





import unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script



@require_cpu
class 							_lowerCAmelCase     (      unittest.TestCase				):


  '''simple docstring'''







  def UpperCamelCase_			(   self   :       str				):


    '''simple docstring'''





    debug_launcher(test_script.main				)







  def UpperCamelCase_			(   self   :       str				):


    '''simple docstring'''





    debug_launcher(test_ops.main				)









"""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       UpperCAmelCase_    (							unittest.TestCase		):



      def 		snake_case_   (	self	:      Union[str, Any]	,      A	:      Optional[Any]		):
           for model_result in results.values():
                for batch_size, sequence_length in zip(model_result["bs"]	,      model_result["ss"]		):
                     _UpperCAmelCase     :      List[str]														=       model_result["result"][batch_size][sequence_length]
                     self.assertIsNotNone(A		)



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Any														=       "sshleifer/tiny-gpt2"
           _UpperCAmelCase     :      int														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      )
           _UpperCAmelCase     :      List[Any]														=       PyTorchBenchmark(A		)
           _UpperCAmelCase     :      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 		snake_case_   (	self	:      Dict		):
           _UpperCAmelCase     :      List[str]														=       "sgugger/tiny-distilbert-classification"
           _UpperCAmelCase     :      Optional[int]														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      only_pretrain_model=A	,      )
           _UpperCAmelCase     :      Dict														=       PyTorchBenchmark(A		)
           _UpperCAmelCase     :      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 		snake_case_   (	self	:      int		):
           _UpperCAmelCase     :      Any														=       "sshleifer/tiny-gpt2"
           _UpperCAmelCase     :      List[Any]														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      torchscript=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      )
           _UpperCAmelCase     :      Dict														=       PyTorchBenchmark(A		)
           _UpperCAmelCase     :      Optional[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 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase     :      Union[str, Any]														=       "sshleifer/tiny-gpt2"
           _UpperCAmelCase     :      Optional[Any]														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      fpaa=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      )
           _UpperCAmelCase     :      List[str]														=       PyTorchBenchmark(A		)
           _UpperCAmelCase     :      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 		snake_case_   (	self	:      List[Any]		):
           _UpperCAmelCase     :      Optional[Any]														=       "sshleifer/tiny-gpt2"
           _UpperCAmelCase     :      int														=       AutoConfig.from_pretrained(A		)
           # set architectures equal to `None`
           _UpperCAmelCase     :      Optional[Any]														=       None
           _UpperCAmelCase     :      Optional[Any]														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      )
           _UpperCAmelCase     :      List[Any]														=       PyTorchBenchmark(A	,      configs=[config]		)
           _UpperCAmelCase     :      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 		snake_case_   (	self	:      int		):
           _UpperCAmelCase     :      Dict														=       "sshleifer/tiny-gpt2"
           _UpperCAmelCase     :      Union[str, Any]														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      )
           _UpperCAmelCase     :      Tuple														=       PyTorchBenchmark(A		)
           _UpperCAmelCase     :      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		)



      @unittest.skipIf(torch_device == "cpu"	,      "Can't do half precision"		)
      def 		snake_case_   (	self	:      List[Any]		):
           _UpperCAmelCase     :      Optional[int]														=       "sshleifer/tiny-gpt2"
           _UpperCAmelCase     :      str														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      fpaa=A	,      multi_process=A	,      )
           _UpperCAmelCase     :      Tuple														=       PyTorchBenchmark(A		)
           _UpperCAmelCase     :      List[Any]														=       benchmark.run()
           self.check_results_dict_not_empty(results.time_train_result		)
           self.check_results_dict_not_empty(results.memory_train_result		)



      def 		snake_case_   (	self	:      List[Any]		):
           _UpperCAmelCase     :      Optional[Any]														=       "sshleifer/tiny-gpt2"
           _UpperCAmelCase     :      Union[str, Any]														=       AutoConfig.from_pretrained(A		)
           _UpperCAmelCase     :      Dict														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      )
           _UpperCAmelCase     :      List[Any]														=       PyTorchBenchmark(A	,      configs=[config]		)
           _UpperCAmelCase     :      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 		snake_case_   (	self	:      str		):
           _UpperCAmelCase     :      List[str]														=       "sshleifer/tinier_bart"
           _UpperCAmelCase     :      Any														=       AutoConfig.from_pretrained(A		)
           _UpperCAmelCase     :      Optional[int]														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      )
           _UpperCAmelCase     :      int														=       PyTorchBenchmark(A	,      configs=[config]		)
           _UpperCAmelCase     :      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 		snake_case_   (	self	:      Any		):
           _UpperCAmelCase     :      Tuple														=       "sshleifer/tiny-gpt2"
           _UpperCAmelCase     :      Tuple														=       AutoConfig.from_pretrained(A		)
           _UpperCAmelCase     :      List[Any]														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      )
           _UpperCAmelCase     :      int														=       PyTorchBenchmark(A	,      configs=[config]		)
           _UpperCAmelCase     :      Optional[int]														=       benchmark.run()
           self.check_results_dict_not_empty(results.time_train_result		)
           self.check_results_dict_not_empty(results.memory_train_result		)



      def 		snake_case_   (	self	:      str		):
           _UpperCAmelCase     :      Optional[int]														=       "sshleifer/tinier_bart"
           _UpperCAmelCase     :      int														=       AutoConfig.from_pretrained(A		)
           _UpperCAmelCase     :      List[Any]														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      )
           _UpperCAmelCase     :      str														=       PyTorchBenchmark(A	,      configs=[config]		)
           _UpperCAmelCase     :      int														=       benchmark.run()
           self.check_results_dict_not_empty(results.time_train_result		)
           self.check_results_dict_not_empty(results.memory_train_result		)



      def 		snake_case_   (	self	:      Dict		):
           _UpperCAmelCase     :      int														=       "sshleifer/tiny-gpt2"
           with tempfile.TemporaryDirectory() as tmp_dir:
                _UpperCAmelCase     :      Union[str, 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	,      )
                _UpperCAmelCase     :      int														=       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 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase     :      List[Any]														=       "sshleifer/tiny-gpt2"

           def _check_summary_is_not_empty(A	:      int		):
                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:
                _UpperCAmelCase     :      int														=       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	,      )
                _UpperCAmelCase     :      int														=       PyTorchBenchmark(A		)
                _UpperCAmelCase     :      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()		)
