






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()

_a      	=  2





class __lowerCamelCase :





	"""simple docstring"""






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







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

	def __eq__(							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							return self.indices == other.indices

	def __getitem__(							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							if idx < len(self.symbols    ):
													return self.symbols[idx]
							return self.unk_word

	def __len__(							self    ):
							"""simple docstring"""







							return len(self.symbols    )

	def __contains__(							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							return sym in self.indices

	@classmethod
	def        UpperCamelCase    (							cls       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			cls()
							d.add_from_file(UpperCAmelCase    )
							return d

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase=1       ,    UpperCAmelCase=False    ):
							"""simple docstring"""







							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(UpperCAmelCase    )
													self.count.append(UpperCAmelCase    )
													return idx

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							return 0

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							if isinstance(UpperCAmelCase       ,    UpperCAmelCase    ):
													try:
																			with open(UpperCAmelCase       ,    'r'       ,    encoding='utf-8'    ) as fd:
																									self.add_from_file(UpperCAmelCase    )
													except FileNotFoundError as fnfe:
																			raise fnfe
													except UnicodeError:
																			raise Exception('Incorrect encoding detected in {}, please rebuild the dataset'.format(UpperCAmelCase    )    )
													return

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

							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(UpperCAmelCase    )
																			_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(UpperCAmelCase    )    )
																			self.add_symbol(UpperCAmelCase       ,    n=UpperCAmelCase       ,    overwrite=UpperCAmelCase    )
													except ValueError:
																			raise ValueError('Incorrect dictionary format, expected \'<token> <cnt> [flags]\''    )



def __A    (      __lowerCAmelCase	)->  str:





						"""simple docstring"""



						_UpperCAmelCase									=			dict((re.sub(R'@@$' ,		'' ,		__lowerCAmelCase	), v) if k.endswith('@@'	) else (re.sub(R'$' ,		'</w>' ,		__lowerCAmelCase	), 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 __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  str:





						"""simple docstring"""



						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

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

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

						# dicts
						_UpperCAmelCase									=			os.path.join(__lowerCAmelCase ,		'dict.txt'	)
						if not os.path.isfile(__lowerCAmelCase	):
												raise ValueError(F"""path to the file {dict_file} does not exist!"""	)
						_UpperCAmelCase									=			Dictionary.load(__lowerCAmelCase	)
						_UpperCAmelCase									=			rewrite_dict_keys(src_dict.indices	)
						_UpperCAmelCase									=			len(__lowerCAmelCase	)
						_UpperCAmelCase									=			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)
						_UpperCAmelCase									=			os.path.join(__lowerCAmelCase ,		'bpecodes'	)
						if not os.path.isfile(__lowerCAmelCase	):
												raise ValueError(F"""path to the file {bpecodes_file} does not exist!"""	)

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

						# model config
						_UpperCAmelCase									=			os.path.join(__lowerCAmelCase ,		'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(__lowerCAmelCase ,		'w' ,		encoding='utf-8'	) as f:
												f.write(json.dumps(__lowerCAmelCase ,		ensure_ascii=__lowerCAmelCase ,		indent=__lowerCAmelCase	)	)

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

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

						# model
						_UpperCAmelCase									=			chkpt['model']

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

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

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

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

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

						print('Conversion is done!'	)


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








import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory


def __A    (      )->  Tuple:





						"""simple docstring"""



						raise RuntimeError('CUDA out of memory.'	)





class __lowerCamelCase (      nn.Module):





	"""simple docstring"""






	def __init__(							self    ):
							"""simple docstring"""







							super().__init__()
							_UpperCAmelCase									=			nn.Linear(3       ,    4    )
							_UpperCAmelCase									=			nn.BatchNormad(4    )
							_UpperCAmelCase									=			nn.Linear(4       ,    5    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							return self.lineara(self.batchnorm(self.lineara(UpperCAmelCase    )    )    )





class __lowerCamelCase (      unittest.TestCase):





	"""simple docstring"""






	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			[]

							@find_executable_batch_size(starting_batch_size=128    )
							def mock_training_loop_function(UpperCAmelCase    ):
													nonlocal batch_sizes
													batch_sizes.append(UpperCAmelCase    )
													if batch_size != 8:
																			raise_fake_out_of_memory()

							mock_training_loop_function()
							self.assertListEqual(UpperCAmelCase       ,    [128, 64, 32, 16, 8]    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			[]

							@find_executable_batch_size(starting_batch_size=128    )
							def mock_training_loop_function(UpperCAmelCase       ,    UpperCAmelCase    ):
													nonlocal batch_sizes
													batch_sizes.append(UpperCAmelCase    )
													if batch_size != 8:
																			raise_fake_out_of_memory()
													return batch_size, arga

							_UpperCAmelCase					, _UpperCAmelCase									=			mock_training_loop_function('hello'    )
							self.assertListEqual(UpperCAmelCase       ,    [128, 64, 32, 16, 8]    )
							self.assertListEqual([bs, arga]       ,    [8, 'hello']    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							@find_executable_batch_size(starting_batch_size=0    )
							def mock_training_loop_function(UpperCAmelCase    ):
													pass

							with self.assertRaises(UpperCAmelCase    ) as cm:
													mock_training_loop_function()
													self.assertIn('No executable batch size found, reached zero.'       ,    cm.exception.args[0]    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							@find_executable_batch_size(starting_batch_size=16    )
							def mock_training_loop_function(UpperCAmelCase    ):
													if batch_size > 0:
																			raise_fake_out_of_memory()
													pass

							with self.assertRaises(UpperCAmelCase    ) as cm:
													mock_training_loop_function()
													self.assertIn('No executable batch size found, reached zero.'       ,    cm.exception.args[0]    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							@find_executable_batch_size(starting_batch_size=128    )
							def mock_training_loop_function(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
													if batch_size != 8:
																			raise raise_fake_out_of_memory()

							with self.assertRaises(UpperCAmelCase    ) as cm:
													mock_training_loop_function(128       ,    'hello'       ,    'world'    )
													self.assertIn('Batch size was passed into `f`'       ,    cm.exception.args[0]    )
													self.assertIn('`f(arg1=\'hello\', arg2=\'world\')'       ,    cm.exception.args[0]    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							@find_executable_batch_size(starting_batch_size=16    )
							def mock_training_loop_function(UpperCAmelCase    ):
													raise ValueError('Oops, we had an error!'    )

							with self.assertRaises(UpperCAmelCase    ) as cm:
													mock_training_loop_function()
													self.assertIn('Oops, we had an error!'       ,    cm.exception.args[0]    )

	@require_cuda
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			torch.cuda.memory_allocated()
							_UpperCAmelCase									=			ModelForTest()
							model.cuda()
							self.assertGreater(torch.cuda.memory_allocated()       ,    UpperCAmelCase    )
							_UpperCAmelCase									=			release_memory(UpperCAmelCase    )
							self.assertEqual(torch.cuda.memory_allocated()       ,    UpperCAmelCase    )
