from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


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 .multicontrolnet import MultiControlNetModel
 from .pipeline_controlnet import StableDiffusionControlNetPipeline
 from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
 from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
 from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline







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  ,  A  ,  A  ,  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  ,  A						:     Optional[int]         =line.rstrip().rsplit(' '      ,	1     )
                       if field == "#fairseq:overwrite":
                              A						:     int         =True
                              A  ,  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)






