




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        a				(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Any       )				->	int:
     for model_result in results.values():
      for batch_size, sequence_length in zip(model_result["bs"]       ,     model_result["ss"]       ):
       lowerCAmelCase__          =			model_result["result"][batch_size][sequence_length]
       self.assertIsNotNone(SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Optional[Any]       )				->	Any:
     lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
     lowerCAmelCase__          =			PyTorchBenchmarkArguments(
         models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
     lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
     lowerCAmelCase__          =			benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result       )
     self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					int       )				->	Optional[Any]:
     lowerCAmelCase__          =			"sgugger/tiny-distilbert-classification"
     lowerCAmelCase__          =			PyTorchBenchmarkArguments(
         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__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
     lowerCAmelCase__          =			benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result       )
     self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					Optional[Any]       )				->	int:
     lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
     lowerCAmelCase__          =			PyTorchBenchmarkArguments(
         models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     torchscript=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
     lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
     lowerCAmelCase__          =			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        a				(	self					:					Dict       )				->	Optional[Any]:
     lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
     lowerCAmelCase__          =			PyTorchBenchmarkArguments(
         models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     fpaa=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
     lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
     lowerCAmelCase__          =			benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result       )
     self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					Union[str, Any]       )				->	Tuple:
     lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
     lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
     # set architectures equal to `None`
     lowerCAmelCase__          =			None
     lowerCAmelCase__          =			PyTorchBenchmarkArguments(
         models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
     lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     configs=[config]       )
     lowerCAmelCase__          =			benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result       )
     self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					Any       )				->	Optional[Any]:
     lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
     lowerCAmelCase__          =			PyTorchBenchmarkArguments(
         models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
     lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
     lowerCAmelCase__          =			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        a				(	self					:					int       )				->	Dict:
     lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
     lowerCAmelCase__          =			PyTorchBenchmarkArguments(
         models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     fpaa=SCREAMING_SNAKE_CASE__       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
     lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
     lowerCAmelCase__          =			benchmark.run()
     self.check_results_dict_not_empty(results.time_train_result       )
     self.check_results_dict_not_empty(results.memory_train_result       )


    def        a				(	self					:					Optional[int]       )				->	Union[str, Any]:
     lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
     lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
     lowerCAmelCase__          =			PyTorchBenchmarkArguments(
         models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
     lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     configs=[config]       )
     lowerCAmelCase__          =			benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result       )
     self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					Optional[Any]       )				->	Optional[Any]:
     lowerCAmelCase__          =			"sshleifer/tinier_bart"
     lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
     lowerCAmelCase__          =			PyTorchBenchmarkArguments(
         models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
     lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     configs=[config]       )
     lowerCAmelCase__          =			benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result       )
     self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					List[str]       )				->	Dict:
     lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
     lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
     lowerCAmelCase__          =			PyTorchBenchmarkArguments(
         models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
     lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     configs=[config]       )
     lowerCAmelCase__          =			benchmark.run()
     self.check_results_dict_not_empty(results.time_train_result       )
     self.check_results_dict_not_empty(results.memory_train_result       )


    def        a				(	self					:					Optional[int]       )				->	Optional[int]:
     lowerCAmelCase__          =			"sshleifer/tinier_bart"
     lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
     lowerCAmelCase__          =			PyTorchBenchmarkArguments(
         models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
     lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     configs=[config]       )
     lowerCAmelCase__          =			benchmark.run()
     self.check_results_dict_not_empty(results.time_train_result       )
     self.check_results_dict_not_empty(results.memory_train_result       )


    def        a				(	self					:					List[Any]       )				->	Optional[int]:
     lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
     with tempfile.TemporaryDirectory() as tmp_dir:
      lowerCAmelCase__          =			PyTorchBenchmarkArguments(
          models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     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"       )       ,     train_memory_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,     "train_mem.csv"       )       ,     inference_memory_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,     "inf_mem.csv"       )       ,     train_time_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,     "train_time.csv"       )       ,     env_info_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,     "env.csv"       )       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
      lowerCAmelCase__          =			PyTorchBenchmark(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__       ,     "train_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__       ,     "train_mem.csv"       )       ).exists()       )
      self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "env.csv"       )       ).exists()       )


    def        a				(	self					:					Optional[Any]       )				->	Any:
     lowerCAmelCase__          =			"sshleifer/tiny-gpt2"

     def _check_summary_is_not_empty(SCREAMING_SNAKE_CASE__					:					List[Any]       ):
      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__          =			PyTorchBenchmarkArguments(
          models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     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__       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
      lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
      lowerCAmelCase__          =			benchmark.run()
      _check_summary_is_not_empty(result.inference_summary       )
      _check_summary_is_not_empty(result.train_summary       )
      self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "log.txt"       )       ).exists()       )






import string



def 							_A       (						lowerCAmelCase_					: str      ):
     """simple docstring"""

     for key in range(len(string.ascii_uppercase      )      ):
          lowerCAmelCase__          =			""
          for symbol in message:
               if symbol in string.ascii_uppercase:
                    lowerCAmelCase__          =			string.ascii_uppercase.find(lowerCAmelCase_      )
                    lowerCAmelCase__          =			num - key
                    if num < 0:
                         lowerCAmelCase__          =			num + len(string.ascii_uppercase      )
                    lowerCAmelCase__          =			translated + string.ascii_uppercase[num]
               else:
                    lowerCAmelCase__          =			translated + symbol
          print(F'Decryption using Key #{key}: {translated}'      )



def 							_A       (						):
     """simple docstring"""

     lowerCAmelCase__          =			input("Encrypted message: "      )
     lowerCAmelCase__          =			message.upper()
     decrypt(lowerCAmelCase_      )


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()
