




# 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)
UpperCAmelCase_    =1e-10
UpperCAmelCase_    ="""This is incredible! I love it, this is the best chicken I have ever had."""
UpperCAmelCase_    =100


class 				__UpperCamelCase      (			nn.Module	):





				'''simple docstring'''



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



				def    __snake_case		(			self    ):
											return self.classifier_head



				def    __snake_case		(			self    ):
											for param in self.encoder.parameters():
																		lowerCAmelCase =		False
											self.classifier_head.train()



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






				def    __snake_case		(			self							,			UpperCAmelCase_    ):
											if self.cached_mode:
																		lowerCAmelCase =		x.to(self.device    )
											else:
																		lowerCAmelCase =		self.avg_representation(x.to(self.device    )    )

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

											return probs




class 				__UpperCamelCase      (			data.Dataset	):





				'''simple docstring'''



				def __init__(			self							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											lowerCAmelCase =		X
											lowerCAmelCase =		y



				def __len__(			self    ):
											return len(self.X    )






				def __getitem__(			self							,			UpperCAmelCase_    ):
											lowerCAmelCase =		{}
											lowerCAmelCase =		self.X[index]
											lowerCAmelCase =		self.y[index]
											return data




def    UpperCAmelCase							(  _snake_case				):
							def pad_sequences(_snake_case				):
														lowerCAmelCase =		[len(_snake_case				) for seq in sequences]

														lowerCAmelCase =		torch.zeros(len(_snake_case				)    ,			max(_snake_case				)				).long()  # padding value = 0

														for i, seq in enumerate(_snake_case				):
																					lowerCAmelCase =		lengths[i]
																					lowerCAmelCase =		seq[:end]

														return padded_sequences, lengths

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

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

							return x_batch, y_batch



def    UpperCAmelCase							(  _snake_case				):
							lowerCAmelCase =		{}
							for key in data[0].keys():
														lowerCAmelCase =		[d[key] for d in data]

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

							return x_batch, y_batch



def    UpperCAmelCase							(  _snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case=0    ,			_snake_case=10    ,			_snake_case="cpu"				):
							lowerCAmelCase =		0
							discriminator.train_custom()
							for batch_idx, (input_t, target_t) in enumerate(_snake_case				):
														lowerCAmelCase ,  lowerCAmelCase =		input_t.to(_snake_case				), target_t.to(_snake_case				)

														optimizer.zero_grad()

														lowerCAmelCase =		discriminator(_snake_case				)
														lowerCAmelCase =		nn.functional.nll_loss(_snake_case    ,			_snake_case				)
														loss.backward(retain_graph=_snake_case				)
														optimizer.step()

														samples_so_far += len(_snake_case				)

														if batch_idx % log_interval == 0:
																					print(
																					    '''Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'''.format(
																					        epoch + 1    ,			_snake_case    ,			len(data_loader.dataset				)    ,			100 * samples_so_far / len(data_loader.dataset				)    ,			loss.item()    ,			)				)



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

							test_loss /= len(data_loader.dataset				)

							print(
							    '''Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'''.format(
							        _snake_case    ,			_snake_case    ,			len(data_loader.dataset				)    ,			100.0 * correct / len(data_loader.dataset				)				)				)



def    UpperCAmelCase							(  _snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case=False    ,			_snake_case="cpu"				):
							lowerCAmelCase =		model.tokenizer.encode(_snake_case				)
							lowerCAmelCase =		torch.tensor([input_t]    ,			dtype=torch.long    ,			device=_snake_case				)
							if cached:
														lowerCAmelCase =		model.avg_representation(_snake_case				)

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



def    UpperCAmelCase							(  _snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case=False    ,			_snake_case="cpu"				):
							lowerCAmelCase =		torch.utils.data.DataLoader(dataset=_snake_case    ,			batch_size=_snake_case    ,			collate_fn=_snake_case				)

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

							lowerCAmelCase =		torch.utils.data.DataLoader(
							    dataset=Dataset(_snake_case    ,			_snake_case				)    ,			batch_size=_snake_case    ,			shuffle=_snake_case    ,			collate_fn=_snake_case				)

							return data_loader



def    UpperCAmelCase							(  _snake_case    ,			_snake_case=None    ,			_snake_case="gpt2-medium"    ,			_snake_case=10    ,			_snake_case=64    ,			_snake_case=10    ,			_snake_case=False    ,			_snake_case=False    ,			_snake_case=False    ,			):
							lowerCAmelCase =		'''cuda''' if torch.cuda.is_available() and not no_cuda else '''cpu'''

							print('''Preprocessing {} dataset...'''.format(_snake_case				)				)
							lowerCAmelCase =		time.time()

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

														lowerCAmelCase =		Discriminator(
														    class_size=len(_snake_case				)    ,			pretrained_model=_snake_case    ,			cached_mode=_snake_case    ,			device=_snake_case				).to(_snake_case				)

														lowerCAmelCase =		torchtext_data.Field()
														lowerCAmelCase =		torchtext_data.Field(sequential=_snake_case				)
														lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase =		datasets.SST.splits(
														    _snake_case    ,			_snake_case    ,			fine_grained=_snake_case    ,			train_subtrees=_snake_case    ,			)

														lowerCAmelCase =		[]
														lowerCAmelCase =		[]
														for i in trange(len(_snake_case				)    ,			ascii=_snake_case				):
																					lowerCAmelCase =		TreebankWordDetokenizer().detokenize(vars(train_data[i]				)['''text''']				)
																					lowerCAmelCase =		discriminator.tokenizer.encode(_snake_case				)
																					lowerCAmelCase =		torch.tensor([50256] + seq    ,			device=_snake_case    ,			dtype=torch.long				)
																					x.append(_snake_case				)
																					y.append(classaidx[vars(train_data[i]				)['''label''']]				)
														lowerCAmelCase =		Dataset(_snake_case    ,			_snake_case				)

														lowerCAmelCase =		[]
														lowerCAmelCase =		[]
														for i in trange(len(_snake_case				)    ,			ascii=_snake_case				):
																					lowerCAmelCase =		TreebankWordDetokenizer().detokenize(vars(test_data[i]				)['''text''']				)
																					lowerCAmelCase =		discriminator.tokenizer.encode(_snake_case				)
																					lowerCAmelCase =		torch.tensor([50256] + seq    ,			device=_snake_case    ,			dtype=torch.long				)
																					test_x.append(_snake_case				)
																					test_y.append(classaidx[vars(test_data[i]				)['''label''']]				)
														lowerCAmelCase =		Dataset(_snake_case    ,			_snake_case				)

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

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

														lowerCAmelCase =		Discriminator(
														    class_size=len(_snake_case				)    ,			pretrained_model=_snake_case    ,			cached_mode=_snake_case    ,			device=_snake_case				).to(_snake_case				)

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

																																			if len(_snake_case				) < max_length_seq:
																																										lowerCAmelCase =		torch.tensor([50256] + seq    ,			device=_snake_case    ,			dtype=torch.long				)
																																			else:
																																										print('''Line {} is longer than maximum length {}'''.format(_snake_case    ,			_snake_case				)				)
																																										continue
																																			x.append(_snake_case				)
																																			y.append(d['''label''']				)
																												except Exception:
																																			print('''Error evaluating / tokenizing line {}, skipping it'''.format(_snake_case				)				)
																																			pass

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

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

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

														lowerCAmelCase =		Discriminator(
														    class_size=len(_snake_case				)    ,			pretrained_model=_snake_case    ,			cached_mode=_snake_case    ,			device=_snake_case				).to(_snake_case				)

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

																																			if len(_snake_case				) < max_length_seq:
																																										lowerCAmelCase =		torch.tensor([50256] + seq    ,			device=_snake_case    ,			dtype=torch.long				)
																																			else:
																																										print('''Line {} is longer than maximum length {}'''.format(_snake_case    ,			_snake_case				)				)
																																										continue
																																			x.append(_snake_case				)
																																			y.append(int(np.sum(d['''label''']				) > 0				)				)
																												except Exception:
																																			print('''Error evaluating / tokenizing line {}, skipping it'''.format(_snake_case				)				)
																																			pass

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

														lowerCAmelCase =		{
														    '''class_size''': len(_snake_case				),
														    '''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.'''				)

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

														lowerCAmelCase =		sorted(_snake_case				)
														lowerCAmelCase =		{c: i for i, c in enumerate(_snake_case				)}

														lowerCAmelCase =		Discriminator(
														    class_size=len(_snake_case				)    ,			pretrained_model=_snake_case    ,			cached_mode=_snake_case    ,			device=_snake_case				).to(_snake_case				)

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

																																			try:
																																										lowerCAmelCase =		discriminator.tokenizer.encode(_snake_case				)
																																										if len(_snake_case				) < max_length_seq:
																																																	lowerCAmelCase =		torch.tensor([50256] + seq    ,			device=_snake_case    ,			dtype=torch.long				)

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

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

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

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

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

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

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

														lowerCAmelCase =		time.time()

														lowerCAmelCase =		get_cached_data_loader(_snake_case    ,			_snake_case    ,			_snake_case    ,			shuffle=_snake_case    ,			device=_snake_case				)

														lowerCAmelCase =		get_cached_data_loader(_snake_case    ,			_snake_case    ,			_snake_case    ,			device=_snake_case				)

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

							else:
														lowerCAmelCase =		torch.utils.data.DataLoader(
														    dataset=_snake_case    ,			batch_size=_snake_case    ,			shuffle=_snake_case    ,			collate_fn=_snake_case				)
														lowerCAmelCase =		torch.utils.data.DataLoader(dataset=_snake_case    ,			batch_size=_snake_case    ,			collate_fn=_snake_case				)

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

							lowerCAmelCase =		optim.Adam(discriminator.parameters()    ,			lr=0.0001				)

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

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

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

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

														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(_snake_case    ,			epoch + 1				)    ,			)


if __name__ == "__main__":
		UpperCAmelCase_    =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""")
		UpperCAmelCase_    =parser.parse_args()

		train_discriminator(**(vars(args)))









import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
		import torch


UpperCAmelCase_    =logging.get_logger(__name__)


class 				__UpperCamelCase      :





				'''simple docstring'''



				def __init__(			self							,			UpperCAmelCase_							,			UpperCAmelCase_=21							,			UpperCAmelCase_=[]							,			UpperCAmelCase_=None    ):
											lowerCAmelCase =		model
											lowerCAmelCase =		trace_batch_nums
											lowerCAmelCase =		abort_after_batch_num

											# keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
											lowerCAmelCase =		collections.deque([]							,			UpperCAmelCase_    )
											lowerCAmelCase =		[]
											lowerCAmelCase =		0
											lowerCAmelCase =		0
											lowerCAmelCase =		False
											lowerCAmelCase =		'''                 '''

											self.analyse_model()

											self.register_forward_hook()



				def    __snake_case		(			self							,			UpperCAmelCase_=None    ):
											if frame is not None:
																		self.expand_frame(UpperCAmelCase_    )
											self.frames.append('''\n'''.join(self.frame    )    )
											lowerCAmelCase =		[]  # start a new frame



				def    __snake_case		(			self							,			UpperCAmelCase_    ):
											self.frame.append(UpperCAmelCase_    )



				def    __snake_case		(			self    ):
											print('''\n'''.join(self.frames    )    )
											lowerCAmelCase =		[]



				def    __snake_case		(			self    ):
											lowerCAmelCase =		[]



				def    __snake_case		(			self    ):
											print(F"""\nDetected inf/nan during batch_number={self.batch_number}"""    )
											print(F"""Last {len(self.frames    )} forward frames:"""    )
											print(F"""{"abs min":8} {"abs max":8} metadata"""    )
											print('''\n'''.join(self.frames    )    )
											print('''\n\n'''    )
											lowerCAmelCase =		[]



				def    __snake_case		(			self    ):
											# extract the fully qualified module names, to be able to report at run time. e.g.:
											# encoder.block.2.layer.0.SelfAttention.o
											#
											# for shared weights only the first shared module name will be registered
											lowerCAmelCase =		{m: name for name, m in self.model.named_modules()}
											# self.longest_module_name = max(len(v) for v in self.module_names.values())



				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											if torch.is_tensor(UpperCAmelCase_    ):
																		self.expand_frame(get_abs_min_max(UpperCAmelCase_							,			UpperCAmelCase_    )    )
																		if detect_overflow(UpperCAmelCase_							,			UpperCAmelCase_    ):
																									lowerCAmelCase =		True
											elif var is None:
																		self.expand_frame(F"""{"None":>17} {ctx}"""    )
											else:
																		self.expand_frame(F"""{"not a tensor":>17} {ctx}"""    )



				def    __snake_case		(			self    ):
											self.expand_frame(F"""\n\n{self.prefix} *** Starting batch number={self.batch_number} ***"""    )
											self.expand_frame(F"""{"abs min":8} {"abs max":8} metadata"""    )



				def    __snake_case		(			self    ):
											self.expand_frame(F"""{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"""    )



				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											self.expand_frame(F"""{self.prefix} {self.module_names[module]} {module.__class__.__name__}"""    )

											# params
											for name, p in module.named_parameters(recurse=UpperCAmelCase_    ):
																		self.analyse_variable(UpperCAmelCase_							,			UpperCAmelCase_    )

											# inputs
											if isinstance(UpperCAmelCase_							,			UpperCAmelCase_    ):
																		for i, x in enumerate(UpperCAmelCase_    ):
																									self.analyse_variable(UpperCAmelCase_							,			F"""input[{i}]"""    )
											else:
																		self.analyse_variable(UpperCAmelCase_							,			'''input'''    )

											# outputs
											if isinstance(UpperCAmelCase_							,			UpperCAmelCase_    ):
																		for i, x in enumerate(UpperCAmelCase_    ):
																									# possibly a tuple of tuples
																									if isinstance(UpperCAmelCase_							,			UpperCAmelCase_    ):
																																for j, y in enumerate(UpperCAmelCase_    ):
																																							self.analyse_variable(UpperCAmelCase_							,			F"""output[{i}][{j}]"""    )
																									else:
																																self.analyse_variable(UpperCAmelCase_							,			F"""output[{i}]"""    )
											else:
																		self.analyse_variable(UpperCAmelCase_							,			'''output'''    )

											self.save_frame()



				def    __snake_case		(			self    ):
											self.model.apply(self._register_forward_hook    )



				def    __snake_case		(			self							,			UpperCAmelCase_    ):
											module.register_forward_hook(self.forward_hook    )






				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											# - input is a tuple of packed inputs (could be non-Tensors)
											# - output could be a Tensor or a tuple of Tensors and non-Tensors

											lowerCAmelCase =		False

											lowerCAmelCase =		True if self.batch_number in self.trace_batch_nums else False
											if trace_mode:
																		self.reset_saved_frames()

											if self.total_calls == 0:
																		self.batch_start_frame()
											self.total_calls += 1

											# count batch numbers - the very first forward hook of the batch will be called when the
											# batch completes - i.e. it gets called very last - we know this batch has finished
											if module == self.model:
																		self.batch_number += 1
																		lowerCAmelCase =		True

											self.create_frame(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    )

											# if last_frame_of_batch:
											#     self.batch_end_frame()

											if trace_mode:
																		self.trace_frames()

											if last_frame_of_batch:
																		self.batch_start_frame()

											if self.detected_overflow and not trace_mode:
																		self.dump_saved_frames()

																		# now we can abort, as it's pointless to continue running
																		raise ValueError(
																		    '''DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. '''
																		    '''Please scroll up above this traceback to see the activation values prior to this event.'''    )

											# abort after certain batch if requested to do so
											if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
																		raise ValueError(
																		    F"""DebugUnderflowOverflow: aborting after {self.batch_number} batches due to"""
																		    F""" `abort_after_batch_num={self.abort_after_batch_num}` arg"""    )




def    UpperCAmelCase							(  _snake_case    ,			_snake_case				):
							lowerCAmelCase =		var.abs()
							return F"""{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"""



def    UpperCAmelCase							(  _snake_case    ,			_snake_case				):
							lowerCAmelCase =		False
							if torch.isnan(_snake_case				).any().item():
														lowerCAmelCase =		True
														print(F"""{ctx} has nans"""				)
							if torch.isinf(_snake_case				).any().item():
														lowerCAmelCase =		True
														print(F"""{ctx} has infs"""				)

							# if needed to monitor large elements can enable the following
							if 0:  # and detected:
														lowerCAmelCase =		var[torch.ge(var.abs()    ,			100				)]
														if naaa.numel() > 0:
																					print(F"""{ctx}:  n100={naaa.numel()}"""				)
														lowerCAmelCase =		var[torch.ge(var.abs()    ,			1000				)]
														if naaaa.numel() > 0:
																					print(F"""{ctx}: n1000={naaaa.numel()}"""				)
														lowerCAmelCase =		var[torch.ge(var.abs()    ,			10000				)]
														if naaaaa.numel() > 0:
																					print(F"""{ctx}: n10000={naaaaa.numel()}"""				)

							if 0:
														print(F"""min={var.min():9.2e} max={var.max():9.2e}"""				)

							if 0:
														print(F"""min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})"""				)

							return detected


class 				__UpperCamelCase      (			__UpperCAmelCase	):





				'''simple docstring'''



				__a				:			Tuple					    ="""underflow_overflow"""
				__a				:			Dict					    ="""tpu_metrics_debug"""



