





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   __SCREAMING_SNAKE_CASE     :







       def __init__( self	:      List[Any]						,   A	:      Any						,   A	:      Optional[int]=1_3						,   A	:      List[str]=7						,   A	:      List[Any]=True						,   A	:      Union[str, Any]=True						,   A	:      Any=True						,   A	:      Tuple=True						,   A	:      int=9_9						,   A	:      Optional[int]=3_2						,   A	:      Optional[Any]=2						,   A	:      Optional[Any]=4						,   A	:      Optional[int]=3_7						,   A	:      Any="gelu"						,   A	:      List[Any]=0.1						,   A	:      Dict=0.1						,   A	:      int=5_1_2						,   A	:      str=1_6						,   A	:      Tuple=2						,   A	:      str=0.02						,   A	:      Optional[int]=3						,   A	:      Any=4						,   A	:      Union[str, Any]=None						,   A	:      Any=1_0_0_0						,   )  ->Optional[Any]:
          lowerCamelCase__  :		List[Any]     =				parent
          lowerCamelCase__  :		Any     =				batch_size
          lowerCamelCase__  :		List[str]     =				seq_length
          lowerCamelCase__  :		List[Any]     =				is_training
          lowerCamelCase__  :		Optional[Any]     =				use_input_mask
          lowerCamelCase__  :		Optional[Any]     =				use_token_type_ids
          lowerCamelCase__  :		Union[str, Any]     =				use_labels
          lowerCamelCase__  :		int     =				vocab_size
          lowerCamelCase__  :		str     =				hidden_size
          lowerCamelCase__  :		int     =				num_hidden_layers
          lowerCamelCase__  :		List[Any]     =				num_attention_heads
          lowerCamelCase__  :		List[str]     =				intermediate_size
          lowerCamelCase__  :		Union[str, Any]     =				hidden_act
          lowerCamelCase__  :		Dict     =				hidden_dropout_prob
          lowerCamelCase__  :		int     =				attention_probs_dropout_prob
          lowerCamelCase__  :		List[str]     =				max_position_embeddings
          lowerCamelCase__  :		Any     =				type_vocab_size
          lowerCamelCase__  :		str     =				type_sequence_label_size
          lowerCamelCase__  :		Optional[int]     =				initializer_range
          lowerCamelCase__  :		List[Any]     =				num_labels
          lowerCamelCase__  :		Tuple     =				num_choices
          lowerCamelCase__  :		int     =				scope
          lowerCamelCase__  :		Optional[Any]     =				range_bbox







       def 		__lowerCamelCase       ( self	:      Dict     )  ->str:
          lowerCamelCase__  :		Any     =				ids_tensor([self.batch_size, self.seq_length]						,   self.vocab_size     )

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

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

          lowerCamelCase__  :		Optional[int]     =				None
          if self.use_token_type_ids:
             lowerCamelCase__  :		Union[str, Any]     =				ids_tensor([self.batch_size, self.seq_length]						,   self.type_vocab_size     )

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

          lowerCamelCase__  :		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	:      Optional[Any]						,   A	:      Dict						,   A	:      Optional[Any]						,   A	:      List[str]						,   A	:      Tuple						,   A	:      Optional[int]						,   A	:      List[str]						,   A	:      Dict						,   A	:      Dict     )  ->Union[str, Any]:
          lowerCamelCase__  :		Optional[Any]     =				TFLayoutLMModel(config=A     )

          lowerCamelCase__  :		Union[str, Any]     =				model(A						,   A						,   attention_mask=A						,   token_type_ids=A     )
          lowerCamelCase__  :		Union[str, Any]     =				model(A						,   A						,   token_type_ids=A     )
          lowerCamelCase__  :		List[str]     =				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 		__lowerCamelCase       ( self	:      Dict						,   A	:      Optional[Any]						,   A	:      Optional[Any]						,   A	:      List[str]						,   A	:      Tuple						,   A	:      int						,   A	:      List[str]						,   A	:      Tuple						,   A	:      List[str]     )  ->Tuple:
          lowerCamelCase__  :		List[Any]     =				TFLayoutLMForMaskedLM(config=A     )

          lowerCamelCase__  :		Optional[int]     =				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 		__lowerCamelCase       ( self	:      Optional[int]						,   A	:      int						,   A	:      Dict						,   A	:      int						,   A	:      List[str]						,   A	:      Any						,   A	:      int						,   A	:      Optional[int]						,   A	:      int     )  ->str:
          lowerCamelCase__  :		Union[str, Any]     =				self.num_labels
          lowerCamelCase__  :		Tuple     =				TFLayoutLMForSequenceClassification(config=A     )

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







       def 		__lowerCamelCase       ( self	:      Tuple						,   A	:      int						,   A	:      Union[str, Any]						,   A	:      List[Any]						,   A	:      Tuple						,   A	:      List[str]						,   A	:      Tuple						,   A	:      Union[str, Any]						,   A	:      Tuple     )  ->Tuple:
          lowerCamelCase__  :		str     =				self.num_labels
          lowerCamelCase__  :		List[str]     =				TFLayoutLMForTokenClassification(config=A     )

          lowerCamelCase__  :		Optional[int]     =				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 		__lowerCamelCase       ( self	:      int						,   A	:      int						,   A	:      List[Any]						,   A	:      List[str]						,   A	:      Optional[int]						,   A	:      Any						,   A	:      Dict						,   A	:      Any						,   A	:      Optional[Any]     )  ->int:
          lowerCamelCase__  :		Optional[Any]     =				TFLayoutLMForQuestionAnswering(config=A     )

          lowerCamelCase__  :		int     =				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 		__lowerCamelCase       ( self	:      Dict     )  ->Optional[int]:
          lowerCamelCase__  :		int     =				self.prepare_config_and_inputs()
          (
              (
              lowerCamelCase__						
          )  ,				(
              lowerCamelCase__						
          )  ,				(
              lowerCamelCase__						
          )  ,				(
              lowerCamelCase__						
          )  ,				(
              lowerCamelCase__						
          )  ,				(
              lowerCamelCase__						
          )  ,				(
              lowerCamelCase__						
          )  ,				(
              lowerCamelCase__						
          )  ,										
          )  :		List[Any]     =				config_and_inputs
          lowerCamelCase__  :		int     =				{
              '''input_ids''': input_ids,
              '''bbox''': bbox,
              '''token_type_ids''': token_type_ids,
              '''attention_mask''': input_mask,
          }
          return config, inputs_dict


@require_tf
class   __SCREAMING_SNAKE_CASE     (					lowerCAmelCase_	,lowerCAmelCase_	,unittest.TestCase   ):
       _UpperCAmelCase :    Optional[Any]					       =       (
           (
               TFLayoutLMModel,
               TFLayoutLMForMaskedLM,
               TFLayoutLMForTokenClassification,
               TFLayoutLMForSequenceClassification,
               TFLayoutLMForQuestionAnswering,
           )
           if is_tf_available()
           else ()
       )
       _UpperCAmelCase :    Any					       =       (
           {
               "feature-extraction": TFLayoutLMModel,
               "fill-mask": TFLayoutLMForMaskedLM,
               "text-classification": TFLayoutLMForSequenceClassification,
               "token-classification": TFLayoutLMForTokenClassification,
               "zero-shot": TFLayoutLMForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )
       _UpperCAmelCase :    Optional[int]					       =       False
       _UpperCAmelCase :    str					       =       True
       _UpperCAmelCase :    Optional[int]					       =       1_0







       def 		__lowerCamelCase       ( self	:      Dict     )  ->Any:
          lowerCamelCase__  :		Optional[Any]     =				TFLayoutLMModelTester(self     )
          lowerCamelCase__  :		Optional[Any]     =				ConfigTester(self						,   config_class=A						,   hidden_size=3_7     )







       def 		__lowerCamelCase       ( self	:      str     )  ->Optional[int]:
          self.config_tester.run_common_tests()







       def 		__lowerCamelCase       ( self	:      Union[str, Any]     )  ->Any:
          lowerCamelCase__  :		int     =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A     )







       def 		__lowerCamelCase       ( self	:      Tuple     )  ->Optional[int]:
          lowerCamelCase__  :		List[Any]     =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*A     )







       def 		__lowerCamelCase       ( self	:      List[str]     )  ->Any:
          lowerCamelCase__  :		str     =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*A     )







       def 		__lowerCamelCase       ( self	:      Tuple     )  ->Any:
          lowerCamelCase__  :		List[str]     =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*A     )







       def 		__lowerCamelCase       ( self	:      Tuple     )  ->Any:
          lowerCamelCase__  :		int     =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*A     )







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

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







       @unittest.skip('''Onnx compliancy broke with TF 2.10'''     )
       def 		__lowerCamelCase       ( self	:      Tuple     )  ->List[Any]:
          pass



def 			_a		(       )							->							List[Any]:







   """simple docstring"""
   # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
   # fmt: off
   lowerCamelCase__  :		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
   lowerCamelCase__  :		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
   lowerCamelCase__  :		Union[str, Any]     =				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
   lowerCamelCase__  :		Dict     =				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)
   lowerCamelCase__  :		Optional[Any]     =				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   __SCREAMING_SNAKE_CASE     (					unittest.TestCase   ):







       @slow
       def 		__lowerCamelCase       ( self	:      Any     )  ->Optional[Any]:
          lowerCamelCase__  :		List[Any]     =				TFLayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased'''     )

          lowerCamelCase__  ,				lowerCamelCase__  ,				lowerCamelCase__  ,				lowerCamelCase__  ,				lowerCamelCase__  :		Dict     =				prepare_layoutlm_batch_inputs()

          # forward pass
          lowerCamelCase__  :		Any     =				model(input_ids=A						,   bbox=A						,   attention_mask=A						,   token_type_ids=A     )

          # test the sequence output on [0, :3, :3]
          lowerCamelCase__  :		Tuple     =				tf.convert_to_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]]						,   )

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

          # test the pooled output on [1, :3]
          lowerCamelCase__  :		str     =				tf.convert_to_tensor([-0.65_80, -0.02_14, 0.85_52]     )

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







       @slow
       def 		__lowerCamelCase       ( self	:      Optional[int]     )  ->Any:
          lowerCamelCase__  :		List[Any]     =				TFLayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''						,   num_labels=2     )

          lowerCamelCase__  ,				lowerCamelCase__  ,				lowerCamelCase__  ,				lowerCamelCase__  ,				lowerCamelCase__  :		Tuple     =				prepare_layoutlm_batch_inputs()

          # forward pass
          lowerCamelCase__  :		str     =				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
          lowerCamelCase__  :		Dict     =				outputs.loss
          lowerCamelCase__  :		Union[str, Any]     =				(2,)
          self.assertEqual(loss.shape						,   A     )

          # test the shape of the logits
          lowerCamelCase__  :		Any     =				outputs.logits
          lowerCamelCase__  :		str     =				(2, 2)
          self.assertEqual(logits.shape						,   A     )







       @slow
       def 		__lowerCamelCase       ( self	:      str     )  ->int:
          lowerCamelCase__  :		Any     =				TFLayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''						,   num_labels=1_3     )

          lowerCamelCase__  ,				lowerCamelCase__  ,				lowerCamelCase__  ,				lowerCamelCase__  ,				lowerCamelCase__  :		int     =				prepare_layoutlm_batch_inputs()

          # forward pass
          lowerCamelCase__  :		str     =				model(
              input_ids=A						,   bbox=A						,   attention_mask=A						,   token_type_ids=A						,   labels=A     )

          # test the shape of the logits
          lowerCamelCase__  :		int     =				outputs.logits
          lowerCamelCase__  :		Any     =				tf.convert_to_tensor((2, 2_5, 1_3)     )
          self.assertEqual(logits.shape						,   A     )







       @slow
       def 		__lowerCamelCase       ( self	:      Any     )  ->Optional[int]:
          lowerCamelCase__  :		int     =				TFLayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased'''     )

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

          # forward pass
          lowerCamelCase__  :		Any     =				model(input_ids=A						,   bbox=A						,   attention_mask=A						,   token_type_ids=A     )

          # test the shape of the logits
          lowerCamelCase__  :		str     =				tf.convert_to_tensor((2, 2_5)     )
          self.assertEqual(outputs.start_logits.shape						,   A     )
          self.assertEqual(outputs.end_logits.shape						,   A     )








import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

a__   :					Optional[Any] 						=      logging.getLogger()

def 	_lowerCAmelCase (    ):
      lowercase__  =	argparse.ArgumentParser()
      parser.add_argument('-f'						)
      lowercase__  =	parser.parse_args()
      return args.f



class    UpperCAmelCase__(				lowerCamelCase						):

   '''simple docstring'''




   def       UpperCAmelCase		(    self	:      List[str])						->					None:




         """simple docstring"""

         lowercase__  =	logging.StreamHandler(sys.stdout)
         logger.addHandler(lowerCAmelCase)


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




         """simple docstring"""

         lowercase__  =	get_gpu_count()

         if n_gpu > 1:
               pass
               # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
               # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
               # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
               # cmd = [sys.executable] + distributed_args + args
               # execute_subprocess_async(cmd, env=self.get_env())
               # XXX: test the results - need to save them first into .json file
         else:
               args.insert(0      ,					'run_glue_deebert.py')
               with patch.object(lowerCAmelCase      ,					'argv'      ,					lowerCAmelCase):
                     lowercase__  =	run_glue_deebert.main()
                     for value in result.values():
                           self.assertGreaterEqual(lowerCAmelCase      ,					0.6_66)



   @slow
   @require_torch_non_multi_gpu
   def       UpperCAmelCase		(    self	:      Optional[Any])						->					Union[str, Any]:




         """simple docstring"""

         lowercase__  =	'\n            --model_type roberta\n            --model_name_or_path roberta-base\n            --task_name MRPC\n            --do_train\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --max_seq_length 128\n            --per_gpu_eval_batch_size=1\n            --per_gpu_train_batch_size=8\n            --learning_rate 2e-4\n            --num_train_epochs 3\n            --overwrite_output_dir\n            --seed 42\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --save_steps 0\n            --overwrite_cache\n            --eval_after_first_stage\n            '.split()
         self.run_and_check(lowerCAmelCase)

         lowercase__  =	'\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --eval_each_highway\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            '.split()
         self.run_and_check(lowerCAmelCase)

         lowercase__  =	'\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --early_exit_entropy 0.1\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            '.split()
         self.run_and_check(lowerCAmelCase)
