"""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()

SCREAMING_SNAKE_CASE__   =					2



class 					lowerCAmelCase_      :



    """simple docstring"""

    def __init__(			self						,      *,  # begin keyword-only arguments
        lowerCAmelCase="<s>"						,      lowerCAmelCase="<pad>"						,      lowerCAmelCase="</s>"						,      lowerCAmelCase="<unk>"						,      lowerCAmelCase=None						,      ):






          """simple docstring"""

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

    def __eq__(			self						,      lowerCAmelCase      ):






          """simple docstring"""

          return self.indices == other.indices

    def __getitem__(			self						,      lowerCAmelCase      ):






          """simple docstring"""

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

    def __len__(			self      ):






          """simple docstring"""

          return len(self.symbols      )

    def __contains__(			self						,      lowerCAmelCase      ):






          """simple docstring"""

          return sym in self.indices

    @classmethod
    def 						snake_case	(			cls						,      lowerCAmelCase      ):






          """simple docstring"""

          snake_case        =     cls()
          d.add_from_file(lowerCAmelCase      )
          return d

    def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase=1						,      lowerCAmelCase=False      ):






          """simple docstring"""

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

    def 						snake_case	(			self						,      lowerCAmelCase      ):






          """simple docstring"""

          return 0

    def 						snake_case	(			self						,      lowerCAmelCase      ):






          """simple docstring"""

          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

          snake_case        =     f.readlines()
          snake_case        =     self._load_meta(lowerCAmelCase      )

          for line in lines[indices_start_line:]:
                try:
                      snake_case   ,snake_case        =     line.rstrip().rsplit(' '						,      1      )
                      if field == "#fairseq:overwrite":
                            snake_case        =     True
                            snake_case   ,snake_case        =     line.rsplit(' '						,      1      )
                      else:
                            snake_case        =     False
                      snake_case        =     int(lowerCAmelCase      )
                      snake_case        =     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__      (     _UpperCamelCase    :			Optional[Any]   )   ->			List[Any]:





      """simple docstring"""






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





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Optional[Any]	,     _UpperCamelCase    :			Tuple   )   ->			int:





      """simple docstring"""






      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

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

      snake_case        =     chkpt['cfg']['model']

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

      snake_case        =     os.path.join(_UpperCamelCase	,     VOCAB_FILES_NAMES['merges_file']   )
      shutil.copyfile(_UpperCamelCase	,     _UpperCamelCase   )

      # model config
      snake_case        =     os.path.join(_UpperCamelCase	,     'config.json'   )

      snake_case        =     {
          '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-1_2,
          '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
      snake_case        =     os.path.join(_UpperCamelCase	,     _UpperCamelCase   )

      snake_case        =     {
          '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
      snake_case        =     chkpt['model']

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

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

      snake_case        =     BioGptConfig.from_pretrained(_UpperCamelCase   )
      snake_case        =     BioGptForCausalLM(_UpperCamelCase   )

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

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

      print('Conversion is done!'   )


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



"""simple docstring"""
def 				lowerCAmelCase__      (     _UpperCamelCase    :			str   )   ->			bool:





						"""simple docstring"""






						if not all(x.isalpha() for x in string   ):
												raise ValueError('String must only contain alphabetic characters.'   )

						snake_case        =     sorted(string.lower()   )
						return len(_UpperCamelCase   ) == len(set(_UpperCamelCase   )   )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   =					input("Enter a string ").strip()

					SCREAMING_SNAKE_CASE__   =					is_isogram(input_str)
					print(f"""{input_str} is {'an' if isogram else 'not an'} isogram.""")


