


'''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				:					Any    						=			{
    '''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__	(    lowerCamelCase					:      Union[str, Any]       ):
       _A					:     Optional[int]     = {}
       state_dict.pop('pixel_mean'  ,lowerCamelCase       )
       state_dict.pop('pixel_std'  ,lowerCamelCase       )

       _A					:     Dict     = 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:
                            _A					:     Optional[Any]     = key.replace(lowerCamelCase  ,lowerCamelCase       )

              if re.match(lowerCamelCase  ,lowerCamelCase       ):
                     _A					:     List[str]     = int(re.match(lowerCamelCase  ,lowerCamelCase       ).group(2       )       )
                     if layer_nb == 0:
                            _A					:     Optional[Any]     = key.replace('layers.0'  ,'proj_in'       )
                     elif layer_nb == 1:
                            _A					:     List[str]     = key.replace('layers.1'  ,'layers.0'       )
                     elif layer_nb == 2:
                            _A					:     Optional[Any]     = key.replace('layers.2'  ,'proj_out'       )

              _A					:     Any     = value

       _A					:     Optional[int]     = model_state_dict[
           'prompt_encoder.shared_embedding.positional_embedding'
       ]

       return model_state_dict






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[Any]  ,lowerCamelCase					:      int  ,lowerCamelCase					:      Any  ,lowerCamelCase					:      Optional[Any]="ybelkada/segment-anything"       ):
       _A					:     int     = hf_hub_download(lowerCamelCase  ,F'checkpoints/{model_name}.pth'       )

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

              _A					:     Union[str, Any]     = SamConfig(
                  vision_config=lowerCamelCase  ,)
       elif "sam_vit_h" in model_name:
              _A					:     int     = SamVisionConfig(
                  hidden_size=1280  ,num_hidden_layers=32  ,num_attention_heads=16  ,global_attn_indexes=[7, 15, 23, 31]  ,)

              _A					:     Any     = SamConfig(
                  vision_config=lowerCamelCase  ,)

       _A					:     Dict     = torch.load(lowerCamelCase  ,map_location='cpu'       )
       _A					:     Any     = replace_keys(lowerCamelCase       )

       _A					:     Union[str, Any]     = SamImageProcessor()

       _A					:     List[str]     = SamProcessor(image_processor=lowerCamelCase       )
       _A					:     List[str]     = SamModel(lowerCamelCase       )

       hf_model.load_state_dict(lowerCamelCase       )
       _A					:     str     = hf_model.to('cuda'       )

       _A					:     Optional[int]     = 'https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'
       _A					:     int     = Image.open(requests.get(lowerCamelCase  ,stream=lowerCamelCase       ).raw       ).convert('RGB'       )

       _A					:     Union[str, Any]     = [[[400, 650]]]
       _A					:     Optional[int]     = [[1]]

       _A					:     Optional[int]     = processor(images=np.array(lowerCamelCase       )  ,return_tensors='pt'       ).to('cuda'       )

       with torch.no_grad():
              _A					:     str     = hf_model(**lowerCamelCase       )
       _A					:     str     = output.iou_scores.squeeze()

       if model_name == "sam_vit_h_4b8939":
              assert scores[-1].item() == 0.579_890_251_159_668

              _A					:     Optional[int]     = processor(
                  images=np.array(lowerCamelCase       )  ,input_points=lowerCamelCase  ,input_labels=lowerCamelCase  ,return_tensors='pt'       ).to('cuda'       )

              with torch.no_grad():
                     _A					:     Tuple     = hf_model(**lowerCamelCase       )
              _A					:     Any     = output.iou_scores.squeeze()

              assert scores[-1].item() == 0.9_712_603_092_193_604

              _A					:     int     = ((75, 275, 1725, 850),)

              _A					:     Union[str, Any]     = processor(images=np.array(lowerCamelCase       )  ,input_boxes=lowerCamelCase  ,return_tensors='pt'       ).to('cuda'       )

              with torch.no_grad():
                     _A					:     Optional[Any]     = hf_model(**lowerCamelCase       )
              _A					:     int     = output.iou_scores.squeeze()

              assert scores[-1].item() == 0.8_686_015_605_926_514

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

              _A					:     Optional[int]     = processor(
                  images=np.array(lowerCamelCase       )  ,input_points=lowerCamelCase  ,input_labels=lowerCamelCase  ,return_tensors='pt'       ).to('cuda'       )

              with torch.no_grad():
                     _A					:     Any     = hf_model(**lowerCamelCase       )
              _A					:     Tuple     = output.iou_scores.squeeze()

              assert scores[-1].item() == 0.9_936_047_792_434_692


if __name__ == "__main__":
   A				:					str    						=			argparse.ArgumentParser()
   A				:					Any    						=			['''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[int]    						=			parser.parse_args()

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










'''simple docstring'''







A				:					Dict    						=			'''Alexander Joslin'''

import operator as op

from .stack import Stack






def 				lowerCAmelCase__	(    lowerCamelCase					:      str       ):
       _A					:     Dict     = {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}

       _A					:     Stack[int]     = Stack()
       _A					:     Stack[str]     = Stack()

       for i in equation:
              if i.isdigit():
                     # RULE 1
                     operand_stack.push(int(lowerCamelCase       )       )
              elif i in operators:
                     # RULE 2
                     operator_stack.push(lowerCamelCase       )
              elif i == ")":
                     # RULE 4
                     _A					:     List[str]     = operator_stack.peek()
                     operator_stack.pop()
                     _A					:     Dict     = operand_stack.peek()
                     operand_stack.pop()
                     _A					:     Optional[int]     = operand_stack.peek()
                     operand_stack.pop()

                     _A					:     Dict     = operators[opr](lowerCamelCase  ,lowerCamelCase       )
                     operand_stack.push(lowerCamelCase       )

    # RULE 5
       return operand_stack.peek()


if __name__ == "__main__":
   A				:					Dict    						=			'''(5 + ((4 * 2) * (2 + 3)))'''
   # answer = 45
   print(f"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")






