



from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
	import tensorflow as tf

	from transformers import TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
	from PIL import Image

	from transformers import ConvNextImageProcessor







class 							_a							:

			def __init__(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=13,      SCREAMING_SNAKE_CASE_=32,      SCREAMING_SNAKE_CASE_=3,      SCREAMING_SNAKE_CASE_=4,      SCREAMING_SNAKE_CASE_=[10, 20, 30, 40],      SCREAMING_SNAKE_CASE_=[2, 2, 3, 2],      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=37,      SCREAMING_SNAKE_CASE_="gelu",      SCREAMING_SNAKE_CASE_=10,      SCREAMING_SNAKE_CASE_=0.0_2,      SCREAMING_SNAKE_CASE_=3,      SCREAMING_SNAKE_CASE_=None,      )					->      Tuple:
						UpperCAmelCase_:		Union[str, Any]       			=    parent
						UpperCAmelCase_:		Dict       			=    batch_size
						UpperCAmelCase_:		str       			=    image_size
						UpperCAmelCase_:		Optional[int]       			=    num_channels
						UpperCAmelCase_:		List[Any]       			=    num_stages
						UpperCAmelCase_:		Optional[int]       			=    hidden_sizes
						UpperCAmelCase_:		Tuple       			=    depths
						UpperCAmelCase_:		str       			=    is_training
						UpperCAmelCase_:		Optional[Any]       			=    use_labels
						UpperCAmelCase_:		int       			=    intermediate_size
						UpperCAmelCase_:		int       			=    hidden_act
						UpperCAmelCase_:		int       			=    type_sequence_label_size
						UpperCAmelCase_:		Tuple       			=    initializer_range
						UpperCAmelCase_:		int       			=    scope

			def     __snake_case					(self				)					->      List[str]:
						UpperCAmelCase_:		Optional[Any]       			=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

						UpperCAmelCase_:		Union[str, Any]       			=    None
						if self.use_labels:
									UpperCAmelCase_:		List[str]       			=    ids_tensor([self.batch_size],      self.type_sequence_label_size				)

						UpperCAmelCase_:		Union[str, Any]       			=    self.get_config()

						return config, pixel_values, labels

			def     __snake_case					(self				)					->      List[Any]:
						return ConvNextConfig(
						    num_channels=self.num_channels,      hidden_sizes=self.hidden_sizes,      depths=self.depths,      num_stages=self.num_stages,      hidden_act=self.hidden_act,      is_decoder=SCREAMING_SNAKE_CASE_,      initializer_range=self.initializer_range,      )

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)					->      Optional[Any]:
						UpperCAmelCase_:		Dict       			=    TFConvNextModel(config=SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		Tuple       			=    model(SCREAMING_SNAKE_CASE_,      training=SCREAMING_SNAKE_CASE_				)
						# expected last hidden states: B, C, H // 32, W // 32
						self.parent.assertEqual(
						    result.last_hidden_state.shape,      (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),      )

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)					->      List[str]:
						UpperCAmelCase_:		int       			=    self.type_sequence_label_size
						UpperCAmelCase_:		List[Any]       			=    TFConvNextForImageClassification(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		Any       			=    model(SCREAMING_SNAKE_CASE_,      labels=SCREAMING_SNAKE_CASE_,      training=SCREAMING_SNAKE_CASE_				)
						self.parent.assertEqual(result.logits.shape,      (self.batch_size, self.type_sequence_label_size)				)

			def     __snake_case					(self				)					->      Any:
						UpperCAmelCase_:		Tuple       			=    self.prepare_config_and_inputs()
						UpperCAmelCase_:		Any       			=    config_and_inputs
						UpperCAmelCase_:		int       			=    {"""pixel_values""": pixel_values}
						return config, inputs_dict







@require_tf
class 							_a							(      _lowerCAmelCase   ,		_lowerCAmelCase   ,		unittest.TestCase				):
			A					=  (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
			A					=  (
			    {'''feature-extraction''': TFConvNextModel, '''image-classification''': TFConvNextForImageClassification}
			    if is_tf_available()
			    else {}
			)

			A					=  False
			A					=  False
			A					=  False
			A					=  False
			A					=  False

			def     __snake_case					(self				)					->      Dict:
						UpperCAmelCase_:		List[str]       			=    TFConvNextModelTester(self				)
						UpperCAmelCase_:		Any       			=    ConfigTester(
						    self,      config_class=SCREAMING_SNAKE_CASE_,      has_text_modality=SCREAMING_SNAKE_CASE_,      hidden_size=37,      )

			@unittest.skip(reason="""ConvNext does not use inputs_embeds"""				)
			def     __snake_case					(self				)					->      int:
						pass

			@unittest.skipIf(
			    not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""				)				) == 0,      reason="""TF does not support backprop for grouped convolutions on CPU.""",      )
			@slow
			def     __snake_case					(self				)					->      Tuple:
						super().test_keras_fit()

			@unittest.skip(reason="""ConvNext does not support input and output embeddings"""				)
			def     __snake_case					(self				)					->      List[str]:
						pass

			def     __snake_case					(self				)					->      Optional[Any]:
						UpperCAmelCase_:		Dict       			=    self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									UpperCAmelCase_:		Optional[Any]       			=    model_class(SCREAMING_SNAKE_CASE_				)
									UpperCAmelCase_:		Optional[int]       			=    inspect.signature(model.call				)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									UpperCAmelCase_:		Any       			=    [*signature.parameters.keys()]

									UpperCAmelCase_:		int       			=    ["""pixel_values"""]
									self.assertListEqual(arg_names[:1],      SCREAMING_SNAKE_CASE_				)

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

			@unittest.skipIf(
			    not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""				)				) == 0,      reason="""TF does not support backprop for grouped convolutions on CPU.""",      )
			def     __snake_case					(self				)					->      Dict:
						super().test_dataset_conversion()

			def     __snake_case					(self				)					->      Optional[Any]:
						def check_hidden_states_output(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				):
									UpperCAmelCase_:		Dict       			=    model_class(SCREAMING_SNAKE_CASE_				)

									UpperCAmelCase_:		Optional[Any]       			=    model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)				)
									UpperCAmelCase_:		Dict       			=    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

									UpperCAmelCase_:		Optional[Any]       			=    self.model_tester.num_stages
									self.assertEqual(len(SCREAMING_SNAKE_CASE_				),      expected_num_stages + 1				)

									# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
									self.assertListEqual(
									    list(hidden_states[0].shape[-2:]				),      [self.model_tester.image_size // 4, self.model_tester.image_size // 4],      )

						UpperCAmelCase_:		Optional[int]       			=    self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									UpperCAmelCase_:		List[str]       			=    True
									check_hidden_states_output(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									UpperCAmelCase_:		Optional[Any]       			=    True

									check_hidden_states_output(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)

			def     __snake_case					(self				)					->      List[Any]:
						UpperCAmelCase_:		str       			=    self.model_tester.prepare_config_and_inputs_for_common()

						def check_equivalence(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_={}				):
									UpperCAmelCase_:		List[Any]       			=    model(SCREAMING_SNAKE_CASE_,      return_dict=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_				)
									UpperCAmelCase_:		Optional[Any]       			=    model(SCREAMING_SNAKE_CASE_,      return_dict=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_				).to_tuple()

									def recursive_check(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				):
												if isinstance(SCREAMING_SNAKE_CASE_,      (List, Tuple)				):
															for tuple_iterable_value, dict_iterable_value in zip(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				):
																		recursive_check(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)
												elif tuple_object is None:
															return
												else:
															self.assertTrue(
															    all(tf.equal(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)				),      msg=(
															        """Tuple and dict output are not equal. Difference:"""
															        f' {tf.math.reduce_max(tf.abs(tuple_object - dict_object				)				)}'
															    ),      )

												recursive_check(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)

						for model_class in self.all_model_classes:
									UpperCAmelCase_:		Optional[int]       			=    model_class(SCREAMING_SNAKE_CASE_				)

									UpperCAmelCase_:		Optional[int]       			=    self._prepare_for_class(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)
									UpperCAmelCase_:		List[Any]       			=    self._prepare_for_class(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)
									check_equivalence(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)

									UpperCAmelCase_:		Any       			=    self._prepare_for_class(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      return_labels=SCREAMING_SNAKE_CASE_				)
									UpperCAmelCase_:		Dict       			=    self._prepare_for_class(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      return_labels=SCREAMING_SNAKE_CASE_				)
									check_equivalence(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)

									UpperCAmelCase_:		Any       			=    self._prepare_for_class(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)
									UpperCAmelCase_:		Dict       			=    self._prepare_for_class(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)
									check_equivalence(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      {"""output_hidden_states""": True}				)

									UpperCAmelCase_:		Any       			=    self._prepare_for_class(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      return_labels=SCREAMING_SNAKE_CASE_				)
									UpperCAmelCase_:		Tuple       			=    self._prepare_for_class(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      return_labels=SCREAMING_SNAKE_CASE_				)
									check_equivalence(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      {"""output_hidden_states""": True}				)

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

			@slow
			def     __snake_case					(self				)					->      Dict:
						UpperCAmelCase_:		Optional[Any]       			=    TFConvNextModel.from_pretrained("""facebook/convnext-tiny-224"""				)
						self.assertIsNotNone(SCREAMING_SNAKE_CASE_				)







def    lowerCAmelCase_  ():





			"""simple docstring"""


			UpperCAmelCase_:		Optional[Any]       			=    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
			return image







@require_tf
@require_vision
class 							_a							(      unittest.TestCase				):

			@cached_property
			def     __snake_case					(self				)					->      Optional[Any]:
						return ConvNextImageProcessor.from_pretrained("""facebook/convnext-tiny-224"""				) if is_vision_available() else None

			@slow
			def     __snake_case					(self				)					->      str:
						UpperCAmelCase_:		Tuple       			=    TFConvNextForImageClassification.from_pretrained("""facebook/convnext-tiny-224"""				)

						UpperCAmelCase_:		Dict       			=    self.default_image_processor
						UpperCAmelCase_:		Any       			=    prepare_img()
						UpperCAmelCase_:		str       			=    image_processor(images=SCREAMING_SNAKE_CASE_,      return_tensors="""tf"""				)

						# forward pass
						UpperCAmelCase_:		List[Any]       			=    model(**SCREAMING_SNAKE_CASE_				)

						# verify the logits
						UpperCAmelCase_:		Dict       			=    tf.TensorShape((1, 1000)				)
						self.assertEqual(outputs.logits.shape,      SCREAMING_SNAKE_CASE_				)

						UpperCAmelCase_:		Optional[Any]       			=    tf.constant([-0.0_2_6_0, -0.4_7_3_9, 0.1_9_1_1]				)

						tf.debugging.assert_near(outputs.logits[0, :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






