




'''simple docstring'''






import copy
import logging
import random

import joblib
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup


A_ 			=		logging.getLogger(__name__)






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE		)						->      Union[str, Any]:
						random.seed(__SCREAMING_SNAKE_CASE		)
						np.random.seed(__SCREAMING_SNAKE_CASE		)
						torch.manual_seed(__SCREAMING_SNAKE_CASE		)
						torch.cuda.manual_seed_all(__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Any:
						model.eval()
						snake_case__					:      List[str]  			=						next(model.parameters()		).device
						snake_case__					:      Optional[Any]  			=						1
						snake_case__					:      Dict  			=						torch.zeros((eval_batch_size, context_len)							,			dtype=torch.long							,			device=__SCREAMING_SNAKE_CASE		)
						snake_case__					:      str  			=						DataLoader(__SCREAMING_SNAKE_CASE							,			shuffle=__SCREAMING_SNAKE_CASE							,			batch_size=__SCREAMING_SNAKE_CASE		)
						snake_case__					:      List[Any]  			=						torch.zeros(1							,			device=__SCREAMING_SNAKE_CASE		)
						snake_case__					:      Union[str, Any]  			=						0
						for batch in eval_dataloader:
												batch.to(__SCREAMING_SNAKE_CASE		)
												# pad
												context.zero_()
												for i in range(__SCREAMING_SNAKE_CASE		):
																		snake_case__					:      Optional[int]  			=						batch[i]
												snake_case__					:      Union[str, Any]  			=						model(__SCREAMING_SNAKE_CASE							,			labels=__SCREAMING_SNAKE_CASE		)
												eval_loss += outputs[0].sum().item()
												nb_eval_examples += batch.size(0		)
						snake_case__					:      Optional[int]  			=						eval_loss / nb_eval_examples
						snake_case__					:      Tuple  			=						torch.exp(__SCREAMING_SNAKE_CASE		)
						model.train()
						return perplexity






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE="gpt2"		)						->      Any:
						snake_case__					:      Dict  			=						GPTaLMHeadModel.from_pretrained(__SCREAMING_SNAKE_CASE							,			output_hidden_states=__SCREAMING_SNAKE_CASE		)
						torch.save(model.state_dict()							,			model_name + 'local.pt'		)
						return model






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      List[Any]:
						snake_case__					:      Dict  			=						copy.deepcopy(__SCREAMING_SNAKE_CASE		)
						model.to(__SCREAMING_SNAKE_CASE		)

						snake_case__					:      List[str]  			=						['bias', 'LayerNorm.weight']
						snake_case__					:      Optional[Any]  			=						[
						    {
						        'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay		)],
						        'weight_decay': 0.0,
						    },
						    {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay		)], 'weight_decay': 0.0},
						]
						snake_case__					:      int  			=						AdamW(__SCREAMING_SNAKE_CASE							,			lr=5E-5							,			eps=1E-8		)
						snake_case__					:      List[str]  			=						get_linear_schedule_with_warmup(__SCREAMING_SNAKE_CASE							,			0							,			__SCREAMING_SNAKE_CASE		)
						torch.cuda.empty_cache()
						return model, lm_optimizer, lm_scheduler






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Union[str, Any]:
						snake_case__					:      Any  			=						np.array(__SCREAMING_SNAKE_CASE		).mean()
						snake_case__					:      int  			=						np.array(__SCREAMING_SNAKE_CASE		).std()
						snake_case__					:      Union[str, Any]  			=						(real_perps - avg) / std
						snake_case__					:      Optional[int]  			=						list(zip(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)		)
						joblib.dump(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE="dev.jbl"							,			__SCREAMING_SNAKE_CASE=recopy_gpta							,			)						->      Optional[int]:
						snake_case__					:      Optional[Any]  			=						[]
						snake_case__					:      Dict  			=						[]
						snake_case__					:      Optional[int]  			=						[]

						# Initialize the transformer model
						snake_case__					:      Optional[int]  			=						copy.deepcopy(__SCREAMING_SNAKE_CASE		)
						orig_model.to(device='cpu'		)
						torch.cuda.empty_cache()

						# Compute perplexity of initial transformer model for comparison
						model.train()
						snake_case__			,       snake_case__			,       snake_case__					:      Union[str, Any]  			=						recopy_model(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)

						for step in tqdm(range(__SCREAMING_SNAKE_CASE		)		):
												snake_case__					:      List[str]  			=						torch.zeros((1, context_len)							,			dtype=torch.long							,			device=__SCREAMING_SNAKE_CASE		)
												snake_case__					:      str  			=						random.choice(__SCREAMING_SNAKE_CASE		)
												snake_case__					:      Optional[Any]  			=						random.randint(0							,			len(story[0]		) - context_len - 1		)
												snake_case__					:      Optional[Any]  			=						story[0][start : start + context_len]
												lm_optimizer.zero_grad()
												snake_case__					:      List[str]  			=						model(__SCREAMING_SNAKE_CASE							,			labels=__SCREAMING_SNAKE_CASE		)
												snake_case__					:      Any  			=						outputs[0]
												snake_case__					:      Optional[Any]  			=						compute_perplexity(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)
												model.train()
												lm_loss.backward()
												# Do LM backprop
												torch.nn.utils.clip_grad_norm_(model.parameters()							,			3.0		)
												lm_optimizer.step()
												lm_scheduler.step()  # Update learning rate schedule

												# Compute perplexity after back-propagating on the selected context
												snake_case__					:      Dict  			=						compute_perplexity(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)

												# Periodically save the stored (X, IG(X)) pairs
												if step % 1000 == 0 and step > 1:
																		intermittent_save(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)

												# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
												snake_case__			,       snake_case__			,       snake_case__					:      List[Any]  			=						recopy_model(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)

												past_perps.append(past_perp.item()		)
												real_perps.append(orig_perp - real_perp.item()		)
												contexts.append(np.array(context.cpu()		)		)

						intermittent_save(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE="data/tokenized_stories_train_wikitext103.jbl"							,			__SCREAMING_SNAKE_CASE=100							,			__SCREAMING_SNAKE_CASE=1026							,			__SCREAMING_SNAKE_CASE=True		)						->      Tuple:
						snake_case__					:      List[Any]  			=						joblib.load(__SCREAMING_SNAKE_CASE		)
						print('data loaded'		)
						snake_case__					:      List[str]  			=						[]
						if trim:
												for i, example in enumerate(__SCREAMING_SNAKE_CASE		):
																		if len(example[0]		) > min_len:
																								snake_case__					:      Union[str, Any]  			=						random.randint(0							,			len(example[0]		) - context_len - 1		)
																								objective_set.append(example[0, start : start + context_len]		)
																		if len(__SCREAMING_SNAKE_CASE		) >= number:
																								break
												snake_case__					:      int  			=						[]
												for j in range(i + 1							,			len(__SCREAMING_SNAKE_CASE		)		):
																		if len(data[j][0]		) > min_len:
																								train_data.append(data[j]		)
						else:
												snake_case__					:      Dict  			=						data[0:number]
												snake_case__					:      Optional[int]  			=						data[number:]

						joblib.dump(__SCREAMING_SNAKE_CASE							,			'objective_set.jbl'		)
						print('objective set saved'		)
						return train_data, objective_set






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE=50							,			__SCREAMING_SNAKE_CASE="secondary_learner.pt"		)						->      Optional[int]:
						snake_case__					:      Dict  			=						torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'		)
						# We will use the first 512 pairs from our dataset as a test set for
						# our secondary learner and the rest to train
						snake_case__					:      Optional[int]  			=						train_dataset[:512]
						snake_case__					:      Optional[Any]  			=						train_dataset[512:]
						snake_case__					:      Dict  			=						DataLoader(__SCREAMING_SNAKE_CASE							,			shuffle=__SCREAMING_SNAKE_CASE							,			batch_size=__SCREAMING_SNAKE_CASE		)
						snake_case__					:      Any  			=						DataLoader(__SCREAMING_SNAKE_CASE							,			shuffle=__SCREAMING_SNAKE_CASE							,			batch_size=__SCREAMING_SNAKE_CASE		)

						# secondary learner model set up
						snake_case__					:      str  			=						nn.MSELoss()
						snake_case__					:      Dict  			=						nn.MSELoss(reduction='sum'		)
						secondary_learner.to(__SCREAMING_SNAKE_CASE		)
						snake_case__					:      Optional[int]  			=						torch.optim.Adam(secondary_learner.parameters()							,			lr=0.00001		)
						secondary_learner.train()

						# TODO in original code this is written as number of actual batches seen
						# not number of items seen but other places it is number of items instead.
						# improve consistency! changed this to epochs for clarity
						snake_case__					:      Optional[Any]  			=						float('inf'		)
						# Iterate through batches until we've used max_steps batches
						for epoch in range(int(__SCREAMING_SNAKE_CASE		)		):
												snake_case__					:      str  			=						0.0
												secondary_learner.train()
												for step, batch in enumerate(__SCREAMING_SNAKE_CASE		):
																		snake_case__					:      Tuple  			=						batch[0].to(__SCREAMING_SNAKE_CASE		)
																		snake_case__					:      Optional[Any]  			=						batch[1].to(__SCREAMING_SNAKE_CASE		)
																		snake_case__					:      List[Any]  			=						secondary_learner(__SCREAMING_SNAKE_CASE		)
																		q_optimizer.zero_grad()
																		snake_case__					:      List[Any]  			=						loss(__SCREAMING_SNAKE_CASE							,			real_q.float()		)
																		q_loss.backward()
																		q_optimizer.step()
																		tr_q_loss += q_loss.item()

																		# model trains fairly quickly so we won't wait for a full epoch
																		# eval is triggered at eval_freq and end of epochs
																		if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(__SCREAMING_SNAKE_CASE		)):
																								snake_case__					:      Optional[int]  			=						tr_q_loss / (step + 1)

																								secondary_learner.eval()
																								snake_case__					:      List[str]  			=						0.0
																								snake_case__					:      Union[str, Any]  			=						0.0
																								snake_case__					:      int  			=						[]
																								snake_case__					:      Optional[Any]  			=						[]
																								# Compute performance of the secondary learner after this batch
																								for stepa, batcha in enumerate(__SCREAMING_SNAKE_CASE		):
																														snake_case__					:      Tuple  			=						batcha[0].to(__SCREAMING_SNAKE_CASE		)
																														snake_case__					:      int  			=						batcha[1].to(__SCREAMING_SNAKE_CASE		)
																														snake_case__					:      Union[str, Any]  			=						secondary_learner(__SCREAMING_SNAKE_CASE		)
																														q_lossa += test_loss(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		).item()
																														sum_qa += torch.sum(__SCREAMING_SNAKE_CASE		).item()
																														for ei, i in enumerate(predicted_qa.cpu().detach().numpy()		):
																																				predicted.append(i.item()		)
																														for ei, i in enumerate(real_qa.cpu().detach().numpy()		):
																																				actual.append(i.item()		)

																								q_lossa /= len(__SCREAMING_SNAKE_CASE		)
																								print(
																								    'Epoch: '							,			__SCREAMING_SNAKE_CASE							,			'step: '							,			__SCREAMING_SNAKE_CASE							,			'Avg. q:'							,			sum_qa / len(__SCREAMING_SNAKE_CASE		)							,			'Train Loss: '							,			__SCREAMING_SNAKE_CASE							,			'Test Loss: '							,			__SCREAMING_SNAKE_CASE							,			)
																								if q_lossa < best_test_loss:
																														joblib.dump((predicted, actual)							,			'pred_vs_actual.jbl'		)
																														torch.save(secondary_learner.state_dict()							,			__SCREAMING_SNAKE_CASE		)
																														snake_case__					:      Any  			=						q_lossa

																		secondary_learner.train()
						return secondary_learner






class 					lowercase_			(	nn.Module       ):





					def __init__(    self    :				List[Any]    ,       __lowerCamelCase    :				Optional[Any] ):
											super(UpperCamelCase__    ,       self ).__init__()
											snake_case__					:      str  			=						model.transformer.wte
											snake_case__					:      Any  			=						copy.deepcopy(model.transformer.wte.weight )
											snake_case__					:      int  			=						nn.Convad(self.embeddings.weight.size(1 )    ,       256    ,       3    ,       padding=1 )
											snake_case__					:      str  			=						nn.Sequential(nn.Linear(256    ,       32 )    ,       nn.Dropout(p=0.1 )    ,       nn.Linear(32    ,       32 )    ,       nn.Linear(32    ,       1 ) )





					def  _lowerCAmelCase			(    self    :				Optional[Any]    ,       __lowerCamelCase    :				str ):
											snake_case__					:      Any  			=						torch.max(self.conv(self.embeddings(UpperCamelCase__ ).squeeze(1 ).transpose(1    ,       2 ) )    ,       2 )[0]
											snake_case__					:      int  			=						self.fc(UpperCamelCase__ )
											return qs.squeeze(1 )





					@classmethod
					def  _lowerCAmelCase			(    cls    :				Optional[int]    ,       __lowerCamelCase    :				Optional[int]    ,       __lowerCamelCase    :				Dict ):
											snake_case__					:      str  			=						cls(UpperCamelCase__ )  # this calls __init__
											snake_case__					:      Any  			=						torch.load(UpperCamelCase__ )
											secondary_learner.load_state_dict(UpperCamelCase__ )
											snake_case__					:      Optional[int]  			=						model.transformer.wte
											snake_case__					:      str  			=						copy.deepcopy(model.transformer.wte.weight )
											return secondary_learner






'''simple docstring'''






import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
      from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


A_ 			=		get_tests_dir("fixtures/test_sentencepiece.model")






@require_torch
@require_torchaudio
@require_sentencepiece
class 					lowercase_			(	unittest.TestCase       ):





     def  _lowerCAmelCase			(    self    :				List[str] ):
           snake_case__					:      Dict  			=						tempfile.mkdtemp()

           snake_case__					:      Union[str, Any]  			=						['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
           snake_case__					:      str  			=						dict(zip(__lowerCamelCase    ,       range(len(__lowerCamelCase ) ) ) )
           snake_case__					:      Union[str, Any]  			=						Path(self.tmpdirname )
           save_json(__lowerCamelCase    ,       save_dir / VOCAB_FILES_NAMES['vocab_file'] )
           if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
                 copyfile(__lowerCamelCase    ,       save_dir / VOCAB_FILES_NAMES['spm_file'] )

           snake_case__					:      Optional[Any]  			=						SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
           tokenizer.save_pretrained(self.tmpdirname )

           snake_case__					:      str  			=						{
               'feature_size': 24,
               'num_mel_bins': 24,
               'padding_value': 0.0,
               'sampling_rate': 16000,
               'return_attention_mask': False,
               'do_normalize': True,
           }
           save_json(__lowerCamelCase    ,       save_dir / FEATURE_EXTRACTOR_NAME )





     def  _lowerCAmelCase			(    self    :				str    ,       **__lowerCamelCase    :				int ):
           return SpeechaTextTokenizer.from_pretrained(self.tmpdirname    ,       **__lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				int    ,       **__lowerCamelCase    :				List[Any] ):
           return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname    ,       **__lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				List[Any] ):
           shutil.rmtree(self.tmpdirname )





     def  _lowerCAmelCase			(    self    :				Dict ):
           snake_case__					:      Dict  			=						self.get_tokenizer()
           snake_case__					:      Any  			=						self.get_feature_extractor()

           snake_case__					:      Tuple  			=						SpeechaTextProcessor(tokenizer=__lowerCamelCase    ,       feature_extractor=__lowerCamelCase )

           processor.save_pretrained(self.tmpdirname )
           snake_case__					:      Any  			=						SpeechaTextProcessor.from_pretrained(self.tmpdirname )

           self.assertEqual(processor.tokenizer.get_vocab()    ,       tokenizer.get_vocab() )
           self.assertIsInstance(processor.tokenizer    ,       __lowerCamelCase )

           self.assertEqual(processor.feature_extractor.to_json_string()    ,       feature_extractor.to_json_string() )
           self.assertIsInstance(processor.feature_extractor    ,       __lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				List[Any] ):
           snake_case__					:      Any  			=						SpeechaTextProcessor(
               tokenizer=self.get_tokenizer()    ,       feature_extractor=self.get_feature_extractor() )
           processor.save_pretrained(self.tmpdirname )

           snake_case__					:      Tuple  			=						self.get_tokenizer(bos_token='(BOS)'    ,       eos_token='(EOS)' )
           snake_case__					:      int  			=						self.get_feature_extractor(do_normalize=__lowerCamelCase    ,       padding_value=1.0 )

           snake_case__					:      Any  			=						SpeechaTextProcessor.from_pretrained(
               self.tmpdirname    ,       bos_token='(BOS)'    ,       eos_token='(EOS)'    ,       do_normalize=__lowerCamelCase    ,       padding_value=1.0 )

           self.assertEqual(processor.tokenizer.get_vocab()    ,       tokenizer_add_kwargs.get_vocab() )
           self.assertIsInstance(processor.tokenizer    ,       __lowerCamelCase )

           self.assertEqual(processor.feature_extractor.to_json_string()    ,       feature_extractor_add_kwargs.to_json_string() )
           self.assertIsInstance(processor.feature_extractor    ,       __lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				Any ):
           snake_case__					:      int  			=						self.get_feature_extractor()
           snake_case__					:      Optional[Any]  			=						self.get_tokenizer()

           snake_case__					:      List[str]  			=						SpeechaTextProcessor(tokenizer=__lowerCamelCase    ,       feature_extractor=__lowerCamelCase )

           snake_case__					:      List[str]  			=						floats_list((3, 1000) )

           snake_case__					:      Tuple  			=						feature_extractor(__lowerCamelCase    ,       return_tensors='np' )
           snake_case__					:      str  			=						processor(__lowerCamelCase    ,       return_tensors='np' )

           for key in input_feat_extract.keys():
                 self.assertAlmostEqual(input_feat_extract[key].sum()    ,       input_processor[key].sum()    ,       delta=1E-2 )





     def  _lowerCAmelCase			(    self    :				Dict ):
           snake_case__					:      Optional[int]  			=						self.get_feature_extractor()
           snake_case__					:      Optional[Any]  			=						self.get_tokenizer()

           snake_case__					:      Tuple  			=						SpeechaTextProcessor(tokenizer=__lowerCamelCase    ,       feature_extractor=__lowerCamelCase )

           snake_case__					:      Optional[int]  			=						'This is a test string'

           snake_case__					:      int  			=						processor(text=__lowerCamelCase )

           snake_case__					:      Tuple  			=						tokenizer(__lowerCamelCase )

           for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]    ,       encoded_processor[key] )





     def  _lowerCAmelCase			(    self    :				int ):
           snake_case__					:      Optional[int]  			=						self.get_feature_extractor()
           snake_case__					:      int  			=						self.get_tokenizer()

           snake_case__					:      str  			=						SpeechaTextProcessor(tokenizer=__lowerCamelCase    ,       feature_extractor=__lowerCamelCase )

           snake_case__					:      str  			=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           snake_case__					:      List[str]  			=						processor.batch_decode(__lowerCamelCase )
           snake_case__					:      Optional[int]  			=						tokenizer.batch_decode(__lowerCamelCase )

           self.assertListEqual(__lowerCamelCase    ,       __lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				str ):
           snake_case__					:      List[str]  			=						self.get_feature_extractor()
           snake_case__					:      int  			=						self.get_tokenizer()

           snake_case__					:      int  			=						SpeechaTextProcessor(tokenizer=__lowerCamelCase    ,       feature_extractor=__lowerCamelCase )

           self.assertListEqual(
               processor.model_input_names    ,       feature_extractor.model_input_names    ,       msg='`processor` and `feature_extractor` model input names do not match'    ,       )
