

'''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 :      Optional[int]					 =     {
    "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					(						_UpperCamelCase						:     Tuple				)      ->			Optional[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    ={}
     state_dict.pop('pixel_mean'							, _UpperCamelCase				)
     state_dict.pop('pixel_std'							, _UpperCamelCase				)

     _SCREAMING_SNAKE_CASE							    =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:
                    _SCREAMING_SNAKE_CASE							    =key.replace(_UpperCamelCase							, _UpperCamelCase				)

          if re.match(_UpperCamelCase							, _UpperCamelCase				):
               _SCREAMING_SNAKE_CASE							    =int(re.match(_UpperCamelCase							, _UpperCamelCase				).group(2				)				)
               if layer_nb == 0:
                    _SCREAMING_SNAKE_CASE							    =key.replace('layers.0'							, 'proj_in'				)
               elif layer_nb == 1:
                    _SCREAMING_SNAKE_CASE							    =key.replace('layers.1'							, 'layers.0'				)
               elif layer_nb == 2:
                    _SCREAMING_SNAKE_CASE							    =key.replace('layers.2'							, 'proj_out'				)

          _SCREAMING_SNAKE_CASE							    =value

     _SCREAMING_SNAKE_CASE							    =model_state_dict[
         'prompt_encoder.shared_embedding.positional_embedding'
     ]

     return model_state_dict


def        _lowerCAmelCase					(						_UpperCamelCase						:     List[Any]							, _UpperCamelCase						:     Dict							, _UpperCamelCase						:     str							, _UpperCamelCase						:     Dict="ybelkada/segment-anything"				)      ->			Tuple:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =hf_hub_download(_UpperCamelCase							, f"checkpoints/{model_name}.pth"				)

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

          _SCREAMING_SNAKE_CASE							    =SamConfig(
              vision_config=_UpperCamelCase							, )
     elif "sam_vit_h" in model_name:
          _SCREAMING_SNAKE_CASE							    =SamVisionConfig(
              hidden_size=12_80							, num_hidden_layers=32							, num_attention_heads=16							, global_attn_indexes=[7, 15, 23, 31]							, )

          _SCREAMING_SNAKE_CASE							    =SamConfig(
              vision_config=_UpperCamelCase							, )

     _SCREAMING_SNAKE_CASE							    =torch.load(_UpperCamelCase							, map_location='cpu'				)
     _SCREAMING_SNAKE_CASE							    =replace_keys(_UpperCamelCase				)

     _SCREAMING_SNAKE_CASE							    =SamImageProcessor()

     _SCREAMING_SNAKE_CASE							    =SamProcessor(image_processor=_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =SamModel(_UpperCamelCase				)

     hf_model.load_state_dict(_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =hf_model.to('cuda'				)

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

     _SCREAMING_SNAKE_CASE							    =[[[4_00, 6_50]]]
     _SCREAMING_SNAKE_CASE							    =[[1]]

     _SCREAMING_SNAKE_CASE							    =processor(images=np.array(_UpperCamelCase				)							, return_tensors='pt'				).to('cuda'				)

     with torch.no_grad():
          _SCREAMING_SNAKE_CASE							    =hf_model(**_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =output.iou_scores.squeeze()

     if model_name == "sam_vit_h_4b8939":
          assert scores[-1].item() == 0.5_79_89_02_51_15_96_68

          _SCREAMING_SNAKE_CASE							    =processor(
              images=np.array(_UpperCamelCase				)							, input_points=_UpperCamelCase							, input_labels=_UpperCamelCase							, return_tensors='pt'				).to('cuda'				)

          with torch.no_grad():
               _SCREAMING_SNAKE_CASE							    =hf_model(**_UpperCamelCase				)
          _SCREAMING_SNAKE_CASE							    =output.iou_scores.squeeze()

          assert scores[-1].item() == 0.97_12_60_30_92_19_36_04

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

          _SCREAMING_SNAKE_CASE							    =processor(images=np.array(_UpperCamelCase				)							, input_boxes=_UpperCamelCase							, return_tensors='pt'				).to('cuda'				)

          with torch.no_grad():
               _SCREAMING_SNAKE_CASE							    =hf_model(**_UpperCamelCase				)
          _SCREAMING_SNAKE_CASE							    =output.iou_scores.squeeze()

          assert scores[-1].item() == 0.86_86_01_56_05_92_65_14

          # Test with 2 points and 1 image.
          _SCREAMING_SNAKE_CASE							    =[[[4_00, 6_50], [8_00, 6_50]]]
          _SCREAMING_SNAKE_CASE							    =[[1, 1]]

          _SCREAMING_SNAKE_CASE							    =processor(
              images=np.array(_UpperCamelCase				)							, input_points=_UpperCamelCase							, input_labels=_UpperCamelCase							, return_tensors='pt'				).to('cuda'				)

          with torch.no_grad():
               _SCREAMING_SNAKE_CASE							    =hf_model(**_UpperCamelCase				)
          _SCREAMING_SNAKE_CASE							    =output.iou_scores.squeeze()

          assert scores[-1].item() == 0.99_36_04_77_92_43_46_92


if __name__ == "__main__":
     lowerCamelCase :      Optional[Any]					 =     argparse.ArgumentParser()
     lowerCamelCase :      int					 =     ["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 :      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"""





# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets



def       lowerCamelCase__		(			__snake_case	)							->		Tuple:


					"""simple docstring"""





					return 1 / (1 + np.exp(-z	))



def       lowerCamelCase__		(			__snake_case,     __snake_case	)							->		List[str]:


					"""simple docstring"""





					return (-y * np.log(__snake_case	) - (1 - y) * np.log(1 - h	)).mean()



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case	)							->		int:


					"""simple docstring"""





					_UpperCamelCase							 =  np.dot(__snake_case,     __snake_case	)
					return np.sum(y * scores - np.log(1 + np.exp(__snake_case	)	)	)



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case=7_00_00	)							->		Union[str, Any]:


					"""simple docstring"""





					_UpperCamelCase							 =  np.zeros(x.shape[1]	)

					for iterations in range(__snake_case	):
										_UpperCamelCase							 =  np.dot(__snake_case,     __snake_case	)
										_UpperCamelCase							 =  sigmoid_function(__snake_case	)
										_UpperCamelCase							 =  np.dot(x.T,     h - y	) / y.size
										_UpperCamelCase							 =  theta - alpha * gradient  # updating the weights
										_UpperCamelCase							 =  np.dot(__snake_case,     __snake_case	)
										_UpperCamelCase							 =  sigmoid_function(__snake_case	)
										_UpperCamelCase							 =  cost_function(__snake_case,     __snake_case	)
										if iterations % 1_00 == 0:
															print(F'''loss: {j} \t'''	)  # printing the loss after every 100 iterations
					return theta


# In[68]:

if __name__ == "__main__":
				_a														=		datasets.load_iris()
				_a														=		iris.data[:, :2]
				_a														=		(iris.target != 0) * 1

				_a														=		0.1
				_a														=		logistic_reg(alpha, x, y, max_iterations=7_0000)
				print("""theta: """, theta)  # printing the theta i.e our weights vector



				def       lowerCamelCase__		(			__snake_case	)							->		Tuple:


									"""simple docstring"""





									return sigmoid_function(
									    np.dot(__snake_case,     __snake_case	)	)  # predicting the value of probability from the logistic regression algorithm

				plt.figure(figsize=(10, 6))
				plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color="""b""", label="""0""")
				plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color="""r""", label="""1""")
				((_a)							,							(_a))														=		(x[:, 0].min(), x[:, 0].max())
				((_a)							,							(_a))														=		(x[:, 1].min(), x[:, 1].max())
				((_a)							,							(_a))														=		np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
				_a														=		np.c_[xxa.ravel(), xxa.ravel()]
				_a														=		predict_prob(grid).reshape(xxa.shape)
				plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="""black""")

				plt.legend()
				plt.show()






