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 		_UpperCAmelCase (		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]							):
   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(__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   List[str]							):
   __a							=	"sshleifer/tiny-gpt2"
   __a							=	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  ,				)
   __a							=	TensorFlowBenchmark(__SCREAMING_SNAKE_CASE							)
   __a							=	benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result							)
   self.check_results_dict_not_empty(results.memory_inference_result							)

  def 		_UpperCAmelCase (		self			:   int							):
   __a							=	"sgugger/tiny-distilbert-classification"
   __a							=	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  ,				)
   __a							=	TensorFlowBenchmark(__SCREAMING_SNAKE_CASE							)
   __a							=	benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result							)
   self.check_results_dict_not_empty(results.memory_inference_result							)

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

  def 		_UpperCAmelCase (		self			:   str							):
   __a							=	"sshleifer/tiny-gpt2"
   __a							=	AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE							)
   __a							=	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  ,				)
   __a							=	TensorFlowBenchmark(__SCREAMING_SNAKE_CASE  ,				[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 		_UpperCAmelCase (		self			:   Dict							):
   __a							=	"sshleifer/tiny-gpt2"
   __a							=	AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE							)
   __a							=	TensorFlowBenchmarkArguments(
       models=[MODEL_ID]  ,				training=__SCREAMING_SNAKE_CASE  ,				inference=__SCREAMING_SNAKE_CASE  ,				sequence_lengths=[8]  ,				batch_sizes=[1]  ,				multi_process=__SCREAMING_SNAKE_CASE  ,				)
   __a							=	TensorFlowBenchmark(__SCREAMING_SNAKE_CASE  ,				[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 		_UpperCAmelCase (		self			:   str							):
   __a							=	"sshleifer/tiny-gpt2"
   __a							=	TensorFlowBenchmarkArguments(
       models=[MODEL_ID]  ,				training=__SCREAMING_SNAKE_CASE  ,				inference=__SCREAMING_SNAKE_CASE  ,				sequence_lengths=[8]  ,				batch_sizes=[1]  ,				multi_process=__SCREAMING_SNAKE_CASE  ,				)
   __a							=	TensorFlowBenchmark(__SCREAMING_SNAKE_CASE							)
   __a							=	benchmark.run()
   self.check_results_dict_not_empty(results.time_train_result							)
   self.check_results_dict_not_empty(results.memory_train_result							)

  def 		_UpperCAmelCase (		self			:   int							):
   __a							=	"sshleifer/tiny-gpt2"
   __a							=	AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE							)
   __a							=	TensorFlowBenchmarkArguments(
       models=[MODEL_ID]  ,				training=__SCREAMING_SNAKE_CASE  ,				inference=__SCREAMING_SNAKE_CASE  ,				sequence_lengths=[8]  ,				batch_sizes=[1]  ,				multi_process=__SCREAMING_SNAKE_CASE  ,				)
   __a							=	TensorFlowBenchmark(__SCREAMING_SNAKE_CASE  ,				[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 		_UpperCAmelCase (		self			:   Tuple							):
   __a							=	"patrickvonplaten/t5-tiny-random"
   __a							=	AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE							)
   __a							=	TensorFlowBenchmarkArguments(
       models=[MODEL_ID]  ,				training=__SCREAMING_SNAKE_CASE  ,				inference=__SCREAMING_SNAKE_CASE  ,				sequence_lengths=[8]  ,				batch_sizes=[1]  ,				multi_process=__SCREAMING_SNAKE_CASE  ,				)
   __a							=	TensorFlowBenchmark(__SCREAMING_SNAKE_CASE  ,				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 		_UpperCAmelCase (		self			:   Tuple							):
   __a							=	"sshleifer/tiny-gpt2"
   __a							=	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  ,				)
   __a							=	TensorFlowBenchmark(__SCREAMING_SNAKE_CASE							)
   __a							=	benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result							)
   self.check_results_dict_not_empty(results.memory_inference_result							)

  def 		_UpperCAmelCase (		self			:   Tuple							):
   __a							=	"sshleifer/tiny-gpt2"
   with tempfile.TemporaryDirectory() as tmp_dir:
    __a							=	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  ,				)
    __a							=	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 		_UpperCAmelCase (		self			:   Optional[Any]							):
   __a							=	"sshleifer/tiny-gpt2"

   def _check_summary_is_not_empty(__SCREAMING_SNAKE_CASE			:   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:
    __a							=	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  ,				)
    __a							=	TensorFlowBenchmark(__SCREAMING_SNAKE_CASE							)
    __a							=	benchmark.run()
    _check_summary_is_not_empty(result.inference_summary							)
    self.assertTrue(Path(os.path.join(__SCREAMING_SNAKE_CASE  ,				"log.txt"							)							).exists()							)



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 return x if y == 0 else greatest_common_divisor(_A				,					x % y       )



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 return (x * y) // greatest_common_divisor(_A				,					_A       )



def 	__A		(      _A = 20       ):



 """simple docstring"""


 __a							=	1
 for i in range(1				,					n + 1       ):
  __a							=	lcm(_A				,					_A       )
 return g


if __name__ == "__main__":
  print(f'''{solution() = }''')


