


'''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()

UpperCAmelCase__  			=				2
class 						a__					:





			'''simple docstring'''

			def __init__(  self							:			Optional[Any]			,    *,  # begin keyword-only arguments
			    lowerCAmelCase_							:			List[str]="<s>"			,    lowerCAmelCase_							:			Union[str, Any]="<pad>"			,    lowerCAmelCase_							:			List[str]="</s>"			,    lowerCAmelCase_							:			List[Any]="<unk>"			,    lowerCAmelCase_							:			str=None			,    )      ->		Tuple:
						__A,	__A,	__A,	__A=	bos, unk, pad, eos
						__A=	[]
						__A=	[]
						__A=	{}
						__A=	self.add_symbol(lowerCAmelCase_	)
						__A=	self.add_symbol(lowerCAmelCase_	)
						__A=	self.add_symbol(lowerCAmelCase_	)
						__A=	self.add_symbol(lowerCAmelCase_	)
						if extra_special_symbols:
									for s in extra_special_symbols:
												self.add_symbol(lowerCAmelCase_	)
						__A=	len(self.symbols	)
			def __eq__(  self							:			int			,    lowerCAmelCase_							:			str	)      ->		Union[str, Any]:
						return self.indices == other.indices
			def __getitem__(  self							:			Optional[Any]			,    lowerCAmelCase_							:			Optional[int]	)      ->		Optional[int]:
						if idx < len(self.symbols	):
									return self.symbols[idx]
						return self.unk_word
			def __len__(  self							:			List[str]	)      ->		Union[str, Any]:
						return len(self.symbols	)
			def __contains__(  self							:			Optional[Any]			,    lowerCAmelCase_							:			Dict	)      ->		List[str]:
						return sym in self.indices
			@classmethod
			def 					lowerCAmelCase     (  cls							:			List[str]			,    lowerCAmelCase_							:			List[str]	)      ->		str:
						__A=	cls()
						d.add_from_file(lowerCAmelCase_	)
						return d
			def 					lowerCAmelCase     (  self							:			List[Any]			,    lowerCAmelCase_							:			Dict			,    lowerCAmelCase_							:			List[str]=1			,    lowerCAmelCase_							:			str=False	)      ->		Optional[int]:
						if word in self.indices and not overwrite:
									__A=	self.indices[word]
									__A=	self.count[idx] + n
									return idx
						else:
									__A=	len(self.symbols	)
									__A=	idx
									self.symbols.append(lowerCAmelCase_	)
									self.count.append(lowerCAmelCase_	)
									return idx
			def 					lowerCAmelCase     (  self							:			Tuple			,    lowerCAmelCase_							:			str	)      ->		Optional[Any]:
						return 0

			def 					lowerCAmelCase     (  self							:			Any			,    lowerCAmelCase_							:			Tuple	)      ->		int:
						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=	f.readlines()
						__A=	self._load_meta(lowerCAmelCase_	)

						for line in lines[indices_start_line:]:
									try:
												__A,	__A=	line.rstrip().rsplit(' '			,    1	)
												if field == "#fairseq:overwrite":
															__A=	True
															__A,	__A=	line.rsplit(' '			,    1	)
												else:
															__A=	False
												__A=	int(lowerCAmelCase_	)
												__A=	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 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: List[Any] ):






			"""simple docstring"""




			__A=	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() )
			__A=	'<s> <pad> </s> <unk>'.split()
			# restore the special tokens
			for k in keep_keys:
						del da[f"""{k}</w>"""]
						__A=	d[k]  # restore
			return da






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int,_SCREAMING_SNAKE_CASE					: List[str] ):






			"""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

			__A=	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!""" )
			__A=	torch.load(_SCREAMING_SNAKE_CASE,map_location='cpu' )

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

			# dicts
			__A=	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!""" )
			__A=	Dictionary.load(_SCREAMING_SNAKE_CASE )
			__A=	rewrite_dict_keys(src_dict.indices )
			__A=	len(_SCREAMING_SNAKE_CASE )
			__A=	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)
			__A=	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!""" )

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

			# model config
			__A=	os.path.join(_SCREAMING_SNAKE_CASE,'config.json' )

			__A=	{
			    '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(_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
			__A=	os.path.join(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )

			__A=	{
			    '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
			__A=	chkpt['model']

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

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

			__A=	BioGptConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
			__A=	BioGptForCausalLM(_SCREAMING_SNAKE_CASE )

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

			# save
			__A=	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__":
					UpperCAmelCase__  			=				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.'''
					)
					UpperCAmelCase__  			=				parser.parse_args()
					convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)







'''simple docstring'''





# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


UpperCAmelCase__  			=				subprocess.check_output('''git merge-base main HEAD'''.split()).decode('''utf-8''')
UpperCAmelCase__  			=				subprocess.check_output(F"""git diff --name-only {fork_point_sha}""".split()).decode('''utf-8''').split()

UpperCAmelCase__  			=				'''|'''.join(sys.argv[1:])
UpperCAmelCase__  			=				re.compile(rF"""^({joined_dirs}).*?\.py$""")

UpperCAmelCase__  			=				[x for x in modified_files if regex.match(x)]
print(''' '''.join(relevant_modified_files), end='''''')



