





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__			:



     '''simple docstring'''

     def __init__(							self						:   Any						,lowerCamelCase__						:   Tuple						,lowerCamelCase__						:   str=13						,lowerCamelCase__						:   Dict=7						,lowerCamelCase__						:   str=True						,lowerCamelCase__						:   Optional[int]=True						,lowerCamelCase__						:   Union[str, Any]=True						,lowerCamelCase__						:   Union[str, Any]=True						,lowerCamelCase__						:   Any=99						,lowerCamelCase__						:   Dict=32						,lowerCamelCase__						:   List[str]=2						,lowerCamelCase__						:   int=4						,lowerCamelCase__						:   Optional[Any]=37						,lowerCamelCase__						:   Optional[int]="gelu"						,lowerCamelCase__						:   Optional[Any]=0.1						,lowerCamelCase__						:   Union[str, Any]=0.1						,lowerCamelCase__						:   Optional[int]=512						,lowerCamelCase__						:   Optional[int]=16						,lowerCamelCase__						:   Optional[Any]=2						,lowerCamelCase__						:   str=0.02						,lowerCamelCase__						:   Union[str, Any]=3						,lowerCamelCase__						:   List[str]=4						,lowerCamelCase__						:   Union[str, Any]=None						,lowerCamelCase__						:   Dict=1000						,)					->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						parent
           SCREAMING_SNAKE_CASE		=						batch_size
           SCREAMING_SNAKE_CASE		=						seq_length
           SCREAMING_SNAKE_CASE		=						is_training
           SCREAMING_SNAKE_CASE		=						use_input_mask
           SCREAMING_SNAKE_CASE		=						use_token_type_ids
           SCREAMING_SNAKE_CASE		=						use_labels
           SCREAMING_SNAKE_CASE		=						vocab_size
           SCREAMING_SNAKE_CASE		=						hidden_size
           SCREAMING_SNAKE_CASE		=						num_hidden_layers
           SCREAMING_SNAKE_CASE		=						num_attention_heads
           SCREAMING_SNAKE_CASE		=						intermediate_size
           SCREAMING_SNAKE_CASE		=						hidden_act
           SCREAMING_SNAKE_CASE		=						hidden_dropout_prob
           SCREAMING_SNAKE_CASE		=						attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE		=						max_position_embeddings
           SCREAMING_SNAKE_CASE		=						type_vocab_size
           SCREAMING_SNAKE_CASE		=						type_sequence_label_size
           SCREAMING_SNAKE_CASE		=						initializer_range
           SCREAMING_SNAKE_CASE		=						num_labels
           SCREAMING_SNAKE_CASE		=						num_choices
           SCREAMING_SNAKE_CASE		=						scope
           SCREAMING_SNAKE_CASE		=						range_bbox






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						ids_tensor([self.batch_size, self.seq_length]						,self.vocab_size	)

           # convert bbox to numpy since TF does not support item assignment
           SCREAMING_SNAKE_CASE		=						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]:
                             SCREAMING_SNAKE_CASE		=						bbox[i, j, 3]
                             SCREAMING_SNAKE_CASE		=						bbox[i, j, 1]
                             SCREAMING_SNAKE_CASE		=						t
                       if bbox[i, j, 2] < bbox[i, j, 0]:
                             SCREAMING_SNAKE_CASE		=						bbox[i, j, 2]
                             SCREAMING_SNAKE_CASE		=						bbox[i, j, 0]
                             SCREAMING_SNAKE_CASE		=						t
           SCREAMING_SNAKE_CASE		=						tf.convert_to_tensor(lowerCamelCase__	)

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

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

           SCREAMING_SNAKE_CASE		=						None
           SCREAMING_SNAKE_CASE		=						None
           SCREAMING_SNAKE_CASE		=						None
           if self.use_labels:
                 SCREAMING_SNAKE_CASE		=						ids_tensor([self.batch_size]						,self.type_sequence_label_size	)
                 SCREAMING_SNAKE_CASE		=						ids_tensor([self.batch_size, self.seq_length]						,self.num_labels	)
                 SCREAMING_SNAKE_CASE		=						ids_tensor([self.batch_size]						,self.num_choices	)

           SCREAMING_SNAKE_CASE		=						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     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   List[str]						,lowerCamelCase__						:   Any						,lowerCamelCase__						:   List[str]						,lowerCamelCase__						:   Dict						,lowerCamelCase__						:   Any						,lowerCamelCase__						:   Tuple						,lowerCamelCase__						:   Tuple	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						TFLayoutLMModel(config=lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						model(lowerCamelCase__						,lowerCamelCase__						,attention_mask=lowerCamelCase__						,token_type_ids=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						model(lowerCamelCase__						,lowerCamelCase__						,token_type_ids=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						model(lowerCamelCase__						,lowerCamelCase__	)
           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     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]						,lowerCamelCase__						:   Tuple						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   Tuple						,lowerCamelCase__						:   Dict						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   str						,lowerCamelCase__						:   Union[str, Any]						,lowerCamelCase__						:   Optional[int]	)					->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						TFLayoutLMForMaskedLM(config=lowerCamelCase__	)

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]						,lowerCamelCase__						:   Any						,lowerCamelCase__						:   List[str]						,lowerCamelCase__						:   Union[str, Any]						,lowerCamelCase__						:   Tuple						,lowerCamelCase__						:   Any						,lowerCamelCase__						:   Dict						,lowerCamelCase__						:   Any						,lowerCamelCase__						:   Optional[Any]	)					->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.num_labels
           SCREAMING_SNAKE_CASE		=						TFLayoutLMForSequenceClassification(config=lowerCamelCase__	)

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any						,lowerCamelCase__						:   Optional[int]						,lowerCamelCase__						:   List[str]						,lowerCamelCase__						:   Tuple						,lowerCamelCase__						:   Union[str, Any]						,lowerCamelCase__						:   Optional[Any]						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   str						,lowerCamelCase__						:   Tuple	)					->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.num_labels
           SCREAMING_SNAKE_CASE		=						TFLayoutLMForTokenClassification(config=lowerCamelCase__	)

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   int						,lowerCamelCase__						:   Tuple						,lowerCamelCase__						:   Dict						,lowerCamelCase__						:   str						,lowerCamelCase__						:   Union[str, Any]						,lowerCamelCase__						:   Optional[Any]						,lowerCamelCase__						:   Tuple						,lowerCamelCase__						:   Any						,lowerCamelCase__						:   Optional[Any]	)					->		Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						TFLayoutLMForQuestionAnswering(config=lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						model(lowerCamelCase__						,lowerCamelCase__						,attention_mask=lowerCamelCase__						,token_type_ids=lowerCamelCase__	)
           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     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.prepare_config_and_inputs()
           (
               (
               SCREAMING_SNAKE_CASE
           ), (
               SCREAMING_SNAKE_CASE
           ), (
               SCREAMING_SNAKE_CASE
           ), (
               SCREAMING_SNAKE_CASE
           ), (
               SCREAMING_SNAKE_CASE
           ), (
               SCREAMING_SNAKE_CASE
           ), (
               SCREAMING_SNAKE_CASE
           ), (
               SCREAMING_SNAKE_CASE
           ), 
           )		=						config_and_inputs
           SCREAMING_SNAKE_CASE		=						{
               """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       ):



     '''simple docstring'''

     __snake_case   :     Optional[Any]		     =      (
         (
             TFLayoutLMModel,
             TFLayoutLMForMaskedLM,
             TFLayoutLMForTokenClassification,
             TFLayoutLMForSequenceClassification,
             TFLayoutLMForQuestionAnswering,
         )
         if is_tf_available()
         else ()
     )
     __snake_case   :     List[str]		     =      (
         {
             "feature-extraction": TFLayoutLMModel,
             "fill-mask": TFLayoutLMForMaskedLM,
             "text-classification": TFLayoutLMForSequenceClassification,
             "token-classification": TFLayoutLMForTokenClassification,
             "zero-shot": TFLayoutLMForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     __snake_case   :     Union[str, Any]		     =      False
     __snake_case   :     int		     =      True
     __snake_case   :     Dict		     =      10






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]	)					->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						TFLayoutLMModelTester(self	)
           SCREAMING_SNAKE_CASE		=						ConfigTester(self						,config_class=lowerCamelCase__						,hidden_size=37	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		Optional[Any]:




           '''simple docstring'''


           self.config_tester.run_common_tests()






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any	)					->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*lowerCamelCase__	)






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__	)






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




           '''simple docstring'''


           for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 SCREAMING_SNAKE_CASE		=						TFLayoutLMModel.from_pretrained(lowerCamelCase__	)
                 self.assertIsNotNone(lowerCamelCase__	)






     @unittest.skip("""Onnx compliancy broke with TF 2.10"""	)
     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		Union[str, Any]:




           '''simple docstring'''


           pass



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


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						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
      SCREAMING_SNAKE_CASE		=						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
      SCREAMING_SNAKE_CASE		=						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
      SCREAMING_SNAKE_CASE		=						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)
      SCREAMING_SNAKE_CASE		=						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       ):



     '''simple docstring'''

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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						TFLayoutLMModel.from_pretrained("""microsoft/layoutlm-base-uncased"""	)

           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						prepare_layoutlm_batch_inputs()

           # forward pass
           SCREAMING_SNAKE_CASE		=						model(input_ids=lowerCamelCase__						,bbox=lowerCamelCase__						,attention_mask=lowerCamelCase__						,token_type_ids=lowerCamelCase__	)

           # test the sequence output on [0, :3, :3]
           SCREAMING_SNAKE_CASE		=						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]						,lowerCamelCase__						,atol=1e-3	)	)

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

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






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						TFLayoutLMForSequenceClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""						,num_labels=2	)

           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						prepare_layoutlm_batch_inputs()

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

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

           # test the shape of the logits
           SCREAMING_SNAKE_CASE		=						outputs.logits
           SCREAMING_SNAKE_CASE		=						(2, 2)
           self.assertEqual(logits.shape						,lowerCamelCase__	)






     @slow
     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						TFLayoutLMForTokenClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""						,num_labels=13	)

           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						prepare_layoutlm_batch_inputs()

           # forward pass
           SCREAMING_SNAKE_CASE		=						model(
               input_ids=lowerCamelCase__						,bbox=lowerCamelCase__						,attention_mask=lowerCamelCase__						,token_type_ids=lowerCamelCase__						,labels=lowerCamelCase__	)

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






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						TFLayoutLMForQuestionAnswering.from_pretrained("""microsoft/layoutlm-base-uncased"""	)

           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						prepare_layoutlm_batch_inputs()

           # forward pass
           SCREAMING_SNAKE_CASE		=						model(input_ids=lowerCamelCase__						,bbox=lowerCamelCase__						,attention_mask=lowerCamelCase__						,token_type_ids=lowerCamelCase__	)

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









import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict

@dataclass
class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     __snake_case   :     int		     =      ComputeEnvironment.AMAZON_SAGEMAKER
     __snake_case   :     List[Any]		     =      True
     __snake_case   :     Optional[int]		     =      "ml.p3.2xlarge"
     __snake_case   :     List[str]		     =      "accelerate_sagemaker_execution_role"
     __snake_case   :     Tuple		     =      "hf-sm"
     __snake_case   :     Any		     =      "us-east-1"
     __snake_case   :     Union[str, Any]		     =      1
     __snake_case   :     Dict		     =      "accelerate-sagemaker-1"
     __snake_case   :     Tuple		     =      "1.6"
     __snake_case   :     List[str]		     =      "4.4"
     __snake_case   :     str		     =      "train.py"
     __snake_case   :     List[str]		     =      [
         "--model_name_or_path",
         "bert",
         "--do_train",
         "False",
         "--epochs",
         "3",
         "--learning_rate",
         "5e-5",
         "--max_steps",
         "50.5",
     ]
     __snake_case   :     Optional[int]		     =      [
         "--model_name_or_path",
         "bert",
         "--do_train",
         "--do_test",
         "False",
         "--do_predict",
         "--epochs",
         "3",
         "--learning_rate",
         "5e-5",
         "--max_steps",
         "50.5",
     ]

class 		UpperCamelCase__			(				unittest.TestCase       ):



     '''simple docstring'''

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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args	)
           assert isinstance(converted_args["""model_name_or_path"""]						,lowerCamelCase__	)
           assert isinstance(converted_args["""do_train"""]						,lowerCamelCase__	)
           assert isinstance(converted_args["""epochs"""]						,lowerCamelCase__	)
           assert isinstance(converted_args["""learning_rate"""]						,lowerCamelCase__	)
           assert isinstance(converted_args["""max_steps"""]						,lowerCamelCase__	)

           with pytest.raises(lowerCamelCase__	):
                 _convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args	)


