

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


__snake_case							:			Any      		= logging.getLogger(__name__)


def 				_UpperCAmelCase							(	a__):

			'''simple docstring'''



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


def 				_UpperCAmelCase							(	a__     ,  a__     ,  a__):

			'''simple docstring'''



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


def 				_UpperCAmelCase							(	a__="gpt2"):

			'''simple docstring'''



			a_	:	str          =   GPTaLMHeadModel.from_pretrained(a__     ,  output_hidden_states=a__)
			torch.save(model.state_dict()     ,  model_name + """local.pt""")
			return model


def 				_UpperCAmelCase							(	a__     ,  a__     ,  a__):

			'''simple docstring'''



			a_	:	Dict          =   copy.deepcopy(a__)
			model.to(a__)

			a_	:	List[str]          =   ["""bias""", """LayerNorm.weight"""]
			a_	:	Union[str, 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},
			]
			a_	:	int          =   AdamW(a__     ,  lr=5e-5     ,  eps=1e-8)
			a_	:	List[str]          =   get_linear_schedule_with_warmup(a__     ,  0     ,  a__)
			torch.cuda.empty_cache()
			return model, lm_optimizer, lm_scheduler


def 				_UpperCAmelCase							(	a__     ,  a__     ,  a__     ,  a__):

			'''simple docstring'''



			a_	:	int          =   np.array(a__).mean()
			a_	:	Union[str, Any]          =   np.array(a__).std()
			a_	:	List[str]          =   (real_perps - avg) / std
			a_	:	str          =   list(zip(a__     ,  a__     ,  a__))
			joblib.dump(a__     ,  a__)


def 				_UpperCAmelCase							(	a__     ,  a__     ,  a__     ,  a__     ,  a__     ,  a__     ,  a__     ,  a__="dev.jbl"     ,  a__=recopy_gpta     ,  ):

			'''simple docstring'''



			a_	:	Union[str, Any]          =   []
			a_	:	str          =   []
			a_	:	Union[str, Any]          =   []

			# Initialize the transformer model
			a_	:	int          =   copy.deepcopy(a__)
			orig_model.to(device="""cpu""")
			torch.cuda.empty_cache()

			# Compute perplexity of initial transformer model for comparison
			model.train()
			a_       ,				a_       ,				a_	:	Dict          =   recopy_model(a__     ,  a__     ,  a__)

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

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

						# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
						a_       ,				a_       ,				a_	:	Union[str, Any]          =   recopy_model(a__     ,  a__     ,  a__)

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

			intermittent_save(a__     ,  a__     ,  a__     ,  a__)


def 				_UpperCAmelCase							(	a__     ,  a__="data/tokenized_stories_train_wikitext103.jbl"     ,  a__=1_0_0     ,  a__=1_0_2_6     ,  a__=True):

			'''simple docstring'''



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

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


def 				_UpperCAmelCase							(	a__     ,  a__     ,  a__     ,  a__     ,  a__=5_0     ,  a__="secondary_learner.pt"):

			'''simple docstring'''



			a_	:	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
			a_	:	Optional[Any]          =   train_dataset[:5_1_2]
			a_	:	Dict          =   train_dataset[5_1_2:]
			a_	:	Optional[int]          =   DataLoader(a__     ,  shuffle=a__     ,  batch_size=a__)
			a_	:	List[Any]          =   DataLoader(a__     ,  shuffle=a__     ,  batch_size=a__)

			# secondary learner model set up
			a_	:	Dict          =   nn.MSELoss()
			a_	:	Dict          =   nn.MSELoss(reduction="""sum""")
			secondary_learner.to(a__)
			a_	:	int          =   torch.optim.Adam(secondary_learner.parameters()     ,  lr=0.0_0001)
			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
			a_	:	List[Any]          =   float("""inf""")
			# Iterate through batches until we've used max_steps batches
			for epoch in range(int(a__)):
						a_	:	Dict          =   0.0
						secondary_learner.train()
						for step, batch in enumerate(a__):
									a_	:	Optional[Any]          =   batch[0].to(a__)
									a_	:	Union[str, Any]          =   batch[1].to(a__)
									a_	:	List[str]          =   secondary_learner(a__)
									q_optimizer.zero_grad()
									a_	:	Optional[Any]          =   loss(a__     ,  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(a__)):
												a_	:	Tuple          =   tr_q_loss / (step + 1)

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

									secondary_learner.train()
			return secondary_learner



class 					A__(nn.Module       ):
						"""simple docstring"""






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





						def  UpperCamelCase__				(						self     ,			_lowercase						)    ->   Optional[Any]:
									a_	:	str          =   torch.max(self.conv(self.embeddings(_lowercase						).squeeze(1						).transpose(1     ,			2						)						)     ,			2						)[0]
									a_	:	Union[str, Any]          =   self.fc(_lowercase						)
									return qs.squeeze(1						)





						@classmethod
						def  UpperCamelCase__				(						cls     ,			_lowercase     ,			_lowercase						)    ->   Any:
									a_	:	int          =   cls(_lowercase						)  # this calls __init__
									a_	:	Any          =   torch.load(_lowercase						)
									secondary_learner.load_state_dict(_lowercase						)
									a_	:	Tuple          =   model.transformer.wte
									a_	:	int          =   copy.deepcopy(model.transformer.wte.weight						)
									return secondary_learner





# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# 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.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__snake_case							:			Any      		= {
    """configuration_convnextv2""": [
        """CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ConvNextV2Config""",
    ]
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case							:			Dict      		= [
	    """CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """ConvNextV2ForImageClassification""",
	    """ConvNextV2Model""",
	    """ConvNextV2PreTrainedModel""",
	    """ConvNextV2Backbone""",
	]


if TYPE_CHECKING:
	from .configuration_convnextva import (
	    CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    ConvNextVaConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_convnextva import (
		    CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ConvNextVaBackbone,
		    ConvNextVaForImageClassification,
		    ConvNextVaModel,
		    ConvNextVaPreTrainedModel,
		)

else:
	import sys

	__snake_case							:			Dict      		= _LazyModule(__name__, globals()["""__file__"""], _import_structure)


