






import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= [
					    '''decoder.version''',
					    '''decoder.output_projection.weight''',
					    '''_float_tensor''',
					    '''decoder.embed_positions._float_tensor''',
					]
					for k in ignore_keys:
										state_dict.pop(UpperCamelCase__					,			UpperCamelCase__ )




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A							,_A								= emb.weight.shape
					_A								= nn.Linear(UpperCamelCase__					,			UpperCamelCase__					,			bias=UpperCamelCase__ )
					_A								= emb.weight.data
					return lin_layer




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= torch.load(UpperCamelCase__					,			map_location='''cpu''' )
					_A								= Namespace(**checkpoint['''cfg''']['''model'''] )
					_A								= checkpoint['''model''']
					remove_ignore_keys_(UpperCamelCase__ )
					_A								= state_dict['''decoder.embed_tokens.weight'''].shape[0]

					_A								= {key.replace('''decoder'''					,			'''model''' ): val for key, val in state_dict.items()}

					_A								= XGLMConfig(
					    vocab_size=UpperCamelCase__					,			max_position_embeddings=args.max_target_positions					,			num_layers=args.decoder_layers					,			attention_heads=args.decoder_attention_heads					,			ffn_dim=args.decoder_ffn_embed_dim					,			d_model=args.decoder_embed_dim					,			layerdrop=args.decoder_layerdrop					,			dropout=args.dropout					,			attention_dropout=args.attention_dropout					,			activation_dropout=args.activation_dropout					,			activation_function='''gelu'''					,			scale_embedding=not args.no_scale_embedding					,			tie_word_embeddings=args.share_decoder_input_output_embed					,			)

					_A								= XGLMForCausalLM(UpperCamelCase__ )
					_A								= model.load_state_dict(UpperCamelCase__					,			strict=UpperCamelCase__ )
					print(UpperCamelCase__ )
					_A								= make_linear_from_emb(model.model.embed_tokens )

					return model


if __name__ == "__main__":
				__A      =				argparse.ArgumentParser()
				# Required parameters
				parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
				parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
				__A      =				parser.parse_args()
				__A      =				convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
				model.save_pretrained(args.pytorch_dump_folder_path)











import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


__A      =				logging.getLogger(__name__)





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							A_						  =					"sequence-classification"

							def __init__(  self:   str		,     __A:   Union[str, Any]   )    ->				List[str]:
												if type(__A   ) == dict:
																	_A								= Namespace(**__A   )
												_A								= glue_output_modes[hparams.task]
												_A								= glue_tasks_num_labels[hparams.task]

												super().__init__(__A		,     __A		,     self.mode   )

							def       __A (  self:   Optional[Any]		,     **__A:   Union[str, Any]   )    ->				Optional[int]:
												return self.model(**__A   )

							def       __A (  self:   Any		,     __A:   Union[str, Any]		,     __A:   int   )    ->				Optional[Any]:
												_A								= {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

												if self.config.model_type not in ["distilbert", "bart"]:
																	_A								= batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None

												_A								= self(**__A   )
												_A								= outputs[0]

												_A								= self.trainer.lr_schedulers[0]['''scheduler''']
												_A								= {'''loss''': loss, '''rate''': lr_scheduler.get_last_lr()[-1]}
												return {"loss": loss, "log": tensorboard_logs}

							def       __A (  self:   List[str]   )    ->				Dict:
												_A								= self.hparams
												_A								= processors[args.task]()
												_A								= processor.get_labels()

												for mode in ["train", "dev"]:
																	_A								= self._feature_file(__A   )
																	if os.path.exists(__A   ) and not args.overwrite_cache:
																						logger.info('''Loading features from cached file %s'''		,     __A   )
																	else:
																						logger.info('''Creating features from dataset file at %s'''		,     args.data_dir   )
																						_A								= (
																						    processor.get_dev_examples(args.data_dir   )
																						    if mode == '''dev'''
																						    else processor.get_train_examples(args.data_dir   )
																						)
																						_A								= convert_examples_to_features(
																						    __A		,     self.tokenizer		,     max_length=args.max_seq_length		,     label_list=self.labels		,     output_mode=args.glue_output_mode		,     )
																						logger.info('''Saving features into cached file %s'''		,     __A   )
																						torch.save(__A		,     __A   )

							def       __A (  self:   List[str]		,     __A:   str		,     __A:   int		,     __A:   bool = False   )    ->				DataLoader:
												_A								= '''dev''' if mode == '''test''' else mode

												_A								= self._feature_file(__A   )
												logger.info('''Loading features from cached file %s'''		,     __A   )
												_A								= torch.load(__A   )
												_A								= torch.tensor([f.input_ids for f in features]		,     dtype=torch.long   )
												_A								= torch.tensor([f.attention_mask for f in features]		,     dtype=torch.long   )
												_A								= torch.tensor([f.token_type_ids for f in features]		,     dtype=torch.long   )
												if self.hparams.glue_output_mode == "classification":
																	_A								= torch.tensor([f.label for f in features]		,     dtype=torch.long   )
												elif self.hparams.glue_output_mode == "regression":
																	_A								= torch.tensor([f.label for f in features]		,     dtype=torch.float   )

												return DataLoader(
												    TensorDataset(__A		,     __A		,     __A		,     __A   )		,     batch_size=__A		,     shuffle=__A		,     )

							def       __A (  self:   List[str]		,     __A:   str		,     __A:   Tuple   )    ->				str:
												_A								= {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

												if self.config.model_type not in ["distilbert", "bart"]:
																	_A								= batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None

												_A								= self(**__A   )
												_A							,_A								= outputs[:2]
												_A								= logits.detach().cpu().numpy()
												_A								= inputs['''labels'''].detach().cpu().numpy()

												return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}

							def       __A (  self:   str		,     __A:   Dict   )    ->				tuple:
												_A								= torch.stack([x['''val_loss'''] for x in outputs]   ).mean().detach().cpu().item()
												_A								= np.concatenate([x['''pred'''] for x in outputs]		,     axis=0   )

												if self.hparams.glue_output_mode == "classification":
																	_A								= np.argmax(__A		,     axis=1   )
												elif self.hparams.glue_output_mode == "regression":
																	_A								= np.squeeze(__A   )

												_A								= np.concatenate([x['''target'''] for x in outputs]		,     axis=0   )
												_A								= [[] for _ in range(out_label_ids.shape[0]   )]
												_A								= [[] for _ in range(out_label_ids.shape[0]   )]

												_A								= {**{'''val_loss''': val_loss_mean}, **compute_metrics(self.hparams.task		,     __A		,     __A   )}

												_A								= dict(results.items()   )
												_A								= results
												return ret, preds_list, out_label_list

							def       __A (  self:   Any		,     __A:   list   )    ->				dict:
												_A							,_A							,_A								= self._eval_end(__A   )
												_A								= ret['''log''']
												return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

							def       __A (  self:   int		,     __A:   Union[str, Any]   )    ->				dict:
												_A							,_A							,_A								= self._eval_end(__A   )
												_A								= ret['''log''']
												# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
												return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



							@staticmethod
							def       __A (  __A:   Optional[Any]		,     __A:   Optional[Any]   )    ->				Optional[Any]:
												BaseTransformer.add_model_specific_args(__A		,     __A   )
												parser.add_argument(
												    '''--max_seq_length'''		,     default=1_28		,     type=__A		,     help=(
												        '''The maximum total input sequence length after tokenization. Sequences longer '''
												        '''than this will be truncated, sequences shorter will be padded.'''
												    )		,     )

												parser.add_argument(
												    '''--task'''		,     default=''''''		,     type=__A		,     required=__A		,     help='''The GLUE task to run'''		,     )
												parser.add_argument(
												    '''--gpus'''		,     default=0		,     type=__A		,     help='''The number of GPUs allocated for this, it is by default 0 meaning none'''		,     )

												parser.add_argument(
												    '''--overwrite_cache'''		,     action='''store_true'''		,     help='''Overwrite the cached training and evaluation sets'''   )

												return parser




def      __A					(     ):

					'''simple docstring'''







					_A								= argparse.ArgumentParser()
					add_generic_args(_lowercase					,			os.getcwd() )
					_A								= GLUETransformer.add_model_specific_args(_lowercase					,			os.getcwd() )
					_A								= parser.parse_args()

					# If output_dir not provided, a folder will be generated in pwd
					if args.output_dir is None:
										_A								= os.path.join(
										    '''./results'''					,			f"""{args.task}_{time.strftime("%Y%m%d_%H%M%S" )}"""					,			)
										os.makedirs(args.output_dir )

					_A								= GLUETransformer(_lowercase )
					_A								= generic_train(_lowercase					,			_lowercase )

					# Optionally, predict on dev set and write to output_dir
					if args.do_predict:
										_A								= sorted(glob.glob(os.path.join(args.output_dir					,			'''checkpoint-epoch=*.ckpt''' )					,			recursive=_lowercase ) )
										_A								= model.load_from_checkpoint(checkpoints[-1] )
										return trainer.test(_lowercase )


if __name__ == "__main__":
				main()



