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,
)


snake_case__				= {
    '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       __magic_name__(  __UpperCAmelCase		)						->    Any:



      '''simple docstring'''

      _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       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase="ybelkada/segment-anything"		)						->    Any:



      '''simple docstring'''

      _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() == 0.5_7_9_8_9_0_2_5_1_1_5_9_6_6_8

            _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_7_1_2_6_0_3_0_9_2_1_9_3_6_0_4

            _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() == 0.8_6_8_6_0_1_5_6_0_5_9_2_6_5_1_4

            # 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() == 0.9_9_3_6_0_4_7_7_9_2_4_3_4_6_9_2


if __name__ == "__main__":
     snake_case__				= argparse.ArgumentParser()
     snake_case__				= ['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',
     )

     snake_case__				= parser.parse_args()

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

import numpy as np
import pytest
from scipy.linalg import eigh

logging.basicConfig(level=logging.INFO, format='%(message)s')
def       __magic_name__(  __UpperCAmelCase		)						->    np.ndarray:



      '''simple docstring'''

      return input_array.reshape((input_array.size, 1)		)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    np.ndarray:



      '''simple docstring'''

      _lowerCamelCase			    =				np.nan
      for i in range(__UpperCAmelCase		):
            _lowerCamelCase			    =				features[:, labels == i]
            _lowerCamelCase			    =				data.mean(1		)
            # Centralize the data of class i
            _lowerCamelCase			    =				data - column_reshape(__UpperCAmelCase		)
            if i > 0:
                  # If covariance_sum is not None
                  covariance_sum += np.dot(__UpperCAmelCase ,						centered_data.T		)
            else:
                  # If covariance_sum is np.nan (i.e. first loop)
                  _lowerCamelCase			    =				np.dot(__UpperCAmelCase ,						centered_data.T		)

      return covariance_sum / features.shape[1]
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    np.ndarray:



      '''simple docstring'''

      _lowerCamelCase			    =				features.mean(1		)
      _lowerCamelCase			    =				np.nan
      for i in range(__UpperCAmelCase		):
            _lowerCamelCase			    =				features[:, labels == i]
            _lowerCamelCase			    =				data.shape[1]
            _lowerCamelCase			    =				data.mean(1		)
            if i > 0:
                  # If covariance_sum is not None
                  covariance_sum += device_data * np.dot(
                      column_reshape(__UpperCAmelCase		) - column_reshape(__UpperCAmelCase		) ,						(column_reshape(__UpperCAmelCase		) - column_reshape(__UpperCAmelCase		)).T ,						)
            else:
                  # If covariance_sum is np.nan (i.e. first loop)
                  _lowerCamelCase			    =				device_data * np.dot(
                      column_reshape(__UpperCAmelCase		) - column_reshape(__UpperCAmelCase		) ,						(column_reshape(__UpperCAmelCase		) - column_reshape(__UpperCAmelCase		)).T ,						)

      return covariance_sum / features.shape[1]
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    np.ndarray:



      '''simple docstring'''

      if features.any():
            _lowerCamelCase			    =				features.mean(1		)
            # Center the dataset
            _lowerCamelCase			    =				features - np.reshape(__UpperCAmelCase ,						(data_mean.size, 1)		)
            _lowerCamelCase			    =				np.dot(__UpperCAmelCase ,						centered_data.T		) / features.shape[1]
            _lowerCamelCase    ,					_lowerCamelCase			    =				np.linalg.eigh(__UpperCAmelCase		)
            # Take all the columns in the reverse order (-1), and then takes only the first
            _lowerCamelCase			    =				eigenvectors[:, ::-1][:, 0:dimensions]
            # Project the database on the new space
            _lowerCamelCase			    =				np.dot(filtered_eigenvectors.T ,						__UpperCAmelCase		)
            logging.info('''Principal Component Analysis computed'''		)

            return projected_data
      else:
            logging.basicConfig(level=logging.ERROR ,						format='''%(message)s''' ,						force=__UpperCAmelCase		)
            logging.error('''Dataset empty'''		)
            raise AssertionError
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    np.ndarray:



      '''simple docstring'''

      assert classes > dimensions

      # Check if features have been already loaded
      if features.any:
            _lowerCamelCase    ,					_lowerCamelCase			    =				eigh(
                covariance_between_classes(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		) ,						covariance_within_classes(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		) ,						)
            _lowerCamelCase			    =				eigenvectors[:, ::-1][:, :dimensions]
            _lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				np.linalg.svd(__UpperCAmelCase		)
            _lowerCamelCase			    =				svd_matrix[:, 0:dimensions]
            _lowerCamelCase			    =				np.dot(filtered_svd_matrix.T ,						__UpperCAmelCase		)
            logging.info('''Linear Discriminant Analysis computed'''		)

            return projected_data
      else:
            logging.basicConfig(level=logging.ERROR ,						format='''%(message)s''' ,						force=__UpperCAmelCase		)
            logging.error('''Dataset empty'''		)
            raise AssertionError
def       __magic_name__(  )						->    None:



      '''simple docstring'''

      _lowerCamelCase			    =				np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]		)
      _lowerCamelCase			    =				np.array([0, 0, 0, 1, 1]		)
      _lowerCamelCase			    =				2
      _lowerCamelCase			    =				2

      # Assert that the function raises an AssertionError if dimensions > classes
      with pytest.raises(__UpperCAmelCase		) as error_info:
            _lowerCamelCase			    =				linear_discriminant_analysis(
                __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)
            if isinstance(__UpperCAmelCase ,						np.ndarray		):
                  raise AssertionError(
                      '''Did not raise AssertionError for dimensions > classes'''		)
            assert error_info.type is AssertionError
def       __magic_name__(  )						->    None:



      '''simple docstring'''

      _lowerCamelCase			    =				np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]		)
      _lowerCamelCase			    =				2
      _lowerCamelCase			    =				np.array([[6.9_2_8_2_0_3_2_3, 8.6_6_0_2_5_4_0_4, 1_0.3_9_2_3_0_4_8_5], [3.0, 3.0, 3.0]]		)

      with pytest.raises(__UpperCAmelCase		) as error_info:
            _lowerCamelCase			    =				principal_component_analysis(__UpperCAmelCase ,						__UpperCAmelCase		)
            if not np.allclose(__UpperCAmelCase ,						__UpperCAmelCase		):
                  raise AssertionError
            assert error_info.type is AssertionError


if __name__ == "__main__":
     import doctest

     doctest.testmod()