
"""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


_lowercase							 =							logging.getLogger(__name__)



def     lowerCAmelCase__ (		__magic_name__			)	->str:
			random.seed(__magic_name__			)
			np.random.seed(__magic_name__			)
			torch.manual_seed(__magic_name__			)
			torch.cuda.manual_seed_all(__magic_name__			)



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



def     lowerCAmelCase__ (		__magic_name__="gpt2"			)	->Dict:
			__lowercase						      =					GPTaLMHeadModel.from_pretrained(__magic_name__ ,      output_hidden_states=__magic_name__			)
			torch.save(model.state_dict() ,      model_name + "local.pt"			)
			return model



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__ ,      __magic_name__			)	->str:
			__lowercase						      =					copy.deepcopy(__magic_name__			)
			model.to(__magic_name__			)

			__lowercase						      =					["bias", "LayerNorm.weight"]
			__lowercase						      =					[
			    {
			        "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},
			]
			__lowercase						      =					AdamW(__magic_name__ ,      lr=5E-5 ,      eps=1E-8			)
			__lowercase						      =					get_linear_schedule_with_warmup(__magic_name__ ,      0 ,      __magic_name__			)
			torch.cuda.empty_cache()
			return model, lm_optimizer, lm_scheduler



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__ ,      __magic_name__ ,      __magic_name__			)	->Any:
			__lowercase						      =					np.array(__magic_name__			).mean()
			__lowercase						      =					np.array(__magic_name__			).std()
			__lowercase						      =					(real_perps - avg) / std
			__lowercase						      =					list(zip(__magic_name__ ,      __magic_name__ ,      __magic_name__			)			)
			joblib.dump(__magic_name__ ,      __magic_name__			)



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__ ,      __magic_name__ ,      __magic_name__ ,      __magic_name__ ,      __magic_name__ ,      __magic_name__ ,      __magic_name__="dev.jbl" ,      __magic_name__=recopy_gpta ,      )	->Optional[Any]:
			__lowercase						      =					[]
			__lowercase						      =					[]
			__lowercase						      =					[]

			# Initialize the transformer model
			__lowercase						      =					copy.deepcopy(__magic_name__			)
			orig_model.to(device="cpu"			)
			torch.cuda.empty_cache()

			# Compute perplexity of initial transformer model for comparison
			model.train()
			__lowercase      ,				__lowercase      ,				__lowercase						      =					recopy_model(__magic_name__ ,      __magic_name__ ,      __magic_name__			)

			for step in tqdm(range(__magic_name__			)			):
						__lowercase						      =					torch.zeros((1, context_len) ,      dtype=torch.long ,      device=__magic_name__			)
						__lowercase						      =					random.choice(__magic_name__			)
						__lowercase						      =					random.randint(0 ,      len(story[0]			) - context_len - 1			)
						__lowercase						      =					story[0][start : start + context_len]
						lm_optimizer.zero_grad()
						__lowercase						      =					model(__magic_name__ ,      labels=__magic_name__			)
						__lowercase						      =					outputs[0]
						__lowercase						      =					compute_perplexity(__magic_name__ ,      __magic_name__ ,      __magic_name__			)
						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
						__lowercase						      =					compute_perplexity(__magic_name__ ,      __magic_name__ ,      __magic_name__			)

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

						# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
						__lowercase      ,				__lowercase      ,				__lowercase						      =					recopy_model(__magic_name__ ,      __magic_name__ ,      __magic_name__			)

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

			intermittent_save(__magic_name__ ,      __magic_name__ ,      __magic_name__ ,      __magic_name__			)



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__="data/tokenized_stories_train_wikitext103.jbl" ,      __magic_name__=1_0_0 ,      __magic_name__=1_0_2_6 ,      __magic_name__=True			)	->List[str]:
			__lowercase						      =					joblib.load(__magic_name__			)
			print("data loaded"			)
			__lowercase						      =					[]
			if trim:
						for i, example in enumerate(__magic_name__			):
									if len(example[0]			) > min_len:
												__lowercase						      =					random.randint(0 ,      len(example[0]			) - context_len - 1			)
												objective_set.append(example[0, start : start + context_len]			)
									if len(__magic_name__			) >= number:
												break
						__lowercase						      =					[]
						for j in range(i + 1 ,      len(__magic_name__			)			):
									if len(data[j][0]			) > min_len:
												train_data.append(data[j]			)
			else:
						__lowercase						      =					data[0:number]
						__lowercase						      =					data[number:]

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



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__ ,      __magic_name__ ,      __magic_name__ ,      __magic_name__=5_0 ,      __magic_name__="secondary_learner.pt"			)	->Union[str, Any]:
			__lowercase						      =					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
			__lowercase						      =					train_dataset[:5_1_2]
			__lowercase						      =					train_dataset[5_1_2:]
			__lowercase						      =					DataLoader(__magic_name__ ,      shuffle=__magic_name__ ,      batch_size=__magic_name__			)
			__lowercase						      =					DataLoader(__magic_name__ ,      shuffle=__magic_name__ ,      batch_size=__magic_name__			)

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

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

									secondary_learner.train()
			return secondary_learner







class 				__a    (   nn.Module					):



							'''simple docstring'''







							def __init__(    self     ,						_lowerCamelCase					) ->       List[Any]:




										'''simple docstring'''
										super(_lowerCamelCase     ,						self					).__init__()
										__lowercase						      =					model.transformer.wte
										__lowercase						      =					copy.deepcopy(model.transformer.wte.weight					)
										__lowercase						      =					nn.Convad(self.embeddings.weight.size(1					)     ,						256     ,						3     ,						padding=1					)
										__lowercase						      =					nn.Sequential(nn.Linear(256     ,						32					)     ,						nn.Dropout(p=0.1					)     ,						nn.Linear(32     ,						32					)     ,						nn.Linear(32     ,						1					)					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       Any:




										'''simple docstring'''
										__lowercase						      =					torch.max(self.conv(self.embeddings(_lowerCamelCase					).squeeze(1					).transpose(1     ,						2					)					)     ,						2					)[0]
										__lowercase						      =					self.fc(_lowerCamelCase					)
										return qs.squeeze(1					)





							@classmethod
							def      SCREAMING_SNAKE_CASE	(    cls     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       str:




										'''simple docstring'''
										__lowercase						      =					cls(_lowerCamelCase					)  # this calls __init__
										__lowercase						      =					torch.load(_lowerCamelCase					)
										secondary_learner.load_state_dict(_lowerCamelCase					)
										__lowercase						      =					model.transformer.wte
										__lowercase						      =					copy.deepcopy(model.transformer.wte.weight					)
										return secondary_learner







"""simple docstring"""






import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
					from .testing_utils import (
					    floats_tensor,
					    load_hf_numpy,
					    load_image,
					    load_numpy,
					    load_pt,
					    nightly,
					    parse_flag_from_env,
					    print_tensor_test,
					    require_torch_a,
					    require_torch_gpu,
					    skip_mps,
					    slow,
					    torch_all_close,
					    torch_device,
					)
					from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


_lowercase							 =							get_logger(__name__)



def     lowerCAmelCase__ (		__magic_name__			)	->Optional[int]:
			if version.parse(__magic_name__			) < version.parse(__magic_name__			):
						if "dev" in min_version:
									__lowercase						      =					(
									    "This example requires a source install from HuggingFace diffusers (see "
									    "`https://huggingface.co/docs/diffusers/installation#install-from-source`),"
									)
						else:
									__lowercase						      =					F'''This example requires a minimum version of {min_version},'''
						error_message += F''' but the version found is {__version__}.\n'''
						raise ImportError(__magic_name__			)





