



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				):
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						Dict:
          for model_result in results.values():
             for batch_size, sequence_length in zip(model_result['bs'],     model_result['ss']			):
                UpperCamelCase       :    Tuple		      =      model_result['result'][batch_size][sequence_length]
                self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self			)	->						Union[str, Any]:
          UpperCamelCase       :    Optional[Any]		      =      'sshleifer/tiny-gpt2'
          UpperCamelCase       :    List[str]		      =      PyTorchBenchmarkArguments(
              models=[MODEL_ID],     training=SCREAMING_SNAKE_CASE_,     inference=SCREAMING_SNAKE_CASE_,     sequence_lengths=[8],     batch_sizes=[1],     multi_process=SCREAMING_SNAKE_CASE_,     )
          UpperCamelCase       :    List[Any]		      =      PyTorchBenchmark(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    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    snake_case_			(     self			)	->						List[Any]:
          UpperCamelCase       :    Optional[int]		      =      'sgugger/tiny-distilbert-classification'
          UpperCamelCase       :    Union[str, Any]		      =      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_,     )
          UpperCamelCase       :    int		      =      PyTorchBenchmark(SCREAMING_SNAKE_CASE_			)
          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       :    Union[str, Any]		      =      'sshleifer/tiny-gpt2'
          UpperCamelCase       :    Tuple		      =      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_,     )
          UpperCamelCase       :    Dict		      =      PyTorchBenchmark(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    int		      =      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			)	->						Tuple:
          UpperCamelCase       :    Tuple		      =      'sshleifer/tiny-gpt2'
          UpperCamelCase       :    List[Any]		      =      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_,     )
          UpperCamelCase       :    Any		      =      PyTorchBenchmark(SCREAMING_SNAKE_CASE_			)
          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			)	->						Optional[Any]:
          UpperCamelCase       :    Dict		      =      'sshleifer/tiny-gpt2'
          UpperCamelCase       :    Optional[int]		      =      AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_			)
          # set architectures equal to `None`
          UpperCamelCase       :    List[str]		      =      None
          UpperCamelCase       :    Dict		      =      PyTorchBenchmarkArguments(
              models=[MODEL_ID],     training=SCREAMING_SNAKE_CASE_,     inference=SCREAMING_SNAKE_CASE_,     sequence_lengths=[8],     batch_sizes=[1],     multi_process=SCREAMING_SNAKE_CASE_,     )
          UpperCamelCase       :    Any		      =      PyTorchBenchmark(SCREAMING_SNAKE_CASE_,     configs=[config]			)
          UpperCamelCase       :    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    snake_case_			(     self			)	->						Optional[int]:
          UpperCamelCase       :    str		      =      'sshleifer/tiny-gpt2'
          UpperCamelCase       :    Dict		      =      PyTorchBenchmarkArguments(
              models=[MODEL_ID],     training=SCREAMING_SNAKE_CASE_,     inference=SCREAMING_SNAKE_CASE_,     sequence_lengths=[8],     batch_sizes=[1],     multi_process=SCREAMING_SNAKE_CASE_,     )
          UpperCamelCase       :    Optional[int]		      =      PyTorchBenchmark(SCREAMING_SNAKE_CASE_			)
          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			)
       @unittest.skipIf(torch_device == 'cpu',     'Can\'t do half precision'			)
       def    snake_case_			(     self			)	->						List[str]:
          UpperCamelCase       :    int		      =      'sshleifer/tiny-gpt2'
          UpperCamelCase       :    List[Any]		      =      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_,     )
          UpperCamelCase       :    Any		      =      PyTorchBenchmark(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    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			)	->						str:
          UpperCamelCase       :    Any		      =      'sshleifer/tiny-gpt2'
          UpperCamelCase       :    int		      =      AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Dict		      =      PyTorchBenchmarkArguments(
              models=[MODEL_ID],     training=SCREAMING_SNAKE_CASE_,     inference=SCREAMING_SNAKE_CASE_,     sequence_lengths=[8],     batch_sizes=[1],     multi_process=SCREAMING_SNAKE_CASE_,     )
          UpperCamelCase       :    str		      =      PyTorchBenchmark(SCREAMING_SNAKE_CASE_,     configs=[config]			)
          UpperCamelCase       :    int		      =      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			)	->						Tuple:
          UpperCamelCase       :    Optional[Any]		      =      'sshleifer/tinier_bart'
          UpperCamelCase       :    Dict		      =      AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    str		      =      PyTorchBenchmarkArguments(
              models=[MODEL_ID],     training=SCREAMING_SNAKE_CASE_,     inference=SCREAMING_SNAKE_CASE_,     sequence_lengths=[8],     batch_sizes=[1],     multi_process=SCREAMING_SNAKE_CASE_,     )
          UpperCamelCase       :    Any		      =      PyTorchBenchmark(SCREAMING_SNAKE_CASE_,     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			)	->						Union[str, Any]:
          UpperCamelCase       :    Optional[Any]		      =      'sshleifer/tiny-gpt2'
          UpperCamelCase       :    List[str]		      =      AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    str		      =      PyTorchBenchmarkArguments(
              models=[MODEL_ID],     training=SCREAMING_SNAKE_CASE_,     inference=SCREAMING_SNAKE_CASE_,     sequence_lengths=[8],     batch_sizes=[1],     multi_process=SCREAMING_SNAKE_CASE_,     )
          UpperCamelCase       :    str		      =      PyTorchBenchmark(SCREAMING_SNAKE_CASE_,     configs=[config]			)
          UpperCamelCase       :    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[str]:
          UpperCamelCase       :    Dict		      =      'sshleifer/tinier_bart'
          UpperCamelCase       :    List[str]		      =      AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    List[Any]		      =      PyTorchBenchmarkArguments(
              models=[MODEL_ID],     training=SCREAMING_SNAKE_CASE_,     inference=SCREAMING_SNAKE_CASE_,     sequence_lengths=[8],     batch_sizes=[1],     multi_process=SCREAMING_SNAKE_CASE_,     )
          UpperCamelCase       :    Optional[int]		      =      PyTorchBenchmark(SCREAMING_SNAKE_CASE_,     configs=[config]			)
          UpperCamelCase       :    List[str]		      =      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       :    Dict		      =      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_,     )
             UpperCamelCase       :    int		      =      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    snake_case_			(     self			)	->						Union[str, Any]:
          UpperCamelCase       :    Optional[int]		      =      'sshleifer/tiny-gpt2'

          def _check_summary_is_not_empty(SCREAMING_SNAKE_CASE_			):
             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:
             UpperCamelCase       :    List[str]		      =      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_,     )
             UpperCamelCase       :    Union[str, Any]		      =      PyTorchBenchmark(SCREAMING_SNAKE_CASE_			)
             UpperCamelCase       :    List[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(SCREAMING_SNAKE_CASE_,     'log.txt'			)			).exists()			)









'''simple docstring'''


from __future__ import annotations

import math
def 						lowerCamelCase__   (				a    ,	a							):
						__snake_case						 =   u
						for i in range(1    ,	a							):
												__snake_case						 =   temp * (u - i)
						return temp
def 						lowerCamelCase__   (				):
						__snake_case						 =   int(input('enter the numbers of values: '							)							)
						__snake_case						 =   []
						for _ in range(a							):
												y.append([]							)
						for i in range(a							):
												for j in range(a							):
																		y[i].append(a							)
																		__snake_case						 =   0

						print('enter the values of parameters in a list: '							)
						__snake_case						 =   list(map(a    ,	input().split()							)							)

						print('enter the values of corresponding parameters: '							)
						for i in range(a							):
												__snake_case						 =   float(input()							)

						__snake_case						 =   int(input('enter the value to interpolate: '							)							)
						__snake_case						 =   (value - x[0]) / (x[1] - x[0])

						# for calculating forward difference table

						for i in range(1    ,	a							):
												for j in range(n - i							):
																		__snake_case						 =   y[j + 1][i - 1] - y[j][i - 1]

						__snake_case						 =   y[0][0]
						for i in range(1    ,	a							):
												summ += (ucal(a    ,	a							) * y[0][i]) / math.factorial(a							)

						print(f'the value at {value} is {summ}'							)


if __name__ == "__main__":
	main()

