
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 					__lowerCAmelCase    (    a__						) ->						Union[str, Any]:
				random.seed(_UpperCamelCase						)
				np.random.seed(_UpperCamelCase						)
				torch.manual_seed(_UpperCamelCase						)
				torch.cuda.manual_seed_all(_UpperCamelCase						)


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


def 					__lowerCAmelCase    (    a__="gpt2"						) ->						str:
				__a    							=					GPTaLMHeadModel.from_pretrained(_UpperCamelCase		,					output_hidden_states=_UpperCamelCase						)
				torch.save(model.state_dict()		,					model_name + '''local.pt'''						)
				return model


def 					__lowerCAmelCase    (    a__		,					a__		,					a__						) ->						int:
				__a    							=					copy.deepcopy(_UpperCamelCase						)
				model.to(_UpperCamelCase						)

				__a    							=					['''bias''', '''LayerNorm.weight''']
				__a    							=					[
				    {
				        '''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    							=					AdamW(_UpperCamelCase		,					lr=5e-5		,					eps=1e-8						)
				__a    							=					get_linear_schedule_with_warmup(_UpperCamelCase		,					0		,					_UpperCamelCase						)
				torch.cuda.empty_cache()
				return model, lm_optimizer, lm_scheduler


def 					__lowerCAmelCase    (    a__		,					a__		,					a__		,					a__						) ->						List[str]:
				__a    							=					np.array(_UpperCamelCase						).mean()
				__a    							=					np.array(_UpperCamelCase						).std()
				__a    							=					(real_perps - avg) / std
				__a    							=					list(zip(_UpperCamelCase		,					_UpperCamelCase		,					_UpperCamelCase						)						)
				joblib.dump(_UpperCamelCase		,					_UpperCamelCase						)


def 					__lowerCAmelCase    (    a__		,					a__		,					a__		,					a__		,					a__		,					a__		,					a__		,					a__="dev.jbl"		,					a__=recopy_gpta		,					) ->						Any:
				__a    							=					[]
				__a    							=					[]
				__a    							=					[]

				# Initialize the transformer model
				__a    							=					copy.deepcopy(_UpperCamelCase						)
				orig_model.to(device='''cpu'''						)
				torch.cuda.empty_cache()

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

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

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

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

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

				intermittent_save(_UpperCamelCase		,					_UpperCamelCase		,					_UpperCamelCase		,					_UpperCamelCase						)


def 					__lowerCAmelCase    (    a__		,					a__="data/tokenized_stories_train_wikitext103.jbl"		,					a__=100		,					a__=1026		,					a__=True						) ->						str:
				__a    							=					joblib.load(_UpperCamelCase						)
				print('''data loaded'''						)
				__a    							=					[]
				if trim:
								for i, example in enumerate(_UpperCamelCase						):
												if len(example[0]						) > min_len:
																__a    							=					random.randint(0		,					len(example[0]						) - context_len - 1						)
																objective_set.append(example[0, start : start + context_len]						)
												if len(_UpperCamelCase						) >= number:
																break
								__a    							=					[]
								for j in range(i + 1		,					len(_UpperCamelCase						)						):
												if len(data[j][0]						) > min_len:
																train_data.append(data[j]						)
				else:
								__a    							=					data[0:number]
								__a    							=					data[number:]

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


def 					__lowerCAmelCase    (    a__		,					a__		,					a__		,					a__		,					a__=50		,					a__="secondary_learner.pt"						) ->						List[str]:
				__a    							=					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    							=					train_dataset[:512]
				__a    							=					train_dataset[512:]
				__a    							=					DataLoader(_UpperCamelCase		,					shuffle=_UpperCamelCase		,					batch_size=_UpperCamelCase						)
				__a    							=					DataLoader(_UpperCamelCase		,					shuffle=_UpperCamelCase		,					batch_size=_UpperCamelCase						)

				# secondary learner model set up
				__a    							=					nn.MSELoss()
				__a    							=					nn.MSELoss(reduction='''sum'''						)
				secondary_learner.to(_UpperCamelCase						)
				__a    							=					torch.optim.Adam(secondary_learner.parameters()		,					lr=0.00_001						)
				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    							=					float('''inf'''						)
				# Iterate through batches until we've used max_steps batches
				for epoch in range(int(_UpperCamelCase						)						):
								__a    							=					0.0
								secondary_learner.train()
								for step, batch in enumerate(_UpperCamelCase						):
												__a    							=					batch[0].to(_UpperCamelCase						)
												__a    							=					batch[1].to(_UpperCamelCase						)
												__a    							=					secondary_learner(_UpperCamelCase						)
												q_optimizer.zero_grad()
												__a    							=					loss(_UpperCamelCase		,					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(_UpperCamelCase						)):
																__a    							=					tr_q_loss / (step + 1)

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

												secondary_learner.train()
				return secondary_learner







class     __A(				nn.Module    ):




				def __init__(  self	,  _snake_case       )					->   Optional[Any]:


								'''simple docstring'''




								super(a_	,  self       ).__init__()
								__a    							=					model.transformer.wte
								__a    							=					copy.deepcopy(model.transformer.wte.weight       )
								__a    							=					nn.Convad(self.embeddings.weight.size(1       )	,  256	,  3	,  padding=1       )
								__a    							=					nn.Sequential(nn.Linear(256	,  32       )	,  nn.Dropout(p=0.1       )	,  nn.Linear(32	,  32       )	,  nn.Linear(32	,  1       )       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Any:


								'''simple docstring'''




								__a    							=					torch.max(self.conv(self.embeddings(a_       ).squeeze(1       ).transpose(1	,  2       )       )	,  2       )[0]
								__a    							=					self.fc(a_       )
								return qs.squeeze(1       )




				@classmethod
				def        SCREAMING_SNAKE_CASE_	(  cls	,  _snake_case	,  _snake_case       )					->   str:


								'''simple docstring'''




								__a    							=					cls(a_       )  # this calls __init__
								__a    							=					torch.load(a_       )
								secondary_learner.load_state_dict(a_       )
								__a    							=					model.transformer.wte
								__a    							=					copy.deepcopy(model.transformer.wte.weight       )
								return secondary_learner

from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


A  :			int     				=				logging.get_logger(__name__)

A  :			Dict     				=				{
    'layoutlmv2-base-uncased': 'https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json',
    'layoutlmv2-large-uncased': 'https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json',
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
					import detectrona







class     __A(				a    ):
				snake_case_		     =					'''layoutlmv2'''




				def __init__(  self	,  _snake_case=30_522	,  _snake_case=768	,  _snake_case=12	,  _snake_case=12	,  _snake_case=3_072	,  _snake_case="gelu"	,  _snake_case=0.1	,  _snake_case=0.1	,  _snake_case=512	,  _snake_case=2	,  _snake_case=0.02	,  _snake_case=1E-12	,  _snake_case=0	,  _snake_case=1_024	,  _snake_case=128	,  _snake_case=32	,  _snake_case=True	,  _snake_case=256	,  _snake_case=64	,  _snake_case=True	,  _snake_case=[7, 7, 256]	,  _snake_case=128	,  _snake_case=128	,  _snake_case=True	,  _snake_case=True	,  _snake_case=False	,  _snake_case=None	,  **_snake_case	,  )					->   Optional[int]:


								'''simple docstring'''




								super().__init__(
								    vocab_size=_snake_case	,  hidden_size=_snake_case	,  num_hidden_layers=_snake_case	,  num_attention_heads=_snake_case	,  intermediate_size=_snake_case	,  hidden_act=_snake_case	,  hidden_dropout_prob=_snake_case	,  attention_probs_dropout_prob=_snake_case	,  max_position_embeddings=_snake_case	,  type_vocab_size=_snake_case	,  initializer_range=_snake_case	,  layer_norm_eps=_snake_case	,  pad_token_id=_snake_case	,  **_snake_case	,  )
								__a    							=					max_ad_position_embeddings
								__a    							=					max_rel_pos
								__a    							=					rel_pos_bins
								__a    							=					fast_qkv
								__a    							=					max_rel_ad_pos
								__a    							=					rel_ad_pos_bins
								__a    							=					convert_sync_batchnorm
								__a    							=					image_feature_pool_shape
								__a    							=					coordinate_size
								__a    							=					shape_size
								__a    							=					has_relative_attention_bias
								__a    							=					has_spatial_attention_bias
								__a    							=					has_visual_segment_embedding
								__a    							=					(
								    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
								)




				@classmethod
				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Dict:


								'''simple docstring'''




								return {
								    "MODEL.MASK_ON": True,
								    "MODEL.PIXEL_STD": [57.375, 57.120, 58.395],
								    "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
								    "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
								    "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
								    "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
								    "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2_000,
								    "MODEL.RPN.PRE_NMS_TOPK_TEST": 1_000,
								    "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1_000,
								    "MODEL.POST_NMS_TOPK_TEST": 1_000,
								    "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
								    "MODEL.ROI_HEADS.NUM_CLASSES": 5,
								    "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
								    "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
								    "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
								    "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
								    "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
								    "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
								    "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
								    "MODEL.RESNETS.DEPTH": 101,
								    "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
								    "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
								    "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
								    "MODEL.RESNETS.NUM_GROUPS": 32,
								    "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
								    "MODEL.RESNETS.STRIDE_IN_1X1": False,
								}




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Optional[Any]:


								'''simple docstring'''




								__a    							=					detectrona.config.get_cfg()
								for k, v in self.detectrona_config_args.items():
												__a    							=					k.split('''.'''       )
												__a    							=					detectrona_config
												for attribute in attributes[:-1]:
																__a    							=					getattr(_snake_case	,  _snake_case       )
												setattr(_snake_case	,  attributes[-1]	,  _snake_case       )

								return detectrona_config