





"""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()

a_					=     2






class 						__snake_case   :





 """simple docstring"""


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



     '''simple docstring'''




     __A					,			__A					,			__A					,			__A       :		Optional[Any]							=	bos, unk, pad, eos
     __A       :		Optional[Any]							=	[]
     __A       :		Any							=	[]
     __A       :		str							=	{}
     __A       :		List[Any]							=	self.add_symbol(__lowerCamelCase    )
     __A       :		List[Any]							=	self.add_symbol(__lowerCamelCase    )
     __A       :		Any							=	self.add_symbol(__lowerCamelCase    )
     __A       :		Dict							=	self.add_symbol(__lowerCamelCase    )
     if extra_special_symbols:
         for s in extra_special_symbols:
             self.add_symbol(__lowerCamelCase    )
     __A       :		Dict							=	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    UpperCamelCase__(       cls   ,		__lowerCamelCase    ):



     '''simple docstring'''




     __A       :		Optional[Any]							=	cls()
     d.add_from_file(__lowerCamelCase    )
     return d

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase=1   ,		__lowerCamelCase=False    ):



     '''simple docstring'''




     if word in self.indices and not overwrite:
         __A       :		Union[str, Any]							=	self.indices[word]
         __A       :		str							=	self.count[idx] + n
         return idx
     else:
         __A       :		Any							=	len(self.symbols    )
         __A       :		List[Any]							=	idx
         self.symbols.append(__lowerCamelCase    )
         self.count.append(__lowerCamelCase    )
         return idx

 def    UpperCamelCase__(       self   ,		__lowerCamelCase    ):



     '''simple docstring'''




     return 0







 def    UpperCamelCase__(       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

     __A       :		Optional[Any]							=	f.readlines()
     __A       :		Union[str, Any]							=	self._load_meta(__lowerCamelCase    )

     for line in lines[indices_start_line:]:
         try:
             __A					,			__A       :		Optional[int]							=	line.rstrip().rsplit(''' '''   ,		1    )
             if field == "#fairseq:overwrite":
                 __A       :		Optional[int]							=	True
                 __A					,			__A       :		Any							=	line.rsplit(''' '''   ,		1    )
             else:
                 __A       :		Optional[int]							=	False
             __A       :		List[Any]							=	int(__lowerCamelCase    )
             __A       :		Dict							=	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 						__lowercase	(  snake_case_    :    Dict		)  ->Optional[Any]:



    '''simple docstring'''







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







def 						__lowercase	(  snake_case_    :    Any							,snake_case_    :    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

    __A       :		Any							=	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!"""		)
    __A       :		Any							=	torch.load(snake_case_							,map_location='''cpu'''		)

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

    # dicts
    __A       :		List[Any]							=	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!"""		)
    __A       :		List[Any]							=	Dictionary.load(snake_case_		)
    __A       :		Optional[int]							=	rewrite_dict_keys(src_dict.indices		)
    __A       :		List[Any]							=	len(snake_case_		)
    __A       :		Tuple							=	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)
    __A       :		List[Any]							=	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!"""		)

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

    # model config
    __A       :		Optional[int]							=	os.path.join(snake_case_							,'''config.json'''		)

    __A       :		Optional[int]							=	{
        '''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
    __A       :		List[Any]							=	os.path.join(snake_case_							,snake_case_		)

    __A       :		Optional[int]							=	{
        '''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(snake_case_							,'''w'''							,encoding='''utf-8'''		) as f:
        f.write(json.dumps(snake_case_							,ensure_ascii=snake_case_							,indent=snake_case_		)		)

    # model
    __A       :		Union[str, Any]							=	chkpt['''model''']

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

    __A       :		int							=	list(model_state_dict.keys()		)
    for layer_name in layer_names:
        if layer_name.endswith('''output_projection.weight'''		):
            __A       :		Any							=	model_state_dict.pop(snake_case_		)
        else:
            __A       :		Optional[int]							=	model_state_dict.pop(snake_case_		)

    __A       :		List[Any]							=	BioGptConfig.from_pretrained(snake_case_		)
    __A       :		Any							=	BioGptForCausalLM(snake_case_		)

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

    # save
    __A       :		Union[str, Any]							=	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__":
       a_					=     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."""
       )
       a_					=     parser.parse_args()
       convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)











"""simple docstring"""



from collections import defaultdict
from math import gcd







def 						__lowercase	(  snake_case_    :    int = 1500000		)  ->int:



    '''simple docstring'''







    __A       :		defaultdict							=	defaultdict(snake_case_		)
    __A       :		Any							=	2
    while 2 * euclid_m * (euclid_m + 1) <= limit:
        for euclid_n in range((euclid_m % 2) + 1							,snake_case_							,2		):
            if gcd(snake_case_							,snake_case_		) > 1:
                continue
            __A       :		List[Any]							=	2 * euclid_m * (euclid_m + euclid_n)
            for perimeter in range(snake_case_							,limit + 1							,snake_case_		):
                frequencies[perimeter] += 1
        euclid_m += 1
    return sum(1 for frequency in frequencies.values() if frequency == 1		)


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




