



"""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)



"""simple docstring"""



import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase=1024							):
							A__				,							A__       :     Any								=			[], []

							A__       :     List[Any]								=			list(zip(lowerCAmelCase			,  lowerCAmelCase							)							)
							A__				,							A__       :     Optional[int]								=			sorted_examples[0]

							def is_too_big(lowerCAmelCase							):
														return tok(lowerCAmelCase			,  return_tensors="""pt"""							).input_ids.shape[1] > max_tokens

							for src, tgt in tqdm(sorted_examples[1:]							):
														A__       :     List[Any]								=			new_src + """ """ + src
														A__       :     List[str]								=			new_tgt + """ """ + tgt
														if is_too_big(lowerCAmelCase							) or is_too_big(lowerCAmelCase							):  # cant fit, finalize example
																					finished_src.append(lowerCAmelCase							)
																					finished_tgt.append(lowerCAmelCase							)
																					A__				,							A__       :     str								=			src, tgt
														else:  # can fit, keep adding
																					A__				,							A__       :     Union[str, Any]								=			cand_src, cand_tgt

    # cleanup
							if new_src:
														assert new_tgt
														finished_src.append(lowerCAmelCase							)
														finished_tgt.append(lowerCAmelCase							)
							return finished_src, finished_tgt

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Optional[int]								=			Path(lowerCAmelCase							)
							save_path.mkdir(exist_ok=lowerCAmelCase							)
							for split in ["train"]:
														A__				,							A__       :     List[Any]								=			data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
														A__       :     Tuple								=			[x.rstrip() for x in Path(lowerCAmelCase							).open().readlines()]
														A__       :     Union[str, Any]								=			[x.rstrip() for x in Path(lowerCAmelCase							).open().readlines()]
														A__				,							A__       :     Tuple								=			pack_examples(lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							)
														print(F'''packed {split} split from {len(lowerCAmelCase							)} examples -> {len(lowerCAmelCase							)}.'''							)
														Path(save_path / F'''{split}.source'''							).open("""w"""							).write("""\n""".join(lowerCAmelCase							)							)
														Path(save_path / F'''{split}.target'''							).open("""w"""							).write("""\n""".join(lowerCAmelCase							)							)
							for split in ["val", "test"]:
														A__				,							A__       :     Dict								=			data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
														shutil.copyfile(lowerCAmelCase			,  save_path / F'''{split}.source'''							)
														shutil.copyfile(lowerCAmelCase			,  save_path / F'''{split}.target'''							)

def  _A(		):
							A__       :     str								=			argparse.ArgumentParser()
							parser.add_argument("""--tok_name"""			,  type=lowerCAmelCase			,  help="""like facebook/bart-large-cnn,t5-base, etc."""							)
							parser.add_argument("""--max_seq_len"""			,  type=lowerCAmelCase			,  default=128							)
							parser.add_argument("""--data_dir"""			,  type=lowerCAmelCase							)
							parser.add_argument("""--save_path"""			,  type=lowerCAmelCase							)
							A__       :     str								=			parser.parse_args()
							A__       :     Optional[Any]								=			AutoTokenizer.from_pretrained(args.tok_name							)
							return pack_data_dir(lowerCAmelCase			,  Path(args.data_dir							)			,  args.max_seq_len			,  args.save_path							)


if __name__ == "__main__":
				packer_cli()





