'''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()

__UpperCAmelCase     							=2




class     a__      :






     def __init__( self		:		int  ,				*,  # begin keyword-only arguments
         a		:		Dict="<s>"  ,				a		:		Dict="<pad>"  ,				a		:		int="</s>"  ,				a		:		List[Any]="<unk>"  ,				a		:		int=None  ,				):





         """simple docstring"""



         __lowerCamelCase	,  __lowerCamelCase	,  __lowerCamelCase	,  __lowerCamelCase		=  bos, unk, pad, eos
         __lowerCamelCase		=  []
         __lowerCamelCase		=  []
         __lowerCamelCase		=  {}
         __lowerCamelCase		=  self.add_symbol(a    )
         __lowerCamelCase		=  self.add_symbol(a    )
         __lowerCamelCase		=  self.add_symbol(a    )
         __lowerCamelCase		=  self.add_symbol(a    )
         if extra_special_symbols:
             for s in extra_special_symbols:
                 self.add_symbol(a    )
         __lowerCamelCase		=  len(self.symbols    )






     def __eq__( self		:		Dict  ,				a		:		Tuple    ):





         """simple docstring"""



         return self.indices == other.indices






     def __getitem__( self		:		List[Any]  ,				a		:		Tuple    ):





         """simple docstring"""



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






     def __len__( self		:		List[str]    ):





         """simple docstring"""



         return len(self.symbols    )






     def __contains__( self		:		Optional[int]  ,				a		:		List[str]    ):





         """simple docstring"""



         return sym in self.indices






     @classmethod
     def    SCREAMING_SNAKE_CASE__   ( cls		:		str  ,				a		:		Dict    ):





         """simple docstring"""



         __lowerCamelCase		=  cls()
         d.add_from_file(a    )
         return d






     def    SCREAMING_SNAKE_CASE__   ( self		:		Any  ,				a		:		Tuple  ,				a		:		Any=1  ,				a		:		List[str]=False    ):





         """simple docstring"""



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






     def    SCREAMING_SNAKE_CASE__   ( self		:		List[str]  ,				a		:		Any    ):





         """simple docstring"""



         return 0






     def    SCREAMING_SNAKE_CASE__   ( self		:		Any  ,				a		:		str    ):





         """simple docstring"""



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

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

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






def __lowerCAmelCase (							UpperCamelCase__				)	->    Tuple:
    # (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}
    __lowerCamelCase		=  dict((re.sub(r'''@@$'''				,  ''''''				,  UpperCamelCase__				), v) if k.endswith('''@@'''				) else (re.sub(r'''$'''				,  '''</w>'''				,  UpperCamelCase__				), v) for k, v in d.items()				)
    __lowerCamelCase		=  '''<s> <pad> </s> <unk>'''.split()
    # restore the special tokens
    for k in keep_keys:
        del da[f"""{k}</w>"""]
        __lowerCamelCase		=  d[k]  # restore
    return da






def __lowerCAmelCase (							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

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

    __lowerCamelCase		=  chkpt['''cfg''']['''model''']

    # dicts
    __lowerCamelCase		=  os.path.join(UpperCamelCase__				,  '''dict.txt'''				)
    if not os.path.isfile(UpperCamelCase__				):
        raise ValueError(f"""path to the file {dict_file} does not exist!"""				)
    __lowerCamelCase		=  Dictionary.load(UpperCamelCase__				)
    __lowerCamelCase		=  rewrite_dict_keys(src_dict.indices				)
    __lowerCamelCase		=  len(UpperCamelCase__				)
    __lowerCamelCase		=  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)
    __lowerCamelCase		=  os.path.join(UpperCamelCase__				,  '''bpecodes'''				)
    if not os.path.isfile(UpperCamelCase__				):
        raise ValueError(f"""path to the file {bpecodes_file} does not exist!"""				)

    __lowerCamelCase		=  os.path.join(UpperCamelCase__				,  VOCAB_FILES_NAMES['''merges_file''']				)
    shutil.copyfile(UpperCamelCase__				,  UpperCamelCase__				)

    # model config
    __lowerCamelCase		=  os.path.join(UpperCamelCase__				,  '''config.json'''				)

    __lowerCamelCase		=  {
        '''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
    __lowerCamelCase		=  os.path.join(UpperCamelCase__				,  UpperCamelCase__				)

    __lowerCamelCase		=  {
        '''bos_token''': '''<s>''',
        '''eos_token''': '''</s>''',
        '''model_max_length''': 10_24,
        '''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
    __lowerCamelCase		=  chkpt['''model''']

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

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

    __lowerCamelCase		=  BioGptConfig.from_pretrained(UpperCamelCase__				)
    __lowerCamelCase		=  BioGptForCausalLM(UpperCamelCase__				)

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

    # save
    __lowerCamelCase		=  os.path.join(UpperCamelCase__				,  UpperCamelCase__				)
    print(f"""Generating {pytorch_weights_dump_path}"""				)
    torch.save(UpperCamelCase__				,  UpperCamelCase__				)

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


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







def 		A_				(     _lowerCAmelCase = 50     )						-> int:
 UpperCamelCase	:					List[Any]       	=   [[0] * 3 for _ in range(length + 1     )]

 for row_length in range(length + 1     ):
  for tile_length in range(2							,   5     ):
   for tile_start in range(row_length - tile_length + 1     ):
    different_colour_ways_number[row_length][tile_length - 2] += (
        different_colour_ways_number[row_length - tile_start - tile_length][
            tile_length - 2
        ]
        + 1
    )

 return sum(different_colour_ways_number[length]     )


if __name__ == "__main__":
    print(f"""{solution() = }""")
