






from __future__ import annotations

from scipy.special import comb  # type: ignore






class 						UpperCAmelCase__ :







		def __init__(					self       ,A__		):
						_A	:				Tuple   =      list_of_points
						# Degree determines the flexibility of the curve.
						# Degree = 1 will produce a straight line.
						_A	:				List[Any]   =      len(__UpperCAmelCase		) - 1







		def  A__      (					self       ,A__		):
						assert 0 <= t <= 1, "Time t must be between 0 and 1."
						_A	:				Tuple   =      []
						for i in range(len(self.list_of_points		)		):
										# basis function for each i
										output_values.append(
										    comb(self.degree       ,__UpperCAmelCase		) * ((1 - t) ** (self.degree - i)) * (t**i)		)
						# the basis must sum up to 1 for it to produce a valid Bezier curve.
						assert round(sum(__UpperCAmelCase		)       ,5		) == 1
						return output_values







		def  A__      (					self       ,A__		):
						assert 0 <= t <= 1, "Time t must be between 0 and 1."

						_A	:				List[Any]   =      self.basis_function(__UpperCAmelCase		)
						_A	:				Optional[Any]   =      0.0
						_A	:				Optional[int]   =      0.0
						for i in range(len(self.list_of_points		)		):
										# For all points, sum up the product of i-th basis function and i-th point.
										x += basis_function[i] * self.list_of_points[i][0]
										y += basis_function[i] * self.list_of_points[i][1]
						return (x, y)







		def  A__      (					self       ,A__ = 0.01		):
						from matplotlib import pyplot as plt  # type: ignore

						_A	:				Tuple   =      []  # x coordinates of points to plot
						_A	:				Dict   =      []  # y coordinates of points to plot

						_A	:				Optional[Any]   =      0.0
						while t <= 1:
										_A	:				Optional[Any]   =      self.bezier_curve_function(__UpperCAmelCase		)
										to_plot_x.append(value[0]		)
										to_plot_y.append(value[1]		)
										t += step_size

						_A	:				Any   =      [i[0] for i in self.list_of_points]
						_A	:				Any   =      [i[1] for i in self.list_of_points]

						plt.plot(
						    __UpperCAmelCase       ,__UpperCAmelCase       ,color='''blue'''       ,label='''Curve of Degree ''' + str(self.degree		)       ,)
						plt.scatter(__UpperCAmelCase       ,__UpperCAmelCase       ,color='''red'''       ,label='''Control Points'''		)
						plt.legend()
						plt.show()


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
	BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
	BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3








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_								=       {
    """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__      (		_UpperCamelCase      :			Optional[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 		a__      (		_UpperCamelCase      :			Tuple			,_UpperCamelCase      :			Dict			,_UpperCamelCase      :			Any			,_UpperCamelCase      :			Optional[int]="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=10_24			,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=12_80			,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    				=     [[[4_00, 6_50]]]
   __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_890_251_159_668

      __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() == 0.9_712_603_092_193_604

      __lowerCamelCase    				=     ((75, 2_75, 17_25, 8_50),)

      __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() == 0.8_686_015_605_926_514

      # Test with 2 points and 1 image.
      __lowerCamelCase    				=     [[[4_00, 6_50], [8_00, 6_50]]]
      __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() == 0.9_936_047_792_434_692


if __name__ == "__main__":
  a_								=       argparse.ArgumentParser()
  a_								=       ["""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_								=       parser.parse_args()

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






