





"""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 					__magic_name__		:






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

              '''simple docstring'''






              lowercase		:str       							=				bos, unk, pad, eos
              lowercase		:Optional[int]       							=				[]
              lowercase		:List[str]       							=				[]
              lowercase		:Any       							=				{}
              lowercase		:str       							=				self.add_symbol(UpperCamelCase__				)
              lowercase		:Optional[Any]       							=				self.add_symbol(UpperCamelCase__				)
              lowercase		:Optional[Any]       							=				self.add_symbol(UpperCamelCase__				)
              lowercase		:str       							=				self.add_symbol(UpperCamelCase__				)
              if extra_special_symbols:
                     for s in extra_special_symbols:
                            self.add_symbol(UpperCamelCase__				)
              lowercase		:Optional[int]       							=				len(self.symbols				)






       def __eq__(   self  :    List[Any]  ,     snake_case__  :    Union[str, Any]				):

              '''simple docstring'''






              return self.indices == other.indices






       def __getitem__(   self  :    List[str]  ,     snake_case__  :    Optional[Any]				):

              '''simple docstring'''






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






       def __len__(   self  :    Optional[Any]				):

              '''simple docstring'''






              return len(self.symbols				)






       def __contains__(   self  :    List[str]  ,     snake_case__  :    List[Any]				):

              '''simple docstring'''






              return sym in self.indices






       @classmethod
       def  __snake_case						(   cls  :    int  ,     snake_case__  :    str				):

              '''simple docstring'''






              lowercase		:int       							=				cls()
              d.add_from_file(UpperCamelCase__				)
              return d






       def  __snake_case						(   self  :    Dict  ,     snake_case__  :    Union[str, Any]  ,     snake_case__  :    Optional[int]=1  ,     snake_case__  :    Optional[Any]=False				):

              '''simple docstring'''






              if word in self.indices and not overwrite:
                     lowercase		:Optional[Any]       							=				self.indices[word]
                     lowercase		:Any       							=				self.count[idx] + n
                     return idx
              else:
                     lowercase		:Optional[Any]       							=				len(self.symbols				)
                     lowercase		:List[str]       							=				idx
                     self.symbols.append(UpperCamelCase__				)
                     self.count.append(UpperCamelCase__				)
                     return idx






       def  __snake_case						(   self  :    Optional[Any]  ,     snake_case__  :    Tuple				):

              '''simple docstring'''






              return 0







       def  __snake_case						(   self  :    str  ,     snake_case__  :    int				):

              '''simple docstring'''






              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		:List[Any]       							=				f.readlines()
              lowercase		:Any       							=				self._load_meta(UpperCamelCase__				)

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







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






def       lowerCamelCase					(a_						:Tuple				,				a_						:int)     ->							Dict:







       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		:Tuple       							=				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		:Dict       							=				torch.load(__UpperCamelCase				,				map_location='''cpu''')

       lowercase		:Union[str, Any]       							=				chkpt['''cfg''']['''model''']

       # dicts
       lowercase		:Dict       							=				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		:Union[str, Any]       							=				Dictionary.load(__UpperCamelCase)
       lowercase		:Any       							=				rewrite_dict_keys(src_dict.indices)
       lowercase		:Tuple       							=				len(__UpperCamelCase)
       lowercase		:List[str]       							=				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[Any]       							=				os.path.join(__UpperCamelCase				,				'''bpecodes''')
       if not os.path.isfile(__UpperCamelCase):
              raise ValueError(F"""path to the file {bpecodes_file} does not exist!""")

       lowercase		:Optional[int]       							=				os.path.join(__UpperCamelCase				,				VOCAB_FILES_NAMES['''merges_file'''])
       shutil.copyfile(__UpperCamelCase				,				__UpperCamelCase)

       # model config
       lowercase		:Optional[Any]       							=				os.path.join(__UpperCamelCase				,				'''config.json''')

       lowercase		:Optional[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''': 0.02,
           '''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(__UpperCamelCase				,				'''w'''				,				encoding='''utf-8''') as f:
              f.write(json.dumps(__UpperCamelCase				,				ensure_ascii=__UpperCamelCase				,				indent=__UpperCamelCase))

       # tokenizer config
       lowercase		:Tuple       							=				os.path.join(__UpperCamelCase				,				__UpperCamelCase)

       lowercase		:List[Any]       							=				{
           '''bos_token''': '''<s>''',
           '''eos_token''': '''</s>''',
           '''model_max_length''': 1024,
           '''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		:Union[str, Any]       							=				chkpt['''model''']

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

       lowercase		:Optional[int]       							=				list(model_state_dict.keys())
       for layer_name in layer_names:
              if layer_name.endswith('''output_projection.weight'''):
                     lowercase		:int       							=				model_state_dict.pop(__UpperCamelCase)
              else:
                     lowercase		:Tuple       							=				model_state_dict.pop(__UpperCamelCase)

       lowercase		:Optional[int]       							=				BioGptConfig.from_pretrained(__UpperCamelCase)
       lowercase		:Dict       							=				BioGptForCausalLM(__UpperCamelCase)

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

       # save
       lowercase		:Dict       							=				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)





"""simple docstring"""



from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat






def       lowerCamelCase					()     ->							tuple[list[int], int]:
       lowercase		:Any       							=				[randint(-1000				,				1000) for i in range(10)]
       lowercase		:Any       							=				randint(-5000				,				5000)
       return (arr, r)


UpperCAmelCase      		=			make_dataset()






def       lowerCamelCase					(a_						:list[int]				,				a_						:int)     ->							tuple[int, ...]:
       for triplet in permutations(a_				,				3):
              if sum(a_) == target:
                     return tuple(sorted(a_))
       return (0, 0, 0)






def       lowerCamelCase					(a_						:list[int]				,				a_						:int)     ->							tuple[int, int, int]:
       arr.sort()
       lowercase		:Union[str, Any]       							=				len(a_)
       for i in range(n - 1):
              lowercase							,  lowercase		:Union[str, Any]       							=				i + 1, n - 1
              while left < right:
                     if arr[i] + arr[left] + arr[right] == target:
                            return (arr[i], arr[left], arr[right])
                     elif arr[i] + arr[left] + arr[right] < target:
                            left += 1
                     elif arr[i] + arr[left] + arr[right] > target:
                            right -= 1
       return (0, 0, 0)






def       lowerCamelCase					()     ->							tuple[float, float]:
       lowercase		:int       							=				'''
from __main__ import dataset, triplet_sum1, triplet_sum2
'''
       lowercase		:Optional[Any]       							=				'''
triplet_sum1(*dataset)
'''
       lowercase		:Union[str, Any]       							=				'''
triplet_sum2(*dataset)
'''
       lowercase		:Dict       							=				repeat(setup=a_				,				stmt=a_				,				repeat=5				,				number=1_0000)
       lowercase		:Optional[int]       							=				repeat(setup=a_				,				stmt=a_				,				repeat=5				,				number=1_0000)
       return (min(a_), min(a_))


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 UpperCAmelCase      		=			solution_times()
 print(F"""The time for naive implementation is {times[0]}.""")
 print(F"""The time for optimized implementation is {times[1]}.""")

