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()

_lowercase							:      int      							=2



class  SCREAMING_SNAKE_CASE_ :
		'''simple docstring'''


		def __init__(				self    :					List[Any]      ,	*,  # begin keyword-only arguments
		    SCREAMING_SNAKE_CASE__    :					List[Any]="<s>"      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]="<pad>"      ,	SCREAMING_SNAKE_CASE__    :					List[str]="</s>"      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]="<unk>"      ,	SCREAMING_SNAKE_CASE__    :					int=None      ,	)    ->				List[Any]:
									A						:     Optional[Any]         =bos, unk, pad, eos
									A						:     Dict         =[]
									A						:     Union[str, Any]         =[]
									A						:     Any         ={}
									A						:     int         =self.add_symbol(SCREAMING_SNAKE_CASE__     )
									A						:     Any         =self.add_symbol(SCREAMING_SNAKE_CASE__     )
									A						:     List[Any]         =self.add_symbol(SCREAMING_SNAKE_CASE__     )
									A						:     List[str]         =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    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					List[str]     )    ->				str:
									return self.indices == other.indices







		def __getitem__(				self    :					int      ,	SCREAMING_SNAKE_CASE__    :					List[Any]     )    ->				List[Any]:
									if idx < len(self.symbols     ):
																return self.symbols[idx]
									return self.unk_word







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







		def __contains__(				self    :					Dict      ,	SCREAMING_SNAKE_CASE__    :					List[Any]     )    ->				Tuple:
									return sym in self.indices







		@classmethod
		def 		SCREAMING_SNAKE_CASE_		(				cls    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					int     )    ->				Any:
									A						:     Union[str, Any]         =cls()
									d.add_from_file(SCREAMING_SNAKE_CASE__     )
									return d







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







		def 		SCREAMING_SNAKE_CASE_		(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					List[Any]     )    ->				Optional[Any]:
									return 0







		def 		SCREAMING_SNAKE_CASE_		(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					List[str]     )    ->				Optional[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						:     str         =f.readlines()
									A						:     int         =self._load_meta(SCREAMING_SNAKE_CASE__     )

									for line in lines[indices_start_line:]:
																try:
																							A						:     Optional[int]         =line.rstrip().rsplit(' '      ,	1     )
																							if field == "#fairseq:overwrite":
																														A						:     int         =True
																														A						:     Optional[Any]         =line.rsplit(' '      ,	1     )
																							else:
																														A						:     Any         =False
																							A						:     Tuple         =int(SCREAMING_SNAKE_CASE__     )
																							A						:     Optional[int]         =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 A__  (			lowercase:     Union[str, Any]					)				->		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}
							A						:     int         =dict((re.sub(r'@@$',		'',		lowercase					), v) if k.endswith('@@'					) else (re.sub(r'$',		'</w>',		lowercase					), v) for k, v in d.items()					)
							A						:     int         ='<s> <pad> </s> <unk>'.split()
							# restore the special tokens
							for k in keep_keys:
														del da[F'{k}</w>']
														A						:     List[Any]         =d[k]  # restore
							return da



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

							# handle various types of models

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

							A						:     Any         =chkpt['cfg']['model']

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

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

							A						:     List[str]         =os.path.join(lowercase,		VOCAB_FILES_NAMES['merges_file']					)
							shutil.copyfile(lowercase,		lowercase					)

							# model config
							A						:     Tuple         =os.path.join(lowercase,		'config.json'					)

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

							# tokenizer config
							A						:     int         =os.path.join(lowercase,		lowercase					)

							A						:     List[str]         ={
							    'bos_token': '<s>',
							    'eos_token': '</s>',
							    'model_max_length': 1_024,
							    'pad_token': '<pad>',
							    'special_tokens_map_file': None,
							    'tokenizer_class': 'BioGptTokenizer',
							    'unk_token': '<unk>',
							}

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

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

							# remove unneeded keys
							A						:     List[Any]         =[
							    'decoder.version',
							]
							for k in ignore_keys:
														model_state_dict.pop(lowercase,		lowercase					)

							A						:     str         =list(model_state_dict.keys()					)
							for layer_name in layer_names:
														if layer_name.endswith('output_projection.weight'					):
																					A						:     Union[str, Any]         =model_state_dict.pop(lowercase					)
														else:
																					A						:     List[str]         =model_state_dict.pop(lowercase					)

							A						:     Any         =BioGptConfig.from_pretrained(lowercase					)
							A						:     str         =BioGptForCausalLM(lowercase					)

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

							# save
							A						:     Tuple         =os.path.join(lowercase,		lowercase					)
							print(F'Generating {pytorch_weights_dump_path}'					)
							torch.save(lowercase,		lowercase					)

							print('Conversion is done!'					)


if __name__ == "__main__":
				_lowercase							:      Union[str, 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.'''
				)
				_lowercase							:      List[Any]      							=parser.parse_args()
				convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)







import math



def A__  (			lowercase:     int					)				->		list:
       A						:     Optional[Any]         =[True] * n
       A						:     Tuple         =False
       A						:     List[Any]         =False
       A						:     Dict         =True

       for i in range(3,		int(n**0.5 + 1					),		2					):
              A						:     Dict         =i * 2
              while index < n:
                     A						:     Dict         =False
                     A						:     Dict         =index + i

       A						:     Tuple         =[2]

       for i in range(3,		lowercase,		2					):
              if is_prime[i]:
                     primes.append(lowercase					)

       return primes



def A__  (			lowercase:     int = 999_966_663_333					)				->		int:
       A						:     Optional[int]         =math.floor(math.sqrt(lowercase					)					) + 100
       A						:     Optional[int]         =prime_sieve(lowercase					)

       A						:     Optional[Any]         =0
       A						:     List[Any]         =0
       A						:     Union[str, Any]         =primes[prime_index]

       while (last_prime**2) <= limit:
              A						:     Tuple         =primes[prime_index + 1]

              A						:     Optional[int]         =last_prime**2
              A						:     Tuple         =next_prime**2

              # Get numbers divisible by lps(current)
              A						:     int         =lower_bound + last_prime
              while upper_bound > current <= limit:
                     matches_sum += current
                     current += last_prime

              # Reset the upper_bound
              while (upper_bound - next_prime) > limit:
                     upper_bound -= next_prime

              # Add the numbers divisible by ups(current)
              A						:     List[Any]         =upper_bound - next_prime
              while current > lower_bound:
                     matches_sum += current
                     current -= next_prime

              # Remove the numbers divisible by both ups and lps
              A						:     Any         =0
              while upper_bound > current <= limit:
                     if current <= lower_bound:
                            # Increment the current number
                            current += last_prime * next_prime
                            continue

                     if current > limit:
                            break

                     # Remove twice since it was added by both ups and lps
                     matches_sum -= current * 2

                     # Increment the current number
                     current += last_prime * next_prime

              # Setup for next pair
              A						:     List[str]         =next_prime
              prime_index += 1

       return matches_sum


if __name__ == "__main__":
    print(solution())






