
"""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 		a__						(      unittest.TestCase						):
   def 			__magic_name__			(			self	,    _a				):
          for model_result in results.values():
                 for batch_size, sequence_length in zip(model_result["bs"]	,    model_result["ss"]				):
                        lowercase    :				List[str]       		=							model_result['''result'''][batch_size][sequence_length]
                        self.assertIsNotNone(A_				)
   def 			__magic_name__			(			self				):
          lowercase    :				Any       		=							'''sshleifer/tiny-gpt2'''
          lowercase    :				Union[str, Any]       		=							PyTorchBenchmarkArguments(
              models=[MODEL_ID]	,    training=A_	,    inference=A_	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=A_	,    )
          lowercase    :				Tuple       		=							PyTorchBenchmark(A_				)
          lowercase    :				List[Any]       		=							benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result				)
          self.check_results_dict_not_empty(results.memory_inference_result				)
   def 			__magic_name__			(			self				):
          lowercase    :				int       		=							'''sgugger/tiny-distilbert-classification'''
          lowercase    :				Tuple       		=							PyTorchBenchmarkArguments(
              models=[MODEL_ID]	,    training=A_	,    inference=A_	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=A_	,    only_pretrain_model=A_	,    )
          lowercase    :				Union[str, Any]       		=							PyTorchBenchmark(A_				)
          lowercase    :				Tuple       		=							benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result				)
          self.check_results_dict_not_empty(results.memory_inference_result				)
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[Any]       		=							'''sshleifer/tiny-gpt2'''
          lowercase    :				List[Any]       		=							PyTorchBenchmarkArguments(
              models=[MODEL_ID]	,    training=A_	,    inference=A_	,    torchscript=A_	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=A_	,    )
          lowercase    :				int       		=							PyTorchBenchmark(A_				)
          lowercase    :				Dict       		=							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 			__magic_name__			(			self				):
          lowercase    :				Union[str, Any]       		=							'''sshleifer/tiny-gpt2'''
          lowercase    :				Optional[Any]       		=							PyTorchBenchmarkArguments(
              models=[MODEL_ID]	,    training=A_	,    inference=A_	,    fpaa=A_	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=A_	,    )
          lowercase    :				Optional[Any]       		=							PyTorchBenchmark(A_				)
          lowercase    :				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 			__magic_name__			(			self				):
          lowercase    :				Dict       		=							'''sshleifer/tiny-gpt2'''
          lowercase    :				Union[str, Any]       		=							AutoConfig.from_pretrained(A_				)
          # set architectures equal to `None`
          lowercase    :				Optional[int]       		=							None
          lowercase    :				Dict       		=							PyTorchBenchmarkArguments(
              models=[MODEL_ID]	,    training=A_	,    inference=A_	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=A_	,    )
          lowercase    :				Any       		=							PyTorchBenchmark(A_	,    configs=[config]				)
          lowercase    :				str       		=							benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result				)
          self.check_results_dict_not_empty(results.memory_inference_result				)
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							'''sshleifer/tiny-gpt2'''
          lowercase    :				List[str]       		=							PyTorchBenchmarkArguments(
              models=[MODEL_ID]	,    training=A_	,    inference=A_	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=A_	,    )
          lowercase    :				int       		=							PyTorchBenchmark(A_				)
          lowercase    :				Optional[Any]       		=							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 			__magic_name__			(			self				):
          lowercase    :				Dict       		=							'''sshleifer/tiny-gpt2'''
          lowercase    :				Tuple       		=							PyTorchBenchmarkArguments(
              models=[MODEL_ID]	,    training=A_	,    inference=A_	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    fpaa=A_	,    multi_process=A_	,    )
          lowercase    :				List[str]       		=							PyTorchBenchmark(A_				)
          lowercase    :				Tuple       		=							benchmark.run()
          self.check_results_dict_not_empty(results.time_train_result				)
          self.check_results_dict_not_empty(results.memory_train_result				)
   def 			__magic_name__			(			self				):
          lowercase    :				Tuple       		=							'''sshleifer/tiny-gpt2'''
          lowercase    :				int       		=							AutoConfig.from_pretrained(A_				)
          lowercase    :				str       		=							PyTorchBenchmarkArguments(
              models=[MODEL_ID]	,    training=A_	,    inference=A_	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=A_	,    )
          lowercase    :				str       		=							PyTorchBenchmark(A_	,    configs=[config]				)
          lowercase    :				str       		=							benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result				)
          self.check_results_dict_not_empty(results.memory_inference_result				)
   def 			__magic_name__			(			self				):
          lowercase    :				List[Any]       		=							'''sshleifer/tinier_bart'''
          lowercase    :				int       		=							AutoConfig.from_pretrained(A_				)
          lowercase    :				List[str]       		=							PyTorchBenchmarkArguments(
              models=[MODEL_ID]	,    training=A_	,    inference=A_	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=A_	,    )
          lowercase    :				List[Any]       		=							PyTorchBenchmark(A_	,    configs=[config]				)
          lowercase    :				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 			__magic_name__			(			self				):
          lowercase    :				List[str]       		=							'''sshleifer/tiny-gpt2'''
          lowercase    :				Tuple       		=							AutoConfig.from_pretrained(A_				)
          lowercase    :				Any       		=							PyTorchBenchmarkArguments(
              models=[MODEL_ID]	,    training=A_	,    inference=A_	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=A_	,    )
          lowercase    :				Optional[Any]       		=							PyTorchBenchmark(A_	,    configs=[config]				)
          lowercase    :				Union[str, Any]       		=							benchmark.run()
          self.check_results_dict_not_empty(results.time_train_result				)
          self.check_results_dict_not_empty(results.memory_train_result				)
   def 			__magic_name__			(			self				):
          lowercase    :				Any       		=							'''sshleifer/tinier_bart'''
          lowercase    :				int       		=							AutoConfig.from_pretrained(A_				)
          lowercase    :				Union[str, Any]       		=							PyTorchBenchmarkArguments(
              models=[MODEL_ID]	,    training=A_	,    inference=A_	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=A_	,    )
          lowercase    :				Union[str, Any]       		=							PyTorchBenchmark(A_	,    configs=[config]				)
          lowercase    :				Dict       		=							benchmark.run()
          self.check_results_dict_not_empty(results.time_train_result				)
          self.check_results_dict_not_empty(results.memory_train_result				)
   def 			__magic_name__			(			self				):
          lowercase    :				int       		=							'''sshleifer/tiny-gpt2'''
          with tempfile.TemporaryDirectory() as tmp_dir:
                 lowercase    :				Optional[Any]       		=							PyTorchBenchmarkArguments(
                     models=[MODEL_ID]	,    training=A_	,    inference=A_	,    save_to_csv=A_	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    inference_time_csv_file=os.path.join(A_	,    "inf_time.csv"				)	,    train_memory_csv_file=os.path.join(A_	,    "train_mem.csv"				)	,    inference_memory_csv_file=os.path.join(A_	,    "inf_mem.csv"				)	,    train_time_csv_file=os.path.join(A_	,    "train_time.csv"				)	,    env_info_csv_file=os.path.join(A_	,    "env.csv"				)	,    multi_process=A_	,    )
                 lowercase    :				List[str]       		=							PyTorchBenchmark(A_				)
                 benchmark.run()
                 self.assertTrue(Path(os.path.join(A_	,    "inf_time.csv"				)				).exists()				)
                 self.assertTrue(Path(os.path.join(A_	,    "train_time.csv"				)				).exists()				)
                 self.assertTrue(Path(os.path.join(A_	,    "inf_mem.csv"				)				).exists()				)
                 self.assertTrue(Path(os.path.join(A_	,    "train_mem.csv"				)				).exists()				)
                 self.assertTrue(Path(os.path.join(A_	,    "env.csv"				)				).exists()				)

   def 			__magic_name__			(			self				):
          lowercase    :				str       		=							'''sshleifer/tiny-gpt2'''

          def _check_summary_is_not_empty(_a				):
                 self.assertTrue(hasattr(A_	,    "sequential"				)				)
                 self.assertTrue(hasattr(A_	,    "cumulative"				)				)
                 self.assertTrue(hasattr(A_	,    "current"				)				)
                 self.assertTrue(hasattr(A_	,    "total"				)				)

          with tempfile.TemporaryDirectory() as tmp_dir:
                 lowercase    :				Tuple       		=							PyTorchBenchmarkArguments(
                     models=[MODEL_ID]	,    training=A_	,    inference=A_	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    log_filename=os.path.join(A_	,    "log.txt"				)	,    log_print=A_	,    trace_memory_line_by_line=A_	,    multi_process=A_	,    )
                 lowercase    :				Tuple       		=							PyTorchBenchmark(A_				)
                 lowercase    :				Union[str, 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(A_	,    "log.txt"				)				).exists()				)







A__  :  Any								=					'''Tobias Carryer'''

from time import time



class 				__snake_case						:




							def __init__(       self     :       Any				,				A_     :       Tuple				,				A_     :       Dict				,				A_     :       Tuple				,				A_     :       str=int(time())):  # noqa: B008
									lowerCAmelCase_			:     int									= multiplier
									lowerCAmelCase_			:     int									= increment
									lowerCAmelCase_			:     str									= modulo
									lowerCAmelCase_			:     str									= seed







							def    UpperCAmelCase__			(       self     :       int):
									lowerCAmelCase_			:     Optional[int]									= (self.multiplier * self.seed + self.increment) % self.modulo
									return self.seed


if __name__ == "__main__":
						# Show the LCG in action.
						A__  :  Union[str, Any]								=					LinearCongruentialGenerator(166_4525, 10_1390_4223, 2 << 31)
						while True:
												print(lcg.next_number())





