






import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

_a      	=  [
    # (stable-diffusion, HF Diffusers)
    ('''time_embed.0.weight''', '''time_embedding.linear_1.weight'''),
    ('''time_embed.0.bias''', '''time_embedding.linear_1.bias'''),
    ('''time_embed.2.weight''', '''time_embedding.linear_2.weight'''),
    ('''time_embed.2.bias''', '''time_embedding.linear_2.bias'''),
    ('''input_blocks.0.0.weight''', '''conv_in.weight'''),
    ('''input_blocks.0.0.bias''', '''conv_in.bias'''),
    ('''out.0.weight''', '''conv_norm_out.weight'''),
    ('''out.0.bias''', '''conv_norm_out.bias'''),
    ('''out.2.weight''', '''conv_out.weight'''),
    ('''out.2.bias''', '''conv_out.bias'''),
]

_a      	=  [
    # (stable-diffusion, HF Diffusers)
    ('''in_layers.0''', '''norm1'''),
    ('''in_layers.2''', '''conv1'''),
    ('''out_layers.0''', '''norm2'''),
    ('''out_layers.3''', '''conv2'''),
    ('''emb_layers.1''', '''time_emb_proj'''),
    ('''skip_connection''', '''conv_shortcut'''),
]

_a      	=  []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
		# loop over downblocks/upblocks

		for j in range(2):
				# loop over resnets/attentions for downblocks
				_a      	=  F'''down_blocks.{i}.resnets.{j}.'''
				_a      	=  F'''input_blocks.{3*i + j + 1}.0.'''
				unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

				if i < 3:
						# no attention layers in down_blocks.3
						_a      	=  F'''down_blocks.{i}.attentions.{j}.'''
						_a      	=  F'''input_blocks.{3*i + j + 1}.1.'''
						unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

		for j in range(3):
				# loop over resnets/attentions for upblocks
				_a      	=  F'''up_blocks.{i}.resnets.{j}.'''
				_a      	=  F'''output_blocks.{3*i + j}.0.'''
				unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

				if i > 0:
						# no attention layers in up_blocks.0
						_a      	=  F'''up_blocks.{i}.attentions.{j}.'''
						_a      	=  F'''output_blocks.{3*i + j}.1.'''
						unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

		if i < 3:
				# no downsample in down_blocks.3
				_a      	=  F'''down_blocks.{i}.downsamplers.0.conv.'''
				_a      	=  F'''input_blocks.{3*(i+1)}.0.op.'''
				unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

				# no upsample in up_blocks.3
				_a      	=  F'''up_blocks.{i}.upsamplers.0.'''
				_a      	=  F'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
				unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

_a      	=  '''mid_block.attentions.0.'''
_a      	=  '''middle_block.1.'''
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
		_a      	=  F'''mid_block.resnets.{j}.'''
		_a      	=  F'''middle_block.{2*j}.'''
		unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))


def __A    (      __lowerCAmelCase	)->  Optional[Any]:





						"""simple docstring"""



						_UpperCAmelCase									=			{k: k for k in unet_state_dict.keys()}
						for sd_name, hf_name in unet_conversion_map:
												_UpperCAmelCase									=			sd_name
						for k, v in mapping.items():
												if "resnets" in k:
																		for sd_part, hf_part in unet_conversion_map_resnet:
																								_UpperCAmelCase									=			v.replace(__lowerCAmelCase ,		__lowerCAmelCase	)
																		_UpperCAmelCase									=			v
						for k, v in mapping.items():
												for sd_part, hf_part in unet_conversion_map_layer:
																		_UpperCAmelCase									=			v.replace(__lowerCAmelCase ,		__lowerCAmelCase	)
												_UpperCAmelCase									=			v
						_UpperCAmelCase									=			{v: unet_state_dict[k] for k, v in mapping.items()}
						return new_state_dict


# ================#
# VAE Conversion #
# ================#

_a      	=  [
    # (stable-diffusion, HF Diffusers)
    ('''nin_shortcut''', '''conv_shortcut'''),
    ('''norm_out''', '''conv_norm_out'''),
    ('''mid.attn_1.''', '''mid_block.attentions.0.'''),
]

for i in range(4):
		# down_blocks have two resnets
		for j in range(2):
				_a      	=  F'''encoder.down_blocks.{i}.resnets.{j}.'''
				_a      	=  F'''encoder.down.{i}.block.{j}.'''
				vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

		if i < 3:
				_a      	=  F'''down_blocks.{i}.downsamplers.0.'''
				_a      	=  F'''down.{i}.downsample.'''
				vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

				_a      	=  F'''up_blocks.{i}.upsamplers.0.'''
				_a      	=  F'''up.{3-i}.upsample.'''
				vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

		# up_blocks have three resnets
		# also, up blocks in hf are numbered in reverse from sd
		for j in range(3):
				_a      	=  F'''decoder.up_blocks.{i}.resnets.{j}.'''
				_a      	=  F'''decoder.up.{3-i}.block.{j}.'''
				vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
		_a      	=  F'''mid_block.resnets.{i}.'''
		_a      	=  F'''mid.block_{i+1}.'''
		vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


_a      	=  [
    # (stable-diffusion, HF Diffusers)
    ('''norm.''', '''group_norm.'''),
    ('''q.''', '''query.'''),
    ('''k.''', '''key.'''),
    ('''v.''', '''value.'''),
    ('''proj_out.''', '''proj_attn.'''),
]


def __A    (      __lowerCAmelCase	)->  Optional[int]:





						"""simple docstring"""



						return w.reshape(*w.shape ,		1 ,		1	)


def __A    (      __lowerCAmelCase	)->  Optional[int]:





						"""simple docstring"""



						_UpperCAmelCase									=			{k: k for k in vae_state_dict.keys()}
						for k, v in mapping.items():
												for sd_part, hf_part in vae_conversion_map:
																		_UpperCAmelCase									=			v.replace(__lowerCAmelCase ,		__lowerCAmelCase	)
												_UpperCAmelCase									=			v
						for k, v in mapping.items():
												if "attentions" in k:
																		for sd_part, hf_part in vae_conversion_map_attn:
																								_UpperCAmelCase									=			v.replace(__lowerCAmelCase ,		__lowerCAmelCase	)
																		_UpperCAmelCase									=			v
						_UpperCAmelCase									=			{v: vae_state_dict[k] for k, v in mapping.items()}
						_UpperCAmelCase									=			['q', 'k', 'v', 'proj_out']
						for k, v in new_state_dict.items():
												for weight_name in weights_to_convert:
																		if F"""mid.attn_1.{weight_name}.weight""" in k:
																								print(F"""Reshaping {k} for SD format"""	)
																								_UpperCAmelCase									=			reshape_weight_for_sd(__lowerCAmelCase	)
						return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


_a      	=  [
    # (stable-diffusion, HF Diffusers)
    ('''resblocks.''', '''text_model.encoder.layers.'''),
    ('''ln_1''', '''layer_norm1'''),
    ('''ln_2''', '''layer_norm2'''),
    ('''.c_fc.''', '''.fc1.'''),
    ('''.c_proj.''', '''.fc2.'''),
    ('''.attn''', '''.self_attn'''),
    ('''ln_final.''', '''transformer.text_model.final_layer_norm.'''),
    ('''token_embedding.weight''', '''transformer.text_model.embeddings.token_embedding.weight'''),
    ('''positional_embedding''', '''transformer.text_model.embeddings.position_embedding.weight'''),
]
_a      	=  {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
_a      	=  re.compile('''|'''.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
_a      	=  {'''q''': 0, '''k''': 1, '''v''': 2}


def __A    (      __lowerCAmelCase	)->  Union[str, Any]:





						"""simple docstring"""



						_UpperCAmelCase									=			{}
						_UpperCAmelCase									=			{}
						_UpperCAmelCase									=			{}
						for k, v in text_enc_dict.items():
												if (
												    k.endswith('.self_attn.q_proj.weight'	)
												    or k.endswith('.self_attn.k_proj.weight'	)
												    or k.endswith('.self_attn.v_proj.weight'	)
												):
																		_UpperCAmelCase									=			k[: -len('.q_proj.weight'	)]
																		_UpperCAmelCase									=			k[-len('q_proj.weight'	)]
																		if k_pre not in capture_qkv_weight:
																								_UpperCAmelCase									=			[None, None, None]
																		_UpperCAmelCase									=			v
																		continue

												if (
												    k.endswith('.self_attn.q_proj.bias'	)
												    or k.endswith('.self_attn.k_proj.bias'	)
												    or k.endswith('.self_attn.v_proj.bias'	)
												):
																		_UpperCAmelCase									=			k[: -len('.q_proj.bias'	)]
																		_UpperCAmelCase									=			k[-len('q_proj.bias'	)]
																		if k_pre not in capture_qkv_bias:
																								_UpperCAmelCase									=			[None, None, None]
																		_UpperCAmelCase									=			v
																		continue

												_UpperCAmelCase									=			textenc_pattern.sub(lambda __lowerCAmelCase	: protected[re.escape(m.group(0	)	)] ,		__lowerCAmelCase	)
												_UpperCAmelCase									=			v

						for k_pre, tensors in capture_qkv_weight.items():
												if None in tensors:
																		raise Exception('CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'	)
												_UpperCAmelCase									=			textenc_pattern.sub(lambda __lowerCAmelCase	: protected[re.escape(m.group(0	)	)] ,		__lowerCAmelCase	)
												_UpperCAmelCase									=			torch.cat(__lowerCAmelCase	)

						for k_pre, tensors in capture_qkv_bias.items():
												if None in tensors:
																		raise Exception('CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'	)
												_UpperCAmelCase									=			textenc_pattern.sub(lambda __lowerCAmelCase	: protected[re.escape(m.group(0	)	)] ,		__lowerCAmelCase	)
												_UpperCAmelCase									=			torch.cat(__lowerCAmelCase	)

						return new_state_dict


def __A    (      __lowerCAmelCase	)->  Optional[Any]:





						"""simple docstring"""



						return text_enc_dict


if __name__ == "__main__":
		_a      	=  argparse.ArgumentParser()

		parser.add_argument('''--model_path''', default=None, type=str, required=True, help='''Path to the model to convert.''')
		parser.add_argument('''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the output model.''')
		parser.add_argument('''--half''', action='''store_true''', help='''Save weights in half precision.''')
		parser.add_argument(
		    '''--use_safetensors''', action='''store_true''', help='''Save weights use safetensors, default is ckpt.'''
		)

		_a      	=  parser.parse_args()

		assert args.model_path is not None, "Must provide a model path!"

		assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

		# Path for safetensors
		_a      	=  osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.safetensors''')
		_a      	=  osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.safetensors''')
		_a      	=  osp.join(args.model_path, '''text_encoder''', '''model.safetensors''')

		# Load models from safetensors if it exists, if it doesn't pytorch
		if osp.exists(unet_path):
				_a      	=  load_file(unet_path, device='''cpu''')
		else:
				_a      	=  osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.bin''')
				_a      	=  torch.load(unet_path, map_location='''cpu''')

		if osp.exists(vae_path):
				_a      	=  load_file(vae_path, device='''cpu''')
		else:
				_a      	=  osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.bin''')
				_a      	=  torch.load(vae_path, map_location='''cpu''')

		if osp.exists(text_enc_path):
				_a      	=  load_file(text_enc_path, device='''cpu''')
		else:
				_a      	=  osp.join(args.model_path, '''text_encoder''', '''pytorch_model.bin''')
				_a      	=  torch.load(text_enc_path, map_location='''cpu''')

		# Convert the UNet model
		_a      	=  convert_unet_state_dict(unet_state_dict)
		_a      	=  {'''model.diffusion_model.''' + k: v for k, v in unet_state_dict.items()}

		# Convert the VAE model
		_a      	=  convert_vae_state_dict(vae_state_dict)
		_a      	=  {'''first_stage_model.''' + k: v for k, v in vae_state_dict.items()}

		# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
		_a      	=  '''text_model.encoder.layers.22.layer_norm2.bias''' in text_enc_dict

		if is_vaa_model:
				# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
				_a      	=  {'''transformer.''' + k: v for k, v in text_enc_dict.items()}
				_a      	=  convert_text_enc_state_dict_vaa(text_enc_dict)
				_a      	=  {'''cond_stage_model.model.''' + k: v for k, v in text_enc_dict.items()}
		else:
				_a      	=  convert_text_enc_state_dict(text_enc_dict)
				_a      	=  {'''cond_stage_model.transformer.''' + k: v for k, v in text_enc_dict.items()}

		# Put together new checkpoint
		_a      	=  {**unet_state_dict, **vae_state_dict, **text_enc_dict}
		if args.half:
				_a      	=  {k: v.half() for k, v in state_dict.items()}

		if args.use_safetensors:
				save_file(state_dict, args.checkpoint_path)
		else:
				_a      	=  {'''state_dict''': state_dict}
				torch.save(state_dict, args.checkpoint_path)








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)
