



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







    def lowerCamelCase       (    self					:     Any ,     snake_case_					:     List[Any]						):
          for model_result in results.values():
                for batch_size, sequence_length in zip(model_result["""bs"""] ,     model_result["""ss"""]						):
                      snake_case__  :    List[Any]    						=      model_result["""result"""][batch_size][sequence_length]
                      self.assertIsNotNone(snake_case_						)




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




    def lowerCamelCase       (    self					:     Union[str, Any]						):
          snake_case__  :    Any    						=      """sshleifer/tiny-gpt2"""
          snake_case__  :    Dict    						=      PyTorchBenchmarkArguments(
              models=[MODEL_ID] ,     training=snake_case_ ,     inference=snake_case_ ,     sequence_lengths=[8] ,     batch_sizes=[1] ,     multi_process=snake_case_ ,     )
          snake_case__  :    Dict    						=      PyTorchBenchmark(snake_case_						)
          snake_case__  :    Optional[int]    						=      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 lowerCamelCase       (    self					:     Any						):
          snake_case__  :    str    						=      """sshleifer/tiny-gpt2"""
          snake_case__  :    List[str]    						=      PyTorchBenchmarkArguments(
              models=[MODEL_ID] ,     training=snake_case_ ,     inference=snake_case_ ,     sequence_lengths=[8] ,     batch_sizes=[1] ,     fpaa=snake_case_ ,     multi_process=snake_case_ ,     )
          snake_case__  :    Optional[Any]    						=      PyTorchBenchmark(snake_case_						)
          snake_case__  :    int    						=      benchmark.run()
          self.check_results_dict_not_empty(results.time_train_result						)
          self.check_results_dict_not_empty(results.memory_train_result						)




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




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




    def lowerCamelCase       (    self					:     Any						):
          snake_case__  :    Any    						=      """sshleifer/tiny-gpt2"""

          def _check_summary_is_not_empty(snake_case_					:     Any						):
                self.assertTrue(hasattr(snake_case_ ,     """sequential"""						)						)
                self.assertTrue(hasattr(snake_case_ ,     """cumulative"""						)						)
                self.assertTrue(hasattr(snake_case_ ,     """current"""						)						)
                self.assertTrue(hasattr(snake_case_ ,     """total"""						)						)

          with tempfile.TemporaryDirectory() as tmp_dir:
                snake_case__  :    Union[str, Any]    						=      PyTorchBenchmarkArguments(
                    models=[MODEL_ID] ,     training=snake_case_ ,     inference=snake_case_ ,     sequence_lengths=[8] ,     batch_sizes=[1] ,     log_filename=os.path.join(snake_case_ ,     """log.txt"""						) ,     log_print=snake_case_ ,     trace_memory_line_by_line=snake_case_ ,     multi_process=snake_case_ ,     )
                snake_case__  :    Optional[int]    						=      PyTorchBenchmark(snake_case_						)
                snake_case__  :    int    						=      benchmark.run()
                _check_summary_is_not_empty(result.inference_summary						)
                _check_summary_is_not_empty(result.train_summary						)
                self.assertTrue(Path(os.path.join(snake_case_ ,     """log.txt"""						)						).exists()						)

"""simple docstring"""






from __future__ import annotations

from collections.abc import Callable

__lowerCAmelCase    :       str					     =list[list[float | int]]


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Matrix					,		lowerCAmelCase__     :Matrix		) ->    Matrix:
      '''simple docstring'''


      lowercase														=	len(lowerCAmelCase__		)
      lowercase														=	[[0 for _ in range(size + 1		)] for _ in range(lowerCAmelCase__		)]
      lowercase														=	42
      lowercase														=	42
      lowercase														=	42
      lowercase														=	42
      lowercase														=	42
      lowercase														=	42

      for row in range(lowerCAmelCase__		):
            for col in range(lowerCAmelCase__		):
                  lowercase														=	matrix[row][col]

            lowercase														=	vector[row][0]

      lowercase														=	0
      lowercase														=	0
      while row < size and col < size:
            # pivoting
            lowercase														=	max((abs(augmented[rowa][col]		), rowa) for rowa in range(lowerCAmelCase__					,		lowerCAmelCase__		)		)[
                1
            ]
            if augmented[pivot_row][col] == 0:
                  col += 1
                  continue
            else:
                  lowercase							,							lowercase														=	augmented[pivot_row], augmented[row]

            for rowa in range(row + 1					,		lowerCAmelCase__		):
                  lowercase														=	augmented[rowa][col] / augmented[row][col]
                  lowercase														=	0
                  for cola in range(col + 1					,		size + 1		):
                        augmented[rowa][cola] -= augmented[row][cola] * ratio

            row += 1
            col += 1

      # back substitution
      for col in range(1					,		lowerCAmelCase__		):
            for row in range(lowerCAmelCase__		):
                  lowercase														=	augmented[row][col] / augmented[col][col]
                  for cola in range(lowerCAmelCase__					,		size + 1		):
                        augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
      return [
          [round(augmented[row][size] / augmented[row][row]					,		1_0		)] for row in range(lowerCAmelCase__		)
      ]


def 			UpperCAmelCase__       (				lowerCAmelCase__     :list[int]		) ->    Callable[[int], int]:
      '''simple docstring'''


      lowercase														=	len(lowerCAmelCase__		)
      lowercase														=	[[0 for _ in range(lowerCAmelCase__		)] for _ in range(lowerCAmelCase__		)]
      lowercase														=	[[0] for _ in range(lowerCAmelCase__		)]
      lowercase														=	42
      lowercase														=	42
      lowercase														=	42
      lowercase														=	42

      for x_val, y_val in enumerate(lowerCAmelCase__		):
            for col in range(lowerCAmelCase__		):
                  lowercase														=	(x_val + 1) ** (size - col - 1)
            lowercase														=	y_val

      lowercase														=	solve(lowerCAmelCase__					,		lowerCAmelCase__		)

      def interpolated_func(lowerCAmelCase__     :int		) -> int:
            return sum(
                round(coeffs[x_val][0]		) * (var ** (size - x_val - 1))
                for x_val in range(lowerCAmelCase__		)		)

      return interpolated_func


def 			UpperCAmelCase__       (				lowerCAmelCase__     :int		) ->    int:
      '''simple docstring'''


      return (
          1
          - variable
          + variable**2
          - variable**3
          + variable**4
          - variable**5
          + variable**6
          - variable**7
          + variable**8
          - variable**9
          + variable**1_0
      )


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Callable[[int], int] = question_function					,		lowerCAmelCase__     :int = 1_0		) ->    int:
      '''simple docstring'''


      lowercase														=	[func(lowerCAmelCase__		) for x_val in range(1					,		order + 1		)]

      lowercase														=	[
          interpolate(data_points[:max_coeff]		) for max_coeff in range(1					,		order + 1		)
      ]

      lowercase														=	0
      lowercase														=	42
      lowercase														=	42

      for poly in polynomials:
            lowercase														=	1
            while func(lowerCAmelCase__		) == poly(lowerCAmelCase__		):
                  x_val += 1

            ret += poly(lowerCAmelCase__		)

      return ret


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



