
import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_UpperCAmelCase :			List[Any]              =	16
_UpperCAmelCase :			Tuple              =	32


def 				A	( lowercase   ,	lowercase   ,	lowercase   ,	lowercase   ,	lowercase = 16			)       ->  Union[str, Any]:
		'''simple docstring'''

		UpperCamelCase          =					AutoTokenizer.from_pretrained('bert-base-cased'			)
		UpperCamelCase          =					DatasetDict(
		    {
		        'train': dataset['train'].select(UpperCamelCase__			),
		        'validation': dataset['train'].select(UpperCamelCase__			),
		        'test': dataset['validation'],
		    }			)

		def tokenize_function(lowercase			):
				# max_length=None => use the model max length (it's actually the default)
				UpperCamelCase          =					tokenizer(examples['sentence1']   ,	examples['sentence2']   ,	truncation=UpperCamelCase__   ,	max_length=UpperCamelCase__			)
				return outputs

		# Apply the method we just defined to all the examples in all the splits of the dataset
		# starting with the main process first:
		with accelerator.main_process_first():
				UpperCamelCase          =					datasets.map(
				    UpperCamelCase__   ,	batched=UpperCamelCase__   ,	remove_columns=['idx', 'sentence1', 'sentence2']   ,	)

		# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
		# transformers library
		UpperCamelCase          =					tokenized_datasets.rename_column('label'   ,	'labels'			)

		def collate_fn(lowercase			):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				UpperCamelCase          =					128 if accelerator.distributed_type == DistributedType.TPU else None
				# When using mixed precision we want round multiples of 8/16
				if accelerator.mixed_precision == "fp8":
						UpperCamelCase          =					16
				elif accelerator.mixed_precision != "no":
						UpperCamelCase          =					8
				else:
						UpperCamelCase          =					None

				return tokenizer.pad(
				    UpperCamelCase__   ,	padding='longest'   ,	max_length=UpperCamelCase__   ,	pad_to_multiple_of=UpperCamelCase__   ,	return_tensors='pt'   ,	)

		# Instantiate dataloaders.
		UpperCamelCase          =					DataLoader(
		    tokenized_datasets['train']   ,	shuffle=UpperCamelCase__   ,	collate_fn=UpperCamelCase__   ,	batch_size=UpperCamelCase__			)
		UpperCamelCase          =					DataLoader(
		    tokenized_datasets['validation']   ,	shuffle=UpperCamelCase__   ,	collate_fn=UpperCamelCase__   ,	batch_size=UpperCamelCase__			)

		UpperCamelCase          =					DataLoader(
		    tokenized_datasets['test']   ,	shuffle=UpperCamelCase__   ,	collate_fn=UpperCamelCase__   ,	batch_size=UpperCamelCase__			)

		return train_dataloader, eval_dataloader, test_dataloader


def 				A	( lowercase   ,	lowercase			)       ->  List[str]:
		'''simple docstring'''

		UpperCamelCase          =					[]
		# Download the dataset
		UpperCamelCase          =					load_dataset('glue'   ,	'mrpc'			)
		# Create our splits
		UpperCamelCase          =					StratifiedKFold(n_splits=int(args.num_folds			)			)
		# Initialize accelerator
		UpperCamelCase          =					Accelerator(cpu=args.cpu   ,	mixed_precision=args.mixed_precision			)
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		UpperCamelCase          =					config['''lr''']
		UpperCamelCase          =					int(config['num_epochs']			)
		UpperCamelCase          =					int(config['seed']			)
		UpperCamelCase          =					int(config['batch_size']			)

		UpperCamelCase          =					evaluate.load('glue'   ,	'mrpc'			)

		# If the batch size is too big we use gradient accumulation
		UpperCamelCase          =					1
		if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
				UpperCamelCase          =					batch_size // MAX_GPU_BATCH_SIZE
				UpperCamelCase          =					MAX_GPU_BATCH_SIZE

		set_seed(UpperCamelCase__			)

		# New Code #
		# Create our folds:
		UpperCamelCase          =					kfold.split(np.zeros(datasets['train'].num_rows			)   ,	datasets['train']['label']			)
		UpperCamelCase          =					[]
		# Iterate over them
		for i, (train_idxs, valid_idxs) in enumerate(UpperCamelCase__			):
				UpperCamelCase          =					get_fold_dataloaders(
				    UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	)
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				UpperCamelCase          =					AutoModelForSequenceClassification.from_pretrained('bert-base-cased'   ,	return_dict=UpperCamelCase__			)

				# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
				# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
				# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
				UpperCamelCase          =					model.to(accelerator.device			)

				# Instantiate optimizer
				UpperCamelCase          =					AdamW(params=model.parameters()   ,	lr=UpperCamelCase__			)

				# Instantiate scheduler
				UpperCamelCase          =					get_linear_schedule_with_warmup(
				    optimizer=UpperCamelCase__   ,	num_warmup_steps=100   ,	num_training_steps=(len(UpperCamelCase__			) * num_epochs) // gradient_accumulation_steps   ,	)

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				UpperCamelCase          =					accelerator.prepare(
				    UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__			)

				# Now we train the model
				for epoch in range(UpperCamelCase__			):
						model.train()
						for step, batch in enumerate(UpperCamelCase__			):
								# We could avoid this line since we set the accelerator with `device_placement=True`.
								batch.to(accelerator.device			)
								UpperCamelCase          =					model(**UpperCamelCase__			)
								UpperCamelCase          =					outputs.loss
								UpperCamelCase          =					loss / gradient_accumulation_steps
								accelerator.backward(UpperCamelCase__			)
								if step % gradient_accumulation_steps == 0:
										optimizer.step()
										lr_scheduler.step()
										optimizer.zero_grad()

						model.eval()
						for step, batch in enumerate(UpperCamelCase__			):
								# We could avoid this line since we set the accelerator with `device_placement=True`.
								batch.to(accelerator.device			)
								with torch.no_grad():
										UpperCamelCase          =					model(**UpperCamelCase__			)
								UpperCamelCase          =					outputs.logits.argmax(dim=-1			)
								UpperCamelCase          =					accelerator.gather_for_metrics((predictions, batch['labels'])			)
								metric.add_batch(
								    predictions=UpperCamelCase__   ,	references=UpperCamelCase__   ,	)

						UpperCamelCase          =					metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f'''epoch {epoch}:'''   ,	UpperCamelCase__			)

				# New Code #
				# We also run predictions on the test set at the very end
				UpperCamelCase          =					[]
				for step, batch in enumerate(UpperCamelCase__			):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device			)
						with torch.no_grad():
								UpperCamelCase          =					model(**UpperCamelCase__			)
						UpperCamelCase          =					outputs.logits
						UpperCamelCase          =					accelerator.gather_for_metrics((predictions, batch['labels'])			)
						fold_predictions.append(predictions.cpu()			)
						if i == 0:
								# We need all of the test predictions
								test_references.append(references.cpu()			)
        # Use accelerator.print to print only on the main process.
				test_predictions.append(torch.cat(UpperCamelCase__   ,	dim=0			)			)
				# We now need to release all our memory and get rid of the current model, optimizer, etc
				accelerator.free_memory()
		# New Code #
		# Finally we check the accuracy of our folded results:
		UpperCamelCase          =					torch.cat(UpperCamelCase__   ,	dim=0			)
		UpperCamelCase          =					torch.stack(UpperCamelCase__   ,	dim=0			).sum(dim=0			).div(int(args.num_folds			)			).argmax(dim=-1			)
		UpperCamelCase          =					metric.compute(predictions=UpperCamelCase__   ,	references=UpperCamelCase__			)
		accelerator.print('Average test metrics from all folds:'   ,	UpperCamelCase__			)


def 				A	( )       ->  str:
		'''simple docstring'''

		UpperCamelCase          =					argparse.ArgumentParser(description='Simple example of training script.'			)
		parser.add_argument(
		    '--mixed_precision'   ,	type=UpperCamelCase__   ,	default=UpperCamelCase__   ,	choices=['no', 'fp16', 'bf16', 'fp8']   ,	help='Whether to use mixed precision. Choose'
		    'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
		    'and an Nvidia Ampere GPU.'   ,	)
		parser.add_argument('--cpu'   ,	action='store_true'   ,	help='If passed, will train on the CPU.'			)
		# New Code #
		parser.add_argument('--num_folds'   ,	type=UpperCamelCase__   ,	default=3   ,	help='The number of splits to perform across the dataset'			)
		UpperCamelCase          =					parser.parse_args()
		UpperCamelCase          =					{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
		training_function(UpperCamelCase__   ,	UpperCamelCase__			)


if __name__ == "__main__":
					main()


import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
					import torch

					from transformers import (
					    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
					    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
					    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
					    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
					    LayoutLMvaConfig,
					    LayoutLMvaForQuestionAnswering,
					    LayoutLMvaForSequenceClassification,
					    LayoutLMvaForTokenClassification,
					    LayoutLMvaModel,
					)
					from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
					from PIL import Image

					from transformers import LayoutLMvaImageProcessor


class     lowercase :



		def __init__(			self    ,				A_    ,				A_=2    ,				A_=3    ,				A_=4    ,				A_=2    ,				A_=7    ,				A_=True    ,				A_=True    ,				A_=True    ,				A_=True    ,				A_=99    ,				A_=36    ,				A_=3    ,				A_=4    ,				A_=37    ,				A_="gelu"    ,				A_=0.1    ,				A_=0.1    ,				A_=512    ,				A_=16    ,				A_=2    ,				A_=0.02    ,				A_=6    ,				A_=6    ,				A_=3    ,				A_=4    ,				A_=None    ,				A_=1_000    ,				)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					parent
				UpperCamelCase          =					batch_size
				UpperCamelCase          =					num_channels
				UpperCamelCase          =					image_size
				UpperCamelCase          =					patch_size
				UpperCamelCase          =					text_seq_length
				UpperCamelCase          =					is_training
				UpperCamelCase          =					use_input_mask
				UpperCamelCase          =					use_token_type_ids
				UpperCamelCase          =					use_labels
				UpperCamelCase          =					vocab_size
				UpperCamelCase          =					hidden_size
				UpperCamelCase          =					num_hidden_layers
				UpperCamelCase          =					num_attention_heads
				UpperCamelCase          =					intermediate_size
				UpperCamelCase          =					hidden_act
				UpperCamelCase          =					hidden_dropout_prob
				UpperCamelCase          =					attention_probs_dropout_prob
				UpperCamelCase          =					max_position_embeddings
				UpperCamelCase          =					type_vocab_size
				UpperCamelCase          =					type_sequence_label_size
				UpperCamelCase          =					initializer_range
				UpperCamelCase          =					coordinate_size
				UpperCamelCase          =					shape_size
				UpperCamelCase          =					num_labels
				UpperCamelCase          =					num_choices
				UpperCamelCase          =					scope
				UpperCamelCase          =					range_bbox

				# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
				UpperCamelCase          =					text_seq_length
				UpperCamelCase          =					(image_size // patch_size) ** 2 + 1
				UpperCamelCase          =					self.text_seq_length + self.image_seq_length



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					ids_tensor([self.batch_size, self.text_seq_length]    ,				self.vocab_size	)

				UpperCamelCase          =					ids_tensor([self.batch_size, self.text_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          =					bbox[i, j, 3]
										UpperCamelCase          =					bbox[i, j, 1]
										UpperCamelCase          =					t
								if bbox[i, j, 2] < bbox[i, j, 0]:
										UpperCamelCase          =					bbox[i, j, 2]
										UpperCamelCase          =					bbox[i, j, 0]
										UpperCamelCase          =					t

				UpperCamelCase          =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

				UpperCamelCase          =					None
				if self.use_input_mask:
						UpperCamelCase          =					random_attention_mask([self.batch_size, self.text_seq_length]	)

				UpperCamelCase          =					None
				if self.use_token_type_ids:
						UpperCamelCase          =					ids_tensor([self.batch_size, self.text_seq_length]    ,				self.type_vocab_size	)

				UpperCamelCase          =					None
				UpperCamelCase          =					None
				if self.use_labels:
						UpperCamelCase          =					ids_tensor([self.batch_size]    ,				self.type_sequence_label_size	)
						UpperCamelCase          =					ids_tensor([self.batch_size, self.text_seq_length]    ,				self.num_labels	)

				UpperCamelCase          =					LayoutLMvaConfig(
				    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    ,				coordinate_size=self.coordinate_size    ,				shape_size=self.shape_size    ,				input_size=self.image_size    ,				patch_size=self.patch_size    ,				)

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



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					LayoutLMvaModel(config=A_	)
				model.to(A_	)
				model.eval()

				# text + image
				UpperCamelCase          =					model(A_    ,				pixel_values=A_	)
				UpperCamelCase          =					model(
				    A_    ,				bbox=A_    ,				pixel_values=A_    ,				attention_mask=A_    ,				token_type_ids=A_	)
				UpperCamelCase          =					model(A_    ,				bbox=A_    ,				pixel_values=A_    ,				token_type_ids=A_	)
				UpperCamelCase          =					model(A_    ,				bbox=A_    ,				pixel_values=A_	)

				self.parent.assertEqual(result.last_hidden_state.shape    ,				(self.batch_size, self.seq_length, self.hidden_size)	)

				# text only
				UpperCamelCase          =					model(A_	)
				self.parent.assertEqual(
				    result.last_hidden_state.shape    ,				(self.batch_size, self.text_seq_length, self.hidden_size)	)

				# image only
				UpperCamelCase          =					model(pixel_values=A_	)
				self.parent.assertEqual(
				    result.last_hidden_state.shape    ,				(self.batch_size, self.image_seq_length, self.hidden_size)	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.num_labels
				UpperCamelCase          =					LayoutLMvaForSequenceClassification(A_	)
				model.to(A_	)
				model.eval()
				UpperCamelCase          =					model(
				    A_    ,				bbox=A_    ,				pixel_values=A_    ,				attention_mask=A_    ,				token_type_ids=A_    ,				labels=A_    ,				)
				self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.num_labels)	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					self.num_labels
				UpperCamelCase          =					LayoutLMvaForTokenClassification(config=A_	)
				model.to(A_	)
				model.eval()
				UpperCamelCase          =					model(
				    A_    ,				bbox=A_    ,				pixel_values=A_    ,				attention_mask=A_    ,				token_type_ids=A_    ,				labels=A_    ,				)
				self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.text_seq_length, self.num_labels)	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					LayoutLMvaForQuestionAnswering(config=A_	)
				model.to(A_	)
				model.eval()
				UpperCamelCase          =					model(
				    A_    ,				bbox=A_    ,				pixel_values=A_    ,				attention_mask=A_    ,				token_type_ids=A_    ,				start_positions=A_    ,				end_positions=A_    ,				)
				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	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					self.prepare_config_and_inputs()
				(
				    (
				    UpperCamelCase
				)     ,  (
				    UpperCamelCase
				)     ,  (
				    UpperCamelCase
				)     ,  (
				    UpperCamelCase
				)     ,  (
				    UpperCamelCase
				)     ,  (
				    UpperCamelCase
				)     ,  (
				    UpperCamelCase
				)     ,  (
				    UpperCamelCase
				)     ,  
				)          =					config_and_inputs
				UpperCamelCase          =					{
				    'input_ids': input_ids,
				    'bbox': bbox,
				    'pixel_values': pixel_values,
				    'token_type_ids': token_type_ids,
				    'attention_mask': input_mask,
				}
				return config, inputs_dict






@require_torch
class     lowercase (		_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE  ,       unittest.TestCase       ):
		__lowercase	:	Tuple     					=    False
		__lowercase	:	List[Any]     					=    False
		__lowercase	:	str     					=    False

		__lowercase	:	Tuple     					=    (
		    (
		        LayoutLMvaModel,
		        LayoutLMvaForSequenceClassification,
		        LayoutLMvaForTokenClassification,
		        LayoutLMvaForQuestionAnswering,
		    )
		    if is_torch_available()
		    else ()
		)
		__lowercase	:	List[str]     					=    (
		    {"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
		    if is_torch_available()
		    else {}
		)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_	)  -> int:
				"""simple docstring"""
				# `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
				# embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
				# the sequence dimension of the text embedding only.
				# (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
				return True



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					LayoutLMvaModelTester(self	)
				UpperCamelCase          =					ConfigTester(self    ,				config_class=A_    ,				hidden_size=37	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_=False	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					copy.deepcopy(A_	)
				if model_class in get_values(A_	):
						UpperCamelCase          =					{
						    k: v.unsqueeze(1	).expand(-1    ,				self.model_tester.num_choices    ,				-1	).contiguous()
						    if isinstance(A_    ,				torch.Tensor	) and v.ndim > 1
						    else v
						    for k, v in inputs_dict.items()
						}
				if return_labels:
						if model_class in get_values(A_	):
								UpperCamelCase          =					torch.ones(self.model_tester.batch_size    ,				dtype=torch.long    ,				device=A_	)
						elif model_class in get_values(A_	):
								UpperCamelCase          =					torch.zeros(
								    self.model_tester.batch_size    ,				dtype=torch.long    ,				device=A_	)
								UpperCamelCase          =					torch.zeros(
								    self.model_tester.batch_size    ,				dtype=torch.long    ,				device=A_	)
						elif model_class in [
						    *get_values(A_	),
						]:
								UpperCamelCase          =					torch.zeros(
								    self.model_tester.batch_size    ,				dtype=torch.long    ,				device=A_	)
						elif model_class in [
						    *get_values(A_	),
						]:
								UpperCamelCase          =					torch.zeros(
								    (self.model_tester.batch_size, self.model_tester.text_seq_length)    ,				dtype=torch.long    ,				device=A_    ,				)

				return inputs_dict



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				self.config_tester.run_common_tests()



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*A_	)



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				for type in ["absolute", "relative_key", "relative_key_query"]:
						UpperCamelCase          =					type
						self.model_tester.create_and_check_model(*A_	)



		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_sequence_classification(*A_	)



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_token_classification(*A_	)



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_question_answering(*A_	)



		@slow
		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase          =					LayoutLMvaModel.from_pretrained(A_	)
						self.assertIsNotNone(A_	)






def 				A	( )       ->  int:
		'''simple docstring'''

		UpperCamelCase          =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
		return image


@require_torch
class     lowercase (		unittest.TestCase       ):



		@cached_property
		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				return LayoutLMvaImageProcessor(apply_ocr=A_	) if is_vision_available() else None



		@slow
		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					LayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'	).to(A_	)

				UpperCamelCase          =					self.default_image_processor
				UpperCamelCase          =					prepare_img()
				UpperCamelCase          =					image_processor(images=A_    ,				return_tensors='pt'	).pixel_values.to(A_	)

				UpperCamelCase          =					torch.tensor([[1, 2]]	)
				UpperCamelCase          =					torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]	).unsqueeze(0	)

				# forward pass
				UpperCamelCase          =					model(
				    input_ids=input_ids.to(A_	)    ,				bbox=bbox.to(A_	)    ,				pixel_values=pixel_values.to(A_	)    ,				)

				# verify the logits
				UpperCamelCase          =					torch.Size((1, 199, 768)	)
				self.assertEqual(outputs.last_hidden_state.shape    ,				A_	)

				UpperCamelCase          =					torch.tensor(
				    [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]	).to(A_	)

				self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]    ,				A_    ,				atol=1e-4	)	)
