






"""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   A  (				self     :						List[str]    ,						A     :						Union[str, Any]   )  ->  int:
							for model_result in results.values():
									for batch_size, sequence_length in zip(model_result['''bs''']    ,						model_result['''ss''']   ):
											lowercase_ : Tuple							       =   model_result['''result'''][batch_size][sequence_length]
											self.assertIsNotNone(A   )




					def   A  (				self     :						int   )  ->  Optional[Any]:
							lowercase_ : Optional[Any]							       =   '''sshleifer/tiny-gpt2'''
							lowercase_ : int							       =   PyTorchBenchmarkArguments(
							    models=[MODEL_ID]    ,						training=A    ,						inference=A    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=A    ,						)
							lowercase_ : List[str]							       =   PyTorchBenchmark(A   )
							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   A  (				self     :						int   )  ->  Optional[Any]:
							lowercase_ : List[Any]							       =   '''sgugger/tiny-distilbert-classification'''
							lowercase_ : Optional[Any]							       =   PyTorchBenchmarkArguments(
							    models=[MODEL_ID]    ,						training=A    ,						inference=A    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=A    ,						only_pretrain_model=A    ,						)
							lowercase_ : Tuple							       =   PyTorchBenchmark(A   )
							lowercase_ : Optional[int]							       =   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[Any]   )  ->  str:
							lowercase_ : str							       =   '''sshleifer/tiny-gpt2'''
							lowercase_ : Any							       =   PyTorchBenchmarkArguments(
							    models=[MODEL_ID]    ,						training=A    ,						inference=A    ,						torchscript=A    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=A    ,						)
							lowercase_ : List[Any]							       =   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   A  (				self     :						List[Any]   )  ->  str:
							lowercase_ : int							       =   '''sshleifer/tiny-gpt2'''
							lowercase_ : Tuple							       =   PyTorchBenchmarkArguments(
							    models=[MODEL_ID]    ,						training=A    ,						inference=A    ,						fpaa=A    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=A    ,						)
							lowercase_ : Any							       =   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   )




					def   A  (				self     :						Union[str, Any]   )  ->  Optional[int]:
							lowercase_ : Union[str, Any]							       =   '''sshleifer/tiny-gpt2'''
							lowercase_ : Optional[int]							       =   AutoConfig.from_pretrained(A   )
							# set architectures equal to `None`
							lowercase_ : str							       =   None
							lowercase_ : Tuple							       =   PyTorchBenchmarkArguments(
							    models=[MODEL_ID]    ,						training=A    ,						inference=A    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=A    ,						)
							lowercase_ : Any							       =   PyTorchBenchmark(A    ,						configs=[config]   )
							lowercase_ : 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   A  (				self     :						Optional[Any]   )  ->  List[Any]:
							lowercase_ : Optional[Any]							       =   '''sshleifer/tiny-gpt2'''
							lowercase_ : str							       =   PyTorchBenchmarkArguments(
							    models=[MODEL_ID]    ,						training=A    ,						inference=A    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=A    ,						)
							lowercase_ : List[str]							       =   PyTorchBenchmark(A   )
							lowercase_ : Dict							       =   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     :						Optional[Any]   )  ->  Dict:
							lowercase_ : Optional[Any]							       =   '''sshleifer/tiny-gpt2'''
							lowercase_ : str							       =   PyTorchBenchmarkArguments(
							    models=[MODEL_ID]    ,						training=A    ,						inference=A    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						fpaa=A    ,						multi_process=A    ,						)
							lowercase_ : int							       =   PyTorchBenchmark(A   )
							lowercase_ : 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   A  (				self     :						int   )  ->  Optional[Any]:
							lowercase_ : List[Any]							       =   '''sshleifer/tiny-gpt2'''
							lowercase_ : Any							       =   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_ : 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   A  (				self     :						Any   )  ->  List[Any]:
							lowercase_ : Union[str, Any]							       =   '''sshleifer/tinier_bart'''
							lowercase_ : Optional[Any]							       =   AutoConfig.from_pretrained(A   )
							lowercase_ : int							       =   PyTorchBenchmarkArguments(
							    models=[MODEL_ID]    ,						training=A    ,						inference=A    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=A    ,						)
							lowercase_ : Any							       =   PyTorchBenchmark(A    ,						configs=[config]   )
							lowercase_ : int							       =   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]   )  ->  Optional[int]:
							lowercase_ : str							       =   '''sshleifer/tiny-gpt2'''
							lowercase_ : Union[str, Any]							       =   AutoConfig.from_pretrained(A   )
							lowercase_ : List[Any]							       =   PyTorchBenchmarkArguments(
							    models=[MODEL_ID]    ,						training=A    ,						inference=A    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=A    ,						)
							lowercase_ : List[str]							       =   PyTorchBenchmark(A    ,						configs=[config]   )
							lowercase_ : str							       =   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[Any]   )  ->  int:
							lowercase_ : Optional[Any]							       =   '''sshleifer/tinier_bart'''
							lowercase_ : Optional[Any]							       =   AutoConfig.from_pretrained(A   )
							lowercase_ : Tuple							       =   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_train_result   )
							self.check_results_dict_not_empty(results.memory_train_result   )




					def   A  (				self     :						Union[str, Any]   )  ->  Optional[int]:
							lowercase_ : List[Any]							       =   '''sshleifer/tiny-gpt2'''
							with tempfile.TemporaryDirectory() as tmp_dir:
									lowercase_ : 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_ : 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   A  (				self     :						str   )  ->  Tuple:
							lowercase_ : str							       =   '''sshleifer/tiny-gpt2'''

							def _check_summary_is_not_empty(A     :						List[Any]   ):
									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_ : Any							       =   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_ : Optional[int]							       =   PyTorchBenchmark(A   )
									lowercase_ : Dict							       =   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()   )










def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase = 100_0000 )			->     int:
    lowerCamelCase__    :      int					 =		limit + 1
    lowerCamelCase__    :      Optional[Any]					 =		[0] * limit
    for first_term in range(1						,	_UpperCAmelCase ):
        for n in range(_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase ):
            lowerCamelCase__    :      Optional[Any]					 =		first_term + n / first_term
            if common_difference % 4:  # d must be divisble by 4
                continue
            else:
                common_difference /= 4
                if (
                    first_term > common_difference
                    and first_term < 4 * common_difference
                ):  # since x,y,z are positive integers
                    frequency[n] += 1  # so z>0 and a>d ,also 4d<a

    lowerCamelCase__    :      List[str]					 =		sum(1 for x in frequency[1:limit] if x == 10 )

    return count


if __name__ == "__main__":
    print(F"""{solution() = }""")





