"""simple docstring"""




import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	int     )	->				Dict:
  _lowerCAmelCase						:	Dict        =					fname.split(os.path.sep     )[-1]
  return re.search(r"""^(.*)_\d+\.jpg$"""      ,_lowerCamelCase     ).groups()[0]
class 						__A							(			SCREAMING_SNAKE_CASE_		):






     def __init__(		self    ,					a__    ,					a__=None    ,					a__=None  ):
       _lowerCAmelCase						:	int        =					file_names
       _lowerCAmelCase						:	str        =					image_transform
       _lowerCAmelCase						:	List[str]        =					label_to_id






     def __len__(		self  ):
       return len(self.file_names  )






     def __getitem__(		self    ,					a__  ):
       _lowerCAmelCase						:	List[str]        =					self.file_names[idx]
       _lowerCAmelCase						:	List[Any]        =					PIL.Image.open(a__  )
       _lowerCAmelCase						:	List[Any]        =					raw_image.convert("""RGB"""  )
       if self.image_transform is not None:
         _lowerCAmelCase						:	List[Any]        =					self.image_transform(a__  )
       _lowerCAmelCase						:	Optional[Any]        =					extract_label(a__  )
       if self.label_to_id is not None:
         _lowerCAmelCase						:	str        =					self.label_to_id[label]
       return {"image": image, "label": label}







def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Tuple      ,_lowerCamelCase						:	Dict     )	->				Dict:
  # Initialize accelerator
  if args.with_tracking:
    _lowerCAmelCase						:	Optional[int]        =					Accelerator(
        cpu=args.cpu      ,mixed_precision=args.mixed_precision      ,log_with="""all"""      ,project_dir=args.project_dir     )
  else:
    _lowerCAmelCase						:	str        =					Accelerator(cpu=args.cpu      ,mixed_precision=args.mixed_precision     )

  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _lowerCAmelCase						:	int        =					config["""lr"""]
  _lowerCAmelCase						:	Optional[Any]        =					int(config["""num_epochs"""]     )
  _lowerCAmelCase						:	List[str]        =					int(config["""seed"""]     )
  _lowerCAmelCase						:	Optional[Any]        =					int(config["""batch_size"""]     )
  _lowerCAmelCase						:	Optional[int]        =					config["""image_size"""]
  if not isinstance(_lowerCamelCase      ,(list, tuple)     ):
    _lowerCAmelCase						:	int        =					(image_size, image_size)

  # Parse out whether we are saving every epoch or after a certain number of batches
  if hasattr(args.checkpointing_steps      ,"""isdigit"""     ):
    if args.checkpointing_steps == "epoch":
      _lowerCAmelCase						:	Dict        =					args.checkpointing_steps
    elif args.checkpointing_steps.isdigit():
      _lowerCAmelCase						:	int        =					int(args.checkpointing_steps     )
    else:
      raise ValueError(
          f"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."     )
  else:
    _lowerCAmelCase						:	int        =					None

  # We need to initialize the trackers we use, and also store our configuration
  if args.with_tracking:
    _lowerCAmelCase						:	Any        =					os.path.split(_lowerCamelCase     )[-1].split("""."""     )[0]
    accelerator.init_trackers(_lowerCamelCase      ,_lowerCamelCase     )

  # Grab all the image filenames
  _lowerCAmelCase						:	Optional[int]        =					[os.path.join(args.data_dir      ,_lowerCamelCase     ) for fname in os.listdir(args.data_dir     ) if fname.endswith(""".jpg"""     )]

  # Build the label correspondences
  _lowerCAmelCase						:	Optional[Any]        =					[extract_label(_lowerCamelCase     ) for fname in file_names]
  _lowerCAmelCase						:	Union[str, Any]        =					list(set(_lowerCamelCase     )     )
  id_to_label.sort()
  _lowerCAmelCase						:	int        =					{lbl: i for i, lbl in enumerate(_lowerCamelCase     )}

  # Set the seed before splitting the data.
  np.random.seed(_lowerCamelCase     )
  torch.manual_seed(_lowerCamelCase     )
  torch.cuda.manual_seed_all(_lowerCamelCase     )

  # Split our filenames between train and validation
  _lowerCAmelCase						:	Union[str, Any]        =					np.random.permutation(len(_lowerCamelCase     )     )
  _lowerCAmelCase						:	int        =					int(0.8 * len(_lowerCamelCase     )     )
  _lowerCAmelCase						:	List[str]        =					random_perm[:cut]
  _lowerCAmelCase						:	int        =					random_perm[cut:]

  # For training we use a simple RandomResizedCrop
  _lowerCAmelCase						:	List[str]        =					Compose([RandomResizedCrop(_lowerCamelCase      ,scale=(0.5, 1.0)     ), ToTensor()]     )
  _lowerCAmelCase						:	Union[str, Any]        =					PetsDataset(
      [file_names[i] for i in train_split]      ,image_transform=_lowerCamelCase      ,label_to_id=_lowerCamelCase     )

  # For evaluation, we use a deterministic Resize
  _lowerCAmelCase						:	Optional[int]        =					Compose([Resize(_lowerCamelCase     ), ToTensor()]     )
  _lowerCAmelCase						:	Any        =					PetsDataset([file_names[i] for i in eval_split]      ,image_transform=_lowerCamelCase      ,label_to_id=_lowerCamelCase     )

  # Instantiate dataloaders.
  _lowerCAmelCase						:	List[str]        =					DataLoader(_lowerCamelCase      ,shuffle=_lowerCamelCase      ,batch_size=_lowerCamelCase      ,num_workers=4     )
  _lowerCAmelCase						:	Optional[Any]        =					DataLoader(_lowerCamelCase      ,shuffle=_lowerCamelCase      ,batch_size=_lowerCamelCase      ,num_workers=4     )

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  _lowerCAmelCase						:	int        =					create_model("""resnet50d"""      ,pretrained=_lowerCamelCase      ,num_classes=len(_lowerCamelCase     )     )

  # 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).
  _lowerCAmelCase						:	Dict        =					model.to(accelerator.device     )

  # Freezing the base model
  for param in model.parameters():
    _lowerCAmelCase						:	Optional[int]        =					False
  for param in model.get_classifier().parameters():
    _lowerCAmelCase						:	Optional[Any]        =					True

  # We normalize the batches of images to be a bit faster.
  _lowerCAmelCase						:	Optional[int]        =					torch.tensor(model.default_cfg["""mean"""]     )[None, :, None, None].to(accelerator.device     )
  _lowerCAmelCase						:	List[Any]        =					torch.tensor(model.default_cfg["""std"""]     )[None, :, None, None].to(accelerator.device     )

  # Instantiate optimizer
  _lowerCAmelCase						:	List[Any]        =					torch.optim.Adam(params=model.parameters()      ,lr=lr / 25     )

  # Instantiate learning rate scheduler
  _lowerCAmelCase						:	Optional[int]        =					OneCycleLR(optimizer=_lowerCamelCase      ,max_lr=_lowerCamelCase      ,epochs=_lowerCamelCase      ,steps_per_epoch=len(_lowerCamelCase     )     )

  # 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.
  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	Optional[int]        =					accelerator.prepare(
      _lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase     )
  # We need to keep track of how many total steps we have iterated over
  _lowerCAmelCase						:	Any        =					0
  # We also need to keep track of the starting epoch so files are named properly
  _lowerCAmelCase						:	int        =					0

  # Potentially load in the weights and states from a previous save
  if args.resume_from_checkpoint:
    if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
      accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}"     )
      accelerator.load_state(args.resume_from_checkpoint     )
      _lowerCAmelCase						:	Union[str, Any]        =					os.path.basename(args.resume_from_checkpoint     )
    else:
      # Get the most recent checkpoint
      _lowerCAmelCase						:	int        =					[f.name for f in os.scandir(os.getcwd()     ) if f.is_dir()]
      dirs.sort(key=os.path.getctime     )
      _lowerCAmelCase						:	Dict        =					dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract `epoch_{i}` or `step_{i}`
    _lowerCAmelCase						:	Dict        =					os.path.splitext(_lowerCamelCase     )[0]

    if "epoch" in training_difference:
      _lowerCAmelCase						:	int        =					int(training_difference.replace("""epoch_"""      ,""""""     )     ) + 1
      _lowerCAmelCase						:	Union[str, Any]        =					None
    else:
      _lowerCAmelCase						:	Any        =					int(training_difference.replace("""step_"""      ,""""""     )     )
      _lowerCAmelCase						:	Tuple        =					resume_step // len(_lowerCamelCase     )
      resume_step -= starting_epoch * len(_lowerCamelCase     )

    # Now we train the model
  for epoch in range(_lowerCamelCase      ,_lowerCamelCase     ):
    model.train()
    if args.with_tracking:
      _lowerCAmelCase						:	Optional[int]        =					0
    if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
      # We need to skip steps until we reach the resumed step
      _lowerCAmelCase						:	Dict        =					accelerator.skip_first_batches(_lowerCamelCase      ,_lowerCamelCase     )
      overall_step += resume_step
    else:
      # After the first iteration though, we need to go back to the original dataloader
      _lowerCAmelCase						:	Dict        =					train_dataloader
    for batch in active_dataloader:
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      _lowerCAmelCase						:	int        =					{k: v.to(accelerator.device     ) for k, v in batch.items()}
      _lowerCAmelCase						:	int        =					(batch["""image"""] - mean) / std
      _lowerCAmelCase						:	Optional[Any]        =					model(_lowerCamelCase     )
      _lowerCAmelCase						:	Tuple        =					torch.nn.functional.cross_entropy(_lowerCamelCase      ,batch["""label"""]     )
      # We keep track of the loss at each epoch
      if args.with_tracking:
        total_loss += loss.detach().float()
      accelerator.backward(_lowerCamelCase     )
      optimizer.step()
      lr_scheduler.step()
      optimizer.zero_grad()
      overall_step += 1
      if isinstance(_lowerCamelCase      ,_lowerCamelCase     ):
        _lowerCAmelCase						:	Any        =					f"step_{overall_step}"
        if overall_step % checkpointing_steps == 0:
          if args.output_dir is not None:
            _lowerCAmelCase						:	Union[str, Any]        =					os.path.join(args.output_dir      ,_lowerCamelCase     )
          accelerator.save_state(_lowerCamelCase     )
    model.eval()
    _lowerCAmelCase						:	Optional[int]        =					0
    _lowerCAmelCase						:	Optional[int]        =					0
    for step, batch in enumerate(_lowerCamelCase     ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      _lowerCAmelCase						:	Dict        =					{k: v.to(accelerator.device     ) for k, v in batch.items()}
      _lowerCAmelCase						:	Union[str, Any]        =					(batch["""image"""] - mean) / std
      with torch.no_grad():
        _lowerCAmelCase						:	Optional[int]        =					model(_lowerCamelCase     )
      _lowerCAmelCase						:	Union[str, Any]        =					outputs.argmax(dim=-1     )
      _lowerCAmelCase      ,     _lowerCAmelCase						:	Any        =					accelerator.gather_for_metrics((predictions, batch["""label"""])     )
      _lowerCAmelCase						:	Union[str, Any]        =					predictions == references
      num_elems += accurate_preds.shape[0]
      accurate += accurate_preds.long().sum()

    _lowerCAmelCase						:	Optional[Any]        =					accurate.item() / num_elems
    # Use accelerator.print to print only on the main process.
    accelerator.print(f"epoch {epoch}: {100 * eval_metric:.2f}"     )
    if args.with_tracking:
      accelerator.log(
          {
              """accuracy""": 100 * eval_metric,
              """train_loss""": total_loss.item() / len(_lowerCamelCase     ),
              """epoch""": epoch,
          }      ,step=_lowerCamelCase      ,)
    if checkpointing_steps == "epoch":
      _lowerCAmelCase						:	Optional[int]        =					f"epoch_{epoch}"
      if args.output_dir is not None:
        _lowerCAmelCase						:	List[str]        =					os.path.join(args.output_dir      ,_lowerCamelCase     )
      accelerator.save_state(_lowerCamelCase     )

  if args.with_tracking:
    accelerator.end_training()





def 							SCREAMING_SNAKE_CASE					(      )	->				List[str]:
  _lowerCAmelCase						:	List[Any]        =					argparse.ArgumentParser(description="""Simple example of training script."""     )
  parser.add_argument("""--data_dir"""      ,required=_lowerCamelCase      ,help="""The data folder on disk."""     )
  parser.add_argument("""--fp16"""      ,action="""store_true"""      ,help="""If passed, will use FP16 training."""     )
  parser.add_argument(
      """--mixed_precision"""      ,type=_lowerCamelCase      ,default=_lowerCamelCase      ,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."""     )
  parser.add_argument(
      """--checkpointing_steps"""      ,type=_lowerCamelCase      ,default=_lowerCamelCase      ,help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""      ,)
  parser.add_argument(
      """--output_dir"""      ,type=_lowerCamelCase      ,default="""."""      ,help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""      ,)
  parser.add_argument(
      """--resume_from_checkpoint"""      ,type=_lowerCamelCase      ,default=_lowerCamelCase      ,help="""If the training should continue from a checkpoint folder."""      ,)
  parser.add_argument(
      """--with_tracking"""      ,action="""store_true"""      ,help="""Whether to load in all available experiment trackers from the environment and use them for logging."""      ,)
  parser.add_argument(
      """--project_dir"""      ,type=_lowerCamelCase      ,default="""logs"""      ,help="""Location on where to store experiment tracking logs` and relevent project information"""      ,)
  _lowerCAmelCase						:	Any        =					parser.parse_args()
  _lowerCAmelCase						:	List[str]        =					{"""lr""": 3e-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 224}
  training_function(_lowerCamelCase      ,_lowerCamelCase     )


if __name__ == "__main__":
     main()





"""simple docstring"""




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 						__A							:






     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__=0.0_2    ,					a__=6    ,					a__=6    ,					a__=3    ,					a__=4    ,					a__=None    ,					a__=1000    ,					):
       _lowerCAmelCase						:	Union[str, Any]        =					parent
       _lowerCAmelCase						:	Optional[Any]        =					batch_size
       _lowerCAmelCase						:	List[Any]        =					num_channels
       _lowerCAmelCase						:	str        =					image_size
       _lowerCAmelCase						:	str        =					patch_size
       _lowerCAmelCase						:	str        =					text_seq_length
       _lowerCAmelCase						:	List[Any]        =					is_training
       _lowerCAmelCase						:	List[str]        =					use_input_mask
       _lowerCAmelCase						:	List[Any]        =					use_token_type_ids
       _lowerCAmelCase						:	Optional[Any]        =					use_labels
       _lowerCAmelCase						:	Any        =					vocab_size
       _lowerCAmelCase						:	str        =					hidden_size
       _lowerCAmelCase						:	Dict        =					num_hidden_layers
       _lowerCAmelCase						:	List[Any]        =					num_attention_heads
       _lowerCAmelCase						:	str        =					intermediate_size
       _lowerCAmelCase						:	str        =					hidden_act
       _lowerCAmelCase						:	Optional[Any]        =					hidden_dropout_prob
       _lowerCAmelCase						:	Any        =					attention_probs_dropout_prob
       _lowerCAmelCase						:	Tuple        =					max_position_embeddings
       _lowerCAmelCase						:	Dict        =					type_vocab_size
       _lowerCAmelCase						:	Tuple        =					type_sequence_label_size
       _lowerCAmelCase						:	Union[str, Any]        =					initializer_range
       _lowerCAmelCase						:	Dict        =					coordinate_size
       _lowerCAmelCase						:	Optional[int]        =					shape_size
       _lowerCAmelCase						:	str        =					num_labels
       _lowerCAmelCase						:	Optional[Any]        =					num_choices
       _lowerCAmelCase						:	str        =					scope
       _lowerCAmelCase						:	Dict        =					range_bbox

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






     def    __A  (		self  ):
       _lowerCAmelCase						:	Tuple        =					ids_tensor([self.batch_size, self.text_seq_length]    ,					self.vocab_size  )

       _lowerCAmelCase						:	Tuple        =					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]:
             _lowerCAmelCase						:	Optional[Any]        =					bbox[i, j, 3]
             _lowerCAmelCase						:	List[str]        =					bbox[i, j, 1]
             _lowerCAmelCase						:	List[Any]        =					t
           if bbox[i, j, 2] < bbox[i, j, 0]:
             _lowerCAmelCase						:	int        =					bbox[i, j, 2]
             _lowerCAmelCase						:	Optional[int]        =					bbox[i, j, 0]
             _lowerCAmelCase						:	Optional[int]        =					t

       _lowerCAmelCase						:	Dict        =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

       _lowerCAmelCase						:	List[str]        =					None
       if self.use_input_mask:
         _lowerCAmelCase						:	int        =					random_attention_mask([self.batch_size, self.text_seq_length]  )

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

       _lowerCAmelCase						:	int        =					None
       _lowerCAmelCase						:	int        =					None
       if self.use_labels:
         _lowerCAmelCase						:	List[str]        =					ids_tensor([self.batch_size]    ,					self.type_sequence_label_size  )
         _lowerCAmelCase						:	str        =					ids_tensor([self.batch_size, self.text_seq_length]    ,					self.num_labels  )

       _lowerCAmelCase						:	str        =					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    __A  (		self    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__  ):
       _lowerCAmelCase						:	int        =					LayoutLMvaModel(config=a__  )
       model.to(a__  )
       model.eval()

       # text + image
       _lowerCAmelCase						:	Optional[Any]        =					model(a__    ,					pixel_values=a__  )
       _lowerCAmelCase						:	Any        =					model(
           a__    ,					bbox=a__    ,					pixel_values=a__    ,					attention_mask=a__    ,					token_type_ids=a__  )
       _lowerCAmelCase						:	List[Any]        =					model(a__    ,					bbox=a__    ,					pixel_values=a__    ,					token_type_ids=a__  )
       _lowerCAmelCase						:	Tuple        =					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
       _lowerCAmelCase						:	Dict        =					model(a__  )
       self.parent.assertEqual(
           result.last_hidden_state.shape    ,					(self.batch_size, self.text_seq_length, self.hidden_size)  )

       # image only
       _lowerCAmelCase						:	Optional[Any]        =					model(pixel_values=a__  )
       self.parent.assertEqual(
           result.last_hidden_state.shape    ,					(self.batch_size, self.image_seq_length, self.hidden_size)  )






     def    __A  (		self    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__  ):
       _lowerCAmelCase						:	int        =					self.num_labels
       _lowerCAmelCase						:	int        =					LayoutLMvaForSequenceClassification(a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:	Tuple        =					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    __A  (		self    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__  ):
       _lowerCAmelCase						:	Optional[Any]        =					self.num_labels
       _lowerCAmelCase						:	str        =					LayoutLMvaForTokenClassification(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:	List[str]        =					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    __A  (		self    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__  ):
       _lowerCAmelCase						:	Dict        =					LayoutLMvaForQuestionAnswering(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:	str        =					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    __A  (		self  ):
       _lowerCAmelCase						:	List[str]        =					self.prepare_config_and_inputs()
       (
           (
           _lowerCAmelCase
       )      ,     (
           _lowerCAmelCase
       )      ,     (
           _lowerCAmelCase
       )      ,     (
           _lowerCAmelCase
       )      ,     (
           _lowerCAmelCase
       )      ,     (
           _lowerCAmelCase
       )      ,     (
           _lowerCAmelCase
       )      ,     (
           _lowerCAmelCase
       )      ,     
       )						:	Any        =					config_and_inputs
       _lowerCAmelCase						:	Union[str, Any]        =					{
           """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 						__A							(			SCREAMING_SNAKE_CASE_   ,     SCREAMING_SNAKE_CASE_   ,     unittest.TestCase		):
     _UpperCamelCase							:      Tuple		 =		False
     _UpperCamelCase							:      Any		 =		False
     _UpperCamelCase							:      Any		 =		False

     _UpperCamelCase							:      int		 =		(
         (
             LayoutLMvaModel,
             LayoutLMvaForSequenceClassification,
             LayoutLMvaForTokenClassification,
             LayoutLMvaForQuestionAnswering,
         )
         if is_torch_available()
         else ()
     )
     _UpperCamelCase							:      Union[str, Any]		 =		(
         {"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
         if is_torch_available()
         else {}
     )






     def    __A  (		self    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__  ):
       # `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    __A  (		self  ):
       _lowerCAmelCase						:	Any        =					LayoutLMvaModelTester(self  )
       _lowerCAmelCase						:	int        =					ConfigTester(self    ,					config_class=a__    ,					hidden_size=37  )






     def    __A  (		self    ,					a__    ,					a__    ,					a__=False  ):
       _lowerCAmelCase						:	List[str]        =					copy.deepcopy(a__  )
       if model_class in get_values(a__  ):
         _lowerCAmelCase						:	Optional[int]        =					{
             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__  ):
           _lowerCAmelCase						:	List[Any]        =					torch.ones(self.model_tester.batch_size    ,					dtype=torch.long    ,					device=a__  )
         elif model_class in get_values(a__  ):
           _lowerCAmelCase						:	Optional[int]        =					torch.zeros(
               self.model_tester.batch_size    ,					dtype=torch.long    ,					device=a__  )
           _lowerCAmelCase						:	Any        =					torch.zeros(
               self.model_tester.batch_size    ,					dtype=torch.long    ,					device=a__  )
         elif model_class in [
             *get_values(a__  ),
         ]:
           _lowerCAmelCase						:	Optional[int]        =					torch.zeros(
               self.model_tester.batch_size    ,					dtype=torch.long    ,					device=a__  )
         elif model_class in [
             *get_values(a__  ),
         ]:
           _lowerCAmelCase						:	int        =					torch.zeros(
               (self.model_tester.batch_size, self.model_tester.text_seq_length)    ,					dtype=torch.long    ,					device=a__    ,					)

       return inputs_dict






     def    __A  (		self  ):
       self.config_tester.run_common_tests()






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[int]        =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[Any]        =					self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
         _lowerCAmelCase						:	int        =					type
         self.model_tester.create_and_check_model(*a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[Any]        =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Tuple        =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[int]        =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*a__  )






     @slow
     def    __A  (		self  ):
       for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCAmelCase						:	str        =					LayoutLMvaModel.from_pretrained(a__  )
         self.assertIsNotNone(a__  )







def 							SCREAMING_SNAKE_CASE					(      )	->				str:
  _lowerCAmelCase						:	Any        =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
  return image






@require_torch
class 						__A							(			unittest.TestCase		):






     @cached_property
     def    __A  (		self  ):
       return LayoutLMvaImageProcessor(apply_ocr=a__  ) if is_vision_available() else None






     @slow
     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[int]        =					LayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base"""  ).to(a__  )

       _lowerCAmelCase						:	Dict        =					self.default_image_processor
       _lowerCAmelCase						:	Optional[int]        =					prepare_img()
       _lowerCAmelCase						:	Dict        =					image_processor(images=a__    ,					return_tensors="""pt"""  ).pixel_values.to(a__  )

       _lowerCAmelCase						:	Optional[Any]        =					torch.tensor([[1, 2]]  )
       _lowerCAmelCase						:	Dict        =					torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]  ).unsqueeze(0  )

       # forward pass
       _lowerCAmelCase						:	str        =					model(
           input_ids=input_ids.to(a__  )    ,					bbox=bbox.to(a__  )    ,					pixel_values=pixel_values.to(a__  )    ,					)

       # verify the logits
       _lowerCAmelCase						:	Optional[int]        =					torch.Size((1, 199, 768)  )
       self.assertEqual(outputs.last_hidden_state.shape    ,					a__  )

       _lowerCAmelCase						:	Union[str, Any]        =					torch.tensor(
           [[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]]  ).to(a__  )

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




