"""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    __snake_case       :



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





        '''simple docstring'''





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



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





        '''simple docstring'''





        a__:     str										=	ids_tensor([self.batch_size, self.seq_length]    ,			self.vocab_size)

        # convert bbox to numpy since TF does not support item assignment
        a__:     Dict										=	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]:
              a__:     int										=	bbox[i, j, 3]
              a__:     str										=	bbox[i, j, 1]
              a__:     Optional[Any]										=	t
            if bbox[i, j, 2] < bbox[i, j, 0]:
              a__:     List[Any]										=	bbox[i, j, 2]
              a__:     Any										=	bbox[i, j, 0]
              a__:     Dict										=	t
        a__:     List[str]										=	tf.convert_to_tensor(lowercase)

        a__:     Optional[int]										=	None
        if self.use_input_mask:
          a__:     Optional[Any]										=	random_attention_mask([self.batch_size, self.seq_length])

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

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

        a__:     int										=	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   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase)	->			Union[str, Any]:





        '''simple docstring'''





        a__:     Any										=	TFLayoutLMModel(config=lowercase)

        a__:     str										=	model(lowercase    ,			lowercase    ,			attention_mask=lowercase    ,			token_type_ids=lowercase)
        a__:     List[str]										=	model(lowercase    ,			lowercase    ,			token_type_ids=lowercase)
        a__:     Tuple										=	model(lowercase    ,			lowercase)
        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   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase)	->			Optional[int]:





        '''simple docstring'''





        a__:     Optional[Any]										=	TFLayoutLMForMaskedLM(config=lowercase)

        a__:     Any										=	model(lowercase    ,			lowercase    ,			attention_mask=lowercase    ,			token_type_ids=lowercase    ,			labels=lowercase)
        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.seq_length, self.vocab_size))



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase)	->			int:





        '''simple docstring'''





        a__:     Dict										=	self.num_labels
        a__:     Optional[int]										=	TFLayoutLMForSequenceClassification(config=lowercase)

        a__:     Any										=	model(lowercase    ,			lowercase    ,			attention_mask=lowercase    ,			token_type_ids=lowercase)
        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.num_labels))



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





        '''simple docstring'''





        a__:     Dict										=	self.num_labels
        a__:     Optional[int]										=	TFLayoutLMForTokenClassification(config=lowercase)

        a__:     int										=	model(lowercase    ,			lowercase    ,			attention_mask=lowercase    ,			token_type_ids=lowercase    ,			labels=lowercase)
        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.seq_length, self.num_labels))



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase)	->			Tuple:





        '''simple docstring'''





        a__:     Any										=	TFLayoutLMForQuestionAnswering(config=lowercase)

        a__:     Tuple										=	model(lowercase    ,			lowercase    ,			attention_mask=lowercase    ,			token_type_ids=lowercase)
        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   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     Tuple										=	self.prepare_config_and_inputs()
        (
            (
            a__
        )							,    (
            a__
        )							,    (
            a__
        )							,    (
            a__
        )							,    (
            a__
        )							,    (
            a__
        )							,    (
            a__
        )							,    (
            a__
        )							,    
        ):     List[Any]										=	config_and_inputs
        a__:     Tuple										=	{
            'input_ids': input_ids,
            'bbox': bbox,
            'token_type_ids': token_type_ids,
            'attention_mask': input_mask,
        }
        return config, inputs_dict







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



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     Union[str, Any]										=	TFLayoutLMModelTester(self)
        a__:     List[Any]										=	ConfigTester(self    ,			config_class=lowercase    ,			hidden_size=37)



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





        '''simple docstring'''





        self.config_tester.run_common_tests()



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     Union[str, Any]										=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase)



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        a__:     Tuple										=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*lowercase)



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     Optional[int]										=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*lowercase)



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     Union[str, Any]										=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*lowercase)



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     List[str]										=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*lowercase)



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





        '''simple docstring'''





        for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          a__:     Any										=	TFLayoutLMModel.from_pretrained(lowercase)
          self.assertIsNotNone(lowercase)



      @unittest.skip('Onnx compliancy broke with TF 2.10')
      def   lowerCamelCase_   (						self)	->			Optional[int]:





        '''simple docstring'''





        pass







def 						__a   (	)	->Optional[Any]:
  # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
  # fmt: off
  a__:     List[str]										=	tf.convert_to_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]]   )  # noqa: E231
  a__:     Tuple										=	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
  a__:     Tuple										=	tf.convert_to_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]]]   )  # noqa: E231
  a__:     Optional[int]										=	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)
  a__:     Tuple										=	tf.convert_to_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]]   )  # noqa: E231
  # fmt: on

  return input_ids, attention_mask, bbox, token_type_ids, labels




@require_tf
class    __snake_case       (     unittest.TestCase				):



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





        '''simple docstring'''





        a__:     str										=	TFLayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased')

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

        # forward pass
        a__:     Union[str, Any]										=	model(input_ids=lowercase    ,			bbox=lowercase    ,			attention_mask=lowercase    ,			token_type_ids=lowercase)

        # test the sequence output on [0, :3, :3]
        a__:     Optional[int]										=	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]    ,			lowercase    ,			atol=1e-3))

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

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



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





        '''simple docstring'''





        a__:     Any										=	TFLayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'    ,			num_labels=2)

        a__							,    a__							,    a__							,    a__							,    a__:     Dict										=	prepare_layoutlm_batch_inputs()

        # forward pass
        a__:     List[Any]										=	model(
            input_ids=lowercase    ,			bbox=lowercase    ,			attention_mask=lowercase    ,			token_type_ids=lowercase    ,			labels=tf.convert_to_tensor([1, 1])    ,			)

        # test whether we get a loss as a scalar
        a__:     Tuple										=	outputs.loss
        a__:     Union[str, Any]										=	(2,)
        self.assertEqual(loss.shape    ,			lowercase)

        # test the shape of the logits
        a__:     int										=	outputs.logits
        a__:     Dict										=	(2, 2)
        self.assertEqual(logits.shape    ,			lowercase)



      @slow
      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        a__:     List[Any]										=	TFLayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'    ,			num_labels=13)

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

        # forward pass
        a__:     int										=	model(
            input_ids=lowercase    ,			bbox=lowercase    ,			attention_mask=lowercase    ,			token_type_ids=lowercase    ,			labels=lowercase)

        # test the shape of the logits
        a__:     str										=	outputs.logits
        a__:     Any										=	tf.convert_to_tensor((2, 25, 13))
        self.assertEqual(logits.shape    ,			lowercase)



      @slow
      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        a__:     Optional[Any]										=	TFLayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased')

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

        # forward pass
        a__:     Optional[int]										=	model(input_ids=lowercase    ,			bbox=lowercase    ,			attention_mask=lowercase    ,			token_type_ids=lowercase)

        # test the shape of the logits
        a__:     Dict										=	tf.convert_to_tensor((2, 25))
        self.assertEqual(outputs.start_logits.shape    ,			lowercase)
        self.assertEqual(outputs.end_logits.shape    ,			lowercase)



"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__					       =	logging.get_logger(__name__)

lowercase__					       =	{
    'andreasmadsen/efficient_mlm_m0.40': (
        'https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'
    ),
}




class    __snake_case       (     __lowerCAmelCase				):
      a__        =		"""roberta-prelayernorm"""



      def __init__(						self    ,			lowercase=5_02_65    ,			lowercase=7_68    ,			lowercase=12    ,			lowercase=12    ,			lowercase=30_72    ,			lowercase="gelu"    ,			lowercase=0.1    ,			lowercase=0.1    ,			lowercase=5_12    ,			lowercase=2    ,			lowercase=0.02    ,			lowercase=1e-12    ,			lowercase=1    ,			lowercase=0    ,			lowercase=2    ,			lowercase="absolute"    ,			lowercase=True    ,			lowercase=None    ,			**lowercase    ,			)	->			Any:





        '''simple docstring'''





        super().__init__(pad_token_id=lowercase    ,			bos_token_id=lowercase    ,			eos_token_id=lowercase    ,			**lowercase)

        a__:     Union[str, Any]										=	vocab_size
        a__:     str										=	hidden_size
        a__:     Tuple										=	num_hidden_layers
        a__:     List[str]										=	num_attention_heads
        a__:     Dict										=	hidden_act
        a__:     int										=	intermediate_size
        a__:     Tuple										=	hidden_dropout_prob
        a__:     str										=	attention_probs_dropout_prob
        a__:     Tuple										=	max_position_embeddings
        a__:     Tuple										=	type_vocab_size
        a__:     Optional[Any]										=	initializer_range
        a__:     Tuple										=	layer_norm_eps
        a__:     Optional[int]										=	position_embedding_type
        a__:     Any										=	use_cache
        a__:     Dict										=	classifier_dropout







class    __snake_case       (     __lowerCAmelCase				):



      @property
      def   lowerCamelCase_   (						self)	->			Mapping[str, Mapping[int, str]]:





        '''simple docstring'''





        if self.task == "multiple-choice":
          a__:     str										=	{0: 'batch', 1: 'choice', 2: 'sequence'}
        else:
          a__:     Union[str, Any]										=	{0: 'batch', 1: 'sequence'}
        return OrderedDict(
            [
                ('input_ids', dynamic_axis),
                ('attention_mask', dynamic_axis),
            ])


