

"""simple docstring"""

# Copyright (c) 2019 Uber Technologies, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import csv
import json
import math
import time

import numpy as np
import torch
import torch.optim as optim
import torch.utils.data as data
from nltk.tokenize.treebank import TreebankWordDetokenizer
from pplm_classification_head import ClassificationHead
from torch import nn
from torchtext import data as torchtext_data
from torchtext import datasets
from tqdm import tqdm, trange

from transformers import GPTaLMHeadModel, GPTaTokenizer


torch.manual_seed(0)
np.random.seed(0)
a__			:						Optional[int]							=			1E-10
a__			:						str							=			"""This is incredible! I love it, this is the best chicken I have ever had."""
a__			:						Any							=			100


class       _snake_case  (    nn.Module							):







						def __init__(self  ,				UpperCAmelCase_  ,				UpperCAmelCase_="gpt2-medium"  ,				UpperCAmelCase_=False  ,				UpperCAmelCase_="cpu"				)      ->    int:
											super().__init__()
											__lowercase        =      GPTaTokenizer.from_pretrained(UpperCAmelCase_				)
											__lowercase        =      GPTaLMHeadModel.from_pretrained(UpperCAmelCase_				)
											__lowercase        =      self.encoder.transformer.config.hidden_size
											__lowercase        =      ClassificationHead(class_size=UpperCAmelCase_  ,				embed_size=self.embed_size				)
											__lowercase        =      cached_mode
											__lowercase        =      device







						def 							_UpperCamelCase     (self				)      ->    Any:
											return self.classifier_head







						def 							_UpperCamelCase     (self				)      ->    Any:
											for param in self.encoder.parameters():
																__lowercase        =      False
											self.classifier_head.train()







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_				)      ->    int:
											__lowercase        =      x.ne(0				).unsqueeze(2				).repeat(1  ,				1  ,				self.embed_size				).float().to(self.device				).detach()
											__lowercase        =      self.encoder.transformer(UpperCAmelCase_				)['''last_hidden_state''']
											__lowercase        =      hidden * mask
											__lowercase        =      torch.sum(UpperCAmelCase_  ,				dim=1				) / (torch.sum(UpperCAmelCase_  ,				dim=1				).detach() + EPSILON)
											return avg_hidden







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_				)      ->    List[Any]:
											if self.cached_mode:
																__lowercase        =      x.to(self.device				)
											else:
																__lowercase        =      self.avg_representation(x.to(self.device				)				)

											__lowercase        =      self.classifier_head(UpperCAmelCase_				)
											__lowercase        =      nn.functional.log_softmax(UpperCAmelCase_  ,				dim=-1				)

											return probs



class       _snake_case  (    data.Dataset							):







						def __init__(self  ,				UpperCAmelCase_  ,				UpperCAmelCase_				)      ->    Union[str, Any]:
											__lowercase        =      X
											__lowercase        =      y







						def __len__(self				)      ->    Dict:
											return len(self.X				)







						def __getitem__(self  ,				UpperCAmelCase_				)      ->    str:
											__lowercase        =      {}
											__lowercase        =      self.X[index]
											__lowercase        =      self.y[index]
											return data





def 					__UpperCamelCase (     a__:			Dict      )					->      List[str]:




					"""simple docstring"""



					def pad_sequences(a__:			Tuple      ):
										__lowercase        =      [len(a__      ) for seq in sequences]

										__lowercase        =      torch.zeros(len(a__      )			,						max(a__      )      ).long()  # padding value = 0

										for i, seq in enumerate(a__      ):
															__lowercase        =      lengths[i]
															__lowercase        =      seq[:end]

										return padded_sequences, lengths

					__lowercase        =      {}
					for key in data[0].keys():
										__lowercase        =      [d[key] for d in data]

					__lowercase    ,			__lowercase        =      pad_sequences(item_info['''X''']      )
					__lowercase        =      torch.tensor(item_info['''y''']			,						dtype=torch.long      )

					return x_batch, y_batch





def 					__UpperCamelCase (     a__:			Any      )					->      List[Any]:




					"""simple docstring"""



					__lowercase        =      {}
					for key in data[0].keys():
										__lowercase        =      [d[key] for d in data]

					__lowercase        =      torch.cat(item_info['''X''']			,						0      )
					__lowercase        =      torch.tensor(item_info['''y''']			,						dtype=torch.long      )

					return x_batch, y_batch





def 					__UpperCamelCase (     a__:			Union[str, Any]			,						a__:			Optional[Any]			,						a__:			Union[str, Any]			,						a__:			List[str]=0			,						a__:			List[str]=10			,						a__:			Union[str, Any]="cpu"      )					->      List[str]:




					"""simple docstring"""



					__lowercase        =      0
					discriminator.train_custom()
					for batch_idx, (input_t, target_t) in enumerate(a__      ):
										__lowercase    ,			__lowercase        =      input_t.to(a__      ), target_t.to(a__      )

										optimizer.zero_grad()

										__lowercase        =      discriminator(a__      )
										__lowercase        =      nn.functional.nll_loss(a__			,						a__      )
										loss.backward(retain_graph=a__      )
										optimizer.step()

										samples_so_far += len(a__      )

										if batch_idx % log_interval == 0:
															print(
															    '''Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'''.format(
															        epoch + 1			,						a__			,						len(data_loader.dataset      )			,						1_00 * samples_so_far / len(data_loader.dataset      )			,						loss.item()			,						)      )





def 					__UpperCamelCase (     a__:			List[str]			,						a__:			Dict			,						a__:			Union[str, Any]="cpu"      )					->      Optional[Any]:




					"""simple docstring"""



					discriminator.eval()
					__lowercase        =      0
					__lowercase        =      0
					with torch.no_grad():
										for input_t, target_t in data_loader:
															__lowercase    ,			__lowercase        =      input_t.to(a__      ), target_t.to(a__      )
															__lowercase        =      discriminator(a__      )
															# sum up batch loss
															test_loss += nn.functional.nll_loss(a__			,						a__			,						reduction='''sum'''      ).item()
															# get the index of the max log-probability
															__lowercase        =      output_t.argmax(dim=1			,						keepdim=a__      )
															correct += pred_t.eq(target_t.view_as(a__      )      ).sum().item()

					test_loss /= len(data_loader.dataset      )

					print(
					    '''Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'''.format(
					        a__			,						a__			,						len(data_loader.dataset      )			,						1_00.0 * correct / len(data_loader.dataset      )      )      )





def 					__UpperCamelCase (     a__:			Dict			,						a__:			Dict			,						a__:			List[Any]			,						a__:			Any=False			,						a__:			int="cpu"      )					->      List[str]:




					"""simple docstring"""



					__lowercase        =      model.tokenizer.encode(a__      )
					__lowercase        =      torch.tensor([input_t]			,						dtype=torch.long			,						device=a__      )
					if cached:
										__lowercase        =      model.avg_representation(a__      )

					__lowercase        =      model(a__      ).data.cpu().numpy().flatten().tolist()
					print('''Input sentence:'''			,						a__      )
					print(
					    '''Predictions:'''			,						''', '''.join('''{}: {:.4f}'''.format(a__			,						math.exp(a__      )      ) for c, log_prob in zip(a__			,						a__      )      )			,						)





def 					__UpperCamelCase (     a__:			Optional[Any]			,						a__:			Dict			,						a__:			Optional[Any]			,						a__:			List[Any]=False			,						a__:			Dict="cpu"      )					->      List[Any]:




					"""simple docstring"""



					__lowercase        =      torch.utils.data.DataLoader(dataset=a__			,						batch_size=a__			,						collate_fn=a__      )

					__lowercase        =      []
					__lowercase        =      []
					for batch_idx, (x, y) in enumerate(tqdm(a__			,						ascii=a__      )      ):
										with torch.no_grad():
															__lowercase        =      x.to(a__      )
															__lowercase        =      discriminator.avg_representation(a__      ).cpu().detach()
															__lowercase        =      torch.unbind(avg_rep.unsqueeze(1      )      )
															xs += avg_rep_list
															ys += y.cpu().numpy().tolist()

					__lowercase        =      torch.utils.data.DataLoader(
					    dataset=Dataset(a__			,						a__      )			,						batch_size=a__			,						shuffle=a__			,						collate_fn=a__      )

					return data_loader





def 					__UpperCamelCase (     a__:			Union[str, Any]			,						a__:			Optional[Any]=None			,						a__:			int="gpt2-medium"			,						a__:			Union[str, Any]=10			,						a__:			Union[str, Any]=64			,						a__:			Optional[Any]=10			,						a__:			int=False			,						a__:			int=False			,						a__:			Optional[Any]=False			,						)					->      List[Any]:




					"""simple docstring"""



					__lowercase        =      '''cuda''' if torch.cuda.is_available() and not no_cuda else '''cpu'''

					print('''Preprocessing {} dataset...'''.format(a__      )      )
					__lowercase        =      time.time()

					if dataset == "SST":
										__lowercase        =      ['''positive''', '''negative''', '''very positive''', '''very negative''', '''neutral''']
										__lowercase        =      {c: i for i, c in enumerate(a__      )}

										__lowercase        =      Discriminator(
										    class_size=len(a__      )			,						pretrained_model=a__			,						cached_mode=a__			,						device=a__      ).to(a__      )

										__lowercase        =      torchtext_data.Field()
										__lowercase        =      torchtext_data.Field(sequential=a__      )
										__lowercase    ,			__lowercase    ,			__lowercase        =      datasets.SST.splits(
										    a__			,						a__			,						fine_grained=a__			,						train_subtrees=a__			,						)

										__lowercase        =      []
										__lowercase        =      []
										for i in trange(len(a__      )			,						ascii=a__      ):
															__lowercase        =      TreebankWordDetokenizer().detokenize(vars(train_data[i]      )['''text''']      )
															__lowercase        =      discriminator.tokenizer.encode(a__      )
															__lowercase        =      torch.tensor([5_02_56] + seq			,						device=a__			,						dtype=torch.long      )
															x.append(a__      )
															y.append(classaidx[vars(train_data[i]      )['''label''']]      )
										__lowercase        =      Dataset(a__			,						a__      )

										__lowercase        =      []
										__lowercase        =      []
										for i in trange(len(a__      )			,						ascii=a__      ):
															__lowercase        =      TreebankWordDetokenizer().detokenize(vars(test_data[i]      )['''text''']      )
															__lowercase        =      discriminator.tokenizer.encode(a__      )
															__lowercase        =      torch.tensor([5_02_56] + seq			,						device=a__			,						dtype=torch.long      )
															test_x.append(a__      )
															test_y.append(classaidx[vars(test_data[i]      )['''label''']]      )
										__lowercase        =      Dataset(a__			,						a__      )

										__lowercase        =      {
										    '''class_size''': len(a__      ),
										    '''embed_size''': discriminator.embed_size,
										    '''pretrained_model''': pretrained_model,
										    '''class_vocab''': classaidx,
										    '''default_class''': 2,
										}

					elif dataset == "clickbait":
										__lowercase        =      ['''non_clickbait''', '''clickbait''']
										__lowercase        =      {c: i for i, c in enumerate(a__      )}

										__lowercase        =      Discriminator(
										    class_size=len(a__      )			,						pretrained_model=a__			,						cached_mode=a__			,						device=a__      ).to(a__      )

										with open('''datasets/clickbait/clickbait_train_prefix.txt'''      ) as f:
															__lowercase        =      []
															for i, line in enumerate(a__      ):
																				try:
																									data.append(eval(a__      )      )
																				except Exception:
																									print('''Error evaluating line {}: {}'''.format(a__			,						a__      )      )
																									continue
										__lowercase        =      []
										__lowercase        =      []
										with open('''datasets/clickbait/clickbait_train_prefix.txt'''      ) as f:
															for i, line in enumerate(tqdm(a__			,						ascii=a__      )      ):
																				try:
																									__lowercase        =      eval(a__      )
																									__lowercase        =      discriminator.tokenizer.encode(d['''text''']      )

																									if len(a__      ) < max_length_seq:
																														__lowercase        =      torch.tensor([5_02_56] + seq			,						device=a__			,						dtype=torch.long      )
																									else:
																														print('''Line {} is longer than maximum length {}'''.format(a__			,						a__      )      )
																														continue
																									x.append(a__      )
																									y.append(d['''label''']      )
																				except Exception:
																									print('''Error evaluating / tokenizing line {}, skipping it'''.format(a__      )      )
																									pass

										__lowercase        =      Dataset(a__			,						a__      )
										__lowercase        =      int(0.9 * len(a__      )      )
										__lowercase        =      len(a__      ) - train_size
										__lowercase    ,			__lowercase        =      torch.utils.data.random_split(a__			,						[train_size, test_size]      )

										__lowercase        =      {
										    '''class_size''': len(a__      ),
										    '''embed_size''': discriminator.embed_size,
										    '''pretrained_model''': pretrained_model,
										    '''class_vocab''': classaidx,
										    '''default_class''': 1,
										}

					elif dataset == "toxic":
										__lowercase        =      ['''non_toxic''', '''toxic''']
										__lowercase        =      {c: i for i, c in enumerate(a__      )}

										__lowercase        =      Discriminator(
										    class_size=len(a__      )			,						pretrained_model=a__			,						cached_mode=a__			,						device=a__      ).to(a__      )

										__lowercase        =      []
										__lowercase        =      []
										with open('''datasets/toxic/toxic_train.txt'''      ) as f:
															for i, line in enumerate(tqdm(a__			,						ascii=a__      )      ):
																				try:
																									__lowercase        =      eval(a__      )
																									__lowercase        =      discriminator.tokenizer.encode(d['''text''']      )

																									if len(a__      ) < max_length_seq:
																														__lowercase        =      torch.tensor([5_02_56] + seq			,						device=a__			,						dtype=torch.long      )
																									else:
																														print('''Line {} is longer than maximum length {}'''.format(a__			,						a__      )      )
																														continue
																									x.append(a__      )
																									y.append(int(np.sum(d['''label''']      ) > 0      )      )
																				except Exception:
																									print('''Error evaluating / tokenizing line {}, skipping it'''.format(a__      )      )
																									pass

										__lowercase        =      Dataset(a__			,						a__      )
										__lowercase        =      int(0.9 * len(a__      )      )
										__lowercase        =      len(a__      ) - train_size
										__lowercase    ,			__lowercase        =      torch.utils.data.random_split(a__			,						[train_size, test_size]      )

										__lowercase        =      {
										    '''class_size''': len(a__      ),
										    '''embed_size''': discriminator.embed_size,
										    '''pretrained_model''': pretrained_model,
										    '''class_vocab''': classaidx,
										    '''default_class''': 0,
										}

					else:  # if dataset == "generic":
										# This assumes the input dataset is a TSV with the following structure:
										# class \t text

										if dataset_fp is None:
															raise ValueError('''When generic dataset is selected, dataset_fp needs to be specified aswell.'''      )

										__lowercase        =      set()
										with open(a__      ) as f:
															__lowercase        =      csv.reader(a__			,						delimiter='''\t'''      )
															for row in tqdm(a__			,						ascii=a__      ):
																				if row:
																									classes.add(row[0]      )

										__lowercase        =      sorted(a__      )
										__lowercase        =      {c: i for i, c in enumerate(a__      )}

										__lowercase        =      Discriminator(
										    class_size=len(a__      )			,						pretrained_model=a__			,						cached_mode=a__			,						device=a__      ).to(a__      )

										__lowercase        =      []
										__lowercase        =      []
										with open(a__      ) as f:
															__lowercase        =      csv.reader(a__			,						delimiter='''\t'''      )
															for i, row in enumerate(tqdm(a__			,						ascii=a__      )      ):
																				if row:
																									__lowercase        =      row[0]
																									__lowercase        =      row[1]

																									try:
																														__lowercase        =      discriminator.tokenizer.encode(a__      )
																														if len(a__      ) < max_length_seq:
																																			__lowercase        =      torch.tensor([5_02_56] + seq			,						device=a__			,						dtype=torch.long      )

																														else:
																																			print('''Line {} is longer than maximum length {}'''.format(a__			,						a__      )      )
																																			continue

																														x.append(a__      )
																														y.append(classaidx[label]      )

																									except Exception:
																														print('''Error tokenizing line {}, skipping it'''.format(a__      )      )
																														pass

										__lowercase        =      Dataset(a__			,						a__      )
										__lowercase        =      int(0.9 * len(a__      )      )
										__lowercase        =      len(a__      ) - train_size
										__lowercase    ,			__lowercase        =      torch.utils.data.random_split(a__			,						[train_size, test_size]      )

										__lowercase        =      {
										    '''class_size''': len(a__      ),
										    '''embed_size''': discriminator.embed_size,
										    '''pretrained_model''': pretrained_model,
										    '''class_vocab''': classaidx,
										    '''default_class''': 0,
										}

					__lowercase        =      time.time()
					print('''Preprocessed {} data points'''.format(len(a__      ) + len(a__      )      )      )
					print('''Data preprocessing took: {:.3f}s'''.format(end - start      )      )

					if cached:
										print('''Building representation cache...'''      )

										__lowercase        =      time.time()

										__lowercase        =      get_cached_data_loader(a__			,						a__			,						a__			,						shuffle=a__			,						device=a__      )

										__lowercase        =      get_cached_data_loader(a__			,						a__			,						a__			,						device=a__      )

										__lowercase        =      time.time()
										print('''Building representation cache took: {:.3f}s'''.format(end - start      )      )

					else:
										__lowercase        =      torch.utils.data.DataLoader(
										    dataset=a__			,						batch_size=a__			,						shuffle=a__			,						collate_fn=a__      )
										__lowercase        =      torch.utils.data.DataLoader(dataset=a__			,						batch_size=a__			,						collate_fn=a__      )

					if save_model:
										with open('''{}_classifier_head_meta.json'''.format(a__      )			,						'''w'''      ) as meta_file:
															json.dump(a__			,						a__      )

					__lowercase        =      optim.Adam(discriminator.parameters()			,						lr=0.00_01      )

					for epoch in range(a__      ):
										__lowercase        =      time.time()
										print('''\nEpoch'''			,						epoch + 1      )

										train_epoch(
										    discriminator=a__			,						data_loader=a__			,						optimizer=a__			,						epoch=a__			,						log_interval=a__			,						device=a__			,						)
										evaluate_performance(data_loader=a__			,						discriminator=a__			,						device=a__      )

										__lowercase        =      time.time()
										print('''Epoch took: {:.3f}s'''.format(end - start      )      )

										print('''\nExample prediction'''      )
										predict(a__			,						a__			,						a__			,						cached=a__			,						device=a__      )

										if save_model:
															# torch.save(discriminator.state_dict(),
															#           "{}_discriminator_{}.pt".format(
															#               args.dataset, epoch + 1
															#               ))
															torch.save(
															    discriminator.get_classifier().state_dict()			,						'''{}_classifier_head_epoch_{}.pt'''.format(a__			,						epoch + 1      )			,						)


if __name__ == "__main__":
							a__			:						Union[str, Any]							=			argparse.ArgumentParser(description="""Train a discriminator on top of GPT-2 representations""")
							parser.add_argument(
							    """--dataset""",
							    type=str,
							    default="""SST""",
							    choices=("""SST""", """clickbait""", """toxic""", """generic"""),
							    help=(
							        """dataset to train the discriminator on."""
							        """In case of generic, the dataset is expected"""
							        """to be a TSBV file with structure: class \\t text"""
							    ),
							)
							parser.add_argument(
							    """--dataset_fp""",
							    type=str,
							    default="""""",
							    help="""File path of the dataset to use. Needed only in case of generic datadset""",
							)
							parser.add_argument(
							    """--pretrained_model""", type=str, default="""gpt2-medium""", help="""Pretrained model to use as encoder"""
							)
							parser.add_argument("""--epochs""", type=int, default=10, metavar="""N""", help="""Number of training epochs""")
							parser.add_argument(
							    """--batch_size""", type=int, default=64, metavar="""N""", help="""input batch size for training (default: 64)"""
							)
							parser.add_argument(
							    """--log_interval""",
							    type=int,
							    default=10,
							    metavar="""N""",
							    help="""how many batches to wait before logging training status""",
							)
							parser.add_argument("""--save_model""", action="""store_true""", help="""whether to save the model""")
							parser.add_argument("""--cached""", action="""store_true""", help="""whether to cache the input representations""")
							parser.add_argument("""--no_cuda""", action="""store_true""", help="""use to turn off cuda""")
							a__			:						str							=			parser.parse_args()

							train_discriminator(**(vars(args)))





"""simple docstring"""

import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig





def 					__UpperCamelCase (     a__:			List[Any]			,						a__:			int			,						a__:			Dict=0      )					->      Any:




					"""simple docstring"""



					if name is None:
										__lowercase        =      None
					else:
										__lowercase        =      '''.''' * max(0			,						spaces - 2      ) + '''# {:''' + str(50 - spaces      ) + '''s}'''
										__lowercase        =      fmt.format(a__      )

					# Print and recurse (if needed).
					if isinstance(a__			,						a__      ):
										if msg is not None:
															print(a__      )
										for k in val.keys():
															recursive_print(a__			,						val[k]			,						spaces + 2      )
					elif isinstance(a__			,						torch.Tensor      ):
										print(a__			,						''':'''			,						val.size()      )
					else:
										print(a__			,						''':'''			,						a__      )





def 					__UpperCamelCase (     a__:			Optional[int]			,						a__:			Tuple			,						a__:			List[Any]			,						a__:			List[str]      )					->      str:




					"""simple docstring"""



					__lowercase        =      param.size()
					# other versions store [num_heads * num_splits * hidden_size, :]
					__lowercase        =      (num_heads, num_splits, hidden_size) + input_shape[1:]
					__lowercase        =      param.view(*a__      )
					__lowercase        =      param.transpose(0			,						1      ).contiguous()
					__lowercase        =      param.view(*a__      )
					return param





def 					__UpperCamelCase (     a__:			Tuple			,						a__:			Optional[int]      )					->      List[str]:




					"""simple docstring"""



					__lowercase        =      config.n_positions
					__lowercase        =      config.n_layer
					__lowercase        =      config.vocab_size
					__lowercase        =      config.n_head
					__lowercase        =      config.n_embd // config.n_head

					__lowercase        =      sd_megatron['''model.language_model.embedding.word_embeddings.weight'''][:vocab_size, :]
					__lowercase        =      {
					    '''transformer.wte.weight''': word_embeddings,
					    '''transformer.wpe.weight''': sd_megatron['''model.language_model.embedding.position_embeddings.weight'''],
					    '''transformer.ln_f.weight''': sd_megatron['''model.language_model.encoder.final_layernorm.weight'''],
					    '''transformer.ln_f.bias''': sd_megatron['''model.language_model.encoder.final_layernorm.bias'''],
					}

					__lowercase        =      '''model.language_model.encoder.layers.'''
					for i in range(a__      ):
										__lowercase        =      torch.tril(torch.ones((n_positions, n_positions)			,						dtype=torch.bool      )      )
										__lowercase        =      causal_mask.view(1			,						1			,						a__			,						a__      )
										__lowercase        =      causal_mask
										__lowercase        =      torch.tensor(-1e4			,						dtype=torch.bfloataa      )

										__lowercase        =      sd_megatron[f'''{pf}{i}.input_layernorm.weight''']
										__lowercase        =      sd_megatron[f'''{pf}{i}.input_layernorm.bias''']

										__lowercase        =      sd_megatron[f'''{pf}{i}.self_attention.query_key_value.weight''']
										__lowercase        =      fix_query_key_value_ordering(a__			,						3			,						a__			,						a__      )
										__lowercase        =      vala.transpose(0			,						1      ).contiguous()

										__lowercase        =      sd_megatron[f'''{pf}{i}.self_attention.query_key_value.bias''']
										__lowercase        =      fix_query_key_value_ordering(a__			,						3			,						a__			,						a__      )
										__lowercase        =      vala

										__lowercase        =      sd_megatron[f'''{pf}{i}.self_attention.dense.weight'''].transpose(
										    0			,						1      )
										__lowercase        =      sd_megatron[f'''{pf}{i}.self_attention.dense.bias''']
										__lowercase        =      sd_megatron[f'''{pf}{i}.post_attention_layernorm.weight''']
										__lowercase        =      sd_megatron[f'''{pf}{i}.post_attention_layernorm.bias''']
										__lowercase        =      sd_megatron[f'''{pf}{i}.mlp.dense_h_to_4h.weight'''].transpose(0			,						1      )
										__lowercase        =      sd_megatron[f'''{pf}{i}.mlp.dense_h_to_4h.bias''']
										__lowercase        =      sd_megatron[f'''{pf}{i}.mlp.dense_4h_to_h.weight'''].transpose(
										    0			,						1      )
										__lowercase        =      sd_megatron[f'''{pf}{i}.mlp.dense_4h_to_h.bias''']

					# For LM head, transformers' wants the matrix to weight embeddings.
					__lowercase        =      word_embeddings

					return sd_hf





def 					__UpperCamelCase (     a__:			Any			,						a__:			List[Any]      )					->      List[Any]:




					"""simple docstring"""



					__lowercase        =      6_40_00
					__lowercase        =      config_megatron['''encoder_seq_length''']
					__lowercase        =      config_megatron['''hidden_size''']
					__lowercase        =      config_megatron['''num_layers''']
					__lowercase        =      config_megatron['''num_attention_heads''']
					__lowercase        =      config_megatron['''ffn_hidden_size''']
					__lowercase        =      '''gelu'''
					__lowercase        =      0.1
					__lowercase        =      0.1
					__lowercase        =      0.1
					__lowercase        =      config_megatron['''layernorm_epsilon''']  # 1e-5
					__lowercase        =      config_megatron['''init_method_std''']  # 0.02
					__lowercase        =      config_megatron['''apply_query_key_layer_scaling''']  # True
					__lowercase        =      True
					__lowercase        =      True

					# This identifies the 6.7B (7B) model which uses a different tokenizer
					if config_megatron["hidden_size"] == 40_96:
										__lowercase        =      1  # <|endoftext|>
										__lowercase        =      1  # <|endoftext|>
										__lowercase        =      0  # <unk>
					else:
										__lowercase        =      2  # <s>
										__lowercase        =      3  # <|endoftext|>
										__lowercase        =      0  # <pad>

					return config_hf





def 					__UpperCamelCase (     a__:			Dict      )					->      Union[str, Any]:




					"""simple docstring"""



					print(a__      )

					__lowercase        =      args.checkpoint_path
					__lowercase        =      args.save_path
					if isfile(a__      ):
										raise FileNotFoundError(f'''ERROR! could not find file {checkpoint_path}'''      )

					# Load the model.
					__lowercase        =      torch.load(a__			,						map_location='''cpu'''      )

					# Load the config.
					__lowercase        =      checkpoint['''hyper_parameters''']['''cfg''']
					__lowercase        =      GPTaConfig()
					__lowercase        =      copy_config(config_hf=a__			,						config_megatron=a__      )
					__lowercase        =      ['''GPT2LMHeadModel''']

					__lowercase        =      checkpoint['''state_dict''']

					# Convert.
					print('''Converting'''      )
					__lowercase        =      convert_megatron_checkpoint(a__			,						a__      )

					# Print the structure of converted state dict.
					if args.print_checkpoint_structure:
										recursive_print(a__			,						a__      )

					__lowercase        =      '''GPTSw3Tokenizer'''

					# Store the config to file.
					print('''Saving config'''      )
					config_hf.save_pretrained(a__      )

					# Store the state_dict to file.
					__lowercase        =      os.path.join(a__			,						'''pytorch_model.bin'''      )
					print(f'''Saving checkpoint to "{output_checkpoint_file}"'''      )
					torch.save(a__			,						a__      )


if __name__ == "__main__":
							a__			:						Union[str, Any]							=			argparse.ArgumentParser()
							parser.add_argument(
							    """--checkpoint_path""",
							    type=str,
							    required=True,
							    help="""e.g. megatron_gpt--val_loss=2.42-step=38000-consumed_samples=54720000""",
							)
							parser.add_argument("""--save_path""", type=str, required=True, help="""e.g. /home/user/gpt-sw3/hf""")
							parser.add_argument("""--print-checkpoint-structure""", action="""store_true""")
							a__			:						Dict							=			parser.parse_args()
							main(_args)


