






"""simple docstring"""





import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


UpperCamelCase			=      logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

UpperCamelCase			=      {
    """base""": AutoModel,
    """sequence-classification""": AutoModelForSequenceClassification,
    """question-answering""": AutoModelForQuestionAnswering,
    """pretraining""": AutoModelForPreTraining,
    """token-classification""": AutoModelForTokenClassification,
    """language-modeling""": AutoModelWithLMHead,
    """summarization""": AutoModelForSeqaSeqLM,
    """translation""": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
UpperCamelCase			=      {
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
UpperCamelCase			=      sorted(arg_to_scheduler.keys())
UpperCamelCase			=      """{""" + """, """.join(arg_to_scheduler_choices) + """}"""







class 		_a       (						pl.LightningModule   ):





   '''simple docstring'''






   def __init__(   self			,				UpperCAmelCase_			,				UpperCAmelCase_=None			,				UpperCAmelCase_="base"			,				UpperCAmelCase_=None			,				UpperCAmelCase_=None			,				UpperCAmelCase_=None			,				**UpperCAmelCase_			,				)	-> List[Any]:




          '''simple docstring'''




          super().__init__()
          # TODO: move to self.save_hyperparameters()
          # self.save_hyperparameters()
          # can also expand arguments into trainer signature for easier reading

          self.save_hyperparameters(UpperCAmelCase_)
          lowercase__:    str        =      0
          lowercase__:    Optional[int]        =      Path(self.hparams.output_dir)
          lowercase__:    List[str]        =      self.hparams.cache_dir if self.hparams.cache_dir else None
          if config is None:
                 lowercase__:    Optional[int]        =      AutoConfig.from_pretrained(
                     self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path			,				**({"num_labels": num_labels} if num_labels is not None else {})			,				cache_dir=UpperCAmelCase_			,				**UpperCAmelCase_			,				)
          else:
                 lowercase__:    PretrainedConfig        =      config

          lowercase__:    Tuple        =      ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
          for p in extra_model_params:
                 if getattr(self.hparams			,				UpperCAmelCase_			,				UpperCAmelCase_):
                        assert hasattr(self.config			,				UpperCAmelCase_), F"""model config doesn't have a `{p}` attribute"""
                        setattr(self.config			,				UpperCAmelCase_			,				getattr(self.hparams			,				UpperCAmelCase_))

          if tokenizer is None:
                 lowercase__:    str        =      AutoTokenizer.from_pretrained(
                     self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path			,				cache_dir=UpperCAmelCase_			,				)
          else:
                 lowercase__:    PreTrainedTokenizer        =      tokenizer
          lowercase__:    str        =      MODEL_MODES[mode]
          if model is None:
                 lowercase__:    Optional[int]        =      self.model_type.from_pretrained(
                     self.hparams.model_name_or_path			,				from_tf=bool(".ckpt" in self.hparams.model_name_or_path)			,				config=self.config			,				cache_dir=UpperCAmelCase_			,				)
          else:
                 lowercase__:    Dict        =      model





   def __lowercase    (   self			,				*UpperCAmelCase_			,				**UpperCAmelCase_)	-> Optional[int]:




          '''simple docstring'''




          lowercase__:    Union[str, Any]        =      self.model_type.from_pretrained(*UpperCAmelCase_			,				**UpperCAmelCase_)





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




          '''simple docstring'''




          lowercase__:    int        =      arg_to_scheduler[self.hparams.lr_scheduler]
          lowercase__:    Tuple        =      get_schedule_func(
              self.opt			,				num_warmup_steps=self.hparams.warmup_steps			,				num_training_steps=self.total_steps())
          lowercase__:    Union[str, Any]        =      {"scheduler": scheduler, "interval": "step", "frequency": 1}
          return scheduler





   def __lowercase    (   self)	-> Dict:




          '''simple docstring'''




          lowercase__:    List[str]        =      self.model
          lowercase__:    int        =      ["bias", "LayerNorm.weight"]
          lowercase__:    Optional[Any]        =      [
              {
                  "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
                  "weight_decay": self.hparams.weight_decay,
              },
              {
                  "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
                  "weight_decay": 0.0,
              },
          ]
          if self.hparams.adafactor:
                 lowercase__:    int        =      Adafactor(
                     UpperCAmelCase_			,				lr=self.hparams.learning_rate			,				scale_parameter=UpperCAmelCase_			,				relative_step=UpperCAmelCase_)

          else:
                 lowercase__:    Optional[int]        =      AdamW(
                     UpperCAmelCase_			,				lr=self.hparams.learning_rate			,				eps=self.hparams.adam_epsilon)
          lowercase__:    Union[str, Any]        =      optimizer

          lowercase__:    Optional[int]        =      self.get_lr_scheduler()

          return [optimizer], [scheduler]





   def __lowercase    (   self			,				UpperCAmelCase_			,				UpperCAmelCase_)	-> List[Any]:




          '''simple docstring'''




          return self.validation_step(UpperCAmelCase_			,				UpperCAmelCase_)





   def __lowercase    (   self			,				UpperCAmelCase_)	-> List[Any]:




          '''simple docstring'''




          return self.validation_end(UpperCAmelCase_)





   def __lowercase    (   self)	-> int:




          '''simple docstring'''




          lowercase__:    Dict        =      max(1			,				self.hparams.gpus)  # TODO: consider num_tpu_cores
          lowercase__:    Union[str, Any]        =      self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
          return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs





   def __lowercase    (   self			,				UpperCAmelCase_)	-> Union[str, Any]:




          '''simple docstring'''




          if mode == "test":
                 lowercase__:    Union[str, Any]        =      len(self.test_dataloader().dataset)
          else:
                 lowercase__:    Any        =      self.get_dataloader("train"			,				self.hparams.train_batch_size			,				shuffle=UpperCAmelCase_)
                 lowercase__:    Any        =      len(self.train_dataloader().dataset)





   def __lowercase    (   self			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_ = False)	-> Optional[int]:




          '''simple docstring'''




          raise NotImplementedError("You must implement this for your task")





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




          '''simple docstring'''




          return self.train_loader





   def __lowercase    (   self)	-> Any:




          '''simple docstring'''




          return self.get_dataloader("dev"			,				self.hparams.eval_batch_size			,				shuffle=UpperCAmelCase_)





   def __lowercase    (   self)	-> Dict:




          '''simple docstring'''




          return self.get_dataloader("test"			,				self.hparams.eval_batch_size			,				shuffle=UpperCAmelCase_)





   def __lowercase    (   self			,				UpperCAmelCase_)	-> List[str]:




          '''simple docstring'''




          return os.path.join(
              self.hparams.data_dir			,				"cached_{}_{}_{}".format(
                  UpperCAmelCase_			,				list(filter(UpperCAmelCase_			,				self.hparams.model_name_or_path.split("/"))).pop()			,				str(self.hparams.max_seq_length)			,				)			,				)





   @pl.utilities.rank_zero_only
   def __lowercase    (   self			,				UpperCAmelCase_)	-> None:




          '''simple docstring'''




          lowercase__:    Optional[int]        =      self.output_dir.joinpath("best_tfmr")
          lowercase__:    Any        =      self.step_count
          self.model.save_pretrained(UpperCAmelCase_)
          self.tokenizer.save_pretrained(UpperCAmelCase_)





   @staticmethod
   def __lowercase    (   UpperCAmelCase_			,				UpperCAmelCase_)	-> List[Any]:




          '''simple docstring'''




          parser.add_argument(
              "--model_name_or_path"			,				default=UpperCAmelCase_			,				type=UpperCAmelCase_			,				required=UpperCAmelCase_			,				help="Path to pretrained model or model identifier from huggingface.co/models"			,				)
          parser.add_argument(
              "--config_name"			,				default=""			,				type=UpperCAmelCase_			,				help="Pretrained config name or path if not the same as model_name")
          parser.add_argument(
              "--tokenizer_name"			,				default=UpperCAmelCase_			,				type=UpperCAmelCase_			,				help="Pretrained tokenizer name or path if not the same as model_name"			,				)
          parser.add_argument(
              "--cache_dir"			,				default=""			,				type=UpperCAmelCase_			,				help="Where do you want to store the pre-trained models downloaded from huggingface.co"			,				)
          parser.add_argument(
              "--encoder_layerdrop"			,				type=UpperCAmelCase_			,				help="Encoder layer dropout probability (Optional). Goes into model.config"			,				)
          parser.add_argument(
              "--decoder_layerdrop"			,				type=UpperCAmelCase_			,				help="Decoder layer dropout probability (Optional). Goes into model.config"			,				)
          parser.add_argument(
              "--dropout"			,				type=UpperCAmelCase_			,				help="Dropout probability (Optional). Goes into model.config"			,				)
          parser.add_argument(
              "--attention_dropout"			,				type=UpperCAmelCase_			,				help="Attention dropout probability (Optional). Goes into model.config"			,				)
          parser.add_argument("--learning_rate"			,				default=5E-5			,				type=UpperCAmelCase_			,				help="The initial learning rate for Adam.")
          parser.add_argument(
              "--lr_scheduler"			,				default="linear"			,				choices=UpperCAmelCase_			,				metavar=UpperCAmelCase_			,				type=UpperCAmelCase_			,				help="Learning rate scheduler"			,				)
          parser.add_argument("--weight_decay"			,				default=0.0			,				type=UpperCAmelCase_			,				help="Weight decay if we apply some.")
          parser.add_argument("--adam_epsilon"			,				default=1E-8			,				type=UpperCAmelCase_			,				help="Epsilon for Adam optimizer.")
          parser.add_argument("--warmup_steps"			,				default=0			,				type=UpperCAmelCase_			,				help="Linear warmup over warmup_steps.")
          parser.add_argument("--num_workers"			,				default=4			,				type=UpperCAmelCase_			,				help="kwarg passed to DataLoader")
          parser.add_argument("--num_train_epochs"			,				dest="max_epochs"			,				default=3			,				type=UpperCAmelCase_)
          parser.add_argument("--train_batch_size"			,				default=32			,				type=UpperCAmelCase_)
          parser.add_argument("--eval_batch_size"			,				default=32			,				type=UpperCAmelCase_)
          parser.add_argument("--adafactor"			,				action="store_true")







class 		_a       (						pl.Callback   ):





   '''simple docstring'''






   def __lowercase    (   self			,				UpperCAmelCase_			,				UpperCAmelCase_)	-> Union[str, Any]:




          '''simple docstring'''




          lowercase__:    Optional[Any]        =      trainer.lr_schedulers[0]["scheduler"]
          lowercase__:    Optional[int]        =      {F"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr())}
          pl_module.logger.log_metrics(UpperCAmelCase_)





   def __lowercase    (   self			,				UpperCAmelCase_			,				UpperCAmelCase_)	-> List[Any]:




          '''simple docstring'''




          rank_zero_info("***** Validation results *****")
          lowercase__:    Optional[int]        =      trainer.callback_metrics
          # Log results
          for key in sorted(UpperCAmelCase_):
                 if key not in ["log", "progress_bar"]:
                        rank_zero_info("{} = {}\n".format(UpperCAmelCase_			,				str(metrics[key])))





   def __lowercase    (   self			,				UpperCAmelCase_			,				UpperCAmelCase_)	-> str:




          '''simple docstring'''




          rank_zero_info("***** Test results *****")
          lowercase__:    Optional[int]        =      trainer.callback_metrics
          # Log and save results to file
          lowercase__:    Union[str, Any]        =      os.path.join(pl_module.hparams.output_dir			,				"test_results.txt")
          with open(UpperCAmelCase_			,				"w") as writer:
                 for key in sorted(UpperCAmelCase_):
                        if key not in ["log", "progress_bar"]:
                               rank_zero_info("{} = {}\n".format(UpperCAmelCase_			,				str(metrics[key])))
                               writer.write("{} = {}\n".format(UpperCAmelCase_			,				str(metrics[key])))







def     A(  snake_case_						,       snake_case_		):
       """simple docstring"""





       parser.add_argument(
           "--output_dir"						,       default=snake_case_						,       type=snake_case_						,       required=snake_case_						,       help="The output directory where the model predictions and checkpoints will be written."						,       )
       parser.add_argument(
           "--fp16"						,       action="store_true"						,       help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"						,       )

       parser.add_argument(
           "--fp16_opt_level"						,       type=snake_case_						,       default="O2"						,       help=(
               "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
               "See details at https://nvidia.github.io/apex/amp.html"
           )						,       )
       parser.add_argument("--n_tpu_cores"						,       dest="tpu_cores"						,       type=snake_case_		)
       parser.add_argument("--max_grad_norm"						,       dest="gradient_clip_val"						,       default=1.0						,       type=snake_case_						,       help="Max gradient norm"		)
       parser.add_argument("--do_train"						,       action="store_true"						,       help="Whether to run training."		)
       parser.add_argument("--do_predict"						,       action="store_true"						,       help="Whether to run predictions on the test set."		)
       parser.add_argument(
           "--gradient_accumulation_steps"						,       dest="accumulate_grad_batches"						,       type=snake_case_						,       default=1						,       help="Number of updates steps to accumulate before performing a backward/update pass."						,       )
       parser.add_argument("--seed"						,       type=snake_case_						,       default=42						,       help="random seed for initialization"		)
       parser.add_argument(
           "--data_dir"						,       default=snake_case_						,       type=snake_case_						,       required=snake_case_						,       help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."						,       )







def     A(  snake_case_						,       snake_case_						,       snake_case_=None						,       snake_case_=True						,       snake_case_=[]						,       snake_case_=None						,       snake_case_=None						,       **snake_case_						,       ):
       """simple docstring"""





       pl.seed_everything(args.seed		)

       # init model
       lowercase__:    Any        =      Path(model.hparams.output_dir		)
       odir.mkdir(exist_ok=snake_case_		)

       # add custom checkpoints
       if checkpoint_callback is None:
              lowercase__:    List[str]        =      pl.callbacks.ModelCheckpoint(
                  filepath=args.output_dir						,       prefix="checkpoint"						,       monitor="val_loss"						,       mode="min"						,       save_top_k=1		)
       if early_stopping_callback:
              extra_callbacks.append(snake_case_		)
       if logging_callback is None:
              lowercase__:    List[Any]        =      LoggingCallback()

       lowercase__:    Dict        =      {}

       # TODO: remove with PyTorch 1.6 since pl uses native amp
       if args.fpaa:
              lowercase__:    List[str]        =      16
              lowercase__:    List[Any]        =      args.fpaa_opt_level

       if args.gpus > 1:
              lowercase__:    List[Any]        =      "ddp"

       lowercase__:    int        =      args.accumulate_grad_batches
       lowercase__:    Optional[Any]        =      extra_train_kwargs.get("accelerator"						,       snake_case_		)
       lowercase__:    Tuple        =      extra_train_kwargs.get("profiler"						,       snake_case_		)

       lowercase__:    Tuple        =      pl.Trainer.from_argparse_args(
           snake_case_						,       weights_summary=snake_case_						,       callbacks=[logging_callback] + extra_callbacks						,       logger=snake_case_						,       checkpoint_callback=snake_case_						,       **snake_case_						,       )

       if args.do_train:
              trainer.fit(snake_case_		)

       return trainer














"""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       :





   '''simple docstring'''






   def __init__(   self			,				UpperCAmelCase_			,				UpperCAmelCase_=13			,				UpperCAmelCase_=7			,				UpperCAmelCase_=True			,				UpperCAmelCase_=True			,				UpperCAmelCase_=True			,				UpperCAmelCase_=True			,				UpperCAmelCase_=99			,				UpperCAmelCase_=32			,				UpperCAmelCase_=5			,				UpperCAmelCase_=4			,				UpperCAmelCase_=37			,				UpperCAmelCase_="gelu"			,				UpperCAmelCase_=0.1			,				UpperCAmelCase_=0.1			,				UpperCAmelCase_=512			,				UpperCAmelCase_=16			,				UpperCAmelCase_=2			,				UpperCAmelCase_=0.02			,				UpperCAmelCase_=3			,				UpperCAmelCase_=4			,				UpperCAmelCase_=None			,				UpperCAmelCase_=1_000			,				)	-> Optional[int]:




          '''simple docstring'''




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





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




          '''simple docstring'''




          lowercase__:    int        =      ids_tensor([self.batch_size, self.seq_length]			,				self.vocab_size)

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

          lowercase__:    Dict        =      None
          if self.use_input_mask:
                 lowercase__:    str        =      ids_tensor([self.batch_size, self.seq_length]			,				vocab_size=2)

          lowercase__:    str        =      None
          if self.use_token_type_ids:
                 lowercase__:    List[Any]        =      ids_tensor([self.batch_size, self.seq_length]			,				self.type_vocab_size)

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

          lowercase__:    int        =      self.get_config()

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





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




          '''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 __lowercase    (   self			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_)	-> Optional[Any]:




          '''simple docstring'''




          lowercase__:    Any        =      LayoutLMModel(config=UpperCAmelCase_)
          model.to(UpperCAmelCase_)
          model.eval()
          lowercase__:    Tuple        =      model(UpperCAmelCase_			,				UpperCAmelCase_			,				attention_mask=UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_)
          lowercase__:    Dict        =      model(UpperCAmelCase_			,				UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_)
          lowercase__:    Any        =      model(UpperCAmelCase_			,				UpperCAmelCase_)
          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 __lowercase    (   self			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_)	-> List[Any]:




          '''simple docstring'''




          lowercase__:    int        =      LayoutLMForMaskedLM(config=UpperCAmelCase_)
          model.to(UpperCAmelCase_)
          model.eval()
          lowercase__:    str        =      model(UpperCAmelCase_			,				UpperCAmelCase_			,				attention_mask=UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_			,				labels=UpperCAmelCase_)
          self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.seq_length, self.vocab_size))





   def __lowercase    (   self			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_)	-> Optional[int]:




          '''simple docstring'''




          lowercase__:    Union[str, Any]        =      self.num_labels
          lowercase__:    Optional[Any]        =      LayoutLMForSequenceClassification(UpperCAmelCase_)
          model.to(UpperCAmelCase_)
          model.eval()
          lowercase__:    Optional[Any]        =      model(
              UpperCAmelCase_			,				UpperCAmelCase_			,				attention_mask=UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_			,				labels=UpperCAmelCase_)
          self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_labels))





   def __lowercase    (   self			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_)	-> List[str]:




          '''simple docstring'''




          lowercase__:    List[str]        =      self.num_labels
          lowercase__:    List[str]        =      LayoutLMForTokenClassification(config=UpperCAmelCase_)
          model.to(UpperCAmelCase_)
          model.eval()
          lowercase__:    List[Any]        =      model(UpperCAmelCase_			,				UpperCAmelCase_			,				attention_mask=UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_			,				labels=UpperCAmelCase_)
          self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.seq_length, self.num_labels))





   def __lowercase    (   self			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_)	-> str:




          '''simple docstring'''




          lowercase__:    Dict        =      LayoutLMForQuestionAnswering(config=UpperCAmelCase_)
          model.to(UpperCAmelCase_)
          model.eval()
          lowercase__:    List[Any]        =      model(
              UpperCAmelCase_			,				bbox=UpperCAmelCase_			,				attention_mask=UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_			,				start_positions=UpperCAmelCase_			,				end_positions=UpperCAmelCase_			,				)
          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 __lowercase    (   self)	-> Tuple:




          '''simple docstring'''




          lowercase__:    int        =      self.prepare_config_and_inputs()
          (
              (
              lowercase__
          )  ,     (
              lowercase__
          )  ,     (
              lowercase__
          )  ,     (
              lowercase__
          )  ,     (
              lowercase__
          )  ,     (
              lowercase__
          )  ,     (
              lowercase__
          )  ,     (
              lowercase__
          )  ,     
          ):    int        =      config_and_inputs
          lowercase__:    Optional[int]        =      {
              "input_ids": input_ids,
              "bbox": bbox,
              "token_type_ids": token_type_ids,
              "attention_mask": input_mask,
          }
          return config, inputs_dict







@require_torch
class 		_a       (						lowercase_    ,					lowercase_    ,					unittest.TestCase   ):





   '''simple docstring'''






   UpperCamelCase__      					=    (
       (
           LayoutLMModel,
           LayoutLMForMaskedLM,
           LayoutLMForSequenceClassification,
           LayoutLMForTokenClassification,
           LayoutLMForQuestionAnswering,
       )
       if is_torch_available()
       else None
   )
   UpperCamelCase__      					=    (
       {
           """document-question-answering""": LayoutLMForQuestionAnswering,
           """feature-extraction""": LayoutLMModel,
           """fill-mask""": LayoutLMForMaskedLM,
           """text-classification""": LayoutLMForSequenceClassification,
           """token-classification""": LayoutLMForTokenClassification,
           """zero-shot""": LayoutLMForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   UpperCamelCase__      					=    True





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




          '''simple docstring'''




          lowercase__:    List[str]        =      LayoutLMModelTester(self)
          lowercase__:    List[str]        =      ConfigTester(self			,				config_class=UpperCAmelCase_			,				hidden_size=37)





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




          '''simple docstring'''




          self.config_tester.run_common_tests()





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




          '''simple docstring'''




          lowercase__:    int        =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*UpperCAmelCase_)





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




          '''simple docstring'''




          lowercase__:    List[Any]        =      self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
                 lowercase__:    Union[str, Any]        =      type
                 self.model_tester.create_and_check_model(*UpperCAmelCase_)





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




          '''simple docstring'''




          lowercase__:    Any        =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase_)





   def __lowercase    (   self)	-> Dict:




          '''simple docstring'''




          lowercase__:    List[Any]        =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase_)





   def __lowercase    (   self)	-> int:




          '''simple docstring'''




          lowercase__:    Dict        =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase_)





   def __lowercase    (   self)	-> str:




          '''simple docstring'''




          lowercase__:    Optional[Any]        =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase_)





   @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
   def __lowercase    (   self)	-> List[Any]:




          '''simple docstring'''




          pass







def     A(  ):
       """simple docstring"""





       lowercase__:    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=snake_case_		)  # noqa: E231
       lowercase__:    Dict        =      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=snake_case_		)  # noqa: E231
       lowercase__:    Optional[Any]        =      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=snake_case_		)  # noqa: E231
       lowercase__:    Dict        =      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=snake_case_		)  # noqa: E231
       # these are sequence labels (i.e. at the token level)
       lowercase__:    int        =      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=snake_case_		)  # noqa: E231
       # fmt: on

       return input_ids, attention_mask, bbox, token_type_ids, labels







@require_torch
class 		_a       (						unittest.TestCase   ):





   '''simple docstring'''






   @slow
   def __lowercase    (   self)	-> Any:




          '''simple docstring'''




          lowercase__:    str        =      LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased").to(UpperCAmelCase_)

          lowercase__  ,     lowercase__  ,     lowercase__  ,     lowercase__  ,     lowercase__:    Dict        =      prepare_layoutlm_batch_inputs()

          # forward pass
          lowercase__:    str        =      model(input_ids=UpperCAmelCase_			,				bbox=UpperCAmelCase_			,				attention_mask=UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_)

          # test the sequence output on [0, :3, :3]
          lowercase__:    Optional[int]        =      torch.tensor(
              [[0.17_85, -0.19_47, -0.04_25], [-0.32_54, -0.28_07, 0.25_53], [-0.53_91, -0.33_22, 0.33_64]]			,				device=UpperCAmelCase_			,				)

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

          # test the pooled output on [1, :3]
          lowercase__:    List[Any]        =      torch.tensor([-0.65_80, -0.02_14, 0.85_52]			,				device=UpperCAmelCase_)

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





   @slow
   def __lowercase    (   self)	-> Optional[Any]:




          '''simple docstring'''




          lowercase__:    int        =      LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased"			,				num_labels=2).to(
              UpperCAmelCase_)

          lowercase__  ,     lowercase__  ,     lowercase__  ,     lowercase__  ,     lowercase__:    List[Any]        =      prepare_layoutlm_batch_inputs()

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

          # test whether we get a loss as a scalar
          lowercase__:    Dict        =      outputs.loss
          lowercase__:    int        =      torch.Size([])
          self.assertEqual(loss.shape			,				UpperCAmelCase_)

          # test the shape of the logits
          lowercase__:    str        =      outputs.logits
          lowercase__:    Any        =      torch.Size((2, 2))
          self.assertEqual(logits.shape			,				UpperCAmelCase_)





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




          '''simple docstring'''




          lowercase__:    Tuple        =      LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"			,				num_labels=13).to(
              UpperCAmelCase_)

          lowercase__  ,     lowercase__  ,     lowercase__  ,     lowercase__  ,     lowercase__:    List[Any]        =      prepare_layoutlm_batch_inputs()

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

          # 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
          lowercase__:    int        =      outputs.logits
          lowercase__:    int        =      torch.Size((2, 25, 13))
          self.assertEqual(logits.shape			,				UpperCAmelCase_)





   @slow
   def __lowercase    (   self)	-> Tuple:




          '''simple docstring'''




          lowercase__:    Tuple        =      LayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased").to(UpperCAmelCase_)

          lowercase__  ,     lowercase__  ,     lowercase__  ,     lowercase__  ,     lowercase__:    Tuple        =      prepare_layoutlm_batch_inputs()

          # forward pass
          lowercase__:    str        =      model(input_ids=UpperCAmelCase_			,				bbox=UpperCAmelCase_			,				attention_mask=UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_)

          # test the shape of the logits
          lowercase__:    str        =      torch.Size((2, 25))
          self.assertEqual(outputs.start_logits.shape			,				UpperCAmelCase_)
          self.assertEqual(outputs.end_logits.shape			,				UpperCAmelCase_)






