


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 _lowerCamelCase:
       def __init__(      self,   lowerCamelCase,   lowerCamelCase=13,   lowerCamelCase=7,   lowerCamelCase=True,   lowerCamelCase=True,   lowerCamelCase=True,   lowerCamelCase=True,   lowerCamelCase=99,   lowerCamelCase=32,   lowerCamelCase=2,   lowerCamelCase=4,   lowerCamelCase=37,   lowerCamelCase="gelu",   lowerCamelCase=0.1,   lowerCamelCase=0.1,   lowerCamelCase=5_12,   lowerCamelCase=16,   lowerCamelCase=2,   lowerCamelCase=0.0_2,   lowerCamelCase=3,   lowerCamelCase=4,   lowerCamelCase=None,   lowerCamelCase=10_00,   ) ->     Optional[int]:




           """simple docstring"""

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




           """simple docstring"""

           _lowercase :							Tuple   	=	ids_tensor([self.batch_size, self.seq_length],   self.vocab_size)

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

           _lowercase :							List[str]   	=	None
           if self.use_input_mask:
               _lowercase :							str   	=	random_attention_mask([self.batch_size, self.seq_length])

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

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

           _lowercase :							str   	=	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,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							List[Any]   	=	TFLayoutLMModel(config=lowerCamelCase)

           _lowercase :							List[str]   	=	model(lowerCamelCase,   lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase)
           _lowercase :							Tuple   	=	model(lowerCamelCase,   lowerCamelCase,   token_type_ids=lowerCamelCase)
           _lowercase :							List[Any]   	=	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     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     List[Any]:




           """simple docstring"""

           _lowercase :							str   	=	TFLayoutLMForMaskedLM(config=lowerCamelCase)

           _lowercase :							Dict   	=	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     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							int   	=	self.num_labels
           _lowercase :							Any   	=	TFLayoutLMForSequenceClassification(config=lowerCamelCase)

           _lowercase :							Any   	=	model(lowerCamelCase,   lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase)
           self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.num_labels))
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							str   	=	self.num_labels
           _lowercase :							Union[str, Any]   	=	TFLayoutLMForTokenClassification(config=lowerCamelCase)

           _lowercase :							Dict   	=	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     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Tuple:




           """simple docstring"""

           _lowercase :							Any   	=	TFLayoutLMForQuestionAnswering(config=lowerCamelCase)

           _lowercase :							Dict   	=	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     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

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






@require_tf
class _lowerCamelCase(		_a,				_a,				unittest.TestCase     ):
       lowercase_				:			Dict									=    (
           (
               TFLayoutLMModel,
               TFLayoutLMForMaskedLM,
               TFLayoutLMForTokenClassification,
               TFLayoutLMForSequenceClassification,
               TFLayoutLMForQuestionAnswering,
           )
           if is_tf_available()
           else ()
       )
       lowercase_				:			List[str]									=    (
           {
               """feature-extraction""": TFLayoutLMModel,
               """fill-mask""": TFLayoutLMForMaskedLM,
               """text-classification""": TFLayoutLMForSequenceClassification,
               """token-classification""": TFLayoutLMForTokenClassification,
               """zero-shot""": TFLayoutLMForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )
       lowercase_				:			List[str]									=    False
       lowercase_				:			Dict									=    True
       lowercase_				:			Union[str, Any]									=    10
       def     UpperCamelCase  (      self) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							str   	=	TFLayoutLMModelTester(self)
           _lowercase :							int   	=	ConfigTester(self,   config_class=lowerCamelCase,   hidden_size=37)
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           self.config_tester.run_common_tests()
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							Optional[int]   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							int   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							List[Any]   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     Any:




           """simple docstring"""

           _lowercase :							str   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*lowerCamelCase)
       @slow
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _lowercase :							int   	=	TFLayoutLMModel.from_pretrained(lowerCamelCase)
               self.assertIsNotNone(lowerCamelCase)




       @unittest.skip('Onnx compliancy broke with TF 2.10')
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           pass



def        UpperCamelCase_(   )					->		int:
    # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
    # fmt: off
    _lowercase :							Optional[int]   	=	tf.convert_to_tensor([[101,1019,1014,1016,1037,1_2849,4747,1004,1_4246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,1_1300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,1_9274,2772,6205,2_7814,1_6147,1_6147,4343,2047,1_0283,1_0969,1_4389,1012,2338,102]]				)  # noqa: E231
    _lowercase :							List[str]   	=	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
    _lowercase :							int   	=	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
    _lowercase :							Optional[Any]   	=	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)
    _lowercase :							List[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 _lowerCamelCase(		unittest.TestCase     ):
       @slow
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							Optional[int]   	=	TFLayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased')

           _lowercase	,       _lowercase	,       _lowercase	,       _lowercase	,       _lowercase :							Optional[Any]   	=	prepare_layoutlm_batch_inputs()

           # forward pass
           _lowercase :							Tuple   	=	model(input_ids=lowerCamelCase,   bbox=lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase)

           # test the sequence output on [0, :3, :3]
           _lowercase :							Any   	=	tf.convert_to_tensor(
               [[0.1_7_8_5, -0.1_9_4_7, -0.0_4_2_5], [-0.3_2_5_4, -0.2_8_0_7, 0.2_5_5_3], [-0.5_3_9_1, -0.3_3_2_2, 0.3_3_6_4]],   )

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

           # test the pooled output on [1, :3]
           _lowercase :							Union[str, Any]   	=	tf.convert_to_tensor([-0.6_5_8_0, -0.0_2_1_4, 0.8_5_5_2])

           self.assertTrue(np.allclose(outputs.pooler_output[1, :3],   lowerCamelCase,   atol=1E-3))
       @slow
       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

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

           _lowercase	,       _lowercase	,       _lowercase	,       _lowercase	,       _lowercase :							Optional[Any]   	=	prepare_layoutlm_batch_inputs()

           # forward pass
           _lowercase :							List[str]   	=	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
           _lowercase :							Optional[int]   	=	outputs.loss
           _lowercase :							Any   	=	(2,)
           self.assertEqual(loss.shape,   lowerCamelCase)

           # test the shape of the logits
           _lowercase :							List[Any]   	=	outputs.logits
           _lowercase :							int   	=	(2, 2)
           self.assertEqual(logits.shape,   lowerCamelCase)
       @slow
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

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

           _lowercase	,       _lowercase	,       _lowercase	,       _lowercase	,       _lowercase :							Union[str, Any]   	=	prepare_layoutlm_batch_inputs()

           # forward pass
           _lowercase :							str   	=	model(
               input_ids=lowerCamelCase,   bbox=lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   labels=lowerCamelCase)

           # test the shape of the logits
           _lowercase :							List[Any]   	=	outputs.logits
           _lowercase :							List[str]   	=	tf.convert_to_tensor((2, 25, 13))
           self.assertEqual(logits.shape,   lowerCamelCase)




       @slow
       def     UpperCamelCase  (      self) ->     Any:




           """simple docstring"""

           _lowercase :							str   	=	TFLayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased')

           _lowercase	,       _lowercase	,       _lowercase	,       _lowercase	,       _lowercase :							Any   	=	prepare_layoutlm_batch_inputs()

           # forward pass
           _lowercase :							Optional[Any]   	=	model(input_ids=lowerCamelCase,   bbox=lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase)

           # test the shape of the logits
           _lowercase :							List[Any]   	=	tf.convert_to_tensor((2, 25))
           self.assertEqual(outputs.start_logits.shape,   lowerCamelCase)
           self.assertEqual(outputs.end_logits.shape,   lowerCamelCase)






import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
SCREAMING_SNAKE_CASE :   Optional[Any]							      =				logging.getLogger()



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		Optional[Any]:
    _lowercase :							List[Any]   	=	'\n'.join(lowerCamelCase_				)
    Path(lowerCamelCase_				).open('w'				).writelines(lowerCamelCase_				)


SCREAMING_SNAKE_CASE :   str							      =				"patrickvonplaten/t5-tiny-random"
SCREAMING_SNAKE_CASE :   List[Any]							      =				"sshleifer/bart-tiny-random"
SCREAMING_SNAKE_CASE :   int							      =				"sshleifer/tiny-mbart"

SCREAMING_SNAKE_CASE :   Optional[int]							      =				logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks






class _lowerCamelCase(		_a     ):
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     Dict:




           """simple docstring"""

           _lowercase :							int   	=	Path(self.get_auto_remove_tmp_dir()) / 'utest_input.source'
           _lowercase :							str   	=	input_file_name.parent / 'utest_output.txt'
           assert not output_file_name.exists()
           _lowercase :							str   	=	[' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
           _dump_articles(lowerCamelCase,   lowerCamelCase)

           _lowercase :							List[str]   	=	str(Path(self.get_auto_remove_tmp_dir()) / 'scores.json')
           _lowercase :							Optional[int]   	=	'translation_en_to_de' if model == T5_TINY else 'summarization'
           _lowercase :							Any   	=	F'''
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            '''.split()

           with patch.object(lowerCamelCase,   'argv',   lowerCamelCase):
               run_generate()
               assert Path(lowerCamelCase).exists()
               # os.remove(Path(output_file_name))
       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           self.run_eval_tester(lowerCamelCase)
       @parameterized.expand([BART_TINY, MBART_TINY])
       @slow
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     int:




           """simple docstring"""

           self.run_eval_tester(lowerCamelCase)




       @parameterized.expand([T5_TINY, MBART_TINY])
       @slow
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     List[str]:




           """simple docstring"""

           _lowercase :							str   	=	Path(self.get_auto_remove_tmp_dir()) / 'utest_input.source'
           _lowercase :							Any   	=	input_file_name.parent / 'utest_output.txt'
           assert not output_file_name.exists()

           _lowercase :							List[str]   	=	{
               'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
               'de': [
                   'Maschinelles Lernen ist großartig, oder?',
                   'Ich esse gerne Bananen',
                   'Morgen ist wieder ein toller Tag!',
               ],
           }

           _lowercase :							Optional[Any]   	=	Path(self.get_auto_remove_tmp_dir())
           _lowercase :							Optional[Any]   	=	str(tmp_dir / 'scores.json')
           _lowercase :							str   	=	str(tmp_dir / 'val.target')
           _dump_articles(lowerCamelCase,   text['en'])
           _dump_articles(lowerCamelCase,   text['de'])
           _lowercase :							Tuple   	=	'translation_en_to_de' if model == T5_TINY else 'summarization'
           _lowercase :							Tuple   	=	F'''
            run_eval_search.py
            {model}
            {str(lowerCamelCase)}
            {str(lowerCamelCase)}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            '''.split()
           testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0'])

           with patch.object(lowerCamelCase,   'argv',   lowerCamelCase):
               with CaptureStdout() as cs:
                   run_search()
               _lowercase :							Dict   	=	[' num_beams | length_penalty', model, 'Best score args']
               _lowercase :							Optional[Any]   	=	['Info']
               if "translation" in task:
                   expected_strings.append('bleu')
               else:
                   expected_strings.extend(lowerCamelCase)
               for w in expected_strings:
                   assert w in cs.out
               for w in un_expected_strings:
                   assert w not in cs.out
               assert Path(lowerCamelCase).exists()
               os.remove(Path(lowerCamelCase))


