



'''simple docstring'''





import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
	from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments






@require_torch
class 	lowerCAmelCase_(    unittest.TestCase					):
		'''simple docstring'''




		def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase				)   ->		Dict:
						for model_result in results.values():
										for batch_size, sequence_length in zip(model_result["""bs"""]  ,model_result["""ss"""]				):
														lowerCAmelCase__			:  int											= model_result['''result'''][batch_size][sequence_length]
														self.assertIsNotNone(snake_case_				)




		def 							UpperCAmelCase_     (   self				)   ->		Any:
						lowerCAmelCase__			:  Optional[Any]											= '''sshleifer/tiny-gpt2'''
						lowerCAmelCase__			:  int											= PyTorchBenchmarkArguments(
						    models=[MODEL_ID]  ,training=snake_case_  ,inference=snake_case_  ,sequence_lengths=[8]  ,batch_sizes=[1]  ,multi_process=snake_case_  ,)
						lowerCAmelCase__			:  List[str]											= PyTorchBenchmark(snake_case_				)
						lowerCAmelCase__			:  Dict											= 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				)   ->		Union[str, Any]:
						lowerCAmelCase__			:  Tuple											= '''sgugger/tiny-distilbert-classification'''
						lowerCAmelCase__			:  List[Any]											= PyTorchBenchmarkArguments(
						    models=[MODEL_ID]  ,training=snake_case_  ,inference=snake_case_  ,sequence_lengths=[8]  ,batch_sizes=[1]  ,multi_process=snake_case_  ,only_pretrain_model=snake_case_  ,)
						lowerCAmelCase__			:  Optional[int]											= PyTorchBenchmark(snake_case_				)
						lowerCAmelCase__			:  List[str]											= 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:
						lowerCAmelCase__			:  Tuple											= '''sshleifer/tiny-gpt2'''
						lowerCAmelCase__			:  List[str]											= PyTorchBenchmarkArguments(
						    models=[MODEL_ID]  ,training=snake_case_  ,inference=snake_case_  ,torchscript=snake_case_  ,sequence_lengths=[8]  ,batch_sizes=[1]  ,multi_process=snake_case_  ,)
						lowerCAmelCase__			:  Tuple											= PyTorchBenchmark(snake_case_				)
						lowerCAmelCase__			:  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(torch_device == """cpu"""  ,"""Cant do half precision"""				)
		def 							UpperCAmelCase_     (   self				)   ->		Optional[Any]:
						lowerCAmelCase__			:  Any											= '''sshleifer/tiny-gpt2'''
						lowerCAmelCase__			:  List[str]											= PyTorchBenchmarkArguments(
						    models=[MODEL_ID]  ,training=snake_case_  ,inference=snake_case_  ,fpaa=snake_case_  ,sequence_lengths=[8]  ,batch_sizes=[1]  ,multi_process=snake_case_  ,)
						lowerCAmelCase__			:  List[str]											= PyTorchBenchmark(snake_case_				)
						lowerCAmelCase__			:  List[str]											= 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:
						lowerCAmelCase__			:  Optional[Any]											= '''sshleifer/tiny-gpt2'''
						lowerCAmelCase__			:  Tuple											= AutoConfig.from_pretrained(snake_case_				)
						# set architectures equal to `None`
						lowerCAmelCase__			:  List[Any]											= None
						lowerCAmelCase__			:  Dict											= PyTorchBenchmarkArguments(
						    models=[MODEL_ID]  ,training=snake_case_  ,inference=snake_case_  ,sequence_lengths=[8]  ,batch_sizes=[1]  ,multi_process=snake_case_  ,)
						lowerCAmelCase__			:  Optional[Any]											= PyTorchBenchmark(snake_case_  ,configs=[config]				)
						lowerCAmelCase__			:  Dict											= 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:
						lowerCAmelCase__			:  List[Any]											= '''sshleifer/tiny-gpt2'''
						lowerCAmelCase__			:  Optional[Any]											= PyTorchBenchmarkArguments(
						    models=[MODEL_ID]  ,training=snake_case_  ,inference=snake_case_  ,sequence_lengths=[8]  ,batch_sizes=[1]  ,multi_process=snake_case_  ,)
						lowerCAmelCase__			:  Optional[int]											= PyTorchBenchmark(snake_case_				)
						lowerCAmelCase__			:  str											= benchmark.run()
						self.check_results_dict_not_empty(results.time_train_result				)
						self.check_results_dict_not_empty(results.memory_train_result				)




		@unittest.skipIf(torch_device == """cpu"""  ,"""Can\'t do half precision"""				)
		def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
						lowerCAmelCase__			:  int											= '''sshleifer/tiny-gpt2'''
						lowerCAmelCase__			:  Any											= PyTorchBenchmarkArguments(
						    models=[MODEL_ID]  ,training=snake_case_  ,inference=snake_case_  ,sequence_lengths=[8]  ,batch_sizes=[1]  ,fpaa=snake_case_  ,multi_process=snake_case_  ,)
						lowerCAmelCase__			:  Dict											= PyTorchBenchmark(snake_case_				)
						lowerCAmelCase__			:  int											= 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:
						lowerCAmelCase__			:  Optional[Any]											= '''sshleifer/tiny-gpt2'''
						lowerCAmelCase__			:  List[str]											= AutoConfig.from_pretrained(snake_case_				)
						lowerCAmelCase__			:  Any											= PyTorchBenchmarkArguments(
						    models=[MODEL_ID]  ,training=snake_case_  ,inference=snake_case_  ,sequence_lengths=[8]  ,batch_sizes=[1]  ,multi_process=snake_case_  ,)
						lowerCAmelCase__			:  Optional[int]											= PyTorchBenchmark(snake_case_  ,configs=[config]				)
						lowerCAmelCase__			:  Any											= 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:
						lowerCAmelCase__			:  Union[str, Any]											= '''sshleifer/tinier_bart'''
						lowerCAmelCase__			:  Dict											= AutoConfig.from_pretrained(snake_case_				)
						lowerCAmelCase__			:  Dict											= PyTorchBenchmarkArguments(
						    models=[MODEL_ID]  ,training=snake_case_  ,inference=snake_case_  ,sequence_lengths=[8]  ,batch_sizes=[1]  ,multi_process=snake_case_  ,)
						lowerCAmelCase__			:  Union[str, Any]											= PyTorchBenchmark(snake_case_  ,configs=[config]				)
						lowerCAmelCase__			:  Tuple											= 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				)   ->		Optional[int]:
						lowerCAmelCase__			:  Optional[int]											= '''sshleifer/tiny-gpt2'''
						lowerCAmelCase__			:  int											= AutoConfig.from_pretrained(snake_case_				)
						lowerCAmelCase__			:  str											= PyTorchBenchmarkArguments(
						    models=[MODEL_ID]  ,training=snake_case_  ,inference=snake_case_  ,sequence_lengths=[8]  ,batch_sizes=[1]  ,multi_process=snake_case_  ,)
						lowerCAmelCase__			:  List[str]											= PyTorchBenchmark(snake_case_  ,configs=[config]				)
						lowerCAmelCase__			:  List[str]											= 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:
						lowerCAmelCase__			:  Optional[int]											= '''sshleifer/tinier_bart'''
						lowerCAmelCase__			:  int											= AutoConfig.from_pretrained(snake_case_				)
						lowerCAmelCase__			:  List[Any]											= PyTorchBenchmarkArguments(
						    models=[MODEL_ID]  ,training=snake_case_  ,inference=snake_case_  ,sequence_lengths=[8]  ,batch_sizes=[1]  ,multi_process=snake_case_  ,)
						lowerCAmelCase__			:  List[Any]											= PyTorchBenchmark(snake_case_  ,configs=[config]				)
						lowerCAmelCase__			:  int											= 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				)   ->		Any:
						lowerCAmelCase__			:  List[Any]											= '''sshleifer/tiny-gpt2'''
						with tempfile.TemporaryDirectory() as tmp_dir:
										lowerCAmelCase__			:  Tuple											= PyTorchBenchmarkArguments(
										    models=[MODEL_ID]  ,training=snake_case_  ,inference=snake_case_  ,save_to_csv=snake_case_  ,sequence_lengths=[8]  ,batch_sizes=[1]  ,inference_time_csv_file=os.path.join(snake_case_  ,"""inf_time.csv"""				)  ,train_memory_csv_file=os.path.join(snake_case_  ,"""train_mem.csv"""				)  ,inference_memory_csv_file=os.path.join(snake_case_  ,"""inf_mem.csv"""				)  ,train_time_csv_file=os.path.join(snake_case_  ,"""train_time.csv"""				)  ,env_info_csv_file=os.path.join(snake_case_  ,"""env.csv"""				)  ,multi_process=snake_case_  ,)
										lowerCAmelCase__			:  Any											= PyTorchBenchmark(snake_case_				)
										benchmark.run()
										self.assertTrue(Path(os.path.join(snake_case_  ,"""inf_time.csv"""				)				).exists()				)
										self.assertTrue(Path(os.path.join(snake_case_  ,"""train_time.csv"""				)				).exists()				)
										self.assertTrue(Path(os.path.join(snake_case_  ,"""inf_mem.csv"""				)				).exists()				)
										self.assertTrue(Path(os.path.join(snake_case_  ,"""train_mem.csv"""				)				).exists()				)
										self.assertTrue(Path(os.path.join(snake_case_  ,"""env.csv"""				)				).exists()				)






		def 							UpperCAmelCase_     (   self				)   ->		int:
						lowerCAmelCase__			:  Union[str, Any]											= '''sshleifer/tiny-gpt2'''

						def _check_summary_is_not_empty(__UpperCAmelCase				):
										self.assertTrue(hasattr(snake_case_  ,"""sequential"""				)				)
										self.assertTrue(hasattr(snake_case_  ,"""cumulative"""				)				)
										self.assertTrue(hasattr(snake_case_  ,"""current"""				)				)
										self.assertTrue(hasattr(snake_case_  ,"""total"""				)				)

						with tempfile.TemporaryDirectory() as tmp_dir:
										lowerCAmelCase__			:  Union[str, Any]											= PyTorchBenchmarkArguments(
										    models=[MODEL_ID]  ,training=snake_case_  ,inference=snake_case_  ,sequence_lengths=[8]  ,batch_sizes=[1]  ,log_filename=os.path.join(snake_case_  ,"""log.txt"""				)  ,log_print=snake_case_  ,trace_memory_line_by_line=snake_case_  ,multi_process=snake_case_  ,)
										lowerCAmelCase__			:  Union[str, Any]											= PyTorchBenchmark(snake_case_				)
										lowerCAmelCase__			:  Any											= benchmark.run()
										_check_summary_is_not_empty(result.inference_summary				)
										_check_summary_is_not_empty(result.train_summary				)
										self.assertTrue(Path(os.path.join(snake_case_  ,"""log.txt"""				)				).exists()				)







'''simple docstring'''





class 	lowerCAmelCase_:
  '''simple docstring'''




  def __init__(   self  ,__UpperCAmelCase				)   ->		Tuple:
      lowerCAmelCase__			:  Union[str, Any]											= n
      lowerCAmelCase__			:  int											= [None] * self.n
      lowerCAmelCase__			:  Union[str, Any]											= 0  # index of the first element
      lowerCAmelCase__			:  Union[str, Any]											= 0
      lowerCAmelCase__			:  Union[str, Any]											= 0




  def __len__(   self				)   ->		int:
      return self.size




  def 							UpperCAmelCase_     (   self				)   ->		bool:
      return self.size == 0




  def 							UpperCAmelCase_     (   self				)   ->		Optional[Any]:
      return False if self.is_empty() else self.array[self.front]




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase				)   ->		Union[str, Any]:
      if self.size >= self.n:
          raise Exception("""QUEUE IS FULL"""				)

      lowerCAmelCase__			:  str											= data
      lowerCAmelCase__			:  List[str]											= (self.rear + 1) % self.n
      self.size += 1
      return self






  def 							UpperCAmelCase_     (   self				)   ->		int:
      if self.size == 0:
          raise Exception("""UNDERFLOW"""				)

      lowerCAmelCase__			:  int											= self.array[self.front]
      lowerCAmelCase__			:  Optional[Any]											= None
      lowerCAmelCase__			:  int											= (self.front + 1) % self.n
      self.size -= 1
      return temp


