


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

 lowerCamelCase_      =  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:
    lowerCamelCase_      =  key.replace(UpperCAmelCase_					,				UpperCAmelCase_		)

  if re.match(UpperCAmelCase_					,				UpperCAmelCase_		):
   lowerCamelCase_      =  int(re.match(UpperCAmelCase_					,				UpperCAmelCase_		).group(2		)		)
   if layer_nb == 0:
    lowerCamelCase_      =  key.replace("layers.0"					,				"proj_in"		)
   elif layer_nb == 1:
    lowerCamelCase_      =  key.replace("layers.1"					,				"layers.0"		)
   elif layer_nb == 2:
    lowerCamelCase_      =  key.replace("layers.2"					,				"proj_out"		)

  lowerCamelCase_      =  value

 lowerCamelCase_      =  model_state_dict[
     "prompt_encoder.shared_embedding.positional_embedding"
 ]

 return model_state_dict





def __snake_case					(      UpperCAmelCase_						:						Optional[int]					,				UpperCAmelCase_						:						Any					,				UpperCAmelCase_						:						str					,				UpperCAmelCase_						:						Dict="ybelkada/segment-anything"		):
 lowerCamelCase_      =  hf_hub_download(UpperCAmelCase_					,				F'''checkpoints/{model_name}.pth'''		)

 if "sam_vit_b" in model_name:
  lowerCamelCase_      =  SamConfig()
 elif "sam_vit_l" in model_name:
  lowerCamelCase_      =  SamVisionConfig(
      hidden_size=1024					,				num_hidden_layers=24					,				num_attention_heads=16					,				global_attn_indexes=[5, 11, 17, 23]					,				)

  lowerCamelCase_      =  SamConfig(
      vision_config=UpperCAmelCase_					,				)
 elif "sam_vit_h" in model_name:
  lowerCamelCase_      =  SamVisionConfig(
      hidden_size=1280					,				num_hidden_layers=32					,				num_attention_heads=16					,				global_attn_indexes=[7, 15, 23, 31]					,				)

  lowerCamelCase_      =  SamConfig(
      vision_config=UpperCAmelCase_					,				)

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

 lowerCamelCase_      =  SamImageProcessor()

 lowerCamelCase_      =  SamProcessor(image_processor=UpperCAmelCase_		)
 lowerCamelCase_      =  SamModel(UpperCAmelCase_		)

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

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

 lowerCamelCase_      =  [[[400, 650]]]
 lowerCamelCase_      =  [[1]]

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

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

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

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

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

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

  lowerCamelCase_      =  ((75, 275, 1725, 850),)

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

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

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

  # Test with 2 points and 1 image.
  lowerCamelCase_      =  [[[400, 650], [800, 650]]]
  lowerCamelCase_      =  [[1, 1]]

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

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

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


if __name__ == "__main__":
     a_  :				int						= argparse.ArgumentParser()
     a_  :				List[str]						= ["""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_  :				Optional[Any]						= parser.parse_args()

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








"""simple docstring"""







from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
							from .features import FeatureType
@dataclass
class        __A       :







		'''simple docstring'''





		lowerCAmelCase			:					List[str]
		lowerCAmelCase			:					Optional[str]   	=				None
		# Automatically constructed
		lowerCAmelCase			:					ClassVar[str]   	=				"dict"
		lowerCAmelCase			:					ClassVar[Any]   	=				None
		lowerCAmelCase			:					str   	=				field(default="Translation"     ,init=A_     ,repr=A_    )







		def __call__(	self						:  List[str]						)					->		Any:






				"""simple docstring"""



				return pa.struct({lang: pa.string() for lang in sorted(self.languages						)}						)







		def 					UpperCAmelCase    (	self						:  List[str]						)					->		Union["FeatureType", Dict[str, "FeatureType"]]:






				"""simple docstring"""



				from .features import Value

				return {k: Value('''string'''						) for k in sorted(self.languages						)}





@dataclass
class        __A       :







		'''simple docstring'''





		lowerCAmelCase			:					Optional[List]   	=				None
		lowerCAmelCase			:					Optional[int]   	=				None
		lowerCAmelCase			:					Optional[str]   	=				None
		# Automatically constructed
		lowerCAmelCase			:					ClassVar[str]   	=				"dict"
		lowerCAmelCase			:					ClassVar[Any]   	=				None
		lowerCAmelCase			:					str   	=				field(default="TranslationVariableLanguages"     ,init=A_     ,repr=A_    )







		def 					UpperCAmelCase    (	self						:  List[Any]						)					->		Optional[int]:






				"""simple docstring"""



				lowercase__   :    Optional[int]				=			sorted(set(self.languages						)						) if self.languages else None
				lowercase__   :    Dict				=			len(self.languages						) if self.languages else None







		def __call__(	self						:  List[Any]						)					->		List[Any]:






				"""simple docstring"""



				return pa.struct({'''language''': pa.list_(pa.string()						), '''translation''': pa.list_(pa.string()						)}						)







		def 					UpperCAmelCase    (	self						:  Dict			,_snake_case						:  Tuple						)					->		int:






				"""simple docstring"""



				lowercase__   :    List[Any]				=			set(self.languages						)
				if self.languages and set(_snake_case						) - lang_set:
						raise ValueError(
						    f"""Some languages in example ({", ".join(sorted(set(_snake_case						) - lang_set						)						)}) are not in valid set ({", ".join(_snake_case						)})."""						)

				# Convert dictionary into tuples, splitting out cases where there are
				# multiple translations for a single language.
				lowercase__   :    str				=			[]
				for lang, text in translation_dict.items():
						if isinstance(_snake_case			,_snake_case						):
								translation_tuples.append((lang, text)						)
						else:
								translation_tuples.extend([(lang, el) for el in text]						)

        # Ensure translations are in ascending order by language code.
				lowercase__		,			lowercase__   :    Optional[Any]				=			zip(*sorted(_snake_case						)						)

				return {"language": languages, "translation": translations}







		def 					UpperCAmelCase    (	self						:  List[Any]						)					->		Union["FeatureType", Dict[str, "FeatureType"]]:






				"""simple docstring"""



				from .features import Sequence, Value

				return {
				    "language": Sequence(Value('''string'''						)						),
				    "translation": Sequence(Value('''string'''						)						),
				}
