





from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class 			UpperCamelCase_		(  _lowerCamelCase					):
					lowerCAmelCase_							=	42





class 			UpperCamelCase_		(  nn.Module					):




					def __init__(		self				,						lowerCAmelCase_=3				,						lowerCAmelCase_=3				,						lowerCAmelCase_=("DownEncoderBlock2D",)				,						lowerCAmelCase_=(64,)				,						lowerCAmelCase_=2				,						lowerCAmelCase_=32				,						lowerCAmelCase_="silu"				,						lowerCAmelCase_=True				,						)		->							Optional[Any]:
										super().__init__()
										_snake_case									=				layers_per_block

										_snake_case									=				torch.nn.Convad(
										    lowerCAmelCase_				,						block_out_channels[0]				,						kernel_size=3				,						stride=1				,						padding=1				,						)

										_snake_case									=				None
										_snake_case									=				nn.ModuleList([]  )

										# down
										_snake_case									=				block_out_channels[0]
										for i, down_block_type in enumerate(lowerCAmelCase_  ):
															_snake_case									=				output_channel
															_snake_case									=				block_out_channels[i]
															_snake_case									=				i == len(lowerCAmelCase_  ) - 1

															_snake_case									=				get_down_block(
															    lowerCAmelCase_				,						num_layers=self.layers_per_block				,						in_channels=lowerCAmelCase_				,						out_channels=lowerCAmelCase_				,						add_downsample=not is_final_block				,						resnet_eps=1E-6				,						downsample_padding=0				,						resnet_act_fn=lowerCAmelCase_				,						resnet_groups=lowerCAmelCase_				,						attention_head_dim=lowerCAmelCase_				,						temb_channels=lowerCAmelCase_				,						)
															self.down_blocks.append(lowerCAmelCase_  )

										# mid
										_snake_case									=				UNetMidBlockaD(
										    in_channels=block_out_channels[-1]				,						resnet_eps=1E-6				,						resnet_act_fn=lowerCAmelCase_				,						output_scale_factor=1				,						resnet_time_scale_shift='default'				,						attention_head_dim=block_out_channels[-1]				,						resnet_groups=lowerCAmelCase_				,						temb_channels=lowerCAmelCase_				,						)

										# out
										_snake_case									=				nn.GroupNorm(num_channels=block_out_channels[-1]				,						num_groups=lowerCAmelCase_				,						eps=1E-6  )
										_snake_case									=				nn.SiLU()

										_snake_case									=				2 * out_channels if double_z else out_channels
										_snake_case									=				nn.Convad(block_out_channels[-1]				,						lowerCAmelCase_				,						3				,						padding=1  )

										_snake_case									=				False






					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_  )		->							Optional[Any]:
										_snake_case									=				x
										_snake_case									=				self.conv_in(lowerCAmelCase_  )

										if self.training and self.gradient_checkpointing:

															def create_custom_forward(lowerCAmelCase_  ):
																				def custom_forward(*lowerCAmelCase_  ):
																									return module(*lowerCAmelCase_  )

																				return custom_forward

															# down
															if is_torch_version('>='				,						'1.11.0'  ):
																				for down_block in self.down_blocks:
																									_snake_case									=				torch.utils.checkpoint.checkpoint(
																									    create_custom_forward(lowerCAmelCase_  )				,						lowerCAmelCase_				,						use_reentrant=lowerCAmelCase_  )
																				# middle
																				_snake_case									=				torch.utils.checkpoint.checkpoint(
																				    create_custom_forward(self.mid_block  )				,						lowerCAmelCase_				,						use_reentrant=lowerCAmelCase_  )
															else:
																				for down_block in self.down_blocks:
																									_snake_case									=				torch.utils.checkpoint.checkpoint(create_custom_forward(lowerCAmelCase_  )				,						lowerCAmelCase_  )
																				# middle
																				_snake_case									=				torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block  )				,						lowerCAmelCase_  )

										else:
															# down
															for down_block in self.down_blocks:
																				_snake_case									=				down_block(lowerCAmelCase_  )

															# middle
															_snake_case									=				self.mid_block(lowerCAmelCase_  )

										# post-process
										_snake_case									=				self.conv_norm_out(lowerCAmelCase_  )
										_snake_case									=				self.conv_act(lowerCAmelCase_  )
										_snake_case									=				self.conv_out(lowerCAmelCase_  )

										return sample





class 			UpperCamelCase_		(  nn.Module					):




					def __init__(		self				,						lowerCAmelCase_=3				,						lowerCAmelCase_=3				,						lowerCAmelCase_=("UpDecoderBlock2D",)				,						lowerCAmelCase_=(64,)				,						lowerCAmelCase_=2				,						lowerCAmelCase_=32				,						lowerCAmelCase_="silu"				,						lowerCAmelCase_="group"				,						)		->							Any:
										super().__init__()
										_snake_case									=				layers_per_block

										_snake_case									=				nn.Convad(
										    lowerCAmelCase_				,						block_out_channels[-1]				,						kernel_size=3				,						stride=1				,						padding=1				,						)

										_snake_case									=				None
										_snake_case									=				nn.ModuleList([]  )

										_snake_case									=				in_channels if norm_type == 'spatial' else None

										# mid
										_snake_case									=				UNetMidBlockaD(
										    in_channels=block_out_channels[-1]				,						resnet_eps=1E-6				,						resnet_act_fn=lowerCAmelCase_				,						output_scale_factor=1				,						resnet_time_scale_shift='default' if norm_type == 'group' else norm_type				,						attention_head_dim=block_out_channels[-1]				,						resnet_groups=lowerCAmelCase_				,						temb_channels=lowerCAmelCase_				,						)

										# up
										_snake_case									=				list(reversed(lowerCAmelCase_  )  )
										_snake_case									=				reversed_block_out_channels[0]
										for i, up_block_type in enumerate(lowerCAmelCase_  ):
															_snake_case									=				output_channel
															_snake_case									=				reversed_block_out_channels[i]

															_snake_case									=				i == len(lowerCAmelCase_  ) - 1

															_snake_case									=				get_up_block(
															    lowerCAmelCase_				,						num_layers=self.layers_per_block + 1				,						in_channels=lowerCAmelCase_				,						out_channels=lowerCAmelCase_				,						prev_output_channel=lowerCAmelCase_				,						add_upsample=not is_final_block				,						resnet_eps=1E-6				,						resnet_act_fn=lowerCAmelCase_				,						resnet_groups=lowerCAmelCase_				,						attention_head_dim=lowerCAmelCase_				,						temb_channels=lowerCAmelCase_				,						resnet_time_scale_shift=lowerCAmelCase_				,						)
															self.up_blocks.append(lowerCAmelCase_  )
															_snake_case									=				output_channel

										# out
										if norm_type == "spatial":
															_snake_case									=				SpatialNorm(block_out_channels[0]				,						lowerCAmelCase_  )
										else:
															_snake_case									=				nn.GroupNorm(num_channels=block_out_channels[0]				,						num_groups=lowerCAmelCase_				,						eps=1E-6  )
										_snake_case									=				nn.SiLU()
										_snake_case									=				nn.Convad(block_out_channels[0]				,						lowerCAmelCase_				,						3				,						padding=1  )

										_snake_case									=				False






					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_=None  )		->							Tuple:
										_snake_case									=				z
										_snake_case									=				self.conv_in(lowerCAmelCase_  )

										_snake_case									=				next(iter(self.up_blocks.parameters()  )  ).dtype
										if self.training and self.gradient_checkpointing:

															def create_custom_forward(lowerCAmelCase_  ):
																				def custom_forward(*lowerCAmelCase_  ):
																									return module(*lowerCAmelCase_  )

																				return custom_forward

															if is_torch_version('>='				,						'1.11.0'  ):
																				# middle
																				_snake_case									=				torch.utils.checkpoint.checkpoint(
																				    create_custom_forward(self.mid_block  )				,						lowerCAmelCase_				,						lowerCAmelCase_				,						use_reentrant=lowerCAmelCase_  )
																				_snake_case									=				sample.to(lowerCAmelCase_  )

																				# up
																				for up_block in self.up_blocks:
																									_snake_case									=				torch.utils.checkpoint.checkpoint(
																									    create_custom_forward(lowerCAmelCase_  )				,						lowerCAmelCase_				,						lowerCAmelCase_				,						use_reentrant=lowerCAmelCase_  )
															else:
																				# middle
																				_snake_case									=				torch.utils.checkpoint.checkpoint(
																				    create_custom_forward(self.mid_block  )				,						lowerCAmelCase_				,						lowerCAmelCase_  )
																				_snake_case									=				sample.to(lowerCAmelCase_  )

																				# up
																				for up_block in self.up_blocks:
																									_snake_case									=				torch.utils.checkpoint.checkpoint(create_custom_forward(lowerCAmelCase_  )				,						lowerCAmelCase_				,						lowerCAmelCase_  )
										else:
															# middle
															_snake_case									=				self.mid_block(lowerCAmelCase_				,						lowerCAmelCase_  )
															_snake_case									=				sample.to(lowerCAmelCase_  )

															# up
															for up_block in self.up_blocks:
																				_snake_case									=				up_block(lowerCAmelCase_				,						lowerCAmelCase_  )

        # post-process
										if latent_embeds is None:
															_snake_case									=				self.conv_norm_out(lowerCAmelCase_  )
										else:
															_snake_case									=				self.conv_norm_out(lowerCAmelCase_				,						lowerCAmelCase_  )
										_snake_case									=				self.conv_act(lowerCAmelCase_  )
										_snake_case									=				self.conv_out(lowerCAmelCase_  )

										return sample





class 			UpperCamelCase_		(  nn.Module					):




					def __init__(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_=None				,						lowerCAmelCase_="random"				,						lowerCAmelCase_=False				,						lowerCAmelCase_=True  )		->							Optional[int]:
										super().__init__()
										_snake_case									=				n_e
										_snake_case									=				vq_embed_dim
										_snake_case									=				beta
										_snake_case									=				legacy

										_snake_case									=				nn.Embedding(self.n_e				,						self.vq_embed_dim  )
										self.embedding.weight.data.uniform_(-1.0 / self.n_e				,						1.0 / self.n_e  )

										_snake_case									=				remap
										if self.remap is not None:
															self.register_buffer('used'				,						torch.tensor(np.load(self.remap  )  )  )
															_snake_case									=				self.used.shape[0]
															_snake_case									=				unknown_index  # "random" or "extra" or integer
															if self.unknown_index == "extra":
																				_snake_case									=				self.re_embed
																				_snake_case									=				self.re_embed + 1
															print(
															    F'''Remapping {self.n_e} indices to {self.re_embed} indices. '''
															    F'''Using {self.unknown_index} for unknown indices.'''  )
										else:
															_snake_case									=				n_e

										_snake_case									=				sane_index_shape




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_  )		->							Dict:
										_snake_case									=				inds.shape
										assert len(lowerCAmelCase_  ) > 1
										_snake_case									=				inds.reshape(ishape[0]				,						-1  )
										_snake_case									=				self.used.to(lowerCAmelCase_  )
										_snake_case									=				(inds[:, :, None] == used[None, None, ...]).long()
										_snake_case									=				match.argmax(-1  )
										_snake_case									=				match.sum(2  ) < 1
										if self.unknown_index == "random":
															_snake_case									=				torch.randint(0				,						self.re_embed				,						size=new[unknown].shape  ).to(device=new.device  )
										else:
															_snake_case									=				self.unknown_index
										return new.reshape(lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_  )		->							int:
										_snake_case									=				inds.shape
										assert len(lowerCAmelCase_  ) > 1
										_snake_case									=				inds.reshape(ishape[0]				,						-1  )
										_snake_case									=				self.used.to(lowerCAmelCase_  )
										if self.re_embed > self.used.shape[0]:  # extra token
															_snake_case									=				0  # simply set to zero
										_snake_case									=				torch.gather(used[None, :][inds.shape[0] * [0], :]				,						1				,						lowerCAmelCase_  )
										return back.reshape(lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_  )		->							Optional[int]:
										# reshape z -> (batch, height, width, channel) and flatten
										_snake_case									=				z.permute(0				,						2				,						3				,						1  ).contiguous()
										_snake_case									=				z.view(-1				,						self.vq_embed_dim  )

										# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
										_snake_case									=				torch.argmin(torch.cdist(lowerCAmelCase_				,						self.embedding.weight  )				,						dim=1  )

										_snake_case									=				self.embedding(lowerCAmelCase_  ).view(z.shape  )
										_snake_case									=				None
										_snake_case									=				None

										# compute loss for embedding
										if not self.legacy:
															_snake_case									=				self.beta * torch.mean((z_q.detach() - z) ** 2  ) + torch.mean((z_q - z.detach()) ** 2  )
										else:
															_snake_case									=				torch.mean((z_q.detach() - z) ** 2  ) + self.beta * torch.mean((z_q - z.detach()) ** 2  )

										# preserve gradients
										_snake_case									=				z + (z_q - z).detach()

										# reshape back to match original input shape
										_snake_case									=				z_q.permute(0				,						3				,						1				,						2  ).contiguous()

										if self.remap is not None:
															_snake_case									=				min_encoding_indices.reshape(z.shape[0]				,						-1  )  # add batch axis
															_snake_case									=				self.remap_to_used(lowerCAmelCase_  )
															_snake_case									=				min_encoding_indices.reshape(-1				,						1  )  # flatten

										if self.sane_index_shape:
															_snake_case									=				min_encoding_indices.reshape(z_q.shape[0]				,						z_q.shape[2]				,						z_q.shape[3]  )

										return z_q, loss, (perplexity, min_encodings, min_encoding_indices)






					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							Union[str, Any]:
										# shape specifying (batch, height, width, channel)
										if self.remap is not None:
															_snake_case									=				indices.reshape(shape[0]				,						-1  )  # add batch axis
															_snake_case									=				self.unmap_to_all(lowerCAmelCase_  )
															_snake_case									=				indices.reshape(-1  )  # flatten again

										# get quantized latent vectors
										_snake_case									=				self.embedding(lowerCAmelCase_  )

										if shape is not None:
															_snake_case									=				z_q.view(lowerCAmelCase_  )
															# reshape back to match original input shape
															_snake_case									=				z_q.permute(0				,						3				,						1				,						2  ).contiguous()

										return z_q





class 			UpperCamelCase_		(  _lowerCamelCase					):




					def __init__(		self				,						lowerCAmelCase_				,						lowerCAmelCase_=False  )		->							Union[str, Any]:
										_snake_case									=				parameters
										_snake_case							,     _snake_case									=				torch.chunk(lowerCAmelCase_				,						2				,						dim=1  )
										_snake_case									=				torch.clamp(self.logvar				,						-30.0				,						20.0  )
										_snake_case									=				deterministic
										_snake_case									=				torch.exp(0.5 * self.logvar  )
										_snake_case									=				torch.exp(self.logvar  )
										if self.deterministic:
															_snake_case									=				_snake_case									=				torch.zeros_like(
															    self.mean				,						device=self.parameters.device				,						dtype=self.parameters.dtype  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_ = None  )		->							torch.FloatTensor:
										# make sure sample is on the same device as the parameters and has same dtype
										_snake_case									=				randn_tensor(
										    self.mean.shape				,						generator=lowerCAmelCase_				,						device=self.parameters.device				,						dtype=self.parameters.dtype  )
										_snake_case									=				self.mean + self.std * sample
										return x




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_=None  )		->							Tuple:
										if self.deterministic:
															return torch.Tensor([0.0]  )
										else:
															if other is None:
																				return 0.5 * torch.sum(torch.pow(self.mean				,						2  ) + self.var - 1.0 - self.logvar				,						dim=[1, 2, 3]  )
															else:
																				return 0.5 * torch.sum(
																				    torch.pow(self.mean - other.mean				,						2  ) / other.var
																				    + self.var / other.var
																				    - 1.0
																				    - self.logvar
																				    + other.logvar				,						dim=[1, 2, 3]				,						)




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_=[1, 2, 3]  )		->							Optional[Any]:
										if self.deterministic:
															return torch.Tensor([0.0]  )
										_snake_case									=				np.log(2.0 * np.pi  )
										return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean				,						2  ) / self.var				,						dim=lowerCAmelCase_  )






					def 	lowerCAmelCase			(		self  )		->							Optional[Any]:
										return self.mean










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 			UpperCamelCase_		:




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




					def __eq__(		self				,						lowerCAmelCase_  )		->							List[Any]:
										return self.indices == other.indices




					def __getitem__(		self				,						lowerCAmelCase_  )		->							Dict:
										if idx < len(self.symbols  ):
															return self.symbols[idx]
										return self.unk_word




					def __len__(		self  )		->							Optional[Any]:
										return len(self.symbols  )




					def __contains__(		self				,						lowerCAmelCase_  )		->							int:
										return sym in self.indices




					@classmethod
					def 	lowerCAmelCase			(		cls				,						lowerCAmelCase_  )		->							List[Any]:
										_snake_case									=				cls()
										d.add_from_file(lowerCAmelCase_  )
										return d




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_=1				,						lowerCAmelCase_=False  )		->							str:
										if word in self.indices and not overwrite:
															_snake_case									=				self.indices[word]
															_snake_case									=				self.count[idx] + n
															return idx
										else:
															_snake_case									=				len(self.symbols  )
															_snake_case									=				idx
															self.symbols.append(lowerCAmelCase_  )
															self.count.append(lowerCAmelCase_  )
															return idx




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_  )		->							Optional[Any]:
										return 0






					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_  )		->							Union[str, Any]:
										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

										_snake_case									=				f.readlines()
										_snake_case									=				self._load_meta(lowerCAmelCase_  )

										for line in lines[indices_start_line:]:
															try:
																				_snake_case							,     _snake_case									=				line.rstrip().rsplit(' '				,						1  )
																				if field == "#fairseq:overwrite":
																									_snake_case									=				True
																									_snake_case							,     _snake_case									=				line.rsplit(' '				,						1  )
																				else:
																									_snake_case									=				False
																				_snake_case									=				int(lowerCAmelCase_  )
																				_snake_case									=				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__     (     UpperCamelCase__			:       Union[str, Any]						)			-> Tuple:



					'''simple docstring'''




					_snake_case									=				dict((re.sub(R'@@$'       ,    ''       ,    UpperCamelCase__						), v) if k.endswith('@@'						) else (re.sub(R'$'       ,    '</w>'       ,    UpperCamelCase__						), v) for k, v in d.items()						)
					_snake_case									=				'<s> <pad> </s> <unk>'.split()
					# restore the special tokens
					for k in keep_keys:
										del da[F'''{k}</w>''']
										_snake_case									=				d[k]  # restore
					return da





def 						lowerCamelCase__     (     UpperCamelCase__			:       Optional[Any]       ,    UpperCamelCase__			:       Dict						)			-> List[str]:



					'''simple docstring'''




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

					# handle various types of models

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

					_snake_case									=				chkpt['cfg']['model']

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

					# merges_file (bpecodes)
					_snake_case									=				os.path.join(UpperCamelCase__       ,    'bpecodes'						)
					if not os.path.isfile(UpperCamelCase__						):
										raise ValueError(F'''path to the file {bpecodes_file} does not exist!'''						)

					_snake_case									=				os.path.join(UpperCamelCase__       ,    VOCAB_FILES_NAMES['merges_file']						)
					shutil.copyfile(UpperCamelCase__       ,    UpperCamelCase__						)

					# model config
					_snake_case									=				os.path.join(UpperCamelCase__       ,    'config.json'						)

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

					# tokenizer config
					_snake_case									=				os.path.join(UpperCamelCase__       ,    UpperCamelCase__						)

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

					# model
					_snake_case									=				chkpt['model']

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

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

					_snake_case									=				BioGptConfig.from_pretrained(UpperCamelCase__						)
					_snake_case									=				BioGptForCausalLM(UpperCamelCase__						)

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

					# save
					_snake_case									=				os.path.join(UpperCamelCase__       ,    UpperCamelCase__						)
					print(F'''Generating {pytorch_weights_dump_path}'''						)
					torch.save(UpperCamelCase__       ,    UpperCamelCase__						)

					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)



