
"""simple docstring"""





from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    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.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
				from fairscale.optim import OSS


_a														=		logging.get_logger(__name__)

_a														=		{
    """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,
    """constant""": get_constant_schedule,
    """constant_w_warmup""": get_constant_schedule_with_warmup,
}






class    _UpperCAmelCase(   lowerCamelCase     ):



		def __init__( self    ,		__a=None    ,		__a=None    ,		*__a    ,		**__a)	->	List[str]:






							'''simple docstring'''
							super().__init__(*__a    ,		**__a)

							if config is None:
												assert isinstance(self.model    ,		__a), (
												    "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
												    F''' {self.model.__class__}'''
												)
												_UpperCamelCase										=  self.model.config
							else:
												_UpperCamelCase										=  config

							_UpperCamelCase										=  data_args
							_UpperCamelCase										=  self.config.tgt_vocab_size if isinstance(self.config    ,		__a) else self.config.vocab_size

							if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
												assert self.config.pad_token_id is not None, (
												    "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
												    " calculation or doing label smoothing."
												)

							if self.config.pad_token_id is None and self.config.eos_token_id is not None:
												logger.warning(
												    F'''The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'''
												    ''' padding..''')

							if self.args.label_smoothing == 0:
												_UpperCamelCase										=  torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id)
							else:
												# dynamically import label_smoothed_nll_loss
												from utils import label_smoothed_nll_loss

												_UpperCamelCase										=  label_smoothed_nll_loss



		def 						UpperCAmelCase							( self    ,		__a)	->	Dict:






							'''simple docstring'''
							if self.optimizer is None:
												_UpperCamelCase										=  ['''bias''', '''LayerNorm.weight''']
												_UpperCamelCase										=  [
												    {
												        '''params''': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay)],
												        '''weight_decay''': self.args.weight_decay,
												    },
												    {
												        '''params''': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay)],
												        '''weight_decay''': 0.0,
												    },
												]
												_UpperCamelCase										=  Adafactor if self.args.adafactor else AdamW
												if self.args.adafactor:
																	_UpperCamelCase										=  Adafactor
																	_UpperCamelCase										=  {'''scale_parameter''': False, '''relative_step''': False}
												else:
																	_UpperCamelCase										=  AdamW
																	_UpperCamelCase										=  {
																	    '''betas''': (self.args.adam_betaa, self.args.adam_betaa),
																	    '''eps''': self.args.adam_epsilon,
																	}
												_UpperCamelCase										=  self.args.learning_rate
												if self.sharded_ddp:
																	_UpperCamelCase										=  OSS(
																	    params=__a    ,		optim=__a    ,		**__a    ,		)
												else:
																	_UpperCamelCase										=  optimizer_cls(__a    ,		**__a)

							if self.lr_scheduler is None:
												_UpperCamelCase										=  self._get_lr_scheduler(__a)
							else:  # ignoring --lr_scheduler
												logger.warning('''scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.''')



		def 						UpperCAmelCase							( self    ,		__a)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  arg_to_scheduler[self.args.lr_scheduler]
							if self.args.lr_scheduler == "constant":
												_UpperCamelCase										=  schedule_func(self.optimizer)
							elif self.args.lr_scheduler == "constant_w_warmup":
												_UpperCamelCase										=  schedule_func(self.optimizer    ,		num_warmup_steps=self.args.warmup_steps)
							else:
												_UpperCamelCase										=  schedule_func(
												    self.optimizer    ,		num_warmup_steps=self.args.warmup_steps    ,		num_training_steps=__a)
							return scheduler



		def 						UpperCAmelCase							( self)	->	Optional[torch.utils.data.Sampler]:






							'''simple docstring'''
							if isinstance(self.train_dataset    ,		torch.utils.data.IterableDataset):
												return None
							elif is_torch_tpu_available():
												return get_tpu_sampler(self.train_dataset)
							else:
												if self.args.sortish_sampler:
																	self.train_dataset.make_sortish_sampler(
																	    self.args.per_device_train_batch_size    ,		distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)    ,		)

												return (
												    RandomSampler(self.train_dataset)
												    if self.args.local_rank == -1
												    else DistributedSampler(self.train_dataset)
												)



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a)	->	Union[str, Any]:






							'''simple docstring'''
							if self.args.label_smoothing == 0:
												if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
																	# force training to ignore pad token
																	_UpperCamelCase										=  model(**__a    ,		use_cache=__a)[0]
																	_UpperCamelCase										=  self.loss_fn(logits.view(-1    ,		logits.shape[-1])    ,		labels.view(-1))
												else:
																	# compute usual loss via models
																	_UpperCamelCase			,  _UpperCamelCase										=  model(**__a    ,		labels=__a    ,		use_cache=__a)[:2]
							else:
												# compute label smoothed loss
												_UpperCamelCase										=  model(**__a    ,		use_cache=__a)[0]
												_UpperCamelCase										=  torch.nn.functional.log_softmax(__a    ,		dim=-1)
												_UpperCamelCase			,  _UpperCamelCase										=  self.loss_fn(__a    ,		__a    ,		self.args.label_smoothing    ,		ignore_index=self.config.pad_token_id)
							return loss, logits



		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase										=  inputs.pop('''labels''')
							_UpperCamelCase			,  _UpperCamelCase										=  self._compute_loss(__a    ,		__a    ,		__a)
							return loss



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a = None    ,		)	->	Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:






							'''simple docstring'''
							_UpperCamelCase										=  self._prepare_inputs(__a)

							_UpperCamelCase										=  {
							    '''max_length''': self.data_args.val_max_target_length
							    if self.data_args is not None
							    else self.config.max_length,
							    '''num_beams''': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
							}

							if self.args.predict_with_generate and not self.args.prediction_loss_only:
												_UpperCamelCase										=  self.model.generate(
												    inputs['''input_ids''']    ,		attention_mask=inputs['''attention_mask''']    ,		**__a    ,		)
												# in case the batch is shorter than max length, the output should be padded
												if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
																	_UpperCamelCase										=  self._pad_tensors_to_max_len(__a    ,		gen_kwargs['''max_length'''])

							_UpperCamelCase										=  inputs.pop('''labels''')
							with torch.no_grad():
												# compute loss on predict data
												_UpperCamelCase			,  _UpperCamelCase										=  self._compute_loss(__a    ,		__a    ,		__a)

							_UpperCamelCase										=  loss.mean().detach()
							if self.args.prediction_loss_only:
												return (loss, None, None)

							_UpperCamelCase										=  generated_tokens if self.args.predict_with_generate else logits

							if labels.shape[-1] < gen_kwargs["max_length"]:
												_UpperCamelCase										=  self._pad_tensors_to_max_len(__a    ,		gen_kwargs['''max_length'''])

							return (loss, logits, labels)




		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	List[str]:






							'''simple docstring'''
							# If PAD token is not defined at least EOS token has to be defined
							_UpperCamelCase										=  self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

							if pad_token_id is None:
												raise ValueError(
												    '''Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'''
												    F''' padded to `max_length`={max_length}''')

							_UpperCamelCase										=  pad_token_id * torch.ones(
							    (tensor.shape[0], max_length)    ,		dtype=tensor.dtype    ,		device=tensor.device)
							_UpperCamelCase										=  tensor
							return padded_tensor








"""simple docstring"""





from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers.models.layoutlm.modeling_tf_layoutlm import (
				    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFLayoutLMForMaskedLM,
				    TFLayoutLMForQuestionAnswering,
				    TFLayoutLMForSequenceClassification,
				    TFLayoutLMForTokenClassification,
				    TFLayoutLMModel,
				)






class    _UpperCAmelCase:



		def __init__( self    ,		__a    ,		__a=13    ,		__a=7    ,		__a=True    ,		__a=True    ,		__a=True    ,		__a=True    ,		__a=99    ,		__a=32    ,		__a=2    ,		__a=4    ,		__a=37    ,		__a="gelu"    ,		__a=0.1    ,		__a=0.1    ,		__a=5_12    ,		__a=16    ,		__a=2    ,		__a=0.02    ,		__a=3    ,		__a=4    ,		__a=None    ,		__a=10_00    ,		)	->	str:






							'''simple docstring'''
							_UpperCamelCase										=  parent
							_UpperCamelCase										=  batch_size
							_UpperCamelCase										=  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										=  num_labels
							_UpperCamelCase										=  num_choices
							_UpperCamelCase										=  scope
							_UpperCamelCase										=  range_bbox



		def 						UpperCAmelCase							( self)	->	Tuple:






							'''simple docstring'''
							_UpperCamelCase										=  ids_tensor([self.batch_size, self.seq_length]    ,		self.vocab_size)

							# convert bbox to numpy since TF does not support item assignment
							_UpperCamelCase										=  ids_tensor([self.batch_size, self.seq_length, 4]    ,		self.range_bbox).numpy()
							# 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										=  tf.convert_to_tensor(__a)

							_UpperCamelCase										=  None
							if self.use_input_mask:
												_UpperCamelCase										=  random_attention_mask([self.batch_size, self.seq_length])

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

							_UpperCamelCase										=  None
							_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.seq_length]    ,		self.num_labels)
												_UpperCamelCase										=  ids_tensor([self.batch_size]    ,		self.num_choices)

							_UpperCamelCase										=  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    ,		)

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



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a)	->	List[str]:






							'''simple docstring'''
							_UpperCamelCase										=  TFLayoutLMModel(config=__a)

							_UpperCamelCase										=  model(__a    ,		__a    ,		attention_mask=__a    ,		token_type_ids=__a)
							_UpperCamelCase										=  model(__a    ,		__a    ,		token_type_ids=__a)
							_UpperCamelCase										=  model(__a    ,		__a)
							self.parent.assertEqual(result.last_hidden_state.shape    ,		(self.batch_size, self.seq_length, self.hidden_size))
							self.parent.assertEqual(result.pooler_output.shape    ,		(self.batch_size, self.hidden_size))



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a)	->	Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase										=  TFLayoutLMForMaskedLM(config=__a)

							_UpperCamelCase										=  model(__a    ,		__a    ,		attention_mask=__a    ,		token_type_ids=__a    ,		labels=__a)
							self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.seq_length, self.vocab_size))



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a)	->	int:






							'''simple docstring'''
							_UpperCamelCase										=  self.num_labels
							_UpperCamelCase										=  TFLayoutLMForSequenceClassification(config=__a)

							_UpperCamelCase										=  model(__a    ,		__a    ,		attention_mask=__a    ,		token_type_ids=__a)
							self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.num_labels))



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a)	->	Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase										=  self.num_labels
							_UpperCamelCase										=  TFLayoutLMForTokenClassification(config=__a)

							_UpperCamelCase										=  model(__a    ,		__a    ,		attention_mask=__a    ,		token_type_ids=__a    ,		labels=__a)
							self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.seq_length, self.num_labels))



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a)	->	Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase										=  TFLayoutLMForQuestionAnswering(config=__a)

							_UpperCamelCase										=  model(__a    ,		__a    ,		attention_mask=__a    ,		token_type_ids=__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)	->	List[Any]:






							'''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,
							    '''token_type_ids''': token_type_ids,
							    '''attention_mask''': input_mask,
							}
							return config, inputs_dict






@require_tf
class    _UpperCAmelCase(   lowerCamelCase				,					lowerCamelCase				,					unittest.TestCase     ):
		lowercase__      =				(
		    (
		        TFLayoutLMModel,
		        TFLayoutLMForMaskedLM,
		        TFLayoutLMForTokenClassification,
		        TFLayoutLMForSequenceClassification,
		        TFLayoutLMForQuestionAnswering,
		    )
		    if is_tf_available()
		    else ()
		)
		lowercase__      =				(
		    {
		        'feature-extraction': TFLayoutLMModel,
		        'fill-mask': TFLayoutLMForMaskedLM,
		        'text-classification': TFLayoutLMForSequenceClassification,
		        'token-classification': TFLayoutLMForTokenClassification,
		        'zero-shot': TFLayoutLMForSequenceClassification,
		    }
		    if is_tf_available()
		    else {}
		)
		lowercase__      =				False
		lowercase__      =				True
		lowercase__      =				10



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






							'''simple docstring'''
							_UpperCamelCase										=  TFLayoutLMModelTester(self)
							_UpperCamelCase										=  ConfigTester(self    ,		config_class=__a    ,		hidden_size=37)



		def 						UpperCAmelCase							( self)	->	str:






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



		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__a)



		def 						UpperCAmelCase							( self)	->	Dict:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*__a)



		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*__a)



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






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*__a)



		def 						UpperCAmelCase							( self)	->	Tuple:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*__a)



		@slow
		def 						UpperCAmelCase							( self)	->	str:






							'''simple docstring'''
							for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_UpperCamelCase										=  TFLayoutLMModel.from_pretrained(__a)
												self.assertIsNotNone(__a)




		@unittest.skip('''Onnx compliancy broke with TF 2.10''')
		def 						UpperCAmelCase							( self)	->	List[str]:






							'''simple docstring'''
							pass





def 				lowerCamelCase__		(      )							->		Union[str, Any]:


					"""simple docstring"""





					_UpperCamelCase										=  tf.convert_to_tensor([[1_01,10_19,10_14,10_16,10_37,1_28_49,47_47,10_04,1_42_46,22_78,54_39,45_24,50_02,29_30,21_93,29_30,43_41,32_08,10_05,10_55,21_71,28_48,1_13_00,35_31,1_02],[1_01,40_70,40_34,70_20,10_24,30_58,10_15,10_13,28_61,10_13,60_70,1_92_74,27_72,62_05,2_78_14,1_61_47,1_61_47,43_43,20_47,1_02_83,1_09_69,1_43_89,10_12,23_38,1_02]]     )  # noqa: E231
					_UpperCamelCase										=  tf.convert_to_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],]     )  # noqa: E231
					_UpperCamelCase										=  tf.convert_to_tensor([[[0,0,0,0],[4_23,2_37,4_40,2_51],[4_27,2_72,4_41,2_87],[4_19,1_15,4_37,1_29],[9_61,8_85,9_92,9_12],[2_56,38,3_30,58],[2_56,38,3_30,58],[3_36,42,3_53,57],[3_60,39,4_01,56],[3_60,39,4_01,56],[4_11,39,4_71,59],[4_79,41,5_28,59],[5_33,39,6_30,60],[67,1_13,1_34,1_31],[1_41,1_15,2_09,1_32],[68,1_49,1_33,1_66],[1_41,1_49,1_87,1_64],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[2_95,1_48,3_49,1_65],[4_41,1_49,4_92,1_66],[4_97,1_49,5_46,1_64],[64,2_01,1_25,2_18],[10_00,10_00,10_00,10_00]],[[0,0,0,0],[6_62,1_50,7_54,1_66],[6_65,1_99,7_42,2_11],[5_19,2_13,5_54,2_28],[5_19,2_13,5_54,2_28],[1_34,4_33,1_87,4_54],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[3_14,4_69,3_76,4_82],[5_04,6_84,5_82,7_06],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[6_10,7_49,6_52,7_65],[1_30,6_59,1_68,6_72],[1_76,6_57,2_37,6_72],[2_38,6_57,3_12,6_72],[4_43,6_53,6_28,6_72],[4_43,6_53,6_28,6_72],[7_16,3_01,8_25,3_17],[10_00,10_00,10_00,10_00]]]     )  # noqa: E231
					_UpperCamelCase										=  tf.convert_to_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]]     )  # noqa: E231
					# these are sequence labels (i.e. at the token level)
					_UpperCamelCase										=  tf.convert_to_tensor([[-1_00,10,10,10,9,1,-1_00,7,7,-1_00,7,7,4,2,5,2,8,8,-1_00,-1_00,5,0,3,2,-1_00],[-1_00,12,12,12,-1_00,12,10,-1_00,-1_00,-1_00,-1_00,10,12,9,-1_00,-1_00,-1_00,10,10,10,9,12,-1_00,10,-1_00]]     )  # noqa: E231
					# fmt: on

					return input_ids, attention_mask, bbox, token_type_ids, labels






@require_tf
class    _UpperCAmelCase(   unittest.TestCase     ):



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






							'''simple docstring'''
							_UpperCamelCase										=  TFLayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased''')

							_UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase										=  prepare_layoutlm_batch_inputs()

							# forward pass
							_UpperCamelCase										=  model(input_ids=__a    ,		bbox=__a    ,		attention_mask=__a    ,		token_type_ids=__a)

							# test the sequence output on [0, :3, :3]
							_UpperCamelCase										=  tf.convert_to_tensor(
							    [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]]    ,		)

							self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]    ,		__a    ,		atol=1e-3))

							# test the pooled output on [1, :3]
							_UpperCamelCase										=  tf.convert_to_tensor([-0.6580, -0.0214, 0.8552])

							self.assertTrue(np.allclose(outputs.pooler_output[1, :3]    ,		__a    ,		atol=1e-3))



		@slow
		def 						UpperCAmelCase							( self)	->	Any:






							'''simple docstring'''
							# initialize model with randomly initialized sequence classification head
							_UpperCamelCase										=  TFLayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''    ,		num_labels=2)

							_UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase										=  prepare_layoutlm_batch_inputs()

							# forward pass
							_UpperCamelCase										=  model(
							    input_ids=__a    ,		bbox=__a    ,		attention_mask=__a    ,		token_type_ids=__a    ,		labels=tf.convert_to_tensor([1, 1])    ,		)

							# test whether we get a loss as a scalar
							_UpperCamelCase										=  outputs.loss
							_UpperCamelCase										=  (2,)
							self.assertEqual(loss.shape    ,		__a)

							# test the shape of the logits
							_UpperCamelCase										=  outputs.logits
							_UpperCamelCase										=  (2, 2)
							self.assertEqual(logits.shape    ,		__a)



		@slow
		def 						UpperCAmelCase							( self)	->	List[str]:






							'''simple docstring'''
							# initialize model with randomly initialized token classification head
							_UpperCamelCase										=  TFLayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''    ,		num_labels=13)

							_UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase										=  prepare_layoutlm_batch_inputs()

							# forward pass
							_UpperCamelCase										=  model(
							    input_ids=__a    ,		bbox=__a    ,		attention_mask=__a    ,		token_type_ids=__a    ,		labels=__a)

							# test the shape of the logits
							_UpperCamelCase										=  outputs.logits
							_UpperCamelCase										=  tf.convert_to_tensor((2, 25, 13))
							self.assertEqual(logits.shape    ,		__a)




		@slow
		def 						UpperCAmelCase							( self)	->	Dict:






							'''simple docstring'''
							# initialize model with randomly initialized token classification head
							_UpperCamelCase										=  TFLayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased''')

							_UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase										=  prepare_layoutlm_batch_inputs()

							# forward pass
							_UpperCamelCase										=  model(input_ids=__a    ,		bbox=__a    ,		attention_mask=__a    ,		token_type_ids=__a)

							# test the shape of the logits
							_UpperCamelCase										=  tf.convert_to_tensor((2, 25))
							self.assertEqual(outputs.start_logits.shape    ,		__a)
							self.assertEqual(outputs.end_logits.shape    ,		__a)






