"""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			   = 2


class     UpperCAmelCase						:



    """simple docstring"""

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






    def __eq__(   self    ,			_UpperCAmelCase							):
       return self.indices == other.indices






    def __getitem__(   self    ,			_UpperCAmelCase							):
       if idx < len(self.symbols							):
          return self.symbols[idx]
       return self.unk_word






    def __len__(   self							):
       return len(self.symbols							)






    def __contains__(   self    ,			_UpperCAmelCase							):
       return sym in self.indices






    @classmethod
    def _snake_case			(   cls    ,			_UpperCAmelCase							):
       lowercase__:   str					=      cls()
       d.add_from_file(_UpperCAmelCase							)
       return d






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=1    ,			_UpperCAmelCase=False							):
       if word in self.indices and not overwrite:
          lowercase__:   str					=      self.indices[word]
          lowercase__:   str					=      self.count[idx] + n
          return idx
       else:
          lowercase__:   Tuple					=      len(self.symbols							)
          lowercase__:   List[str]					=      idx
          self.symbols.append(_UpperCAmelCase							)
          self.count.append(_UpperCAmelCase							)
          return idx






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       return 0






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

       lowercase__:   Any					=      f.readlines()
       lowercase__:   Dict					=      self._load_meta(_UpperCAmelCase							)

       for line in lines[indices_start_line:]:
          try:
             lowercase__, lowercase__:   Dict					=      line.rstrip().rsplit(''' '''    ,			1							)
             if field == "#fairseq:overwrite":
                lowercase__:   Union[str, Any]					=      True
                lowercase__, lowercase__:   Optional[int]					=      line.rsplit(''' '''    ,			1							)
             else:
                lowercase__:   Dict					=      False
             lowercase__:   Union[str, Any]					=      int(_UpperCAmelCase							)
             lowercase__:   List[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(_UpperCAmelCase							)							)
             self.add_symbol(_UpperCAmelCase    ,			n=_UpperCAmelCase    ,			overwrite=_UpperCAmelCase							)
          except ValueError:
             raise ValueError('''Incorrect dictionary format, expected \'<token> <cnt> [flags]\''''							)



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  str:
   # (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}
   lowercase__:   Tuple					=      dict((re.sub(R'''@@$'''       ,    ''''''       ,    __UpperCAmelCase ), v) if k.endswith('''@@''' ) else (re.sub(R'''$'''       ,    '''</w>'''       ,    __UpperCAmelCase ), v) for k, v in d.items() )
   lowercase__:   Optional[int]					=      '''<s> <pad> </s> <unk>'''.split()
   # restore the special tokens
   for k in keep_keys:
      del da[F"""{k}</w>"""]
      lowercase__:   List[str]					=      d[k]  # restore
   return da



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  Union[str, Any]:
   # prep
   if not os.path.exists(__UpperCAmelCase ):
      raise ValueError(F"""path {biogpt_checkpoint_path} does not exist!""" )
   os.makedirs(__UpperCAmelCase       ,    exist_ok=__UpperCAmelCase )
   print(F"""Writing results to {pytorch_dump_folder_path}""" )

   # handle various types of models

   lowercase__:   Any					=      os.path.join(__UpperCAmelCase       ,    '''checkpoint.pt''' )
   if not os.path.isfile(__UpperCAmelCase ):
      raise ValueError(F"""path to the file {checkpoint_file} does not exist!""" )
   lowercase__:   List[str]					=      torch.load(__UpperCAmelCase       ,    map_location='''cpu''' )

   lowercase__:   List[str]					=      chkpt['''cfg''']['''model''']

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

   # merges_file (bpecodes)
   lowercase__:   List[str]					=      os.path.join(__UpperCAmelCase       ,    '''bpecodes''' )
   if not os.path.isfile(__UpperCAmelCase ):
      raise ValueError(F"""path to the file {bpecodes_file} does not exist!""" )

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

   # model config
   lowercase__:   Dict					=      os.path.join(__UpperCAmelCase       ,    '''config.json''' )

   lowercase__:   Union[str, Any]					=      {
       '''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''': 0.0_2,
       '''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(__UpperCAmelCase       ,    '''w'''       ,    encoding='''utf-8''' ) as f:
      f.write(json.dumps(__UpperCAmelCase       ,    ensure_ascii=__UpperCAmelCase       ,    indent=__UpperCAmelCase ) )

   # tokenizer config
   lowercase__:   str					=      os.path.join(__UpperCAmelCase       ,    __UpperCAmelCase )

   lowercase__:   Any					=      {
       '''bos_token''': '''<s>''',
       '''eos_token''': '''</s>''',
       '''model_max_length''': 1_0_2_4,
       '''pad_token''': '''<pad>''',
       '''special_tokens_map_file''': None,
       '''tokenizer_class''': '''BioGptTokenizer''',
       '''unk_token''': '''<unk>''',
   }

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

   # model
   lowercase__:   Optional[int]					=      chkpt['''model''']

   # remove unneeded keys
   lowercase__:   Optional[Any]					=      [
       '''decoder.version''',
   ]
   for k in ignore_keys:
      model_state_dict.pop(__UpperCAmelCase       ,    __UpperCAmelCase )

   lowercase__:   Any					=      list(model_state_dict.keys() )
   for layer_name in layer_names:
      if layer_name.endswith('''output_projection.weight''' ):
         lowercase__:   Optional[int]					=      model_state_dict.pop(__UpperCAmelCase )
      else:
         lowercase__:   List[str]					=      model_state_dict.pop(__UpperCAmelCase )

   lowercase__:   List[Any]					=      BioGptConfig.from_pretrained(__UpperCAmelCase )
   lowercase__:   Any					=      BioGptForCausalLM(__UpperCAmelCase )

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

   # save
   lowercase__:   Union[str, Any]					=      os.path.join(__UpperCAmelCase       ,    __UpperCAmelCase )
   print(F"""Generating {pytorch_weights_dump_path}""" )
   torch.save(__UpperCAmelCase       ,    __UpperCAmelCase )

   print('''Conversion is done!''' )


if __name__ == "__main__":
       __A			   = 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			   = parser.parse_args()
       convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)

"""simple docstring"""






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__A			   = logging.get_logger(__name__)

__A			   = {
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}


class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Dict						    = "deta"
    _UpperCAmelCase	:Any						    = {
        "hidden_size": "d_model",
        "num_attention_heads": "encoder_attention_heads",
    }






    def __init__(   self    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=900    ,			_UpperCAmelCase=2048    ,			_UpperCAmelCase=6    ,			_UpperCAmelCase=2048    ,			_UpperCAmelCase=8    ,			_UpperCAmelCase=6    ,			_UpperCAmelCase=1024    ,			_UpperCAmelCase=8    ,			_UpperCAmelCase=0.0    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase="relu"    ,			_UpperCAmelCase=256    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=0.0    ,			_UpperCAmelCase=0.0    ,			_UpperCAmelCase=0.02    ,			_UpperCAmelCase=1.0    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=False    ,			_UpperCAmelCase="sine"    ,			_UpperCAmelCase=5    ,			_UpperCAmelCase=4    ,			_UpperCAmelCase=4    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=300    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=1    ,			_UpperCAmelCase=5    ,			_UpperCAmelCase=2    ,			_UpperCAmelCase=1    ,			_UpperCAmelCase=1    ,			_UpperCAmelCase=5    ,			_UpperCAmelCase=2    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=0.25    ,			**_UpperCAmelCase    ,			):
       if backbone_config is None:
          logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''							)
          lowercase__:   str					=      CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4''']							)
       else:
          if isinstance(_UpperCAmelCase    ,			_UpperCAmelCase							):
             lowercase__:   Tuple					=      backbone_config.pop('''model_type'''							)
             lowercase__:   str					=      CONFIG_MAPPING[backbone_model_type]
             lowercase__:   Optional[int]					=      config_class.from_dict(_UpperCAmelCase							)

       lowercase__:   int					=      backbone_config
       lowercase__:   Any					=      num_queries
       lowercase__:   List[str]					=      max_position_embeddings
       lowercase__:   Optional[Any]					=      d_model
       lowercase__:   List[Any]					=      encoder_ffn_dim
       lowercase__:   Tuple					=      encoder_layers
       lowercase__:   Dict					=      encoder_attention_heads
       lowercase__:   Any					=      decoder_ffn_dim
       lowercase__:   Union[str, Any]					=      decoder_layers
       lowercase__:   List[Any]					=      decoder_attention_heads
       lowercase__:   int					=      dropout
       lowercase__:   List[str]					=      attention_dropout
       lowercase__:   Tuple					=      activation_dropout
       lowercase__:   Tuple					=      activation_function
       lowercase__:   int					=      init_std
       lowercase__:   Optional[Any]					=      init_xavier_std
       lowercase__:   Optional[Any]					=      encoder_layerdrop
       lowercase__:   Optional[int]					=      auxiliary_loss
       lowercase__:   Union[str, Any]					=      position_embedding_type
       # deformable attributes
       lowercase__:   List[str]					=      num_feature_levels
       lowercase__:   Optional[Any]					=      encoder_n_points
       lowercase__:   int					=      decoder_n_points
       lowercase__:   str					=      two_stage
       lowercase__:   Optional[int]					=      two_stage_num_proposals
       lowercase__:   Tuple					=      with_box_refine
       lowercase__:   str					=      assign_first_stage
       if two_stage is True and with_box_refine is False:
          raise ValueError('''If two_stage is True, with_box_refine must be True.'''							)
       # Hungarian matcher
       lowercase__:   Union[str, Any]					=      class_cost
       lowercase__:   Optional[int]					=      bbox_cost
       lowercase__:   int					=      giou_cost
       # Loss coefficients
       lowercase__:   Optional[int]					=      mask_loss_coefficient
       lowercase__:   List[str]					=      dice_loss_coefficient
       lowercase__:   str					=      bbox_loss_coefficient
       lowercase__:   Union[str, Any]					=      giou_loss_coefficient
       lowercase__:   Optional[int]					=      eos_coefficient
       lowercase__:   str					=      focal_alpha
       super().__init__(is_encoder_decoder=_UpperCAmelCase    ,			**_UpperCAmelCase							)






    @property
    def _snake_case			(   self							):
       return self.encoder_attention_heads






    @property
    def _snake_case			(   self							):
       return self.d_model






    def _snake_case			(   self							):
       lowercase__:   Union[str, Any]					=      copy.deepcopy(self.__dict__							)
       lowercase__:   Dict					=      self.backbone_config.to_dict()
       lowercase__:   Union[str, Any]					=      self.__class__.model_type
       return output
