'''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_     :						Tuple    							={
    '''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 snake_case_      (					__snake_case  :							int)			->					Union[str, Any]:
   lowerCAmelCase_			=      {}
   state_dict.pop('''pixel_mean'''	,   __snake_case)
   state_dict.pop('''pixel_std'''	,   __snake_case)

   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(__snake_case	,   __snake_case)

      if re.match(__snake_case	,   __snake_case):
         lowerCAmelCase_			=      int(re.match(__snake_case	,   __snake_case).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 snake_case_      (					__snake_case  :							Optional[int]	,   __snake_case  :							Any	,   __snake_case  :							List[Any]	,   __snake_case  :							str="ybelkada/segment-anything")			->					List[str]:
   lowerCAmelCase_			=      hf_hub_download(__snake_case	,   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=__snake_case	,   )
   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=__snake_case	,   )

   lowerCAmelCase_			=      torch.load(__snake_case	,   map_location='''cpu''')
   lowerCAmelCase_			=      replace_keys(__snake_case)

   lowerCAmelCase_			=      SamImageProcessor()

   lowerCAmelCase_			=      SamProcessor(image_processor=__snake_case)
   lowerCAmelCase_			=      SamModel(__snake_case)

   hf_model.load_state_dict(__snake_case)
   lowerCAmelCase_			=      hf_model.to('''cuda''')

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

   lowerCAmelCase_			=      [[[400, 650]]]
   lowerCAmelCase_			=      [[1]]

   lowerCAmelCase_			=      processor(images=np.array(__snake_case)	,   return_tensors='''pt''').to('''cuda''')

   with torch.no_grad():
      lowerCAmelCase_			=      hf_model(**__snake_case)
   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(__snake_case)	,   input_points=__snake_case	,   input_labels=__snake_case	,   return_tensors='''pt''').to('''cuda''')

      with torch.no_grad():
         lowerCAmelCase_			=      hf_model(**__snake_case)
      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(__snake_case)	,   input_boxes=__snake_case	,   return_tensors='''pt''').to('''cuda''')

      with torch.no_grad():
         lowerCAmelCase_			=      hf_model(**__snake_case)
      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(__snake_case)	,   input_points=__snake_case	,   input_labels=__snake_case	,   return_tensors='''pt''').to('''cuda''')

      with torch.no_grad():
         lowerCAmelCase_			=      hf_model(**__snake_case)
      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__":
 A_     :						Tuple    							=argparse.ArgumentParser()
 A_     :						Dict    							=['''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_     :						str    							=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 gc
import threading
import time

import psutil
import torch


class     __UpperCAmelCase       :





  def __init__(	self ):
     lowerCAmelCase_			=      psutil.Process()
     lowerCAmelCase_			=      False





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      -1

     while True:
        lowerCAmelCase_			=      max(self.process.memory_info().rss    ,     self.cpu_memory_peak )

        # can't sleep or will not catch the peak right (this comment is here on purpose)
        if not self.peak_monitoring:
           break





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      True
     lowerCAmelCase_			=      threading.Thread(target=self.peak_monitor )
     lowerCAmelCase_			=      True
     self.thread.start()







  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      False
     self.thread.join()
     return self.cpu_memory_peak






A_     :						List[str]    							=PeakCPUMemory()



def snake_case_      (					)			->					Tuple:
   # Time
   lowerCAmelCase_			=      {'''time''': time.time()}

   gc.collect()
   torch.cuda.empty_cache()

   # CPU mem
   lowerCAmelCase_			=      psutil.Process().memory_info().rss
   cpu_peak_tracker.start()

   # GPU mem
   for i in range(torch.cuda.device_count()):
      lowerCAmelCase_			=      torch.cuda.memory_allocated(__snake_case)
   torch.cuda.reset_peak_memory_stats()

   return measures



def snake_case_      (					__snake_case  :							Any)			->					List[str]:
   # Time
   lowerCAmelCase_			=      {'''time''': time.time() - start_measures['''time''']}

   gc.collect()
   torch.cuda.empty_cache()

   # CPU mem
   lowerCAmelCase_			=      (psutil.Process().memory_info().rss - start_measures['''cpu''']) / 2**20
   lowerCAmelCase_			=      (cpu_peak_tracker.stop() - start_measures['''cpu''']) / 2**20

   # GPU mem
   for i in range(torch.cuda.device_count()):
      lowerCAmelCase_			=      (torch.cuda.memory_allocated(__snake_case) - start_measures[str(__snake_case)]) / 2**20
      lowerCAmelCase_			=      (torch.cuda.max_memory_allocated(__snake_case) - start_measures[str(__snake_case)]) / 2**20

   return measures






def snake_case_      (					__snake_case  :							Dict	,   __snake_case  :							Optional[int])			->					Dict:
   print(F'''{description}:''')
   print(F'''- Time: {measures['time']:.2f}s''')
   for i in range(torch.cuda.device_count()):
      print(F'''- GPU {i} allocated: {measures[str(__snake_case)]:.2f}MiB''')
      lowerCAmelCase_			=      measures[F'''{i}-peak''']
      print(F'''- GPU {i} peak: {peak:.2f}MiB''')
   print(F'''- CPU RAM allocated: {measures['cpu']:.2f}MiB''')
   print(F'''- CPU RAM peak: {measures['cpu-peak']:.2f}MiB''')





