



from __future__ import annotations

import unittest

from transformers import MPNetConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers.models.mpnet.modeling_tf_mpnet import (
	    TFMPNetForMaskedLM,
	    TFMPNetForMultipleChoice,
	    TFMPNetForQuestionAnswering,
	    TFMPNetForSequenceClassification,
	    TFMPNetForTokenClassification,
	    TFMPNetModel,
	)







class 							_a							:

			def __init__(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=13,      SCREAMING_SNAKE_CASE_=7,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=99,      SCREAMING_SNAKE_CASE_=64,      SCREAMING_SNAKE_CASE_=2,      SCREAMING_SNAKE_CASE_=4,      SCREAMING_SNAKE_CASE_=64,      SCREAMING_SNAKE_CASE_="gelu",      SCREAMING_SNAKE_CASE_=0.1,      SCREAMING_SNAKE_CASE_=0.1,      SCREAMING_SNAKE_CASE_=512,      SCREAMING_SNAKE_CASE_=16,      SCREAMING_SNAKE_CASE_=2,      SCREAMING_SNAKE_CASE_=0.0_2,      SCREAMING_SNAKE_CASE_=3,      SCREAMING_SNAKE_CASE_=4,      SCREAMING_SNAKE_CASE_=None,      )					->      Dict:
						UpperCAmelCase_:		List[Any]       			=    parent
						UpperCAmelCase_:		Tuple       			=    batch_size
						UpperCAmelCase_:		Dict       			=    seq_length
						UpperCAmelCase_:		List[str]       			=    is_training
						UpperCAmelCase_:		int       			=    use_input_mask
						UpperCAmelCase_:		List[str]       			=    use_token_type_ids
						UpperCAmelCase_:		Optional[int]       			=    use_labels
						UpperCAmelCase_:		int       			=    vocab_size
						UpperCAmelCase_:		Optional[Any]       			=    hidden_size
						UpperCAmelCase_:		Union[str, Any]       			=    num_hidden_layers
						UpperCAmelCase_:		List[str]       			=    num_attention_heads
						UpperCAmelCase_:		List[str]       			=    intermediate_size
						UpperCAmelCase_:		str       			=    hidden_act
						UpperCAmelCase_:		Optional[Any]       			=    hidden_dropout_prob
						UpperCAmelCase_:		Tuple       			=    attention_probs_dropout_prob
						UpperCAmelCase_:		Optional[Any]       			=    max_position_embeddings
						UpperCAmelCase_:		Optional[Any]       			=    type_vocab_size
						UpperCAmelCase_:		int       			=    type_sequence_label_size
						UpperCAmelCase_:		str       			=    initializer_range
						UpperCAmelCase_:		List[str]       			=    num_labels
						UpperCAmelCase_:		Dict       			=    num_choices
						UpperCAmelCase_:		Optional[Any]       			=    scope

			def     __snake_case					(self				)					->      List[Any]:
						UpperCAmelCase_:		Union[str, Any]       			=    ids_tensor([self.batch_size, self.seq_length],      self.vocab_size				)

						UpperCAmelCase_:		Dict       			=    None
						if self.use_input_mask:
									UpperCAmelCase_:		List[str]       			=    random_attention_mask([self.batch_size, self.seq_length]				)

						UpperCAmelCase_:		Optional[Any]       			=    None
						UpperCAmelCase_:		List[str]       			=    None
						UpperCAmelCase_:		Optional[Any]       			=    None
						if self.use_labels:
									UpperCAmelCase_:		str       			=    ids_tensor([self.batch_size],      self.type_sequence_label_size				)
									UpperCAmelCase_:		Any       			=    ids_tensor([self.batch_size, self.seq_length],      self.num_labels				)
									UpperCAmelCase_:		Optional[int]       			=    ids_tensor([self.batch_size],      self.num_choices				)

						UpperCAmelCase_:		Tuple       			=    MPNetConfig(
						    vocab_size=self.vocab_size,      hidden_size=self.hidden_size,      num_hidden_layers=self.num_hidden_layers,      num_attention_heads=self.num_attention_heads,      intermediate_size=self.intermediate_size,      hidden_act=self.hidden_act,      hidden_dropout_prob=self.hidden_dropout_prob,      attention_probs_dropout_prob=self.attention_probs_dropout_prob,      max_position_embeddings=self.max_position_embeddings,      initializer_range=self.initializer_range,      )
						return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)					->      Union[str, Any]:
						UpperCAmelCase_:		List[Any]       			=    TFMPNetModel(config=SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		List[str]       			=    {"""input_ids""": input_ids, """attention_mask""": input_mask}
						UpperCAmelCase_:		Dict       			=    model(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		Dict       			=    [input_ids, input_mask]
						UpperCAmelCase_:		Dict       			=    model(SCREAMING_SNAKE_CASE_				)
						self.parent.assertEqual(result.last_hidden_state.shape,      (self.batch_size, self.seq_length, self.hidden_size)				)

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)					->      int:
						UpperCAmelCase_:		Optional[Any]       			=    TFMPNetForMaskedLM(config=SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		Any       			=    {"""input_ids""": input_ids, """attention_mask""": input_mask}
						UpperCAmelCase_:		List[Any]       			=    model(SCREAMING_SNAKE_CASE_				)
						self.parent.assertEqual(result.logits.shape,      (self.batch_size, self.seq_length, self.vocab_size)				)

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)					->      List[str]:
						UpperCAmelCase_:		int       			=    TFMPNetForQuestionAnswering(config=SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		int       			=    {
						    """input_ids""": input_ids,
						    """attention_mask""": input_mask,
						}
						UpperCAmelCase_:		Tuple       			=    model(SCREAMING_SNAKE_CASE_				)
						self.parent.assertEqual(result.start_logits.shape,      (self.batch_size, self.seq_length)				)
						self.parent.assertEqual(result.end_logits.shape,      (self.batch_size, self.seq_length)				)

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)					->      int:
						UpperCAmelCase_:		Dict       			=    self.num_labels
						UpperCAmelCase_:		Optional[int]       			=    TFMPNetForSequenceClassification(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		Union[str, Any]       			=    {"""input_ids""": input_ids, """attention_mask""": input_mask}
						UpperCAmelCase_:		Optional[Any]       			=    model(SCREAMING_SNAKE_CASE_				)
						self.parent.assertEqual(result.logits.shape,      (self.batch_size, self.num_labels)				)

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)					->      Any:
						UpperCAmelCase_:		Union[str, Any]       			=    self.num_choices
						UpperCAmelCase_:		List[str]       			=    TFMPNetForMultipleChoice(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		str       			=    tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_,      1				),      (1, self.num_choices, 1)				)
						UpperCAmelCase_:		Dict       			=    tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_,      1				),      (1, self.num_choices, 1)				)
						UpperCAmelCase_:		List[str]       			=    {
						    """input_ids""": multiple_choice_inputs_ids,
						    """attention_mask""": multiple_choice_input_mask,
						}
						UpperCAmelCase_:		List[Any]       			=    model(SCREAMING_SNAKE_CASE_				)
						self.parent.assertEqual(result.logits.shape,      (self.batch_size, self.num_choices)				)

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)					->      str:
						UpperCAmelCase_:		int       			=    self.num_labels
						UpperCAmelCase_:		List[str]       			=    TFMPNetForTokenClassification(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		Optional[Any]       			=    {"""input_ids""": input_ids, """attention_mask""": input_mask}
						UpperCAmelCase_:		Optional[Any]       			=    model(SCREAMING_SNAKE_CASE_				)
						self.parent.assertEqual(result.logits.shape,      (self.batch_size, self.seq_length, self.num_labels)				)

			def     __snake_case					(self				)					->      List[Any]:
						UpperCAmelCase_:		Optional[int]       			=    self.prepare_config_and_inputs()
						((UpperCAmelCase_)			,       (UpperCAmelCase_)			,       (UpperCAmelCase_)			,       (UpperCAmelCase_)			,       (UpperCAmelCase_)			,       (UpperCAmelCase_)):		Union[str, Any]       			=    config_and_inputs
						UpperCAmelCase_:		Union[str, Any]       			=    {"""input_ids""": input_ids, """attention_mask""": input_mask}
						return config, inputs_dict







@require_tf
class 							_a							(      _lowerCAmelCase   ,		_lowerCAmelCase   ,		unittest.TestCase				):
			A					=  (
			    (
			        TFMPNetForMaskedLM,
			        TFMPNetForMultipleChoice,
			        TFMPNetForQuestionAnswering,
			        TFMPNetForSequenceClassification,
			        TFMPNetForTokenClassification,
			        TFMPNetModel,
			    )
			    if is_tf_available()
			    else ()
			)
			A					=  (
			    {
			        '''feature-extraction''': TFMPNetModel,
			        '''fill-mask''': TFMPNetForMaskedLM,
			        '''question-answering''': TFMPNetForQuestionAnswering,
			        '''text-classification''': TFMPNetForSequenceClassification,
			        '''token-classification''': TFMPNetForTokenClassification,
			        '''zero-shot''': TFMPNetForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			A					=  False
			A					=  False

			def     __snake_case					(self				)					->      Tuple:
						UpperCAmelCase_:		int       			=    TFMPNetModelTester(self				)
						UpperCAmelCase_:		Any       			=    ConfigTester(self,      config_class=SCREAMING_SNAKE_CASE_,      hidden_size=37				)

			def     __snake_case					(self				)					->      int:
						self.config_tester.run_common_tests()

			def     __snake_case					(self				)					->      Optional[Any]:
						UpperCAmelCase_:		List[str]       			=    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mpnet_model(*SCREAMING_SNAKE_CASE_				)

			def     __snake_case					(self				)					->      int:
						UpperCAmelCase_:		List[Any]       			=    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mpnet_for_masked_lm(*SCREAMING_SNAKE_CASE_				)

			def     __snake_case					(self				)					->      Optional[int]:
						UpperCAmelCase_:		Tuple       			=    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mpnet_for_question_answering(*SCREAMING_SNAKE_CASE_				)

			def     __snake_case					(self				)					->      str:
						UpperCAmelCase_:		List[Any]       			=    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mpnet_for_sequence_classification(*SCREAMING_SNAKE_CASE_				)

			def     __snake_case					(self				)					->      Union[str, Any]:
						UpperCAmelCase_:		Union[str, Any]       			=    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mpnet_for_multiple_choice(*SCREAMING_SNAKE_CASE_				)

			def     __snake_case					(self				)					->      Any:
						UpperCAmelCase_:		List[Any]       			=    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mpnet_for_token_classification(*SCREAMING_SNAKE_CASE_				)

			@slow
			def     __snake_case					(self				)					->      List[Any]:
						for model_name in ["microsoft/mpnet-base"]:
									UpperCAmelCase_:		Dict       			=    TFMPNetModel.from_pretrained(SCREAMING_SNAKE_CASE_				)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE_				)







@require_tf
class 							_a							(      unittest.TestCase				):

			@slow
			def     __snake_case					(self				)					->      Tuple:
						UpperCAmelCase_:		Dict       			=    TFMPNetModel.from_pretrained("""microsoft/mpnet-base"""				)
						UpperCAmelCase_:		int       			=    tf.constant([[0, 1, 2, 3, 4, 5]]				)
						UpperCAmelCase_:		List[str]       			=    model(SCREAMING_SNAKE_CASE_				)[0]

						UpperCAmelCase_:		Any       			=    [1, 6, 768]
						self.assertEqual(output.shape,      SCREAMING_SNAKE_CASE_				)

						UpperCAmelCase_:		List[Any]       			=    tf.constant(
						    [
						        [
						            [-0.1_0_6_7_1_7_2, 0.0_8_2_1_6_4_7_3, 0.0_0_2_4_5_4_3],
						            [-0.0_3_4_6_5_8_7_9, 0.8_3_5_4_1_1_8, -0.0_3_2_5_2_2_8_8],
						            [-0.0_6_5_6_9_4_7_6, -0.1_2_4_2_4_1_1_1, -0.0_4_9_4_4_3_6],
						        ]
						    ]				)
						tf.debugging.assert_near(output[:, :3, :3],      SCREAMING_SNAKE_CASE_,      atol=1E-4				)











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







def    lowerCAmelCase_  (lowerCAmelCase__:		Dict     ):





			"""simple docstring"""


			random.seed(lowerCAmelCase__     )
			np.random.seed(lowerCAmelCase__     )
			torch.manual_seed(lowerCAmelCase__     )
			torch.cuda.manual_seed_all(lowerCAmelCase__     )







def    lowerCAmelCase_  (lowerCAmelCase__:		List[str]		,	lowerCAmelCase__:		List[str]		,	lowerCAmelCase__:		Dict     ):





			"""simple docstring"""


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







def    lowerCAmelCase_  (lowerCAmelCase__:		List[Any]="gpt2"     ):





			"""simple docstring"""


			UpperCAmelCase_:		List[str]       			=    GPTaLMHeadModel.from_pretrained(lowerCAmelCase__		,	output_hidden_states=lowerCAmelCase__     )
			torch.save(model.state_dict()		,	model_name + """local.pt"""     )
			return model







def    lowerCAmelCase_  (lowerCAmelCase__:		Dict		,	lowerCAmelCase__:		Tuple		,	lowerCAmelCase__:		str     ):





			"""simple docstring"""


			UpperCAmelCase_:		Optional[Any]       			=    copy.deepcopy(lowerCAmelCase__     )
			model.to(lowerCAmelCase__     )

			UpperCAmelCase_:		Dict       			=    ["""bias""", """LayerNorm.weight"""]
			UpperCAmelCase_:		Optional[int]       			=    [
			    {
			        """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},
			]
			UpperCAmelCase_:		str       			=    AdamW(lowerCAmelCase__		,	lr=5e-5		,	eps=1e-8     )
			UpperCAmelCase_:		Tuple       			=    get_linear_schedule_with_warmup(lowerCAmelCase__		,	0		,	lowerCAmelCase__     )
			torch.cuda.empty_cache()
			return model, lm_optimizer, lm_scheduler







def    lowerCAmelCase_  (lowerCAmelCase__:		int		,	lowerCAmelCase__:		Union[str, Any]		,	lowerCAmelCase__:		Union[str, Any]		,	lowerCAmelCase__:		str     ):





			"""simple docstring"""


			UpperCAmelCase_:		str       			=    np.array(lowerCAmelCase__     ).mean()
			UpperCAmelCase_:		Optional[Any]       			=    np.array(lowerCAmelCase__     ).std()
			UpperCAmelCase_:		Tuple       			=    (real_perps - avg) / std
			UpperCAmelCase_:		Tuple       			=    list(zip(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__     )     )
			joblib.dump(lowerCAmelCase__		,	lowerCAmelCase__     )







def    lowerCAmelCase_  (lowerCAmelCase__:		Any		,	lowerCAmelCase__:		Tuple		,	lowerCAmelCase__:		Optional[int]		,	lowerCAmelCase__:		Any		,	lowerCAmelCase__:		Union[str, Any]		,	lowerCAmelCase__:		int		,	lowerCAmelCase__:		Optional[int]		,	lowerCAmelCase__:		Optional[Any]="dev.jbl"		,	lowerCAmelCase__:		Tuple=recopy_gpta		,	):





			"""simple docstring"""


			UpperCAmelCase_:		int       			=    []
			UpperCAmelCase_:		int       			=    []
			UpperCAmelCase_:		int       			=    []

			# Initialize the transformer model
			UpperCAmelCase_:		Optional[Any]       			=    copy.deepcopy(lowerCAmelCase__     )
			orig_model.to(device="""cpu"""     )
			torch.cuda.empty_cache()

			# Compute perplexity of initial transformer model for comparison
			model.train()
			UpperCAmelCase_			,       UpperCAmelCase_			,       UpperCAmelCase_:		List[str]       			=    recopy_model(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__     )

			for step in tqdm(range(lowerCAmelCase__     )     ):
						UpperCAmelCase_:		Union[str, Any]       			=    torch.zeros((1, context_len)		,	dtype=torch.long		,	device=lowerCAmelCase__     )
						UpperCAmelCase_:		str       			=    random.choice(lowerCAmelCase__     )
						UpperCAmelCase_:		int       			=    random.randint(0		,	len(story[0]     ) - context_len - 1     )
						UpperCAmelCase_:		Optional[int]       			=    story[0][start : start + context_len]
						lm_optimizer.zero_grad()
						UpperCAmelCase_:		Dict       			=    model(lowerCAmelCase__		,	labels=lowerCAmelCase__     )
						UpperCAmelCase_:		List[str]       			=    outputs[0]
						UpperCAmelCase_:		Optional[Any]       			=    compute_perplexity(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__     )
						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
						UpperCAmelCase_:		int       			=    compute_perplexity(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__     )

						# Periodically save the stored (X, IG(X)) pairs
						if step % 1_0_0_0 == 0 and step > 1:
									intermittent_save(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__     )

						# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
						UpperCAmelCase_			,       UpperCAmelCase_			,       UpperCAmelCase_:		str       			=    recopy_model(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__     )

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

			intermittent_save(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__     )







def    lowerCAmelCase_  (lowerCAmelCase__:		Optional[Any]		,	lowerCAmelCase__:		Any="data/tokenized_stories_train_wikitext103.jbl"		,	lowerCAmelCase__:		Any=1_0_0		,	lowerCAmelCase__:		Optional[int]=1_0_2_6		,	lowerCAmelCase__:		Optional[int]=True     ):





			"""simple docstring"""


			UpperCAmelCase_:		Union[str, Any]       			=    joblib.load(lowerCAmelCase__     )
			print("""data loaded"""     )
			UpperCAmelCase_:		List[Any]       			=    []
			if trim:
						for i, example in enumerate(lowerCAmelCase__     ):
									if len(example[0]     ) > min_len:
												UpperCAmelCase_:		Optional[int]       			=    random.randint(0		,	len(example[0]     ) - context_len - 1     )
												objective_set.append(example[0, start : start + context_len]     )
									if len(lowerCAmelCase__     ) >= number:
												break
						UpperCAmelCase_:		Union[str, Any]       			=    []
						for j in range(i + 1		,	len(lowerCAmelCase__     )     ):
									if len(data[j][0]     ) > min_len:
												train_data.append(data[j]     )
			else:
						UpperCAmelCase_:		Optional[int]       			=    data[0:number]
						UpperCAmelCase_:		Tuple       			=    data[number:]

			joblib.dump(lowerCAmelCase__		,	"""objective_set.jbl"""     )
			print("""objective set saved"""     )
			return train_data, objective_set







def    lowerCAmelCase_  (lowerCAmelCase__:		Optional[int]		,	lowerCAmelCase__:		List[str]		,	lowerCAmelCase__:		int		,	lowerCAmelCase__:		Tuple		,	lowerCAmelCase__:		int=5_0		,	lowerCAmelCase__:		List[Any]="secondary_learner.pt"     ):





			"""simple docstring"""


			UpperCAmelCase_:		Tuple       			=    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
			UpperCAmelCase_:		int       			=    train_dataset[:5_1_2]
			UpperCAmelCase_:		Any       			=    train_dataset[5_1_2:]
			UpperCAmelCase_:		Union[str, Any]       			=    DataLoader(lowerCAmelCase__		,	shuffle=lowerCAmelCase__		,	batch_size=lowerCAmelCase__     )
			UpperCAmelCase_:		Optional[int]       			=    DataLoader(lowerCAmelCase__		,	shuffle=lowerCAmelCase__		,	batch_size=lowerCAmelCase__     )

			# secondary learner model set up
			UpperCAmelCase_:		Optional[int]       			=    nn.MSELoss()
			UpperCAmelCase_:		str       			=    nn.MSELoss(reduction="""sum"""     )
			secondary_learner.to(lowerCAmelCase__     )
			UpperCAmelCase_:		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
			UpperCAmelCase_:		Any       			=    float("""inf"""     )
			# Iterate through batches until we've used max_steps batches
			for epoch in range(int(lowerCAmelCase__     )     ):
						UpperCAmelCase_:		Optional[Any]       			=    0.0
						secondary_learner.train()
						for step, batch in enumerate(lowerCAmelCase__     ):
									UpperCAmelCase_:		Union[str, Any]       			=    batch[0].to(lowerCAmelCase__     )
									UpperCAmelCase_:		List[Any]       			=    batch[1].to(lowerCAmelCase__     )
									UpperCAmelCase_:		Tuple       			=    secondary_learner(lowerCAmelCase__     )
									q_optimizer.zero_grad()
									UpperCAmelCase_:		Any       			=    loss(lowerCAmelCase__		,	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(lowerCAmelCase__     )):
												UpperCAmelCase_:		int       			=    tr_q_loss / (step + 1)

												secondary_learner.eval()
												UpperCAmelCase_:		Optional[Any]       			=    0.0
												UpperCAmelCase_:		Union[str, Any]       			=    0.0
												UpperCAmelCase_:		Any       			=    []
												UpperCAmelCase_:		Any       			=    []
												# Compute performance of the secondary learner after this batch
												for stepa, batcha in enumerate(lowerCAmelCase__     ):
															UpperCAmelCase_:		Optional[Any]       			=    batcha[0].to(lowerCAmelCase__     )
															UpperCAmelCase_:		int       			=    batcha[1].to(lowerCAmelCase__     )
															UpperCAmelCase_:		Dict       			=    secondary_learner(lowerCAmelCase__     )
															q_lossa += test_loss(lowerCAmelCase__		,	lowerCAmelCase__     ).item()
															sum_qa += torch.sum(lowerCAmelCase__     ).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(lowerCAmelCase__     )
												print(
												    """Epoch: """		,	lowerCAmelCase__		,	"""step: """		,	lowerCAmelCase__		,	"""Avg. q:"""		,	sum_qa / len(lowerCAmelCase__     )		,	"""Train Loss: """		,	lowerCAmelCase__		,	"""Test Loss: """		,	lowerCAmelCase__		,	)
												if q_lossa < best_test_loss:
															joblib.dump((predicted, actual)		,	"""pred_vs_actual.jbl"""     )
															torch.save(secondary_learner.state_dict()		,	lowerCAmelCase__     )
															UpperCAmelCase_:		str       			=    q_lossa

									secondary_learner.train()
			return secondary_learner







class 							_a							(      nn.Module				):

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

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_				)					->      Any:
						UpperCAmelCase_:		Dict       			=    torch.max(self.conv(self.embeddings(SCREAMING_SNAKE_CASE_				).squeeze(1				).transpose(1,      2				)				),      2				)[0]
						UpperCAmelCase_:		Dict       			=    self.fc(SCREAMING_SNAKE_CASE_				)
						return qs.squeeze(1				)

			@classmethod
			def     __snake_case					(cls,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)					->      Union[str, Any]:
						UpperCAmelCase_:		Optional[int]       			=    cls(SCREAMING_SNAKE_CASE_				)  # this calls __init__
						UpperCAmelCase_:		Any       			=    torch.load(SCREAMING_SNAKE_CASE_				)
						secondary_learner.load_state_dict(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		str       			=    model.transformer.wte
						UpperCAmelCase_:		Optional[Any]       			=    copy.deepcopy(model.transformer.wte.weight				)
						return secondary_learner






