



"""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__              =							{
    '''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_    ( A_   :						Any		):


       '''simple docstring'''





       _lowerCamelCase		:							Tuple            =					{}
       state_dict.pop('''pixel_mean''',							A_		)
       state_dict.pop('''pixel_std''',							A_		)

       _lowerCamelCase		:							List[Any]            =					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		:							Dict            =					key.replace(A_,							A_		)

              if re.match(A_,							A_		):
                     _lowerCamelCase		:							Union[str, Any]            =					int(re.match(A_,							A_		).group(2		)		)
                     if layer_nb == 0:
                            _lowerCamelCase		:							Tuple            =					key.replace('''layers.0''',							'''proj_in'''		)
                     elif layer_nb == 1:
                            _lowerCamelCase		:							Any            =					key.replace('''layers.1''',							'''layers.0'''		)
                     elif layer_nb == 2:
                            _lowerCamelCase		:							Optional[int]            =					key.replace('''layers.2''',							'''proj_out'''		)

              _lowerCamelCase		:							Optional[Any]            =					value

       _lowerCamelCase		:							int            =					model_state_dict[
           '''prompt_encoder.shared_embedding.positional_embedding'''
       ]

       return model_state_dict






def 			snake_case_    ( A_   :						List[Any],							A_   :						int,							A_   :						List[Any],							A_   :						Union[str, Any]="ybelkada/segment-anything"		):


       '''simple docstring'''





       _lowerCamelCase		:							Tuple            =					hf_hub_download(A_,							F'''checkpoints/{model_name}.pth'''		)

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

              _lowerCamelCase		:							List[Any]            =					SamConfig(
                  vision_config=A_,							)
       elif "sam_vit_h" in model_name:
              _lowerCamelCase		:							Dict            =					SamVisionConfig(
                  hidden_size=12_80,							num_hidden_layers=32,							num_attention_heads=16,							global_attn_indexes=[7, 15, 23, 31],							)

              _lowerCamelCase		:							Any            =					SamConfig(
                  vision_config=A_,							)

       _lowerCamelCase		:							List[str]            =					torch.load(A_,							map_location='''cpu'''		)
       _lowerCamelCase		:							int            =					replace_keys(A_		)

       _lowerCamelCase		:							str            =					SamImageProcessor()

       _lowerCamelCase		:							Tuple            =					SamProcessor(image_processor=A_		)
       _lowerCamelCase		:							List[Any]            =					SamModel(A_		)

       hf_model.load_state_dict(A_		)
       _lowerCamelCase		:							Any            =					hf_model.to('''cuda'''		)

       _lowerCamelCase		:							List[str]            =					'''https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'''
       _lowerCamelCase		:							Tuple            =					Image.open(requests.get(A_,							stream=A_		).raw		).convert('''RGB'''		)

       _lowerCamelCase		:							Any            =					[[[4_00, 6_50]]]
       _lowerCamelCase		:							Tuple            =					[[1]]

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

       with torch.no_grad():
              _lowerCamelCase		:							List[str]            =					hf_model(**A_		)
       _lowerCamelCase		:							Tuple            =					output.iou_scores.squeeze()

       if model_name == "sam_vit_h_4b8939":
              assert scores[-1].item() == 0.579890251159668

              _lowerCamelCase		:							int            =					processor(
                  images=np.array(A_		),							input_points=A_,							input_labels=A_,							return_tensors='''pt'''		).to('''cuda'''		)

              with torch.no_grad():
                     _lowerCamelCase		:							str            =					hf_model(**A_		)
              _lowerCamelCase		:							List[Any]            =					output.iou_scores.squeeze()

              assert scores[-1].item() == 0.9712603092193604

              _lowerCamelCase		:							List[str]            =					((75, 2_75, 17_25, 8_50),)

              _lowerCamelCase		:							Tuple            =					processor(images=np.array(A_		),							input_boxes=A_,							return_tensors='''pt'''		).to('''cuda'''		)

              with torch.no_grad():
                     _lowerCamelCase		:							List[Any]            =					hf_model(**A_		)
              _lowerCamelCase		:							List[Any]            =					output.iou_scores.squeeze()

              assert scores[-1].item() == 0.8686015605926514

              # Test with 2 points and 1 image.
              _lowerCamelCase		:							Dict            =					[[[4_00, 6_50], [8_00, 6_50]]]
              _lowerCamelCase		:							Tuple            =					[[1, 1]]

              _lowerCamelCase		:							List[Any]            =					processor(
                  images=np.array(A_		),							input_points=A_,							input_labels=A_,							return_tensors='''pt'''		).to('''cuda'''		)

              with torch.no_grad():
                     _lowerCamelCase		:							Any            =					hf_model(**A_		)
              _lowerCamelCase		:							Optional[Any]            =					output.iou_scores.squeeze()

              assert scores[-1].item() == 0.9936047792434692


if __name__ == "__main__":
   lowerCAmelCase__              =							argparse.ArgumentParser()
   lowerCAmelCase__              =							['''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__              =							parser.parse_args()

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










import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


lowercase    				=   4
lowercase    				=   3




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	pass






def 		__UpperCAmelCase     (	a_):
							for shard in shards:
														for i in range(a_):
																					yield {"i": i, "shard": shard}





def 		__UpperCAmelCase     (	):
							snake_case_           =				int(os.environ['RANK'])
							snake_case_           =				int(os.environ['WORLD_SIZE'])

							snake_case_           =				ArgumentParser()
							parser.add_argument('--streaming'				,	type=a_)
							parser.add_argument('--local_rank'				,	type=a_)
							parser.add_argument('--num_workers'				,	type=a_				,	default=0)
							snake_case_           =				parser.parse_args()
							snake_case_           =				args.streaming
							snake_case_           =				args.num_workers

							snake_case_           =				{'shards': [f'''shard_{shard_idx}''' for shard_idx in range(a_)]}
							snake_case_           =				IterableDataset.from_generator(a_				,	gen_kwargs=a_)
							if not streaming:
														snake_case_           =				Dataset.from_list(list(a_))

							snake_case_           =				split_dataset_by_node(a_				,	rank=a_				,	world_size=a_)
							snake_case_           =				torch.utils.data.DataLoader(a_				,	num_workers=a_)

							snake_case_           =				NUM_SHARDS * NUM_ITEMS_PER_SHARD
							snake_case_           =				full_size // world_size
							expected_local_size += int(rank < (full_size % world_size))

							snake_case_           =				sum(1 for _ in dataloader)
							if local_size != expected_local_size:
														raise FailedTestError(f'''local_size {local_size} != expected_local_size {expected_local_size}''')


if __name__ == "__main__":
	main()





