




"""simple docstring"""


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 _lowerCamelCase    (					unittest.TestCase						):






   def 	_lowerCAmelCase   (		self       :		Any   ,     UpperCamelCase       :		Optional[Any]   )			->						int:

         """simple docstring"""
         for model_result in results.values():
               for batch_size, sequence_length in zip(model_result["""bs"""]   ,     model_result["""ss"""]   ):
                     lowerCAmelCase__      :			List[Any]   =	model_result["result"][batch_size][sequence_length]
                     self.assertIsNotNone(_SCREAMING_SNAKE_CASE   )






   def 	_lowerCAmelCase   (		self       :		List[Any]   )			->						Tuple:

         """simple docstring"""
         lowerCAmelCase__      :			Dict   =	"sshleifer/tiny-gpt2"
         lowerCAmelCase__      :			Optional[Any]   =	TensorFlowBenchmarkArguments(
             models=[MODEL_ID]   ,     training=_SCREAMING_SNAKE_CASE   ,     inference=_SCREAMING_SNAKE_CASE   ,     sequence_lengths=[8]   ,     batch_sizes=[1]   ,     eager_mode=_SCREAMING_SNAKE_CASE   ,     multi_process=_SCREAMING_SNAKE_CASE   ,     )
         lowerCAmelCase__      :			Optional[Any]   =	TensorFlowBenchmark(_SCREAMING_SNAKE_CASE   )
         lowerCAmelCase__      :			Optional[Any]   =	benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result   )
         self.check_results_dict_not_empty(results.memory_inference_result   )






   def 	_lowerCAmelCase   (		self       :		Tuple   )			->						int:

         """simple docstring"""
         lowerCAmelCase__      :			List[Any]   =	"sgugger/tiny-distilbert-classification"
         lowerCAmelCase__      :			Optional[Any]   =	TensorFlowBenchmarkArguments(
             models=[MODEL_ID]   ,     training=_SCREAMING_SNAKE_CASE   ,     inference=_SCREAMING_SNAKE_CASE   ,     sequence_lengths=[8]   ,     batch_sizes=[1]   ,     multi_process=_SCREAMING_SNAKE_CASE   ,     only_pretrain_model=_SCREAMING_SNAKE_CASE   ,     )
         lowerCAmelCase__      :			int   =	TensorFlowBenchmark(_SCREAMING_SNAKE_CASE   )
         lowerCAmelCase__      :			Union[str, Any]   =	benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result   )
         self.check_results_dict_not_empty(results.memory_inference_result   )






   def 	_lowerCAmelCase   (		self       :		List[str]   )			->						Tuple:

         """simple docstring"""
         lowerCAmelCase__      :			List[str]   =	"sshleifer/tiny-gpt2"
         lowerCAmelCase__      :			Optional[Any]   =	TensorFlowBenchmarkArguments(
             models=[MODEL_ID]   ,     training=_SCREAMING_SNAKE_CASE   ,     inference=_SCREAMING_SNAKE_CASE   ,     sequence_lengths=[8]   ,     batch_sizes=[1]   ,     multi_process=_SCREAMING_SNAKE_CASE   ,     )
         lowerCAmelCase__      :			Optional[Any]   =	TensorFlowBenchmark(_SCREAMING_SNAKE_CASE   )
         lowerCAmelCase__      :			int   =	benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result   )
         self.check_results_dict_not_empty(results.memory_inference_result   )






   def 	_lowerCAmelCase   (		self       :		Optional[int]   )			->						int:

         """simple docstring"""
         lowerCAmelCase__      :			Union[str, Any]   =	"sshleifer/tiny-gpt2"
         lowerCAmelCase__      :			List[str]   =	AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE   )
         lowerCAmelCase__      :			int   =	TensorFlowBenchmarkArguments(
             models=[MODEL_ID]   ,     training=_SCREAMING_SNAKE_CASE   ,     inference=_SCREAMING_SNAKE_CASE   ,     sequence_lengths=[8]   ,     batch_sizes=[1]   ,     eager_mode=_SCREAMING_SNAKE_CASE   ,     multi_process=_SCREAMING_SNAKE_CASE   ,     )
         lowerCAmelCase__      :			List[str]   =	TensorFlowBenchmark(_SCREAMING_SNAKE_CASE   ,     [config]   )
         lowerCAmelCase__      :			Optional[int]   =	benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result   )
         self.check_results_dict_not_empty(results.memory_inference_result   )






   def 	_lowerCAmelCase   (		self       :		Dict   )			->						List[str]:

         """simple docstring"""
         lowerCAmelCase__      :			str   =	"sshleifer/tiny-gpt2"
         lowerCAmelCase__      :			int   =	AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE   )
         lowerCAmelCase__      :			Dict   =	TensorFlowBenchmarkArguments(
             models=[MODEL_ID]   ,     training=_SCREAMING_SNAKE_CASE   ,     inference=_SCREAMING_SNAKE_CASE   ,     sequence_lengths=[8]   ,     batch_sizes=[1]   ,     multi_process=_SCREAMING_SNAKE_CASE   ,     )
         lowerCAmelCase__      :			List[Any]   =	TensorFlowBenchmark(_SCREAMING_SNAKE_CASE   ,     [config]   )
         lowerCAmelCase__      :			Union[str, Any]   =	benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result   )
         self.check_results_dict_not_empty(results.memory_inference_result   )






   def 	_lowerCAmelCase   (		self       :		List[Any]   )			->						str:

         """simple docstring"""
         lowerCAmelCase__      :			List[str]   =	"sshleifer/tiny-gpt2"
         lowerCAmelCase__      :			Tuple   =	TensorFlowBenchmarkArguments(
             models=[MODEL_ID]   ,     training=_SCREAMING_SNAKE_CASE   ,     inference=_SCREAMING_SNAKE_CASE   ,     sequence_lengths=[8]   ,     batch_sizes=[1]   ,     multi_process=_SCREAMING_SNAKE_CASE   ,     )
         lowerCAmelCase__      :			List[str]   =	TensorFlowBenchmark(_SCREAMING_SNAKE_CASE   )
         lowerCAmelCase__      :			Dict   =	benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result   )
         self.check_results_dict_not_empty(results.memory_train_result   )






   def 	_lowerCAmelCase   (		self       :		Any   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__      :			str   =	"sshleifer/tiny-gpt2"
         lowerCAmelCase__      :			str   =	AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE   )
         lowerCAmelCase__      :			Dict   =	TensorFlowBenchmarkArguments(
             models=[MODEL_ID]   ,     training=_SCREAMING_SNAKE_CASE   ,     inference=_SCREAMING_SNAKE_CASE   ,     sequence_lengths=[8]   ,     batch_sizes=[1]   ,     multi_process=_SCREAMING_SNAKE_CASE   ,     )
         lowerCAmelCase__      :			Optional[Any]   =	TensorFlowBenchmark(_SCREAMING_SNAKE_CASE   ,     [config]   )
         lowerCAmelCase__      :			str   =	benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result   )
         self.check_results_dict_not_empty(results.memory_train_result   )






   def 	_lowerCAmelCase   (		self       :		List[Any]   )			->						List[str]:

         """simple docstring"""
         lowerCAmelCase__      :			List[str]   =	"patrickvonplaten/t5-tiny-random"
         lowerCAmelCase__      :			Union[str, Any]   =	AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE   )
         lowerCAmelCase__      :			Optional[Any]   =	TensorFlowBenchmarkArguments(
             models=[MODEL_ID]   ,     training=_SCREAMING_SNAKE_CASE   ,     inference=_SCREAMING_SNAKE_CASE   ,     sequence_lengths=[8]   ,     batch_sizes=[1]   ,     multi_process=_SCREAMING_SNAKE_CASE   ,     )
         lowerCAmelCase__      :			List[str]   =	TensorFlowBenchmark(_SCREAMING_SNAKE_CASE   ,     configs=[config]   )
         lowerCAmelCase__      :			List[Any]   =	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 	_lowerCAmelCase   (		self       :		Any   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__      :			int   =	"sshleifer/tiny-gpt2"
         lowerCAmelCase__      :			Tuple   =	TensorFlowBenchmarkArguments(
             models=[MODEL_ID]   ,     training=_SCREAMING_SNAKE_CASE   ,     inference=_SCREAMING_SNAKE_CASE   ,     sequence_lengths=[8]   ,     batch_sizes=[1]   ,     use_xla=_SCREAMING_SNAKE_CASE   ,     multi_process=_SCREAMING_SNAKE_CASE   ,     )
         lowerCAmelCase__      :			List[str]   =	TensorFlowBenchmark(_SCREAMING_SNAKE_CASE   )
         lowerCAmelCase__      :			str   =	benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result   )
         self.check_results_dict_not_empty(results.memory_inference_result   )






   def 	_lowerCAmelCase   (		self       :		Optional[int]   )			->						Tuple:

         """simple docstring"""
         lowerCAmelCase__      :			Union[str, Any]   =	"sshleifer/tiny-gpt2"
         with tempfile.TemporaryDirectory() as tmp_dir:
               lowerCAmelCase__      :			Optional[int]   =	TensorFlowBenchmarkArguments(
                   models=[MODEL_ID]   ,     inference=_SCREAMING_SNAKE_CASE   ,     save_to_csv=_SCREAMING_SNAKE_CASE   ,     sequence_lengths=[8]   ,     batch_sizes=[1]   ,     inference_time_csv_file=os.path.join(_SCREAMING_SNAKE_CASE   ,     """inf_time.csv"""   )   ,     inference_memory_csv_file=os.path.join(_SCREAMING_SNAKE_CASE   ,     """inf_mem.csv"""   )   ,     env_info_csv_file=os.path.join(_SCREAMING_SNAKE_CASE   ,     """env.csv"""   )   ,     multi_process=_SCREAMING_SNAKE_CASE   ,     )
               lowerCAmelCase__      :			Dict   =	TensorFlowBenchmark(_SCREAMING_SNAKE_CASE   )
               benchmark.run()
               self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE   ,     """inf_time.csv"""   )   ).exists()   )
               self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE   ,     """inf_mem.csv"""   )   ).exists()   )
               self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE   ,     """env.csv"""   )   ).exists()   )






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

         """simple docstring"""
         lowerCAmelCase__      :			int   =	"sshleifer/tiny-gpt2"

         def _check_summary_is_not_empty(UpperCamelCase       :		str   ):
               self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,     """sequential"""   )   )
               self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,     """cumulative"""   )   )
               self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,     """current"""   )   )
               self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,     """total"""   )   )

         with tempfile.TemporaryDirectory() as tmp_dir:
               lowerCAmelCase__      :			Union[str, Any]   =	TensorFlowBenchmarkArguments(
                   models=[MODEL_ID]   ,     inference=_SCREAMING_SNAKE_CASE   ,     sequence_lengths=[8]   ,     batch_sizes=[1]   ,     log_filename=os.path.join(_SCREAMING_SNAKE_CASE   ,     """log.txt"""   )   ,     log_print=_SCREAMING_SNAKE_CASE   ,     trace_memory_line_by_line=_SCREAMING_SNAKE_CASE   ,     eager_mode=_SCREAMING_SNAKE_CASE   ,     multi_process=_SCREAMING_SNAKE_CASE   ,     )
               lowerCAmelCase__      :			Tuple   =	TensorFlowBenchmark(_SCREAMING_SNAKE_CASE   )
               lowerCAmelCase__      :			int   =	benchmark.run()
               _check_summary_is_not_empty(result.inference_summary   )
               self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE   ,     """log.txt"""   )   ).exists()   )











"""simple docstring"""


from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *





