

'''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()

lowercase__         =      2




class 	A_			:






     '''simple docstring'''



     def __init__(		self			:					Any					,      *,  # begin keyword-only arguments
         lowercase_			:					int="<s>"					,      lowercase_			:					List[str]="<pad>"					,      lowercase_			:					Tuple="</s>"					,      lowercase_			:					Any="<unk>"					,      lowercase_			:					Dict=None					,      )						->     Optional[int]:
      UpperCAmelCase	:     Any						 =			bos, unk, pad, eos
      UpperCAmelCase	:     List[Any]						 =			[]
      UpperCAmelCase	:     Any						 =			[]
      UpperCAmelCase	:     Tuple						 =			{}
      UpperCAmelCase	:     Optional[Any]						 =			self.add_symbol(lowercase_	)
      UpperCAmelCase	:     Optional[int]						 =			self.add_symbol(lowercase_	)
      UpperCAmelCase	:     Dict						 =			self.add_symbol(lowercase_	)
      UpperCAmelCase	:     List[str]						 =			self.add_symbol(lowercase_	)
      if extra_special_symbols:
       for s in extra_special_symbols:
        self.add_symbol(lowercase_	)
      UpperCAmelCase	:     Any						 =			len(self.symbols	)


     def __eq__(		self			:					Optional[int]					,      lowercase_			:					Any	)						->     str:
      return self.indices == other.indices


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


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


     def __contains__(		self			:					Optional[Any]					,      lowercase_			:					Union[str, Any]	)						->     List[str]:
      return sym in self.indices


     @classmethod
     def        UpperCAmelCase_	(		cls			:					Union[str, Any]					,      lowercase_			:					List[str]	)						->     Optional[int]:
      UpperCAmelCase	:     List[Any]						 =			cls()
      d.add_from_file(lowercase_	)
      return d


     def        UpperCAmelCase_	(		self			:					int					,      lowercase_			:					Dict					,      lowercase_			:					str=1					,      lowercase_			:					Optional[Any]=False	)						->     Optional[int]:
      if word in self.indices and not overwrite:
       UpperCAmelCase	:     List[str]						 =			self.indices[word]
       UpperCAmelCase	:     str						 =			self.count[idx] + n
       return idx
      else:
       UpperCAmelCase	:     List[Any]						 =			len(self.symbols	)
       UpperCAmelCase	:     Dict						 =			idx
       self.symbols.append(lowercase_	)
       self.count.append(lowercase_	)
       return idx


     def        UpperCAmelCase_	(		self			:					Dict					,      lowercase_			:					Any	)						->     List[str]:
      return 0




     def        UpperCAmelCase_	(		self			:					List[str]					,      lowercase_			:					Tuple	)						->     int:
      if isinstance(lowercase_					,      lowercase_	):
       try:
        with open(lowercase_					,      'r'					,      encoding='utf-8'	) as fd:
         self.add_from_file(lowercase_	)
       except FileNotFoundError as fnfe:
        raise fnfe
       except UnicodeError:
        raise Exception('Incorrect encoding detected in {}, please rebuild the dataset'.format(lowercase_	)	)
       return

      UpperCAmelCase	:     List[Any]						 =			f.readlines()
      UpperCAmelCase	:     str						 =			self._load_meta(lowercase_	)

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




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




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 # prep
 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

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

 UpperCAmelCase	:     Dict						 =			chkpt['cfg']['model']

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

 UpperCAmelCase	:     Union[str, Any]						 =			os.path.join(UpperCAmelCase_  ,	VOCAB_FILES_NAMES['merges_file']     )
 shutil.copyfile(UpperCAmelCase_  ,	UpperCAmelCase_     )

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

 UpperCAmelCase	:     List[str]						 =			{
     '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
 UpperCAmelCase	:     Optional[Any]						 =			os.path.join(UpperCAmelCase_  ,	UpperCAmelCase_     )

 UpperCAmelCase	:     Dict						 =			{
     'bos_token': '<s>',
     'eos_token': '</s>',
     'model_max_length': 10_24,
     '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
 UpperCAmelCase	:     str						 =			chkpt['model']

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

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

 UpperCAmelCase	:     Dict						 =			BioGptConfig.from_pretrained(UpperCAmelCase_     )
 UpperCAmelCase	:     str						 =			BioGptForCausalLM(UpperCAmelCase_     )

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

 # save
 UpperCAmelCase	:     Tuple						 =			os.path.join(UpperCAmelCase_  ,	UpperCAmelCase_     )
 print(F"""Generating {pytorch_weights_dump_path}"""     )
 torch.save(UpperCAmelCase_  ,	UpperCAmelCase_     )

 print('Conversion is done!'     )


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







'''simple docstring'''

import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 	A_			(    _snake_case							):






     '''simple docstring'''



     def __init__(		self			:					List[Any]					,      lowercase_			:					VQModel					,      lowercase_			:					UNetaDModel					,      lowercase_			:					DDIMScheduler	)						->     int:
      super().__init__()
      self.register_modules(vqvae=lowercase_					,      unet=lowercase_					,      scheduler=lowercase_	)




     @torch.no_grad()
     def __call__(		self			:					str					,      lowercase_			:					int = 1					,      lowercase_			:					Optional[Union[torch.Generator, List[torch.Generator]]] = None					,      lowercase_			:					float = 0.0					,      lowercase_			:					int = 50					,      lowercase_			:					Optional[str] = "pil"					,      lowercase_			:					bool = True					,      **lowercase_			:					Optional[Any]					,      )						->     Union[Tuple, ImagePipelineOutput]:
      UpperCAmelCase	:     str						 =			randn_tensor(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,      generator=lowercase_					,      )
      UpperCAmelCase	:     Any						 =			latents.to(self.device	)

      # scale the initial noise by the standard deviation required by the scheduler
      UpperCAmelCase	:     Optional[Any]						 =			latents * self.scheduler.init_noise_sigma

      self.scheduler.set_timesteps(lowercase_	)

      # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
      UpperCAmelCase	:     Optional[int]						 =			'eta' in set(inspect.signature(self.scheduler.step	).parameters.keys()	)

      UpperCAmelCase	:     Tuple						 =			{}
      if accepts_eta:
       UpperCAmelCase	:     List[str]						 =			eta

      for t in self.progress_bar(self.scheduler.timesteps	):
       UpperCAmelCase	:     Dict						 =			self.scheduler.scale_model_input(lowercase_					,      lowercase_	)
       # predict the noise residual
       UpperCAmelCase	:     Dict						 =			self.unet(lowercase_					,      lowercase_	).sample
       # compute the previous noisy sample x_t -> x_t-1
       UpperCAmelCase	:     Dict						 =			self.scheduler.step(lowercase_					,      lowercase_					,      lowercase_					,      **lowercase_	).prev_sample

      # decode the image latents with the VAE
      UpperCAmelCase	:     Any						 =			self.vqvae.decode(lowercase_	).sample

      UpperCAmelCase	:     Union[str, Any]						 =			(image / 2 + 0.5).clamp(0					,      1	)
      UpperCAmelCase	:     Optional[Any]						 =			image.cpu().permute(0					,      2					,      3					,      1	).numpy()
      if output_type == "pil":
       UpperCAmelCase	:     Tuple						 =			self.numpy_to_pil(lowercase_	)

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=lowercase_	)




