



'''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'''







import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

A				:					int    						=			2


class 			__lowerCamelCase			:
    """simple docstring"""


    def __init__(  self      :							List[str]				,							*,  # begin keyword-only arguments
        SCREAMING_SNAKE_CASE      :							Optional[Any]="<s>"				,							SCREAMING_SNAKE_CASE      :							int="<pad>"				,							SCREAMING_SNAKE_CASE      :							Optional[Any]="</s>"				,							SCREAMING_SNAKE_CASE      :							Tuple="<unk>"				,							SCREAMING_SNAKE_CASE      :							List[Any]=None				,							):
           _A  ,			_A  ,			_A  ,			_A					:     Any     = bos, unk, pad, eos
           _A					:     Optional[Any]     = []
           _A					:     Optional[Any]     = []
           _A					:     Optional[int]     = {}
           _A					:     Dict     = self.add_symbol(SCREAMING_SNAKE_CASE)
           _A					:     List[str]     = self.add_symbol(SCREAMING_SNAKE_CASE)
           _A					:     str     = self.add_symbol(SCREAMING_SNAKE_CASE)
           _A					:     Any     = self.add_symbol(SCREAMING_SNAKE_CASE)
           if extra_special_symbols:
                  for s in extra_special_symbols:
                         self.add_symbol(SCREAMING_SNAKE_CASE)
           _A					:     List[str]     = len(self.symbols)


    def __eq__(  self      :							int				,							SCREAMING_SNAKE_CASE      :							Optional[Any]):
           return self.indices == other.indices


    def __getitem__(  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							Tuple):
           if idx < len(self.symbols):
                  return self.symbols[idx]
           return self.unk_word


    def __len__(  self      :							Union[str, Any]):
           return len(self.symbols)


    def __contains__(  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							List[str]):
           return sym in self.indices


    @classmethod
    def        A   (  cls      :							Dict				,							SCREAMING_SNAKE_CASE      :							Optional[Any]):
           _A					:     Any     = cls()
           d.add_from_file(SCREAMING_SNAKE_CASE)
           return d


    def        A   (  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							Tuple				,							SCREAMING_SNAKE_CASE      :							Optional[int]=1				,							SCREAMING_SNAKE_CASE      :							int=False):
           if word in self.indices and not overwrite:
                  _A					:     str     = self.indices[word]
                  _A					:     List[str]     = self.count[idx] + n
                  return idx
           else:
                  _A					:     Optional[Any]     = len(self.symbols)
                  _A					:     Union[str, Any]     = idx
                  self.symbols.append(SCREAMING_SNAKE_CASE)
                  self.count.append(SCREAMING_SNAKE_CASE)
                  return idx


    def        A   (  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							Optional[int]):
           return 0


    def        A   (  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							List[Any]):
           if isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  try:
                         with open(SCREAMING_SNAKE_CASE				,							'r'				,							encoding='utf-8') as fd:
                                self.add_from_file(SCREAMING_SNAKE_CASE)
                  except FileNotFoundError as fnfe:
                         raise fnfe
                  except UnicodeError:
                         raise Exception('Incorrect encoding detected in {}, please rebuild the dataset'.format(SCREAMING_SNAKE_CASE))
                  return

           _A					:     Union[str, Any]     = f.readlines()
           _A					:     Any     = self._load_meta(SCREAMING_SNAKE_CASE)

           for line in lines[indices_start_line:]:
                  try:
                         _A  ,			_A					:     List[str]     = line.rstrip().rsplit(' '				,							1)
                         if field == "#fairseq:overwrite":
                                _A					:     int     = True
                                _A  ,			_A					:     List[str]     = line.rsplit(' '				,							1)
                         else:
                                _A					:     Union[str, Any]     = False
                         _A					:     List[str]     = int(SCREAMING_SNAKE_CASE)
                         _A					:     Optional[Any]     = line
                         if word in self and not overwrite:
                                raise RuntimeError(
                                    'Duplicate word found when loading Dictionary: \'{}\'. '
                                    'Duplicate words can overwrite earlier ones by adding the '
                                    '#fairseq:overwrite flag at the end of the corresponding row '
                                    'in the dictionary file. If using the Camembert model, please '
                                    'download an updated copy of the model file.'.format(SCREAMING_SNAKE_CASE))
                         self.add_symbol(SCREAMING_SNAKE_CASE				,							n=SCREAMING_SNAKE_CASE				,							overwrite=SCREAMING_SNAKE_CASE)
                  except ValueError:
                         raise ValueError('Incorrect dictionary format, expected \'<token> <cnt> [flags]\'')






def 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[int]       ):
       # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
       # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
       _A					:     Union[str, Any]     = dict((re.sub(R'@@$'  ,''  ,lowerCamelCase       ), v) if k.endswith('@@'       ) else (re.sub(R'$'  ,'</w>'  ,lowerCamelCase       ), v) for k, v in d.items()       )
       _A					:     Optional[Any]     = '<s> <pad> </s> <unk>'.split()
       # restore the special tokens
       for k in keep_keys:
              del da[F'{k}</w>']
              _A					:     str     = d[k]  # restore
       return da






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[str]  ,lowerCamelCase					:      List[str]       ):
       # prep
       if not os.path.exists(lowerCamelCase       ):
              raise ValueError(F'path {biogpt_checkpoint_path} does not exist!'       )
       os.makedirs(lowerCamelCase  ,exist_ok=lowerCamelCase       )
       print(F'Writing results to {pytorch_dump_folder_path}'       )

       # handle various types of models

       _A					:     Dict     = os.path.join(lowerCamelCase  ,'checkpoint.pt'       )
       if not os.path.isfile(lowerCamelCase       ):
              raise ValueError(F'path to the file {checkpoint_file} does not exist!'       )
       _A					:     int     = torch.load(lowerCamelCase  ,map_location='cpu'       )

       _A					:     Dict     = chkpt['cfg']['model']

       # dicts
       _A					:     Any     = os.path.join(lowerCamelCase  ,'dict.txt'       )
       if not os.path.isfile(lowerCamelCase       ):
              raise ValueError(F'path to the file {dict_file} does not exist!'       )
       _A					:     Any     = Dictionary.load(lowerCamelCase       )
       _A					:     Optional[int]     = rewrite_dict_keys(src_dict.indices       )
       _A					:     List[Any]     = len(lowerCamelCase       )
       _A					:     str     = os.path.join(lowerCamelCase  ,VOCAB_FILES_NAMES['vocab_file']       )
       print(F'Generating {src_vocab_file} of {src_vocab_size} records'       )
       with open(lowerCamelCase  ,'w'  ,encoding='utf-8'       ) as f:
              f.write(json.dumps(lowerCamelCase  ,ensure_ascii=lowerCamelCase  ,indent=lowerCamelCase       )       )

       # merges_file (bpecodes)
       _A					:     Optional[int]     = os.path.join(lowerCamelCase  ,'bpecodes'       )
       if not os.path.isfile(lowerCamelCase       ):
              raise ValueError(F'path to the file {bpecodes_file} does not exist!'       )

       _A					:     Dict     = os.path.join(lowerCamelCase  ,VOCAB_FILES_NAMES['merges_file']       )
       shutil.copyfile(lowerCamelCase  ,lowerCamelCase       )

       # model config
       _A					:     str     = os.path.join(lowerCamelCase  ,'config.json'       )

       _A					:     int     = {
           'activation_dropout': args['activation_dropout'],
           'architectures': ['BioGptForCausalLM'],
           'attention_probs_dropout_prob': args['attention_dropout'],
           'bos_token_id': 0,
           'eos_token_id': 2,
           'hidden_act': args['activation_fn'],
           'hidden_dropout_prob': args['dropout'],
           'hidden_size': args['decoder_embed_dim'],
           'initializer_range': 0.02,
           'intermediate_size': args['decoder_ffn_embed_dim'],
           'layer_norm_eps': 1E-12,
           'layerdrop': args['decoder_layerdrop'],
           'max_position_embeddings': args['max_target_positions'],
           'model_type': 'biogpt',
           'num_attention_heads': args['decoder_attention_heads'],
           'num_hidden_layers': args['decoder_layers'],
           'pad_token_id': 1,
           'scale_embedding': not args['no_scale_embedding'],
           'tie_word_embeddings': args['share_decoder_input_output_embed'],
           'vocab_size': src_vocab_size,
       }

       # good hparam defaults to start with

       print(F'Generating {biogpt_model_config_file}'       )
       with open(lowerCamelCase  ,'w'  ,encoding='utf-8'       ) as f:
              f.write(json.dumps(lowerCamelCase  ,ensure_ascii=lowerCamelCase  ,indent=lowerCamelCase       )       )

       # tokenizer config
       _A					:     Union[str, Any]     = os.path.join(lowerCamelCase  ,lowerCamelCase       )

       _A					:     Any     = {
           'bos_token': '<s>',
           'eos_token': '</s>',
           'model_max_length': 1024,
           'pad_token': '<pad>',
           'special_tokens_map_file': None,
           'tokenizer_class': 'BioGptTokenizer',
           'unk_token': '<unk>',
       }

       print(F'Generating {biogpt_tokenizer_config_file}'       )
       with open(lowerCamelCase  ,'w'  ,encoding='utf-8'       ) as f:
              f.write(json.dumps(lowerCamelCase  ,ensure_ascii=lowerCamelCase  ,indent=lowerCamelCase       )       )

       # model
       _A					:     List[Any]     = chkpt['model']

       # remove unneeded keys
       _A					:     int     = [
           'decoder.version',
       ]
       for k in ignore_keys:
              model_state_dict.pop(lowerCamelCase  ,lowerCamelCase       )

       _A					:     Any     = list(model_state_dict.keys()       )
       for layer_name in layer_names:
              if layer_name.endswith('output_projection.weight'       ):
                     _A					:     str     = model_state_dict.pop(lowerCamelCase       )
              else:
                     _A					:     Dict     = model_state_dict.pop(lowerCamelCase       )

       _A					:     Any     = BioGptConfig.from_pretrained(lowerCamelCase       )
       _A					:     Union[str, Any]     = BioGptForCausalLM(lowerCamelCase       )

       # check that it loads ok
       model_new.load_state_dict(lowerCamelCase       )

       # save
       _A					:     Union[str, Any]     = os.path.join(lowerCamelCase  ,lowerCamelCase       )
       print(F'Generating {pytorch_weights_dump_path}'       )
       torch.save(lowerCamelCase  ,lowerCamelCase       )

       print('Conversion is done!'       )


if __name__ == "__main__":
   A				:					Optional[Any]    						=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--biogpt_checkpoint_path''',
       default=None,
       type=str,
       required=True,
       help=(
           '''Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'''
           ''' bpecodes, etc.'''
       ),
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   A				:					int    						=			parser.parse_args()
   convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)






