



"""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):
   """simple docstring"""



   def  lowercase__						(		self     :    Tuple				,   __UpperCamelCase     :    str   )->Tuple:
       for model_result in results.values():
           for batch_size, sequence_length in zip(model_result['''bs''']				,   model_result['''ss''']   ):
               _UpperCAmelCase					=    model_result['''result'''][batch_size][sequence_length]
               self.assertIsNotNone(__UpperCamelCase   )



   def  lowercase__						(		self     :    Union[str, Any]   )->Optional[Any]:
       _UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
       _UpperCAmelCase					=    PyTorchBenchmarkArguments(
           models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
       _UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
       _UpperCAmelCase					=    benchmark.run()
       self.check_results_dict_not_empty(results.time_inference_result   )
       self.check_results_dict_not_empty(results.memory_inference_result   )



   def  lowercase__						(		self     :    Tuple   )->Optional[Any]:
       _UpperCAmelCase					=    '''sgugger/tiny-distilbert-classification'''
       _UpperCAmelCase					=    PyTorchBenchmarkArguments(
           models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   only_pretrain_model=__UpperCamelCase				,   )
       _UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
       _UpperCAmelCase					=    benchmark.run()
       self.check_results_dict_not_empty(results.time_inference_result   )
       self.check_results_dict_not_empty(results.memory_inference_result   )



   def  lowercase__						(		self     :    Tuple   )->str:
       _UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
       _UpperCAmelCase					=    PyTorchBenchmarkArguments(
           models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   torchscript=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
       _UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
       _UpperCAmelCase					=    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  lowercase__						(		self     :    Any   )->Optional[Any]:
       _UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
       _UpperCAmelCase					=    PyTorchBenchmarkArguments(
           models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   fpaa=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
       _UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
       _UpperCAmelCase					=    benchmark.run()
       self.check_results_dict_not_empty(results.time_inference_result   )
       self.check_results_dict_not_empty(results.memory_inference_result   )



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



   def  lowercase__						(		self     :    List[Any]   )->str:
       _UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
       _UpperCAmelCase					=    PyTorchBenchmarkArguments(
           models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
       _UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
       _UpperCAmelCase					=    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  lowercase__						(		self     :    List[str]   )->int:
       _UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
       _UpperCAmelCase					=    PyTorchBenchmarkArguments(
           models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   fpaa=__UpperCamelCase				,   multi_process=__UpperCamelCase				,   )
       _UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
       _UpperCAmelCase					=    benchmark.run()
       self.check_results_dict_not_empty(results.time_train_result   )
       self.check_results_dict_not_empty(results.memory_train_result   )



   def  lowercase__						(		self     :    str   )->Dict:
       _UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
       _UpperCAmelCase					=    AutoConfig.from_pretrained(__UpperCamelCase   )
       _UpperCAmelCase					=    PyTorchBenchmarkArguments(
           models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
       _UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase				,   configs=[config]   )
       _UpperCAmelCase					=    benchmark.run()
       self.check_results_dict_not_empty(results.time_inference_result   )
       self.check_results_dict_not_empty(results.memory_inference_result   )



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



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



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



   def  lowercase__						(		self     :    Optional[Any]   )->int:
       _UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
       with tempfile.TemporaryDirectory() as tmp_dir:
           _UpperCAmelCase					=    PyTorchBenchmarkArguments(
               models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   save_to_csv=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   inference_time_csv_file=os.path.join(__UpperCamelCase				,   '''inf_time.csv'''   )				,   train_memory_csv_file=os.path.join(__UpperCamelCase				,   '''train_mem.csv'''   )				,   inference_memory_csv_file=os.path.join(__UpperCamelCase				,   '''inf_mem.csv'''   )				,   train_time_csv_file=os.path.join(__UpperCamelCase				,   '''train_time.csv'''   )				,   env_info_csv_file=os.path.join(__UpperCamelCase				,   '''env.csv'''   )				,   multi_process=__UpperCamelCase				,   )
           _UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
           benchmark.run()
           self.assertTrue(Path(os.path.join(__UpperCamelCase				,   '''inf_time.csv'''   )   ).exists()   )
           self.assertTrue(Path(os.path.join(__UpperCamelCase				,   '''train_time.csv'''   )   ).exists()   )
           self.assertTrue(Path(os.path.join(__UpperCamelCase				,   '''inf_mem.csv'''   )   ).exists()   )
           self.assertTrue(Path(os.path.join(__UpperCamelCase				,   '''train_mem.csv'''   )   ).exists()   )
           self.assertTrue(Path(os.path.join(__UpperCamelCase				,   '''env.csv'''   )   ).exists()   )



   def  lowercase__						(		self     :    Tuple   )->List[Any]:
       _UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''

       def _check_summary_is_not_empty(__UpperCamelCase     :    str   ):
           self.assertTrue(hasattr(__UpperCamelCase				,   '''sequential'''   )   )
           self.assertTrue(hasattr(__UpperCamelCase				,   '''cumulative'''   )   )
           self.assertTrue(hasattr(__UpperCamelCase				,   '''current'''   )   )
           self.assertTrue(hasattr(__UpperCamelCase				,   '''total'''   )   )

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








"""simple docstring"""


import functools







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						list[int]					,     _SCREAMING_SNAKE_CASE   :						list[int]				):






				'''simple docstring'''



				if not isinstance(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				) or not all(isinstance(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				) for day in days				):
								raise ValueError('''The parameter days should be a list of integers'''				)

				if len(_SCREAMING_SNAKE_CASE				) != 3 or not all(isinstance(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				) for cost in costs				):
								raise ValueError('''The parameter costs should be a list of three integers'''				)

				if len(_SCREAMING_SNAKE_CASE				) == 0:
								return 0

				if min(_SCREAMING_SNAKE_CASE				) <= 0:
								raise ValueError('''All days elements should be greater than 0'''				)

				if max(_SCREAMING_SNAKE_CASE				) >= 366:
								raise ValueError('''All days elements should be less than 366'''				)

				_UpperCAmelCase					=    set(_SCREAMING_SNAKE_CASE				)

				@functools.cache
				def dynamic_programming(_SCREAMING_SNAKE_CASE   :						int				) -> int:
								if index > 365:
												return 0

								if index not in days_set:
												return dynamic_programming(index + 1				)

								return min(
								    costs[0] + dynamic_programming(index + 1				)					,     costs[1] + dynamic_programming(index + 7				)					,     costs[2] + dynamic_programming(index + 30				)					,     )

				return dynamic_programming(1				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()



