =    SamConfig(
        vision_config=__lowerCamelCase    ,						)

  __UpperCAmelCase				:      str	 =    torch.load(__lowerCamelCase    ,						map_location="""cpu"""						)
  __UpperCAmelCase				:      Optional[Any]	 =    replace_keys(__lowerCamelCase						)

  __UpperCAmelCase				:      Any	 =    SamImageProcessor()

  __UpperCAmelCase				:      Dict	 =    SamProcessor(image_processor=__lowerCamelCase						)
  __UpperCAmelCase				:      int	 =    SamModel(__lowerCamelCase						)

  hf_model.load_state_dict(__lowerCamelCase						)
  __UpperCAmelCase				:      Optional[int]	 =    hf_model.to("""cuda"""						)

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

  __UpperCAmelCase				:      List[str]	 =    [[[400, 650]]]
  __UpperCAmelCase				:      Any	 =    [[1]]

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

  with torch.no_grad():
    __UpperCAmelCase				:      Optional[int]	 =    hf_model(**__lowerCamelCase						)
  __UpperCAmelCase				:      Optional[Any]	 =    output.iou_scores.squeeze()

  if model_name == "sam_vit_h_4b8939":
    assert scores[-1].item() == 0.5_7_9_8_9_0_2_5_1_1_5_9_6_6_8

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

    with torch.no_grad():
      __UpperCAmelCase				:      Tuple	 =    hf_model(**__lowerCamelCase						)
    __UpperCAmelCase				:      List[str]	 =    output.iou_scores.squeeze()

    assert scores[-1].item() == 0.9_7_1_2_6_0_3_0_9_2_1_9_3_6_0_4

    __UpperCAmelCase				:      str	 =    ((75, 275, 1725, 850),)

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

    with torch.no_grad():
      __UpperCAmelCase				:      Any	 =    hf_model(**__lowerCamelCase						)
    __UpperCAmelCase				:      List[Any]	 =    output.iou_scores.squeeze()

    assert scores[-1].item() == 0.8_6_8_6_0_1_5_6_0_5_9_2_6_5_1_4

    # Test with 2 points and 1 image.
    __UpperCAmelCase				:      Dict	 =    [[[400, 650], [800, 650]]]
    __UpperCAmelCase				:      Tuple	 =    [[1, 1]]

    __UpperCAmelCase				:      Any	 =    processor(
        images=np.array(__lowerCamelCase						)    ,						input_points=__lowerCamelCase    ,						input_labels=__lowerCamelCase    ,						return_tensors="""pt"""						).to("""cuda"""						)

    with torch.no_grad():
      __UpperCAmelCase				:      Optional[Any]	 =    hf_model(**__lowerCamelCase						)
    __UpperCAmelCase				:      Dict	 =    output.iou_scores.squeeze()

    assert scores[-1].item() == 0.9_9_3_6_0_4_7_7_9_2_4_3_4_6_9_2


if __name__ == "__main__":
       a						:			Any     	=					argparse.ArgumentParser()
       a						:			Tuple     	=					["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						:			Tuple     	=					parser.parse_args()

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









import copy
import os
from typing import Union

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


SCREAMING_SNAKE_CASE__							:      Tuple         =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__							:      int         =    {
    """google/pix2struct-textcaps-base""": (
        """https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"""
    ),
}







class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    Optional[int]  		=					"""pix2struct_text_model"""
   _UpperCamelCase			:    Dict  		=					["""past_key_values"""]
   _UpperCamelCase			:    Union[str, Any]  		=					{
       """hidden_size""": """hidden_size""",
       """num_attention_heads""": """num_heads""",
       """num_hidden_layers""": """num_layers""",
   }





   def __init__(			self       ,      snake_case=50_244       ,      snake_case=768       ,      snake_case=64       ,      snake_case=2_048       ,      snake_case=12       ,      snake_case=12       ,      snake_case=32       ,      snake_case=128       ,      snake_case=0.1       ,      snake_case=1E-6       ,      snake_case=1.0       ,      snake_case="gelu_new"       ,      snake_case=0       ,      snake_case=False       ,      snake_case=0       ,      snake_case=1       ,      snake_case=False       ,      snake_case=True       ,      **snake_case       ,      )					->	Dict:
      """simple docstring"""





      a__						:    Tuple   		=	vocab_size
      a__						:    List[str]   		=	hidden_size
      a__						:    Optional[Any]   		=	d_kv
      a__						:    List[str]   		=	d_ff
      a__						:    Optional[int]   		=	num_layers
      a__						:    Tuple   		=	num_heads
      a__						:    List[Any]   		=	relative_attention_num_buckets
      a__						:    Optional[int]   		=	relative_attention_max_distance
      a__						:    Optional[int]   		=	dropout_rate
      a__						:    List[Any]   		=	layer_norm_epsilon
      a__						:    List[str]   		=	initializer_factor
      a__						:    Any   		=	use_cache

      a__						:    Any   		=	eos_token_id
      a__						:    List[Any]   		=	decoder_start_token_id

      # for backwards compatibility
      a__						:    Tuple   		=	dense_act_fn

      super().__init__(
          pad_token_id=snake_case       ,      eos_token_id=snake_case       ,      decoder_start_token_id=snake_case       ,      tie_word_embeddings=snake_case       ,      is_decoder=snake_case       ,      **snake_case       ,      )





   @classmethod
   def 						_snake_case			(			cls       ,      snake_case       ,      **snake_case   )					->	"PretrainedConfig":
      """simple docstring"""





      cls._set_token_in_kwargs(snake_case   )

      a__		,  a__						:    Optional[Any]   		=	cls.get_config_dict(snake_case       ,      **snake_case   )

      # get the text config dict if we are loading from Pix2StructConfig
      if config_dict.get("model_type"   ) == "pix2struct":
         a__						:    Optional[int]   		=	config_dict["text_config"]

      if "model_type" in config_dict and hasattr(cls       ,      "model_type"   ) and config_dict["model_type"] != cls.model_type:
         logger.warning(
             F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
             F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""   )

      return cls.from_dict(snake_case       ,      **snake_case   )







class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    int  		=					"""pix2struct_vision_model"""





   def __init__(			self       ,      snake_case=768       ,      snake_case=768       ,      snake_case=2_048       ,      snake_case=64       ,      snake_case=12       ,      snake_case=12       ,      snake_case="gelu_new"       ,      snake_case=1E-6       ,      snake_case=0.0       ,      snake_case=0.0       ,      snake_case=1E-10       ,      snake_case=1.0       ,      snake_case=4_096       ,      snake_case=32       ,      snake_case=128       ,      **snake_case       ,      )					->	Optional[Any]:
      """simple docstring"""





      super().__init__(**snake_case   )

      a__						:    Optional[Any]   		=	hidden_size
      a__						:    Tuple   		=	patch_embed_hidden_size
      a__						:    Tuple   		=	d_ff
      a__						:    List[Any]   		=	dropout_rate
      a__						:    Optional[Any]   		=	num_hidden_layers
      a__						:    Optional[Any]   		=	num_attention_heads
      a__						:    List[str]   		=	initializer_range
      a__						:    List[str]   		=	initializer_factor
      a__						:    List[Any]   		=	attention_dropout
      a__						:    Optional[int]   		=	layer_norm_eps
      a__						:    int   		=	dense_act_fn
      a__						:    Optional[Any]   		=	seq_len
      a__						:    List[Any]   		=	relative_attention_num_buckets
      a__						:    int   		=	relative_attention_max_distance
      a__						:    int   		=	d_kv





   @classmethod
   def 						_snake_case			(			cls       ,      snake_case       ,      **snake_case   )					->	"PretrainedConfig":
      """simple docstring"""





      cls._set_token_in_kwargs(snake_case   )

      a__		,  a__						:    int   		=	cls.get_config_dict(snake_case       ,      **snake_case   )

      # get the vision config dict if we are loading from Pix2StructConfig
      if config_dict.get("model_type"   ) == "pix2struct":
         a__						:    int   		=	config_dict["vision_config"]

      if "model_type" in config_dict and hasattr(cls       ,      "model_type"   ) and config_dict["model_type"] != cls.model_type:
         logger.warning(
             F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
             F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""   )

      return cls.from_dict(snake_case       ,      **snake_case   )







class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    List[Any]  		=					"""pix2struct"""
   _UpperCamelCase			:    Dict  		=					True





   def __init__(			self       ,      snake_case=None       ,      snake_case=None       ,      snake_case=1.0       ,      snake_case=0.02       ,      snake_case=False       ,      snake_case=False       ,      snake_case=True       ,      **snake_case       ,      )					->	Optional[int]:
      """simple docstring"""





      super().__init__(tie_word_embeddings=snake_case       ,      is_encoder_decoder=snake_case       ,      **snake_case   )

      if text_config is None:
         a__						:    Optional[Any]   		=	{}
         logger.info("text_config is None. Initializing the Pix2StructTextConfig with default values."   )

      if vision_config is None:
         a__						:    List[Any]   		=	{}
         logger.info("vision_config is None. Initializing the Pix2StructVisionConfig with default values."   )

      a__						:    List[str]   		=	PixaStructTextConfig(**snake_case   )
      a__						:    List[str]   		=	PixaStructVisionConfig(**snake_case   )

      a__						:    Tuple   		=	self.text_config.decoder_start_token_id
      a__						:    int   		=	self.text_config.pad_token_id
      a__						:    List[Any]   		=	self.text_config.eos_token_id

      a__						:    str   		=	initializer_factor
      a__						:    Dict   		=	initializer_range

      a__						:    Optional[int]   		=	self.initializer_range
      a__						:    Union[str, Any]   		=	self.initializer_range

      a__						:    Union[str, Any]   		=	is_vqa





   @classmethod
   def 						_snake_case			(			cls       ,      snake_case       ,      snake_case       ,      **snake_case   )					->	Dict:
      """simple docstring"""





      return cls(text_config=text_config.to_dict()       ,      vision_config=vision_config.to_dict()       ,      **snake_case   )





   def 						_snake_case			(			self   )					->	int:
      """simple docstring"""





      a__						:    Tuple   		=	copy.deepcopy(self.__dict__   )
      a__						:    List[str]   		=	self.text_config.to_dict()
      a__						:    Union[str, Any]   		=	self.vision_config.to_dict()
      a__						:    List[Any]   		=	self.__class__.model_type
      return output

