





'''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,
)


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      __A						(							a_						:  Optional[int]	):
		lowerCAmelCase     :    Dict						=   {}
		state_dict.pop("pixel_mean"      ,a_	)
		state_dict.pop("pixel_std"      ,a_	)

		lowerCAmelCase     :    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:
								lowerCAmelCase     :    Union[str, Any]						=   key.replace(a_      ,a_	)

				if re.match(a_      ,a_	):
						lowerCAmelCase     :    Any						=   int(re.match(a_      ,a_	).group(2	)	)
						if layer_nb == 0:
								lowerCAmelCase     :    Any						=   key.replace("layers.0"      ,"proj_in"	)
						elif layer_nb == 1:
								lowerCAmelCase     :    str						=   key.replace("layers.1"      ,"layers.0"	)
						elif layer_nb == 2:
								lowerCAmelCase     :    str						=   key.replace("layers.2"      ,"proj_out"	)

				lowerCAmelCase     :    Optional[int]						=   value

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

		return model_state_dict






def      __A						(							a_						:  Union[str, Any]      ,a_						:  Dict      ,a_						:  int      ,a_						:  Dict="ybelkada/segment-anything"	):
		lowerCAmelCase     :    str						=   hf_hub_download(a_      ,f'''checkpoints/{model_name}.pth'''	)

		if "sam_vit_b" in model_name:
				lowerCAmelCase     :    Optional[int]						=   SamConfig()
		elif "sam_vit_l" in model_name:
				lowerCAmelCase     :    Dict						=   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]      ,)

				lowerCAmelCase     :    int						=   SamConfig(
				    vision_config=a_      ,)
		elif "sam_vit_h" in model_name:
				lowerCAmelCase     :    Tuple						=   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]      ,)

				lowerCAmelCase     :    List[str]						=   SamConfig(
				    vision_config=a_      ,)

		lowerCAmelCase     :    Optional[int]						=   torch.load(a_      ,map_location="cpu"	)
		lowerCAmelCase     :    int						=   replace_keys(a_	)

		lowerCAmelCase     :    Optional[int]						=   SamImageProcessor()

		lowerCAmelCase     :    Dict						=   SamProcessor(image_processor=a_	)
		lowerCAmelCase     :    Optional[int]						=   SamModel(a_	)

		hf_model.load_state_dict(a_	)
		lowerCAmelCase     :    Tuple						=   hf_model.to("cuda"	)

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

		lowerCAmelCase     :    Optional[int]						=   [[[4_0_0, 6_5_0]]]
		lowerCAmelCase     :    Optional[Any]						=   [[1]]

		lowerCAmelCase     :    int						=   processor(images=np.array(a_	)      ,return_tensors="pt"	).to("cuda"	)

		with torch.no_grad():
				lowerCAmelCase     :    Union[str, Any]						=   hf_model(**a_	)
		lowerCAmelCase     :    Union[str, 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

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

				with torch.no_grad():
						lowerCAmelCase     :    Any						=   hf_model(**a_	)
				lowerCAmelCase     :    Optional[Any]						=   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

				lowerCAmelCase     :    Optional[int]						=   ((7_5, 2_7_5, 1_7_2_5, 8_5_0),)

				lowerCAmelCase     :    Optional[int]						=   processor(images=np.array(a_	)      ,input_boxes=a_      ,return_tensors="pt"	).to("cuda"	)

				with torch.no_grad():
						lowerCAmelCase     :    Any						=   hf_model(**a_	)
				lowerCAmelCase     :    Union[str, 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.
				lowerCAmelCase     :    List[Any]						=   [[[4_0_0, 6_5_0], [8_0_0, 6_5_0]]]
				lowerCAmelCase     :    Optional[Any]						=   [[1, 1]]

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

				with torch.no_grad():
						lowerCAmelCase     :    Tuple						=   hf_model(**a_	)
				lowerCAmelCase     :    List[Any]						=   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__":
					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)










'''simple docstring'''






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 		lowerCamelCase  (      _A       ):
	snake_case_      =   ["image_processor", "tokenizer"]
	snake_case_      =   "ChineseCLIPImageProcessor"
	snake_case_      =   ("BertTokenizer", "BertTokenizerFast")



	def __init__(   self		,      a_=None		,      a_=None		,      **a_	):
			lowerCAmelCase     :    Any						=   None
			if "feature_extractor" in kwargs:
					warnings.warn(
					    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
					    " instead."		,      a_		,      )
					lowerCAmelCase     :    Optional[Any]						=   kwargs.pop("feature_extractor"	)

			lowerCAmelCase     :    int						=   image_processor if image_processor is not None else feature_extractor
			if image_processor is None:
					raise ValueError("You need to specify an `image_processor`."	)
			if tokenizer is None:
					raise ValueError("You need to specify a `tokenizer`."	)

			super().__init__(a_		,      a_	)
			lowerCAmelCase     :    Dict						=   self.image_processor



	def __call__(   self		,      a_=None		,      a_=None		,      a_=None		,      **a_	):

			if text is None and images is None:
					raise ValueError("You have to specify either text or images. Both cannot be none."	)

			if text is not None:
					lowerCAmelCase     :    List[str]						=   self.tokenizer(a_		,      return_tensors=a_		,      **a_	)

			if images is not None:
					lowerCAmelCase     :    List[Any]						=   self.image_processor(a_		,      return_tensors=a_		,      **a_	)

			if text is not None and images is not None:
					lowerCAmelCase     :    str						=   image_features.pixel_values
					return encoding
			elif text is not None:
					return encoding
			else:
					return BatchEncoding(data=dict(**a_	)		,      tensor_type=a_	)



	def   _lowerCamelCase    (   self		,      *a_		,      **a_	):
			return self.tokenizer.batch_decode(*a_		,      **a_	)



	def   _lowerCamelCase    (   self		,      *a_		,      **a_	):
			return self.tokenizer.decode(*a_		,      **a_	)



	@property
	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    str						=   self.tokenizer.model_input_names
			lowerCAmelCase     :    List[Any]						=   self.image_processor.model_input_names
			return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)





	@property
	def   _lowerCamelCase    (   self	):
			warnings.warn(
			    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,      a_		,      )
			return self.image_processor_class



