




'''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()

_lowerCAmelCase  		=      2
class   __A :



   """simple docstring"""







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

   def __eq__(      self				, _lowerCamelCase						)->       int:
      return self.indices == other.indices

   def __getitem__(      self				, _lowerCamelCase						)->       Optional[int]:




      if idx < len(self.symbols						):
         return self.symbols[idx]
      return self.unk_word

   def __len__(      self						)->       Optional[int]:
      return len(self.symbols						)

   def __contains__(      self				, _lowerCamelCase						)->       Dict:
      return sym in self.indices

   @classmethod
   def snake_case_(      cls				, _lowerCamelCase						)->       Optional[int]:
      lowercase__       							=							cls()
      d.add_from_file(__lowerCamelCase						)
      return d

   def snake_case_(      self				, _lowerCamelCase				, _lowerCamelCase=1				, _lowerCamelCase=False						)->       Union[str, Any]:




      if word in self.indices and not overwrite:
         lowercase__       							=							self.indices[word]
         lowercase__       							=							self.count[idx] + n
         return idx
      else:
         lowercase__       							=							len(self.symbols						)
         lowercase__       							=							idx
         self.symbols.append(__lowerCamelCase						)
         self.count.append(__lowerCamelCase						)
         return idx

   def snake_case_(      self				, _lowerCamelCase						)->       str:
      return 0

   def snake_case_(      self				, _lowerCamelCase						)->       List[Any]:




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

      lowercase__       							=							f.readlines()
      lowercase__       							=							self._load_meta(__lowerCamelCase						)

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






def 		_lowerCAmelCase							(   lowercase							: Dict    )			->Optional[Any]:
   """simple docstring"""




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






def 		_lowerCAmelCase							(   lowercase							: Any					,  lowercase							: List[Any]    )			->Any:
   """simple docstring"""




   if not os.path.exists(snake_case_    ):
      raise ValueError(F'''path {biogpt_checkpoint_path} does not exist!'''    )
   os.makedirs(snake_case_					,  exist_ok=snake_case_    )
   print(F'''Writing results to {pytorch_dump_folder_path}'''    )

   # handle various types of models

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

   lowercase__       							=							chkpt['''cfg''']['''model''']

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

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

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

   # model config
   lowercase__       							=							os.path.join(snake_case_					,  '''config.json'''    )

   lowercase__       							=							{
       '''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(snake_case_					,  '''w'''					,  encoding='''utf-8'''    ) as f:
      f.write(json.dumps(snake_case_					,  ensure_ascii=snake_case_					,  indent=snake_case_    )    )

   # tokenizer config
   lowercase__       							=							os.path.join(snake_case_					,  snake_case_    )

   lowercase__       							=							{
       '''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(snake_case_					,  '''w'''					,  encoding='''utf-8'''    ) as f:
      f.write(json.dumps(snake_case_					,  ensure_ascii=snake_case_					,  indent=snake_case_    )    )

   # model
   lowercase__       							=							chkpt['''model''']

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

   lowercase__       							=							list(model_state_dict.keys()    )
   for layer_name in layer_names:
      if layer_name.endswith('''output_projection.weight'''    ):
         lowercase__       							=							model_state_dict.pop(snake_case_    )
      else:
         lowercase__       							=							model_state_dict.pop(snake_case_    )

   lowercase__       							=							BioGptConfig.from_pretrained(snake_case_    )
   lowercase__       							=							BioGptForCausalLM(snake_case_    )

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

   # save
   lowercase__       							=							os.path.join(snake_case_					,  snake_case_    )
   print(F'''Generating {pytorch_weights_dump_path}'''    )
   torch.save(snake_case_					,  snake_case_    )

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


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











"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a_					=     logging.get_logger(__name__)

a_					=     {
    """sail/poolformer_s12""": """https://huggingface.co/sail/poolformer_s12/resolve/main/config.json""",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}






class 						__snake_case   (						SCREAMING_SNAKE_CASE__	):





 """simple docstring"""


 _lowerCamelCase								=	"""poolformer"""

 def __init__(       self   ,		__lowerCamelCase=3   ,		__lowerCamelCase=16   ,		__lowerCamelCase=16   ,		__lowerCamelCase=3   ,		__lowerCamelCase=4.0   ,		__lowerCamelCase=[2, 2, 6, 2]   ,		__lowerCamelCase=[64, 128, 320, 512]   ,		__lowerCamelCase=[7, 3, 3, 3]   ,		__lowerCamelCase=[4, 2, 2, 2]   ,		__lowerCamelCase=[2, 1, 1, 1]   ,		__lowerCamelCase=4   ,		__lowerCamelCase=0.0   ,		__lowerCamelCase="gelu"   ,		__lowerCamelCase=True   ,		__lowerCamelCase=1e-5   ,		__lowerCamelCase=0.0_2   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     __A       :		str							=	num_channels
     __A       :		List[str]							=	patch_size
     __A       :		str							=	stride
     __A       :		Any							=	padding
     __A       :		Any							=	pool_size
     __A       :		Dict							=	hidden_sizes
     __A       :		Optional[Any]							=	mlp_ratio
     __A       :		Any							=	depths
     __A       :		List[str]							=	patch_sizes
     __A       :		Union[str, Any]							=	strides
     __A       :		List[str]							=	num_encoder_blocks
     __A       :		Optional[int]							=	drop_path_rate
     __A       :		Union[str, Any]							=	hidden_act
     __A       :		Optional[Any]							=	use_layer_scale
     __A       :		List[Any]							=	layer_scale_init_value
     __A       :		List[Any]							=	initializer_range
     super().__init__(**__lowerCamelCase    )






class 						__snake_case   (						SCREAMING_SNAKE_CASE__	):





 """simple docstring"""


 _lowerCamelCase								=	version.parse("""1.11"""	)

 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     return OrderedDict(
         [
             ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
         ]    )







 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     return 2e-3




