



'''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__:     Union[str, Any]										=    2

class        SCREAMING_SNAKE_CASE:







	"""simple docstring"""






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







	def __eq__(		self    :  Union[str, Any]	,    __snake_case    :  Any	)    ->	int:
								return self.indices == other.indices







	def __getitem__(		self    :  Optional[Any]	,    __snake_case    :  str	)    ->	List[Any]:
								if idx < len(self.symbols	):
															return self.symbols[idx]
								return self.unk_word







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







	def __contains__(		self    :  Dict	,    __snake_case    :  Tuple	)    ->	Union[str, Any]:
								return sym in self.indices







	@classmethod
	def   A			(		cls    :  List[str]	,    __snake_case    :  int	)    ->	Any:
								UpperCAmelCase				: List[str]											=	cls()
								d.add_from_file(__snake_case	)
								return d







	def   A			(		self    :  Any	,    __snake_case    :  Any	,    __snake_case    :  Optional[Any]=1	,    __snake_case    :  Dict=False	)    ->	List[Any]:
								if word in self.indices and not overwrite:
															UpperCAmelCase				: int											=	self.indices[word]
															UpperCAmelCase				: Tuple											=	self.count[idx] + n
															return idx
								else:
															UpperCAmelCase				: Optional[int]											=	len(self.symbols	)
															UpperCAmelCase				: Any											=	idx
															self.symbols.append(__snake_case	)
															self.count.append(__snake_case	)
															return idx







	def   A			(		self    :  int	,    __snake_case    :  Any	)    ->	Any:
								return 0







	def   A			(		self    :  Optional[Any]	,    __snake_case    :  Tuple	)    ->	Dict:
								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

								UpperCAmelCase				: Any											=	f.readlines()
								UpperCAmelCase				: List[Any]											=	self._load_meta(__snake_case	)

								for line in lines[indices_start_line:]:
															try:
																						UpperCAmelCase							,	UpperCAmelCase				: Any											=	line.rstrip().rsplit(''' '''	,    1	)
																						if field == "#fairseq:overwrite":
																													UpperCAmelCase				: Any											=	True
																													UpperCAmelCase							,	UpperCAmelCase				: Any											=	line.rsplit(''' '''	,    1	)
																						else:
																													UpperCAmelCase				: List[Any]											=	False
																						UpperCAmelCase				: Union[str, Any]											=	int(__snake_case	)
																						UpperCAmelCase				: List[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 				snake_case_  (      _lowerCAmelCase						:		int						)   ->     List[str]:
							# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
							# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
							UpperCAmelCase				: Any											=	dict((re.sub(R'''@@$'''     ,   ''''''     ,   _lowerCAmelCase						), v) if k.endswith('''@@'''						) else (re.sub(R'''$'''     ,   '''</w>'''     ,   _lowerCAmelCase						), v) for k, v in d.items()						)
							UpperCAmelCase				: Dict											=	'''<s> <pad> </s> <unk>'''.split()
							# restore the special tokens
							for k in keep_keys:
														del da[f"""{k}</w>"""]
														UpperCAmelCase				: Any											=	d[k]  # restore
							return da


def 				snake_case_  (      _lowerCAmelCase						:		Optional[Any]     ,   _lowerCAmelCase						:		Union[str, Any]						)   ->     Tuple:
							# prep
							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				: Dict											=	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				: str											=	torch.load(_lowerCAmelCase     ,   map_location='''cpu'''						)

							UpperCAmelCase				: Dict											=	chkpt['''cfg''']['''model''']

							# dicts
							UpperCAmelCase				: List[str]											=	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				: List[str]											=	Dictionary.load(_lowerCAmelCase						)
							UpperCAmelCase				: int											=	rewrite_dict_keys(src_dict.indices						)
							UpperCAmelCase				: str											=	len(_lowerCAmelCase						)
							UpperCAmelCase				: List[Any]											=	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				: List[str]											=	os.path.join(_lowerCAmelCase     ,   '''bpecodes'''						)
							if not os.path.isfile(_lowerCAmelCase						):
														raise ValueError(f"""path to the file {bpecodes_file} does not exist!"""						)

							UpperCAmelCase				: Optional[Any]											=	os.path.join(_lowerCAmelCase     ,   VOCAB_FILES_NAMES['''merges_file''']						)
							shutil.copyfile(_lowerCAmelCase     ,   _lowerCAmelCase						)

							# model config
							UpperCAmelCase				: Tuple											=	os.path.join(_lowerCAmelCase     ,   '''config.json'''						)

							UpperCAmelCase				: Optional[int]											=	{
							    '''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''': 0.0_2,
							    '''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				: Any											=	os.path.join(_lowerCAmelCase     ,   _lowerCAmelCase						)

							UpperCAmelCase				: Optional[int]											=	{
							    '''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(_lowerCAmelCase     ,   '''w'''     ,   encoding='''utf-8'''						) as f:
														f.write(json.dumps(_lowerCAmelCase     ,   ensure_ascii=_lowerCAmelCase     ,   indent=_lowerCAmelCase						)						)

							# model
							UpperCAmelCase				: str											=	chkpt['''model''']

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

							UpperCAmelCase				: int											=	list(model_state_dict.keys()						)
							for layer_name in layer_names:
														if layer_name.endswith('''output_projection.weight'''						):
																					UpperCAmelCase				: Union[str, Any]											=	model_state_dict.pop(_lowerCAmelCase						)
														else:
																					UpperCAmelCase				: Tuple											=	model_state_dict.pop(_lowerCAmelCase						)

							UpperCAmelCase				: Optional[int]											=	BioGptConfig.from_pretrained(_lowerCAmelCase						)
							UpperCAmelCase				: Any											=	BioGptForCausalLM(_lowerCAmelCase						)

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

							# save
							UpperCAmelCase				: Tuple											=	os.path.join(_lowerCAmelCase     ,   _lowerCAmelCase						)
							print(f"""Generating {pytorch_weights_dump_path}"""						)
							torch.save(_lowerCAmelCase     ,   _lowerCAmelCase						)

							print('''Conversion is done!'''						)


if __name__ == "__main__":
						UpperCamelCase__:     List[Any]										=    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__:     int										=    parser.parse_args()
						convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)










'''simple docstring'''






import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


UpperCamelCase__:     List[str]										=    logging.getLogger(__name__)

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	def   A			(		self    :  str	,    __snake_case    :  Tuple	,    __snake_case    :  int	,    __snake_case    :  List[Any]=None	,    __snake_case    :  List[Any]=None	)    ->	Optional[int]:
								UpperCAmelCase				: Union[str, Any]											=	self.layer[current_layer](__snake_case	,    __snake_case	,    head_mask[current_layer]	)

								UpperCAmelCase				: Optional[Any]											=	layer_outputs[0]

								return hidden_states

@add_start_docstrings(
    """The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top."""							,	A__							,	)
class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	def __init__(		self    :  int	,    __snake_case    :  Any	)    ->	Any:
								super().__init__(__snake_case	)

								UpperCAmelCase				: Any											=	BertEncoderWithPabee(__snake_case	)

								self.init_weights()
								UpperCAmelCase				: Optional[Any]											=	0
								UpperCAmelCase				: List[str]											=	0
								UpperCAmelCase				: Tuple											=	0

								UpperCAmelCase				: List[str]											=	0







	def   A			(		self    :  List[str]	,    __snake_case    :  List[str]	)    ->	List[Any]:
								UpperCAmelCase				: Union[str, Any]											=	threshold







	def   A			(		self    :  str	,    __snake_case    :  List[str]	)    ->	Optional[Any]:
								UpperCAmelCase				: List[str]											=	patience







	def   A			(		self    :  Dict	)    ->	str:
								UpperCAmelCase				: List[Any]											=	0
								UpperCAmelCase				: Dict											=	0







	def   A			(		self    :  Optional[Any]	)    ->	Optional[int]:
								UpperCAmelCase				: Dict											=	self.inference_layers_num / self.inference_instances_num
								UpperCAmelCase				: int											=	(
								    F"""*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ="""
								    F""" {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"""
								)
								print(__snake_case	)







	@add_start_docstrings_to_model_forward(__snake_case	)
	def   A			(		self    :  Union[str, Any]	,    __snake_case    :  Optional[Any]=None	,    __snake_case    :  str=None	,    __snake_case    :  int=None	,    __snake_case    :  Optional[Any]=None	,    __snake_case    :  Tuple=None	,    __snake_case    :  Tuple=None	,    __snake_case    :  Tuple=None	,    __snake_case    :  Optional[Any]=None	,    __snake_case    :  Union[str, Any]=None	,    __snake_case    :  Optional[int]=None	,    __snake_case    :  Optional[int]=False	,    )    ->	List[str]:

								if input_ids is not None and inputs_embeds is not None:
															raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time'''	)
								elif input_ids is not None:
															UpperCAmelCase				: Union[str, Any]											=	input_ids.size()
								elif inputs_embeds is not None:
															UpperCAmelCase				: List[Any]											=	inputs_embeds.size()[:-1]
								else:
															raise ValueError('''You have to specify either input_ids or inputs_embeds'''	)

								UpperCAmelCase				: Optional[Any]											=	input_ids.device if input_ids is not None else inputs_embeds.device

								if attention_mask is None:
															UpperCAmelCase				: Optional[Any]											=	torch.ones(__snake_case	,    device=__snake_case	)
								if token_type_ids is None:
															UpperCAmelCase				: Tuple											=	torch.zeros(__snake_case	,    dtype=torch.long	,    device=__snake_case	)

								# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
								# ourselves in which case we just need to make it broadcastable to all heads.
								UpperCAmelCase				: torch.Tensor											=	self.get_extended_attention_mask(__snake_case	,    __snake_case	,    __snake_case	)

								# If a 2D ou 3D attention mask is provided for the cross-attention
								# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
								if self.config.is_decoder and encoder_hidden_states is not None:
															UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase				: Any											=	encoder_hidden_states.size()
															UpperCAmelCase				: List[Any]											=	(encoder_batch_size, encoder_sequence_length)
															if encoder_attention_mask is None:
																						UpperCAmelCase				: str											=	torch.ones(__snake_case	,    device=__snake_case	)
															UpperCAmelCase				: Tuple											=	self.invert_attention_mask(__snake_case	)
								else:
															UpperCAmelCase				: List[str]											=	None

								# Prepare head mask if needed
								# 1.0 in head_mask indicate we keep the head
								# attention_probs has shape bsz x n_heads x N x N
								# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
								# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
								UpperCAmelCase				: List[Any]											=	self.get_head_mask(__snake_case	,    self.config.num_hidden_layers	)

								UpperCAmelCase				: Any											=	self.embeddings(
								    input_ids=__snake_case	,    position_ids=__snake_case	,    token_type_ids=__snake_case	,    inputs_embeds=__snake_case	)
								UpperCAmelCase				: Optional[int]											=	embedding_output

								if self.training:
															UpperCAmelCase				: str											=	[]
															for i in range(self.config.num_hidden_layers	):
																						UpperCAmelCase				: Optional[int]											=	self.encoder.adaptive_forward(
																						    __snake_case	,    current_layer=__snake_case	,    attention_mask=__snake_case	,    head_mask=__snake_case	)

																						UpperCAmelCase				: List[str]											=	self.pooler(__snake_case	)
																						UpperCAmelCase				: Dict											=	output_layers[i](output_dropout(__snake_case	)	)
																						res.append(__snake_case	)
								elif self.patience == 0:  # Use all layers for inference
															UpperCAmelCase				: Any											=	self.encoder(
															    __snake_case	,    attention_mask=__snake_case	,    head_mask=__snake_case	,    encoder_hidden_states=__snake_case	,    encoder_attention_mask=__snake_case	,    )
															UpperCAmelCase				: Dict											=	self.pooler(encoder_outputs[0]	)
															UpperCAmelCase				: List[Any]											=	[output_layers[self.config.num_hidden_layers - 1](__snake_case	)]
								else:
															UpperCAmelCase				: Tuple											=	0
															UpperCAmelCase				: List[str]											=	None
															UpperCAmelCase				: Optional[Any]											=	0
															for i in range(self.config.num_hidden_layers	):
																						calculated_layer_num += 1
																						UpperCAmelCase				: Optional[Any]											=	self.encoder.adaptive_forward(
																						    __snake_case	,    current_layer=__snake_case	,    attention_mask=__snake_case	,    head_mask=__snake_case	)

																						UpperCAmelCase				: List[str]											=	self.pooler(__snake_case	)
																						UpperCAmelCase				: Optional[int]											=	output_layers[i](__snake_case	)
																						if regression:
																													UpperCAmelCase				: Union[str, Any]											=	logits.detach()
																													if patient_result is not None:
																																				UpperCAmelCase				: List[str]											=	patient_result.detach()
																													if (patient_result is not None) and torch.abs(patient_result - labels	) < self.regression_threshold:
																																				patient_counter += 1
																													else:
																																				UpperCAmelCase				: Dict											=	0
																						else:
																													UpperCAmelCase				: Union[str, Any]											=	logits.detach().argmax(dim=1	)
																													if patient_result is not None:
																																				UpperCAmelCase				: str											=	patient_result.detach().argmax(dim=1	)
																													if (patient_result is not None) and torch.all(labels.eq(__snake_case	)	):
																																				patient_counter += 1
																													else:
																																				UpperCAmelCase				: Union[str, Any]											=	0

																						UpperCAmelCase				: List[str]											=	logits
																						if patient_counter == self.patience:
																													break
															UpperCAmelCase				: List[Any]											=	[patient_result]
															self.inference_layers_num += calculated_layer_num
															self.inference_instances_num += 1

								return res

@add_start_docstrings(
    """Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. """							,	A__							,	)
class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	def __init__(		self    :  str	,    __snake_case    :  str	)    ->	Optional[Any]:
								super().__init__(__snake_case	)
								UpperCAmelCase				: Optional[int]											=	config.num_labels

								UpperCAmelCase				: Optional[Any]											=	BertModelWithPabee(__snake_case	)
								UpperCAmelCase				: Tuple											=	nn.Dropout(config.hidden_dropout_prob	)
								UpperCAmelCase				: Dict											=	nn.ModuleList(
								    [nn.Linear(config.hidden_size	,    self.config.num_labels	) for _ in range(config.num_hidden_layers	)]	)

								self.init_weights()







	@add_start_docstrings_to_model_forward(__snake_case	)
	def   A			(		self    :  Any	,    __snake_case    :  Dict=None	,    __snake_case    :  str=None	,    __snake_case    :  List[Any]=None	,    __snake_case    :  List[Any]=None	,    __snake_case    :  Union[str, Any]=None	,    __snake_case    :  int=None	,    __snake_case    :  str=None	,    )    ->	Union[str, Any]:
								UpperCAmelCase				: Optional[int]											=	self.bert(
								    input_ids=__snake_case	,    attention_mask=__snake_case	,    token_type_ids=__snake_case	,    position_ids=__snake_case	,    head_mask=__snake_case	,    inputs_embeds=__snake_case	,    output_dropout=self.dropout	,    output_layers=self.classifiers	,    regression=self.num_labels == 1	,    )

								UpperCAmelCase				: List[str]											=	(logits[-1],)

								if labels is not None:
															UpperCAmelCase				: Tuple											=	None
															UpperCAmelCase				: Any											=	0
															for ix, logits_item in enumerate(__snake_case	):
																						if self.num_labels == 1:
																													#  We are doing regression
																													UpperCAmelCase				: Optional[Any]											=	MSELoss()
																													UpperCAmelCase				: Optional[Any]											=	loss_fct(logits_item.view(-1	)	,    labels.view(-1	)	)
																						else:
																													UpperCAmelCase				: Dict											=	CrossEntropyLoss()
																													UpperCAmelCase				: Dict											=	loss_fct(logits_item.view(-1	,    self.num_labels	)	,    labels.view(-1	)	)
																						if total_loss is None:
																													UpperCAmelCase				: Dict											=	loss
																						else:
																													total_loss += loss * (ix + 1)
																						total_weights += ix + 1
															UpperCAmelCase				: Any											=	(total_loss / total_weights,) + outputs

								return outputs





