





"""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()

lowerCAmelCase_			=							2



class 	lowerCAmelCase					:






       def __init__(   self    ,  *,  # begin keyword-only arguments
           a__="<s>"    ,  a__="<pad>"    ,  a__="</s>"    ,  a__="<unk>"    ,  a__=None    ,  ):
              _UpperCAmelCase    ,							_UpperCAmelCase    ,							_UpperCAmelCase    ,							_UpperCAmelCase     =     bos, unk, pad, eos
              _UpperCAmelCase     =     []
              _UpperCAmelCase     =     []
              _UpperCAmelCase     =     {}
              _UpperCAmelCase     =     self.add_symbol(a__     )
              _UpperCAmelCase     =     self.add_symbol(a__     )
              _UpperCAmelCase     =     self.add_symbol(a__     )
              _UpperCAmelCase     =     self.add_symbol(a__     )
              if extra_special_symbols:
                     for s in extra_special_symbols:
                            self.add_symbol(a__     )
              _UpperCAmelCase     =     len(self.symbols     )






       def __eq__(   self    ,  a__     ):
              return self.indices == other.indices






       def __getitem__(   self    ,  a__     ):
              if idx < len(self.symbols     ):
                     return self.symbols[idx]
              return self.unk_word






       def __len__(   self     ):
              return len(self.symbols     )






       def __contains__(   self    ,  a__     ):
              return sym in self.indices






       @classmethod
       def 							__A (   cls    ,  a__     ):
              _UpperCAmelCase     =     cls()
              d.add_from_file(a__     )
              return d






       def 							__A (   self    ,  a__    ,  a__=1    ,  a__=False     ):
              if word in self.indices and not overwrite:
                     _UpperCAmelCase     =     self.indices[word]
                     _UpperCAmelCase     =     self.count[idx] + n
                     return idx
              else:
                     _UpperCAmelCase     =     len(self.symbols     )
                     _UpperCAmelCase     =     idx
                     self.symbols.append(a__     )
                     self.count.append(a__     )
                     return idx






       def 							__A (   self    ,  a__     ):
              return 0






       def 							__A (   self    ,  a__     ):
              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

              _UpperCAmelCase     =     f.readlines()
              _UpperCAmelCase     =     self._load_meta(a__     )

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






       """simple docstring"""




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

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       Union[str, Any]:






       """simple docstring"""




       if not os.path.exists(SCREAMING_SNAKE_CASE							):
              raise ValueError(F"""path {biogpt_checkpoint_path} does not exist!"""							)
       os.makedirs(SCREAMING_SNAKE_CASE,exist_ok=SCREAMING_SNAKE_CASE							)
       print(F"""Writing results to {pytorch_dump_folder_path}"""							)

       # handle various types of models

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

       _UpperCAmelCase     =     chkpt['cfg']['model']

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

       # merges_file (bpecodes)
       _UpperCAmelCase     =     os.path.join(SCREAMING_SNAKE_CASE,'bpecodes'							)
       if not os.path.isfile(SCREAMING_SNAKE_CASE							):
              raise ValueError(F"""path to the file {bpecodes_file} does not exist!"""							)

       _UpperCAmelCase     =     os.path.join(SCREAMING_SNAKE_CASE,VOCAB_FILES_NAMES['merges_file']							)
       shutil.copyfile(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)

       # model config
       _UpperCAmelCase     =     os.path.join(SCREAMING_SNAKE_CASE,'config.json'							)

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

       # tokenizer config
       _UpperCAmelCase     =     os.path.join(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)

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

       # model
       _UpperCAmelCase     =     chkpt['model']

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

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

       _UpperCAmelCase     =     BioGptConfig.from_pretrained(SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     BioGptForCausalLM(SCREAMING_SNAKE_CASE							)

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

       # save
       _UpperCAmelCase     =     os.path.join(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)
       print(F"""Generating {pytorch_weights_dump_path}"""							)
       torch.save(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)

       print('Conversion is done!'							)


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













"""simple docstring"""






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
    fastaa_timesteps,
    smartaa_timesteps,
    smartaa_timesteps,
    smartaaa_timesteps,
    smartaaa_timesteps,
    superaa_timesteps,
    superaa_timesteps,
    superaaa_timesteps,
)



@dataclass
class 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  42
       lowerCAmelCase__						 =  42
       lowerCAmelCase__						 =  42


try:
    if not (is_transformers_available() and is_torch_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
    from .pipeline_if import IFPipeline
    from .pipeline_if_imgaimg import IFImgaImgPipeline
    from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
    from .pipeline_if_inpainting import IFInpaintingPipeline
    from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
    from .pipeline_if_superresolution import IFSuperResolutionPipeline
    from .safety_checker import IFSafetyChecker
    from .watermark import IFWatermarker






