





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()

_SCREAMING_SNAKE_CASE					=    2






class 							__lowercase       :







	'''simple docstring'''

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







				'''simple docstring'''





				__lowercase						,		__lowercase						,		__lowercase						,		__lowercase										=							bos, unk, pad, eos
				__lowercase										=							[]
				__lowercase										=							[]
				__lowercase										=							{}
				__lowercase										=							self.add_symbol(_lowerCamelCase )
				__lowercase										=							self.add_symbol(_lowerCamelCase )
				__lowercase										=							self.add_symbol(_lowerCamelCase )
				__lowercase										=							self.add_symbol(_lowerCamelCase )
				if extra_special_symbols:
							for s in extra_special_symbols:
										self.add_symbol(_lowerCamelCase )
				__lowercase										=							len(self.symbols )


	def __eq__(self     ,_lowerCamelCase )							->							List[Any]:







				'''simple docstring'''





				return self.indices == other.indices


	def __getitem__(self     ,_lowerCamelCase )							->							List[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     ,_lowerCamelCase )							->							str:







				'''simple docstring'''





				return sym in self.indices


	@classmethod
	def 						_UpperCAmelCase     (cls     ,_lowerCamelCase )							->							List[Any]:







				'''simple docstring'''





				__lowercase										=							cls()
				d.add_from_file(_lowerCamelCase )
				return d


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase=1     ,_lowerCamelCase=False )							->							List[Any]:







				'''simple docstring'''





				if word in self.indices and not overwrite:
							__lowercase										=							self.indices[word]
							__lowercase										=							self.count[idx] + n
							return idx
				else:
							__lowercase										=							len(self.symbols )
							__lowercase										=							idx
							self.symbols.append(_lowerCamelCase )
							self.count.append(_lowerCamelCase )
							return idx


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							Optional[Any]:







				'''simple docstring'''





				return 0


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							Optional[int]:







				'''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

				__lowercase										=							f.readlines()
				__lowercase										=							self._load_meta(_lowerCamelCase )

				for line in lines[indices_start_line:]:
							try:
										__lowercase						,		__lowercase										=							line.rstrip().rsplit(''' '''     ,1 )
										if field == "#fairseq:overwrite":
													__lowercase										=							True
													__lowercase						,		__lowercase										=							line.rsplit(''' '''     ,1 )
										else:
													__lowercase										=							False
										__lowercase										=							int(_lowerCamelCase )
										__lowercase										=							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    _lowerCAmelCase						(				lowerCamelCase_			:  Tuple				):
			# (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}
			__lowercase										=							dict((re.sub(r'''@@$'''		,       ''''''		,       lowerCamelCase_				), v) if k.endswith('''@@'''				) else (re.sub(r'''$'''		,       '''</w>'''		,       lowerCamelCase_				), v) for k, v in d.items()				)
			__lowercase										=							'''<s> <pad> </s> <unk>'''.split()
			# restore the special tokens
			for k in keep_keys:
						del da[f"{k}</w>"]
						__lowercase										=							d[k]  # restore
			return da




def    _lowerCAmelCase						(				lowerCamelCase_			:  List[str]		,       lowerCamelCase_			:  Optional[Any]				):
			# prep
			if not os.path.exists(lowerCamelCase_				):
						raise ValueError(f"path {biogpt_checkpoint_path} does not exist!"				)
			os.makedirs(lowerCamelCase_		,       exist_ok=lowerCamelCase_				)
			print(f"Writing results to {pytorch_dump_folder_path}"				)

			# handle various types of models

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

			__lowercase										=							chkpt['''cfg''']['''model''']

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

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

			__lowercase										=							os.path.join(lowerCamelCase_		,       VOCAB_FILES_NAMES['''merges_file''']				)
			shutil.copyfile(lowerCamelCase_		,       lowerCamelCase_				)

			# model config
			__lowercase										=							os.path.join(lowerCamelCase_		,       '''config.json'''				)

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

			# tokenizer config
			__lowercase										=							os.path.join(lowerCamelCase_		,       lowerCamelCase_				)

			__lowercase										=							{
			    '''bos_token''': '''<s>''',
			    '''eos_token''': '''</s>''',
			    '''model_max_length''': 1_0_2_4,
			    '''pad_token''': '''<pad>''',
			    '''special_tokens_map_file''': None,
			    '''tokenizer_class''': '''BioGptTokenizer''',
			    '''unk_token''': '''<unk>''',
			}

			print(f"Generating {biogpt_tokenizer_config_file}"				)
			with open(lowerCamelCase_		,       '''w'''		,       encoding='''utf-8'''				) as f:
						f.write(json.dumps(lowerCamelCase_		,       ensure_ascii=lowerCamelCase_		,       indent=lowerCamelCase_				)				)

			# model
			__lowercase										=							chkpt['''model''']

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

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

			__lowercase										=							BioGptConfig.from_pretrained(lowerCamelCase_				)
			__lowercase										=							BioGptForCausalLM(lowerCamelCase_				)

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

			# save
			__lowercase										=							os.path.join(lowerCamelCase_		,       lowerCamelCase_				)
			print(f"Generating {pytorch_weights_dump_path}"				)
			torch.save(lowerCamelCase_		,       lowerCamelCase_				)

			print('''Conversion is done!'''				)


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












'''simple docstring'''



from argparse import ArgumentParser

from .env import EnvironmentCommand




def    _lowerCAmelCase						(				):
			__lowercase										=							ArgumentParser('''Diffusers CLI tool'''		,       usage='''diffusers-cli <command> [<args>]'''				)
			__lowercase										=							parser.add_subparsers(help='''diffusers-cli command helpers'''				)

			# Register commands
			EnvironmentCommand.register_subcommand(lowerCamelCase_				)

			# Let's go
			__lowercase										=							parser.parse_args()

			if not hasattr(lowerCamelCase_		,       '''func'''				):
						parser.print_help()
						exit(1				)

			# Run
			__lowercase										=							args.func(lowerCamelCase_				)
			service.run()


if __name__ == "__main__":
	main()





