



'''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()

lowerCamelCase							:Optional[int]       = 2







class      _lowerCAmelCase							:


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


	def __eq__(self	,				lowercase							):
				return self.indices == other.indices


	def __getitem__(self	,				lowercase							):




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


	def __len__(self							):
				return len(self.symbols							)


	def __contains__(self	,				lowercase							):
				return sym in self.indices


	@classmethod
	def    _a     (cls	,				lowercase							):
				A_     :						List[str]     =					cls()
				d.add_from_file(_snake_case							)
				return d


	def    _a     (self	,				lowercase	,				lowercase=1	,				lowercase=False							):




				if word in self.indices and not overwrite:
							A_     :						Union[str, Any]     =					self.indices[word]
							A_     :						Union[str, Any]     =					self.count[idx] + n
							return idx
				else:
							A_     :						Any     =					len(self.symbols							)
							A_     :						str     =					idx
							self.symbols.append(_snake_case							)
							self.count.append(_snake_case							)
							return idx


	def    _a     (self	,				lowercase							):
				return 0


	def    _a     (self	,				lowercase							):




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

				A_     :						Any     =					f.readlines()
				A_     :						Tuple     =					self._load_meta(_snake_case							)

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






def       a      (			lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						Tuple     =					dict((re.sub(r"""@@$"""						,				""""""						,				a__   ), v) if k.endswith("""@@"""   ) else (re.sub(r"""$"""						,				"""</w>"""						,				a__   ), v) for k, v in d.items()   )
			A_     :						List[str]     =					"""<s> <pad> </s> <unk>""".split()
			# restore the special tokens
			for k in keep_keys:
						del da[f'{k}</w>']
						A_     :						List[str]     =					d[k]  # restore
			return da



def       a      (			lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			if not os.path.exists(a__   ):
						raise ValueError(f'path {biogpt_checkpoint_path} does not exist!'   )
			os.makedirs(a__						,				exist_ok=a__   )
			print(f'Writing results to {pytorch_dump_folder_path}'   )

			# handle various types of models

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

			A_     :						Optional[Any]     =					chkpt["""cfg"""]["""model"""]

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

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

			A_     :						str     =					os.path.join(a__						,				VOCAB_FILES_NAMES["""merges_file"""]   )
			shutil.copyfile(a__						,				a__   )

			# model config
			A_     :						List[str]     =					os.path.join(a__						,				"""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-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(a__						,				"""w"""						,				encoding="""utf-8"""   ) as f:
						f.write(json.dumps(a__						,				ensure_ascii=a__						,				indent=a__   )   )

			# tokenizer config
			A_     :						Optional[int]     =					os.path.join(a__						,				a__   )

			A_     :						int     =					{
			    """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(a__						,				"""w"""						,				encoding="""utf-8"""   ) as f:
						f.write(json.dumps(a__						,				ensure_ascii=a__						,				indent=a__   )   )

			# model
			A_     :						Optional[int]     =					chkpt["""model"""]

			# remove unneeded keys
			A_     :						Optional[int]     =					[
			    """decoder.version""",
			]
			for k in ignore_keys:
						model_state_dict.pop(a__						,				a__   )

			A_     :						List[Any]     =					list(model_state_dict.keys()   )
			for layer_name in layer_names:
						if layer_name.endswith("""output_projection.weight"""   ):
									A_     :						List[Any]     =					model_state_dict.pop(a__   )
						else:
									A_     :						Any     =					model_state_dict.pop(a__   )

			A_     :						Optional[int]     =					BioGptConfig.from_pretrained(a__   )
			A_     :						List[str]     =					BioGptForCausalLM(a__   )

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

			# save
			A_     :						Optional[int]     =					os.path.join(a__						,				a__   )
			print(f'Generating {pytorch_weights_dump_path}'   )
			torch.save(a__						,				a__   )

			print("""Conversion is done!"""   )


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




'''simple docstring'''




def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			return " ".join(input_str.split()[::-1]   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()