






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)
UpperCamelCase								=		logging.getLogger()






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    str:
							_lowercase							:   Optional[int] 							=   '\n'.join(_SCREAMING_SNAKE_CASE				)
							Path(_SCREAMING_SNAKE_CASE				).open('w'				).writelines(_SCREAMING_SNAKE_CASE				)


UpperCamelCase								=		"patrickvonplaten/t5-tiny-random"
UpperCamelCase								=		"sshleifer/bart-tiny-random"
UpperCamelCase								=		"sshleifer/tiny-mbart"

UpperCamelCase								=		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks






class 				lowerCAmelCase_	(		a_			):


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

											_lowercase							:   List[Any] 							=   str(Path(self.get_auto_remove_tmp_dir()      ) / 'scores.json'      )
											_lowercase							:   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 						__a					(					self      ):
											self.run_eval_tester(lowerCAmelCase_      )


				@parameterized.expand([BART_TINY, MBART_TINY]      )
				@slow
				def 						__a					(					self ,						_lowerCAmelCase      ):
											self.run_eval_tester(lowerCAmelCase_      )


				@parameterized.expand([T5_TINY, MBART_TINY]      )
				@slow
				def 						__a					(					self ,						_lowerCAmelCase      ):
											_lowercase							:   List[Any] 							=   Path(self.get_auto_remove_tmp_dir()      ) / 'utest_input.source'
											_lowercase							:   Optional[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							:   List[str] 							=   Path(self.get_auto_remove_tmp_dir()      )
											_lowercase							:   Tuple 							=   str(tmp_dir / 'scores.json'      )
											_lowercase							:   Any 							=   str(tmp_dir / 'val.target'      )
											_dump_articles(lowerCAmelCase_ ,						text['en']      )
											_dump_articles(lowerCAmelCase_ ,						text['de']      )
											_lowercase							:   Any 							=   'translation_en_to_de' if model == T5_TINY else 'summarization'
											_lowercase							:   List[Any] 							=   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							:   Optional[int] 							=   [' num_beams | length_penalty', model, 'Best score args']
																		_lowercase							:   Tuple 							=   ['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_      )      )








'''simple docstring'''





from __future__ import annotations

import unittest

from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow


if is_tf_available():
					from transformers import (
					    AutoConfig,
					    BertConfig,
					    GPTaConfig,
					    TaConfig,
					    TFAutoModel,
					    TFAutoModelForCausalLM,
					    TFAutoModelForMaskedLM,
					    TFAutoModelForPreTraining,
					    TFAutoModelForQuestionAnswering,
					    TFAutoModelForSeqaSeqLM,
					    TFAutoModelForSequenceClassification,
					    TFAutoModelWithLMHead,
					    TFBertForMaskedLM,
					    TFBertForPreTraining,
					    TFBertForQuestionAnswering,
					    TFBertForSequenceClassification,
					    TFBertModel,
					    TFGPTaLMHeadModel,
					    TFRobertaForMaskedLM,
					    TFTaForConditionalGeneration,
					)
					from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
					from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
					from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST

if is_torch_available():
					from transformers import (
					    AutoModel,
					    AutoModelForCausalLM,
					    AutoModelForMaskedLM,
					    AutoModelForPreTraining,
					    AutoModelForQuestionAnswering,
					    AutoModelForSeqaSeqLM,
					    AutoModelForSequenceClassification,
					    AutoModelWithLMHead,
					    BertForMaskedLM,
					    BertForPreTraining,
					    BertForQuestionAnswering,
					    BertForSequenceClassification,
					    BertModel,
					    GPTaLMHeadModel,
					    RobertaForMaskedLM,
					    TaForConditionalGeneration,
					)
@is_pt_tf_cross_test
class 						a__					(					unittest.TestCase   ):





			'''simple docstring'''

			@slow
			def 					lowerCAmelCase     (  self							:			List[str]	)      ->		List[Any]:
						# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						for model_name in ["bert-base-uncased"]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModel.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModel.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			@slow
			def 					lowerCAmelCase     (  self							:			Tuple	)      ->		Optional[Any]:
						# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						for model_name in ["bert-base-uncased"]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModelForPreTraining.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModelForPreTraining.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			@slow
			def 					lowerCAmelCase     (  self							:			int	)      ->		Optional[int]:
						for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModelForCausalLM.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									__A,	__A=	TFAutoModelForCausalLM.from_pretrained(
									    lowerCAmelCase_			,    output_loading_info=lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModelForCausalLM.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									__A,	__A=	AutoModelForCausalLM.from_pretrained(
									    lowerCAmelCase_			,    output_loading_info=lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			@slow
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Optional[Any]:
						for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModelWithLMHead.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModelWithLMHead.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			@slow
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Optional[int]:
						for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModelForMaskedLM.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									__A,	__A=	TFAutoModelForMaskedLM.from_pretrained(
									    lowerCAmelCase_			,    output_loading_info=lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModelForMaskedLM.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									__A,	__A=	AutoModelForMaskedLM.from_pretrained(
									    lowerCAmelCase_			,    output_loading_info=lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			@slow
			def 					lowerCAmelCase     (  self							:			Tuple	)      ->		Dict:
						for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									__A,	__A=	TFAutoModelForSeqaSeqLM.from_pretrained(
									    lowerCAmelCase_			,    output_loading_info=lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									__A,	__A=	AutoModelForSeqaSeqLM.from_pretrained(
									    lowerCAmelCase_			,    output_loading_info=lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			@slow
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		str:
						# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						for model_name in ["bert-base-uncased"]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModelForSequenceClassification.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			@slow
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		List[Any]:
						# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						for model_name in ["bert-base-uncased"]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModelForQuestionAnswering.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModelForQuestionAnswering.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			int	)      ->		List[str]:
						__A=	TFAutoModelWithLMHead.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
						self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
						self.assertEqual(model.num_parameters()			,    14_410	)
						self.assertEqual(model.num_parameters(only_trainable=lowerCAmelCase_	)			,    14_410	)

						__A=	AutoModelWithLMHead.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
						self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
						self.assertEqual(model.num_parameters()			,    14_410	)
						self.assertEqual(model.num_parameters(only_trainable=lowerCAmelCase_	)			,    14_410	)

			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		Tuple:
						__A=	TFAutoModelWithLMHead.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
						self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
						self.assertEqual(model.num_parameters()			,    14_410	)
						self.assertEqual(model.num_parameters(only_trainable=lowerCAmelCase_	)			,    14_410	)

						__A=	AutoModelWithLMHead.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
						self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
						self.assertEqual(model.num_parameters()			,    14_410	)
						self.assertEqual(model.num_parameters(only_trainable=lowerCAmelCase_	)			,    14_410	)



