





'''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


snake_case_  :							List[Any]            =	logging.getLogger(__name__)





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Optional[Any] )			->						Union[str, Any]:
						random.seed(SCREAMING_SNAKE_CASE__ )
						np.random.seed(SCREAMING_SNAKE_CASE__ )
						torch.manual_seed(SCREAMING_SNAKE_CASE__ )
						torch.cuda.manual_seed_all(SCREAMING_SNAKE_CASE__ )





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





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Tuple="gpt2" )			->						Any:
						UpperCAmelCase_      :   List[str]      =	GPTaLMHeadModel.from_pretrained(SCREAMING_SNAKE_CASE__,      output_hidden_states=SCREAMING_SNAKE_CASE__ )
						torch.save(model.state_dict(),      model_name + '''local.pt''' )
						return model





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      List[str],      SCREAMING_SNAKE_CASE__   :      str,      SCREAMING_SNAKE_CASE__   :      Union[str, Any] )			->						List[Any]:
						UpperCAmelCase_      :   Optional[Any]      =	copy.deepcopy(SCREAMING_SNAKE_CASE__ )
						model.to(SCREAMING_SNAKE_CASE__ )

						UpperCAmelCase_      :   List[str]      =	['''bias''', '''LayerNorm.weight''']
						UpperCAmelCase_      :   str      =	[
						    {
						        '''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_      :   Optional[int]      =	AdamW(SCREAMING_SNAKE_CASE__,      lr=5E-5,      eps=1E-8 )
						UpperCAmelCase_      :   Dict      =	get_linear_schedule_with_warmup(SCREAMING_SNAKE_CASE__,      0,      SCREAMING_SNAKE_CASE__ )
						torch.cuda.empty_cache()
						return model, lm_optimizer, lm_scheduler





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      List[Any],      SCREAMING_SNAKE_CASE__   :      int,      SCREAMING_SNAKE_CASE__   :      Optional[int],      SCREAMING_SNAKE_CASE__   :      str )			->						str:
						UpperCAmelCase_      :   Optional[int]      =	np.array(SCREAMING_SNAKE_CASE__ ).mean()
						UpperCAmelCase_      :   Union[str, Any]      =	np.array(SCREAMING_SNAKE_CASE__ ).std()
						UpperCAmelCase_      :   Optional[int]      =	(real_perps - avg) / std
						UpperCAmelCase_      :   Dict      =	list(zip(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ ) )
						joblib.dump(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Tuple,      SCREAMING_SNAKE_CASE__   :      List[str],      SCREAMING_SNAKE_CASE__   :      str,      SCREAMING_SNAKE_CASE__   :      Optional[int],      SCREAMING_SNAKE_CASE__   :      str,      SCREAMING_SNAKE_CASE__   :      str,      SCREAMING_SNAKE_CASE__   :      Union[str, Any],      SCREAMING_SNAKE_CASE__   :      List[Any]="dev.jbl",      SCREAMING_SNAKE_CASE__   :      Optional[Any]=recopy_gpta,      )			->						List[Any]:
						UpperCAmelCase_      :   Union[str, Any]      =	[]
						UpperCAmelCase_      :   List[str]      =	[]
						UpperCAmelCase_      :   Dict      =	[]

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

						# Compute perplexity of initial transformer model for comparison
						model.train()
						UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_      :   Optional[Any]      =	recopy_model(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )

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

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

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

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

						intermittent_save(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )





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

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





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Optional[int],      SCREAMING_SNAKE_CASE__   :      List[Any],      SCREAMING_SNAKE_CASE__   :      Union[str, Any],      SCREAMING_SNAKE_CASE__   :      List[str],      SCREAMING_SNAKE_CASE__   :      int=50,      SCREAMING_SNAKE_CASE__   :      int="secondary_learner.pt" )			->						Tuple:
						UpperCAmelCase_      :   Union[str, Any]      =	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_      :   List[str]      =	train_dataset[:512]
						UpperCAmelCase_      :   List[str]      =	train_dataset[512:]
						UpperCAmelCase_      :   Tuple      =	DataLoader(SCREAMING_SNAKE_CASE__,      shuffle=SCREAMING_SNAKE_CASE__,      batch_size=SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_      :   List[str]      =	DataLoader(SCREAMING_SNAKE_CASE__,      shuffle=SCREAMING_SNAKE_CASE__,      batch_size=SCREAMING_SNAKE_CASE__ )

						# secondary learner model set up
						UpperCAmelCase_      :   List[Any]      =	nn.MSELoss()
						UpperCAmelCase_      :   int      =	nn.MSELoss(reduction='''sum''' )
						secondary_learner.to(SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_      :   Dict      =	torch.optim.Adam(secondary_learner.parameters(),      lr=0.0_00_01 )
						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_      :   Optional[int]      =	float('''inf''' )
						# Iterate through batches until we've used max_steps batches
						for epoch in range(int(SCREAMING_SNAKE_CASE__ ) ):
												UpperCAmelCase_      :   Any      =	0.0
												secondary_learner.train()
												for step, batch in enumerate(SCREAMING_SNAKE_CASE__ ):
																		UpperCAmelCase_      :   int      =	batch[0].to(SCREAMING_SNAKE_CASE__ )
																		UpperCAmelCase_      :   Dict      =	batch[1].to(SCREAMING_SNAKE_CASE__ )
																		UpperCAmelCase_      :   Dict      =	secondary_learner(SCREAMING_SNAKE_CASE__ )
																		q_optimizer.zero_grad()
																		UpperCAmelCase_      :   List[str]      =	loss(SCREAMING_SNAKE_CASE__,      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(SCREAMING_SNAKE_CASE__ )):
																								UpperCAmelCase_      :   Optional[int]      =	tr_q_loss / (step + 1)

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

																		secondary_learner.train()
						return secondary_learner


class        __a   (nn.Module  ):

							def __init__(				self				:  Tuple							,  __magic_name__				:  List[str] )       ->							Union[str, Any]:







													"""simple docstring"""
													super(__magic_name__							,  self ).__init__()
													UpperCAmelCase_      :   Optional[Any]      =	model.transformer.wte
													UpperCAmelCase_      :   int      =	copy.deepcopy(model.transformer.wte.weight )
													UpperCAmelCase_      :   int      =	nn.Convad(self.embeddings.weight.size(1 )							,  2_56							,  3							,  padding=1 )
													UpperCAmelCase_      :   int      =	nn.Sequential(nn.Linear(2_56							,  32 )							,  nn.Dropout(p=0.1 )							,  nn.Linear(32							,  32 )							,  nn.Linear(32							,  1 ) )

							def      UpperCAmelCase__						(				self				:  Dict							,  __magic_name__				:  Union[str, Any] )       ->							int:







													"""simple docstring"""
													UpperCAmelCase_      :   Tuple      =	torch.max(self.conv(self.embeddings(__magic_name__ ).squeeze(1 ).transpose(1							,  2 ) )							,  2 )[0]
													UpperCAmelCase_      :   List[Any]      =	self.fc(__magic_name__ )
													return qs.squeeze(1 )



							@classmethod
							def      UpperCAmelCase__						(				cls				:  Any							,  __magic_name__				:  int							,  __magic_name__				:  Dict )       ->							List[Any]:







													"""simple docstring"""
													UpperCAmelCase_      :   Tuple      =	cls(__magic_name__ )  # this calls __init__
													UpperCAmelCase_      :   List[Any]      =	torch.load(__magic_name__ )
													secondary_learner.load_state_dict(__magic_name__ )
													UpperCAmelCase_      :   Dict      =	model.transformer.wte
													UpperCAmelCase_      :   List[str]      =	copy.deepcopy(model.transformer.wte.weight )
													return secondary_learner












'''simple docstring'''







import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    LayoutLMForMaskedLM,
					    LayoutLMForQuestionAnswering,
					    LayoutLMForSequenceClassification,
					    LayoutLMForTokenClassification,
					    LayoutLMModel,
					)


class        __a   :

							def __init__(				self				:  List[Any]							,  __magic_name__				:  Optional[Any]							,  __magic_name__				:  Dict=13							,  __magic_name__				:  str=7							,  __magic_name__				:  Tuple=True							,  __magic_name__				:  Union[str, Any]=True							,  __magic_name__				:  Union[str, Any]=True							,  __magic_name__				:  Dict=True							,  __magic_name__				:  Any=99							,  __magic_name__				:  str=32							,  __magic_name__				:  int=5							,  __magic_name__				:  str=4							,  __magic_name__				:  Dict=37							,  __magic_name__				:  List[str]="gelu"							,  __magic_name__				:  Optional[Any]=0.1							,  __magic_name__				:  Any=0.1							,  __magic_name__				:  Tuple=5_12							,  __magic_name__				:  Any=16							,  __magic_name__				:  str=2							,  __magic_name__				:  List[Any]=0.0_2							,  __magic_name__				:  List[str]=3							,  __magic_name__				:  Optional[int]=4							,  __magic_name__				:  Dict=None							,  __magic_name__				:  List[str]=10_00							,  )       ->							Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_      :   int      =	parent
													UpperCAmelCase_      :   str      =	batch_size
													UpperCAmelCase_      :   str      =	seq_length
													UpperCAmelCase_      :   Optional[Any]      =	is_training
													UpperCAmelCase_      :   List[Any]      =	use_input_mask
													UpperCAmelCase_      :   Any      =	use_token_type_ids
													UpperCAmelCase_      :   Dict      =	use_labels
													UpperCAmelCase_      :   Union[str, Any]      =	vocab_size
													UpperCAmelCase_      :   Any      =	hidden_size
													UpperCAmelCase_      :   Dict      =	num_hidden_layers
													UpperCAmelCase_      :   Union[str, Any]      =	num_attention_heads
													UpperCAmelCase_      :   Dict      =	intermediate_size
													UpperCAmelCase_      :   int      =	hidden_act
													UpperCAmelCase_      :   int      =	hidden_dropout_prob
													UpperCAmelCase_      :   Union[str, Any]      =	attention_probs_dropout_prob
													UpperCAmelCase_      :   Union[str, Any]      =	max_position_embeddings
													UpperCAmelCase_      :   int      =	type_vocab_size
													UpperCAmelCase_      :   List[Any]      =	type_sequence_label_size
													UpperCAmelCase_      :   int      =	initializer_range
													UpperCAmelCase_      :   Dict      =	num_labels
													UpperCAmelCase_      :   Any      =	num_choices
													UpperCAmelCase_      :   Optional[Any]      =	scope
													UpperCAmelCase_      :   Any      =	range_bbox

							def      UpperCAmelCase__						(				self				:  Any )       ->							str:







													"""simple docstring"""
													UpperCAmelCase_      :   List[str]      =	ids_tensor([self.batch_size, self.seq_length]							,  self.vocab_size )

													UpperCAmelCase_      :   Tuple      =	ids_tensor([self.batch_size, self.seq_length, 4]							,  self.range_bbox )
													# Ensure that bbox is legal
													for i in range(bbox.shape[0] ):
																			for j in range(bbox.shape[1] ):
																									if bbox[i, j, 3] < bbox[i, j, 1]:
																															UpperCAmelCase_      :   Any      =	bbox[i, j, 3]
																															UpperCAmelCase_      :   List[Any]      =	bbox[i, j, 1]
																															UpperCAmelCase_      :   Optional[Any]      =	t
																									if bbox[i, j, 2] < bbox[i, j, 0]:
																															UpperCAmelCase_      :   Union[str, Any]      =	bbox[i, j, 2]
																															UpperCAmelCase_      :   Union[str, Any]      =	bbox[i, j, 0]
																															UpperCAmelCase_      :   Any      =	t

													UpperCAmelCase_      :   Union[str, Any]      =	None
													if self.use_input_mask:
																			UpperCAmelCase_      :   str      =	ids_tensor([self.batch_size, self.seq_length]							,  vocab_size=2 )

													UpperCAmelCase_      :   Dict      =	None
													if self.use_token_type_ids:
																			UpperCAmelCase_      :   Dict      =	ids_tensor([self.batch_size, self.seq_length]							,  self.type_vocab_size )

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

													UpperCAmelCase_      :   str      =	self.get_config()

													return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

							def      UpperCAmelCase__						(				self				:  List[Any] )       ->							Any:







													"""simple docstring"""
													return LayoutLMConfig(
													    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							,  type_vocab_size=self.type_vocab_size							,  initializer_range=self.initializer_range							,  )

							def      UpperCAmelCase__						(				self				:  List[str]							,  __magic_name__				:  Tuple							,  __magic_name__				:  int							,  __magic_name__				:  int							,  __magic_name__				:  List[str]							,  __magic_name__				:  List[Any]							,  __magic_name__				:  List[Any]							,  __magic_name__				:  str							,  __magic_name__				:  Union[str, Any] )       ->							List[Any]:







													"""simple docstring"""
													UpperCAmelCase_      :   str      =	LayoutLMModel(config=__magic_name__ )
													model.to(__magic_name__ )
													model.eval()
													UpperCAmelCase_      :   List[Any]      =	model(__magic_name__							,  __magic_name__							,  attention_mask=__magic_name__							,  token_type_ids=__magic_name__ )
													UpperCAmelCase_      :   List[Any]      =	model(__magic_name__							,  __magic_name__							,  token_type_ids=__magic_name__ )
													UpperCAmelCase_      :   List[Any]      =	model(__magic_name__							,  __magic_name__ )
													self.parent.assertEqual(result.last_hidden_state.shape							,  (self.batch_size, self.seq_length, self.hidden_size) )
													self.parent.assertEqual(result.pooler_output.shape							,  (self.batch_size, self.hidden_size) )

							def      UpperCAmelCase__						(				self				:  List[str]							,  __magic_name__				:  Optional[int]							,  __magic_name__				:  Optional[int]							,  __magic_name__				:  Optional[Any]							,  __magic_name__				:  str							,  __magic_name__				:  int							,  __magic_name__				:  int							,  __magic_name__				:  int							,  __magic_name__				:  str )       ->							Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_      :   Optional[Any]      =	LayoutLMForMaskedLM(config=__magic_name__ )
													model.to(__magic_name__ )
													model.eval()
													UpperCAmelCase_      :   Optional[Any]      =	model(__magic_name__							,  __magic_name__							,  attention_mask=__magic_name__							,  token_type_ids=__magic_name__							,  labels=__magic_name__ )
													self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.seq_length, self.vocab_size) )

							def      UpperCAmelCase__						(				self				:  str							,  __magic_name__				:  Optional[int]							,  __magic_name__				:  List[str]							,  __magic_name__				:  Dict							,  __magic_name__				:  Tuple							,  __magic_name__				:  Dict							,  __magic_name__				:  Union[str, Any]							,  __magic_name__				:  int							,  __magic_name__				:  Optional[int] )       ->							Tuple:







													"""simple docstring"""
													UpperCAmelCase_      :   int      =	self.num_labels
													UpperCAmelCase_      :   Any      =	LayoutLMForSequenceClassification(__magic_name__ )
													model.to(__magic_name__ )
													model.eval()
													UpperCAmelCase_      :   Optional[int]      =	model(
													    __magic_name__							,  __magic_name__							,  attention_mask=__magic_name__							,  token_type_ids=__magic_name__							,  labels=__magic_name__ )
													self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.num_labels) )

							def      UpperCAmelCase__						(				self				:  Any							,  __magic_name__				:  Tuple							,  __magic_name__				:  Union[str, Any]							,  __magic_name__				:  List[Any]							,  __magic_name__				:  Tuple							,  __magic_name__				:  Dict							,  __magic_name__				:  Tuple							,  __magic_name__				:  Optional[int]							,  __magic_name__				:  List[Any] )       ->							str:







													"""simple docstring"""
													UpperCAmelCase_      :   Dict      =	self.num_labels
													UpperCAmelCase_      :   Optional[int]      =	LayoutLMForTokenClassification(config=__magic_name__ )
													model.to(__magic_name__ )
													model.eval()
													UpperCAmelCase_      :   int      =	model(__magic_name__							,  __magic_name__							,  attention_mask=__magic_name__							,  token_type_ids=__magic_name__							,  labels=__magic_name__ )
													self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.seq_length, self.num_labels) )

							def      UpperCAmelCase__						(				self				:  List[Any]							,  __magic_name__				:  Optional[Any]							,  __magic_name__				:  Any							,  __magic_name__				:  Optional[int]							,  __magic_name__				:  Dict							,  __magic_name__				:  Optional[int]							,  __magic_name__				:  Optional[int]							,  __magic_name__				:  Optional[int]							,  __magic_name__				:  Union[str, Any] )       ->							str:







													"""simple docstring"""
													UpperCAmelCase_      :   Optional[int]      =	LayoutLMForQuestionAnswering(config=__magic_name__ )
													model.to(__magic_name__ )
													model.eval()
													UpperCAmelCase_      :   Union[str, Any]      =	model(
													    __magic_name__							,  bbox=__magic_name__							,  attention_mask=__magic_name__							,  token_type_ids=__magic_name__							,  start_positions=__magic_name__							,  end_positions=__magic_name__							,  )
													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      UpperCAmelCase__						(				self				:  Tuple )       ->							Any:







													"""simple docstring"""
													UpperCAmelCase_      :   int      =	self.prepare_config_and_inputs()
													(
													    (
													    UpperCAmelCase_
													)    ,							(
													    UpperCAmelCase_
													)    ,							(
													    UpperCAmelCase_
													)    ,							(
													    UpperCAmelCase_
													)    ,							(
													    UpperCAmelCase_
													)    ,							(
													    UpperCAmelCase_
													)    ,							(
													    UpperCAmelCase_
													)    ,							(
													    UpperCAmelCase_
													)    ,							
													)      :   str      =	config_and_inputs
													UpperCAmelCase_      :   Optional[Any]      =	{
													    '''input_ids''': input_ids,
													    '''bbox''': bbox,
													    '''token_type_ids''': token_type_ids,
													    '''attention_mask''': input_mask,
													}
													return config, inputs_dict





@require_torch
class        __a   (lowerCamelCase							,		lowerCamelCase							,		unittest.TestCase  ):
							__a       : Optional[Any]          =							(
							    (
							        LayoutLMModel,
							        LayoutLMForMaskedLM,
							        LayoutLMForSequenceClassification,
							        LayoutLMForTokenClassification,
							        LayoutLMForQuestionAnswering,
							    )
							    if is_torch_available()
							    else None
							)
							__a       : List[str]          =							(
							    {
							        "document-question-answering": LayoutLMForQuestionAnswering,
							        "feature-extraction": LayoutLMModel,
							        "fill-mask": LayoutLMForMaskedLM,
							        "text-classification": LayoutLMForSequenceClassification,
							        "token-classification": LayoutLMForTokenClassification,
							        "zero-shot": LayoutLMForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							__a       : Any          =							True

							def      UpperCAmelCase__						(				self				:  str )       ->							Dict:







													"""simple docstring"""
													UpperCAmelCase_      :   Dict      =	LayoutLMModelTester(self )
													UpperCAmelCase_      :   str      =	ConfigTester(self							,  config_class=__magic_name__							,  hidden_size=37 )

							def      UpperCAmelCase__						(				self				:  Optional[int] )       ->							int:







													"""simple docstring"""
													self.config_tester.run_common_tests()

							def      UpperCAmelCase__						(				self				:  List[Any] )       ->							Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_      :   List[Any]      =	self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*__magic_name__ )

							def      UpperCAmelCase__						(				self				:  str )       ->							int:







													"""simple docstring"""
													UpperCAmelCase_      :   int      =	self.model_tester.prepare_config_and_inputs()
													for type in ["absolute", "relative_key", "relative_key_query"]:
																			UpperCAmelCase_      :   Any      =	type
																			self.model_tester.create_and_check_model(*__magic_name__ )

							def      UpperCAmelCase__						(				self				:  List[Any] )       ->							Tuple:







													"""simple docstring"""
													UpperCAmelCase_      :   Optional[int]      =	self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_masked_lm(*__magic_name__ )

							def      UpperCAmelCase__						(				self				:  Union[str, Any] )       ->							Dict:







													"""simple docstring"""
													UpperCAmelCase_      :   Union[str, Any]      =	self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_sequence_classification(*__magic_name__ )

							def      UpperCAmelCase__						(				self				:  Optional[Any] )       ->							List[Any]:







													"""simple docstring"""
													UpperCAmelCase_      :   str      =	self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_token_classification(*__magic_name__ )

							def      UpperCAmelCase__						(				self				:  List[str] )       ->							List[str]:







													"""simple docstring"""
													UpperCAmelCase_      :   Optional[Any]      =	self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_question_answering(*__magic_name__ )



							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
							def      UpperCAmelCase__						(				self				:  Dict )       ->							Any:







													"""simple docstring"""
													pass





def 							lowerCamelCase_   (	)			->						List[Any]:
						# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
						# fmt: off
						UpperCAmelCase_      :   Optional[Any]      =	torch.tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]],      device=SCREAMING_SNAKE_CASE__ )  # noqa: E231
						UpperCAmelCase_      :   Optional[Any]      =	torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],],      device=SCREAMING_SNAKE_CASE__ )  # noqa: E231
						UpperCAmelCase_      :   Tuple      =	torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]],      device=SCREAMING_SNAKE_CASE__ )  # noqa: E231
						UpperCAmelCase_      :   int      =	torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]],      device=SCREAMING_SNAKE_CASE__ )  # noqa: E231
						# these are sequence labels (i.e. at the token level)
						UpperCAmelCase_      :   Dict      =	torch.tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]],      device=SCREAMING_SNAKE_CASE__ )  # noqa: E231
						# fmt: on

						return input_ids, attention_mask, bbox, token_type_ids, labels


@require_torch
class        __a   (unittest.TestCase  ):

							@slow
							def      UpperCAmelCase__						(				self				:  Dict )       ->							int:







													"""simple docstring"""
													UpperCAmelCase_      :   Any      =	LayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased''' ).to(__magic_name__ )

													UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_      :   Optional[int]      =	prepare_layoutlm_batch_inputs()

													# forward pass
													UpperCAmelCase_      :   Tuple      =	model(input_ids=__magic_name__							,  bbox=__magic_name__							,  attention_mask=__magic_name__							,  token_type_ids=__magic_name__ )

													# test the sequence output on [0, :3, :3]
													UpperCAmelCase_      :   int      =	torch.tensor(
													    [[0.1_7_8_5, -0.1_9_4_7, -0.0_4_2_5], [-0.3_2_5_4, -0.2_8_0_7, 0.2_5_5_3], [-0.5_3_9_1, -0.3_3_2_2, 0.3_3_6_4]]							,  device=__magic_name__							,  )

													self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]							,  __magic_name__							,  atol=1E-3 ) )

													# test the pooled output on [1, :3]
													UpperCAmelCase_      :   List[str]      =	torch.tensor([-0.6_5_8_0, -0.0_2_1_4, 0.8_5_5_2]							,  device=__magic_name__ )

													self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]							,  __magic_name__							,  atol=1E-3 ) )

							@slow
							def      UpperCAmelCase__						(				self				:  int )       ->							Tuple:







													"""simple docstring"""
													# initialize model with randomly initialized sequence classification head
													UpperCAmelCase_      :   Dict      =	LayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''							,  num_labels=2 ).to(
													    __magic_name__ )

													UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_      :   Optional[int]      =	prepare_layoutlm_batch_inputs()

													# forward pass
													UpperCAmelCase_      :   Dict      =	model(
													    input_ids=__magic_name__							,  bbox=__magic_name__							,  attention_mask=__magic_name__							,  token_type_ids=__magic_name__							,  labels=torch.tensor([1, 1]							,  device=__magic_name__ )							,  )

													# test whether we get a loss as a scalar
													UpperCAmelCase_      :   List[str]      =	outputs.loss
													UpperCAmelCase_      :   Tuple      =	torch.Size([] )
													self.assertEqual(loss.shape							,  __magic_name__ )

													# test the shape of the logits
													UpperCAmelCase_      :   Dict      =	outputs.logits
													UpperCAmelCase_      :   Optional[int]      =	torch.Size((2, 2) )
													self.assertEqual(logits.shape							,  __magic_name__ )

							@slow
							def      UpperCAmelCase__						(				self				:  str )       ->							Union[str, Any]:







													"""simple docstring"""
													# initialize model with randomly initialized token classification head
													UpperCAmelCase_      :   List[Any]      =	LayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''							,  num_labels=13 ).to(
													    __magic_name__ )

													UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_      :   Optional[Any]      =	prepare_layoutlm_batch_inputs()

													# forward pass
													UpperCAmelCase_      :   Any      =	model(
													    input_ids=__magic_name__							,  bbox=__magic_name__							,  attention_mask=__magic_name__							,  token_type_ids=__magic_name__							,  labels=__magic_name__ )

													# test the loss calculation to be around 2.65
													# expected_loss = torch.tensor(2.65, device=torch_device)

													# The loss is currently somewhat random and can vary between 0.1-0.3 atol.
													# self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

													# test the shape of the logits
													UpperCAmelCase_      :   Optional[Any]      =	outputs.logits
													UpperCAmelCase_      :   Dict      =	torch.Size((2, 25, 13) )
													self.assertEqual(logits.shape							,  __magic_name__ )



							@slow
							def      UpperCAmelCase__						(				self				:  Optional[int] )       ->							Dict:







													"""simple docstring"""
													# initialize model with randomly initialized token classification head
													UpperCAmelCase_      :   Any      =	LayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased''' ).to(__magic_name__ )

													UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_      :   Optional[int]      =	prepare_layoutlm_batch_inputs()

													# forward pass
													UpperCAmelCase_      :   Optional[Any]      =	model(input_ids=__magic_name__							,  bbox=__magic_name__							,  attention_mask=__magic_name__							,  token_type_ids=__magic_name__ )

													# test the shape of the logits
													UpperCAmelCase_      :   Union[str, Any]      =	torch.Size((2, 25) )
													self.assertEqual(outputs.start_logits.shape							,  __magic_name__ )
													self.assertEqual(outputs.end_logits.shape							,  __magic_name__ )





