import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase_    :		Optional[int]							=	get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")


@require_sentencepiece
@require_tokenizers
class 						a__							(       __snake_case				,						unittest.TestCase		):
   A__    :       Optional[int]        =      PegasusTokenizer
   A__    :       str        =      PegasusTokenizerFast
   A__    :       Optional[int]        =      True
   A__    :       Union[str, Any]        =      True



   def __SCREAMING_SNAKE_CASE							(						self   )			->   Dict:
        super().setUp()

        # We have a SentencePiece fixture for testing
        __a		 =       PegasusTokenizer(UpperCAmelCase   )
        tokenizer.save_pretrained(self.tmpdirname   )



   @cached_property
   def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
        return PegasusTokenizer.from_pretrained('google/pegasus-large'   )



   def __SCREAMING_SNAKE_CASE							(						self				,						**UpperCAmelCase   )			->   PegasusTokenizer:
        return PegasusTokenizer.from_pretrained(self.tmpdirname				,						**UpperCAmelCase   )



   def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   List[str]:
        return ("This is a test", "This is a test")



   def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
        __a		 =       '</s>'
        __a		 =       1

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase   )				,						UpperCAmelCase   )
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase   )				,						UpperCAmelCase   )



   def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
        __a		 =       list(self.get_tokenizer().get_vocab().keys()   )

        self.assertEqual(vocab_keys[0]				,						'<pad>'   )
        self.assertEqual(vocab_keys[1]				,						'</s>'   )
        self.assertEqual(vocab_keys[-1]				,						'v'   )
        self.assertEqual(len(UpperCAmelCase   )				,						1_1_0_3   )



   def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
        self.assertEqual(self.get_tokenizer().vocab_size				,						1_1_0_3   )



   def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
        __a		 =       self.rust_tokenizer_class.from_pretrained(self.tmpdirname   )
        __a		 =       self.tokenizer_class.from_pretrained(self.tmpdirname   )
        __a		 =       (
            'Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'
            ' </s> <pad> <pad> <pad>'
        )
        __a		 =       rust_tokenizer([raw_input_str]				,						return_tensors=UpperCAmelCase				,						add_special_tokens=UpperCAmelCase   ).input_ids[0]
        __a		 =       py_tokenizer([raw_input_str]				,						return_tensors=UpperCAmelCase				,						add_special_tokens=UpperCAmelCase   ).input_ids[0]
        self.assertListEqual(UpperCAmelCase				,						UpperCAmelCase   )



   def __SCREAMING_SNAKE_CASE							(						self   )			->   List[Any]:
        __a		 =       self._large_tokenizer
        # <mask_1> masks whole sentence while <mask_2> masks single word
        __a		 =       '<mask_1> To ensure a <mask_2> flow of bank resolutions.'
        __a		 =       [2, 4_1_3, 6_1_5, 1_1_4, 3, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
        __a		 =       tokenizer([raw_input_str]				,						return_tensors=UpperCAmelCase   ).input_ids[0]
        self.assertListEqual(UpperCAmelCase				,						UpperCAmelCase   )



   def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
        __a		 =       self._large_tokenizer
        # The tracebacks for the following asserts are **better** without messages or self.assertEqual
        assert tokenizer.vocab_size == 9_6_1_0_3
        assert tokenizer.pad_token_id == 0
        assert tokenizer.eos_token_id == 1
        assert tokenizer.offset == 1_0_3
        assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_0_5
        assert tokenizer.unk_token == "<unk>"
        assert tokenizer.model_max_length == 1_0_2_4
        __a		 =       'To ensure a smooth flow of bank resolutions.'
        __a		 =       [4_1_3, 6_1_5, 1_1_4, 2_2_9_1, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
        __a		 =       tokenizer([raw_input_str]				,						return_tensors=UpperCAmelCase   ).input_ids[0]
        self.assertListEqual(UpperCAmelCase				,						UpperCAmelCase   )
        assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]   ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]



   @require_torch
   def __SCREAMING_SNAKE_CASE							(						self   )			->   List[str]:
        __a		 =       ['This is going to be way too long.' * 1_5_0, 'short example']
        __a		 =       ['not super long but more than 5 tokens', 'tiny']
        __a		 =       self._large_tokenizer(UpperCAmelCase				,						padding=UpperCAmelCase				,						truncation=UpperCAmelCase				,						return_tensors='pt'   )
        __a		 =       self._large_tokenizer(
            text_target=UpperCAmelCase				,						max_length=5				,						padding=UpperCAmelCase				,						truncation=UpperCAmelCase				,						return_tensors='pt'   )

        assert batch.input_ids.shape == (2, 1_0_2_4)
        assert batch.attention_mask.shape == (2, 1_0_2_4)
        assert targets["input_ids"].shape == (2, 5)
        assert len(UpperCAmelCase   ) == 2  # input_ids, attention_mask.







   @slow
   def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
        # fmt: off
        __a		 =       {'input_ids': [[3_8_9_7_9, 1_4_3, 1_8_4_8_5, 6_0_6, 1_3_0, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 5_4_1_8_9, 1_1_2_9, 1_1_1, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 9_1_1_4, 1_4_7_8_7, 1_2_1, 1_3_2_4_9, 1_5_8, 5_9_2, 9_5_6, 1_2_1, 1_4_6_2_1, 3_1_5_7_6, 1_4_3, 6_2_6_1_3, 1_0_8, 9_6_8_8, 9_3_0, 4_3_4_3_0, 1_1_5_6_2, 6_2_6_1_3, 3_0_4, 1_0_8, 1_1_4_4_3, 8_9_7, 1_0_8, 9_3_1_4, 1_7_4_1_5, 6_3_3_9_9, 1_0_8, 1_1_4_4_3, 7_6_1_4, 1_8_3_1_6, 1_1_8, 4_2_8_4, 7_1_4_8, 1_2_4_3_0, 1_4_3, 1_4_0_0, 2_5_7_0_3, 1_5_8, 1_1_1, 4_2_8_4, 7_1_4_8, 1_1_7_7_2, 1_4_3, 2_1_2_9_7, 1_0_6_4, 1_5_8, 1_2_2, 2_0_4, 3_5_0_6, 1_7_5_4, 1_1_3_3, 1_4_7_8_7, 1_5_8_1, 1_1_5, 3_3_2_2_4, 4_4_8_2, 1_1_1, 1_3_5_5, 1_1_0, 2_9_1_7_3, 3_1_7, 5_0_8_3_3, 1_0_8, 2_0_1_4_7, 9_4_6_6_5, 1_1_1, 7_7_1_9_8, 1_0_7, 1], [1_1_0, 6_2_6_1_3, 1_1_7, 6_3_8, 1_1_2, 1_1_3_3, 1_2_1, 2_0_0_9_8, 1_3_5_5, 7_9_0_5_0, 1_3_8_7_2, 1_3_5, 1_5_9_6, 5_3_5_4_1, 1_3_5_2, 1_4_1, 1_3_0_3_9, 5_5_4_2, 1_2_4, 3_0_2, 5_1_8, 1_1_1, 2_6_8, 2_9_5_6, 1_1_5, 1_4_9, 4_4_2_7, 1_0_7, 1, 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, 0, 0, 0, 0], [1_3_9, 1_2_3_5, 2_7_9_9, 1_8_2_8_9, 1_7_7_8_0, 2_0_4, 1_0_9, 9_4_7_4, 1_2_9_6, 1_0_7, 1, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
        # fmt: on

        self.tokenizer_integration_test_util(
            expected_encoding=UpperCAmelCase				,						model_name='google/bigbird-pegasus-large-arxiv'				,						revision='ba85d0851d708441f91440d509690f1ab6353415'				,						)






@require_sentencepiece
@require_tokenizers
class 						a__							(       __snake_case				,						unittest.TestCase		):
   A__    :       int        =      PegasusTokenizer
   A__    :       Dict        =      PegasusTokenizerFast
   A__    :       Any        =      True
   A__    :       str        =      True



   def __SCREAMING_SNAKE_CASE							(						self   )			->   Dict:
        super().setUp()

        # We have a SentencePiece fixture for testing
        __a		 =       PegasusTokenizer(UpperCAmelCase				,						offset=0				,						mask_token_sent=UpperCAmelCase				,						mask_token='[MASK]'   )
        tokenizer.save_pretrained(self.tmpdirname   )



   @cached_property
   def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
        return PegasusTokenizer.from_pretrained('google/bigbird-pegasus-large-arxiv'   )



   def __SCREAMING_SNAKE_CASE							(						self				,						**UpperCAmelCase   )			->   PegasusTokenizer:
        return PegasusTokenizer.from_pretrained(self.tmpdirname				,						**UpperCAmelCase   )



   def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   List[Any]:
        return ("This is a test", "This is a test")



   def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
        __a		 =       self.rust_tokenizer_class.from_pretrained(self.tmpdirname   )
        __a		 =       self.tokenizer_class.from_pretrained(self.tmpdirname   )
        __a		 =       (
            'Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'
            ' <pad> <pad> <pad>'
        )
        __a		 =       rust_tokenizer([raw_input_str]				,						return_tensors=UpperCAmelCase				,						add_special_tokens=UpperCAmelCase   ).input_ids[0]
        __a		 =       py_tokenizer([raw_input_str]				,						return_tensors=UpperCAmelCase				,						add_special_tokens=UpperCAmelCase   ).input_ids[0]
        self.assertListEqual(UpperCAmelCase				,						UpperCAmelCase   )



   @require_torch
   def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
        __a		 =       ['This is going to be way too long.' * 1_0_0_0, 'short example']
        __a		 =       ['not super long but more than 5 tokens', 'tiny']
        __a		 =       self._large_tokenizer(UpperCAmelCase				,						padding=UpperCAmelCase				,						truncation=UpperCAmelCase				,						return_tensors='pt'   )
        __a		 =       self._large_tokenizer(
            text_target=UpperCAmelCase				,						max_length=5				,						padding=UpperCAmelCase				,						truncation=UpperCAmelCase				,						return_tensors='pt'   )

        assert batch.input_ids.shape == (2, 4_0_9_6)
        assert batch.attention_mask.shape == (2, 4_0_9_6)
        assert targets["input_ids"].shape == (2, 5)
        assert len(UpperCAmelCase   ) == 2  # input_ids, attention_mask.







   def __SCREAMING_SNAKE_CASE							(						self   )			->   str:
        __a		 =       (
            'This is an example string that is used to test the original TF implementation against the HF'
            ' implementation'
        )

        __a		 =       self._large_tokenizer(UpperCAmelCase   ).input_ids

        self.assertListEqual(
            UpperCAmelCase				,						[1_8_2, 1_1_7, 1_4_2, 5_8_7, 4_2_1_1, 1_2_0, 1_1_7, 2_6_3, 1_1_2, 8_0_4, 1_0_9, 8_5_6, 2_5_0_1_6, 3_1_3_7, 4_6_4, 1_0_9, 2_6_9_5_5, 3_1_3_7, 1]				,						)





import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
	import tensorflow as tf

	from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments


@require_tf
class 						a__							(       unittest.TestCase		):



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   Optional[Any]:
								for model_result in results.values():
													for batch_size, sequence_length in zip(model_result['bs']				,						model_result['ss']   ):
																		__a		 =       model_result['result'][batch_size][sequence_length]
																		self.assertIsNotNone(UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						eager_mode=UpperCAmelCase				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								__a		 =       'sgugger/tiny-distilbert-classification'
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						only_pretrain_model=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       AutoConfig.from_pretrained(UpperCAmelCase   )
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						eager_mode=UpperCAmelCase				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase				,						[config]   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       AutoConfig.from_pretrained(UpperCAmelCase   )
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase				,						[config]   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_train_result   )
								self.check_results_dict_not_empty(results.memory_train_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       AutoConfig.from_pretrained(UpperCAmelCase   )
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase				,						[config]   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_train_result   )
								self.check_results_dict_not_empty(results.memory_train_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[str]:
								__a		 =       'patrickvonplaten/t5-tiny-random'
								__a		 =       AutoConfig.from_pretrained(UpperCAmelCase   )
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase				,						configs=[config]   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('GPU'   )   ) == 0				,						'Cannot do xla on CPU.'   )
			def __SCREAMING_SNAKE_CASE							(						self   )			->   str:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						use_xla=UpperCAmelCase				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Dict:
								__a		 =       'sshleifer/tiny-gpt2'
								with tempfile.TemporaryDirectory() as tmp_dir:
													__a		 =       TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]				,						inference=UpperCAmelCase				,						save_to_csv=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						inference_time_csv_file=os.path.join(UpperCAmelCase				,						'inf_time.csv'   )				,						inference_memory_csv_file=os.path.join(UpperCAmelCase				,						'inf_mem.csv'   )				,						env_info_csv_file=os.path.join(UpperCAmelCase				,						'env.csv'   )				,						multi_process=UpperCAmelCase				,						)
													__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
													benchmark.run()
													self.assertTrue(Path(os.path.join(UpperCAmelCase				,						'inf_time.csv'   )   ).exists()   )
													self.assertTrue(Path(os.path.join(UpperCAmelCase				,						'inf_mem.csv'   )   ).exists()   )
													self.assertTrue(Path(os.path.join(UpperCAmelCase				,						'env.csv'   )   ).exists()   )







			def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
								__a		 =       'sshleifer/tiny-gpt2'

								def _check_summary_is_not_empty(UpperCAmelCase   ):
													self.assertTrue(hasattr(UpperCAmelCase				,						'sequential'   )   )
													self.assertTrue(hasattr(UpperCAmelCase				,						'cumulative'   )   )
													self.assertTrue(hasattr(UpperCAmelCase				,						'current'   )   )
													self.assertTrue(hasattr(UpperCAmelCase				,						'total'   )   )

								with tempfile.TemporaryDirectory() as tmp_dir:
													__a		 =       TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						log_filename=os.path.join(UpperCAmelCase				,						'log.txt'   )				,						log_print=UpperCAmelCase				,						trace_memory_line_by_line=UpperCAmelCase				,						eager_mode=UpperCAmelCase				,						multi_process=UpperCAmelCase				,						)
													__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
													__a		 =       benchmark.run()
													_check_summary_is_not_empty(result.inference_summary   )
													self.assertTrue(Path(os.path.join(UpperCAmelCase				,						'log.txt'   )   ).exists()   )




