
"""simple docstring"""





from __future__ import annotations

from collections.abc import Callable

UpperCamelCase_					:						Optional[Any]				= list[list[float | int]]
def 							A_     (__a				,   __a				):

					'''simple docstring'''





					A_				 =						len(__a				)
					A_				 =						[[0 for _ in range(size + 1				)] for _ in range(__a				)]
					A_				 =						42
					A_				 =						42
					A_				 =						42
					A_				 =						42
					A_				 =						42
					A_				 =						42

					for row in range(__a				):
										for col in range(__a				):
															A_				 =						matrix[row][col]

										A_				 =						vector[row][0]

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

										for rowa in range(row + 1				,   __a				):
															A_				 =						augmented[rowa][col] / augmented[row][col]
															A_				 =						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				,   __a				):
										for row in range(__a				):
															A_				 =						augmented[row][col] / augmented[col][col]
															for cola in range(__a				,   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]				,   10				)] for row in range(__a				)
					]
def 							A_     (__a				):

					'''simple docstring'''





					A_				 =						len(__a				)
					A_				 =						[[0 for _ in range(__a				)] for _ in range(__a				)]
					A_				 =						[[0] for _ in range(__a				)]
					A_				 =						42
					A_				 =						42
					A_				 =						42
					A_				 =						42

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

					A_				 =						solve(__a				,   __a				)

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

					return interpolated_func
def 							A_     (__a				):

					'''simple docstring'''





					return (
					    1
					    - variable
					    + variable**2
					    - variable**3
					    + variable**4
					    - variable**5
					    + variable**6
					    - variable**7
					    + variable**8
					    - variable**9
					    + variable**10
					)
def 							A_     (__a = question_function				,   __a = 10				):

					'''simple docstring'''





					A_				 =						[func(__a				) for x_val in range(1				,   order + 1				)]

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

					A_				 =						0
					A_				 =						42
					A_				 =						42

					for poly in polynomials:
										A_				 =						1
										while func(__a				) == poly(__a				):
															x_val += 1

										ret += poly(__a				)

					return ret


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






"""simple docstring"""





from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand
def 							A_     (__a				):

					'''simple docstring'''





					return ConvertCommand(
					    args.model_type				,   args.tf_checkpoint				,   args.pytorch_dump_output				,   args.config				,   args.finetuning_task_name				)


UpperCamelCase_					:						Tuple				= '''
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
'''




class __lowerCAmelCase				(					_lowercase      ):




			"""simple docstring"""







			@staticmethod
			def      lowerCamelCase__     (				_snake_case						:					ArgumentParser	)      -> Union[str, Any]:







								"""simple docstring"""
								A_				 =						parser.add_parser(
								    "convert"       ,   help="CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints."       ,   )
								train_parser.add_argument("--model_type"       ,   type=_snake_case       ,   required=_snake_case       ,   help="Model's type."	)
								train_parser.add_argument(
								    "--tf_checkpoint"       ,   type=_snake_case       ,   required=_snake_case       ,   help="TensorFlow checkpoint path or folder."	)
								train_parser.add_argument(
								    "--pytorch_dump_output"       ,   type=_snake_case       ,   required=_snake_case       ,   help="Path to the PyTorch saved model output."	)
								train_parser.add_argument("--config"       ,   type=_snake_case       ,   default=""       ,   help="Configuration file path or folder."	)
								train_parser.add_argument(
								    "--finetuning_task_name"       ,   type=_snake_case       ,   default=_snake_case       ,   help="Optional fine-tuning task name if the TF model was a finetuned model."       ,   )
								train_parser.set_defaults(func=_snake_case	)




			def __init__(				self						:					str       ,   _snake_case						:					str       ,   _snake_case						:					str       ,   _snake_case						:					str       ,   _snake_case						:					str       ,   _snake_case						:					str       ,   *_snake_case						:					Optional[int]       ,   )      -> Union[str, Any]:







								"""simple docstring"""
								A_				 =						logging.get_logger("transformers-cli/converting"	)

								self._logger.info(F'Loading model {model_type}'	)
								A_				 =						model_type
								A_				 =						tf_checkpoint
								A_				 =						pytorch_dump_output
								A_				 =						config
								A_				 =						finetuning_task_name






			def      lowerCamelCase__     (				self						:					Tuple	)      -> Dict:







								"""simple docstring"""
								if self._model_type == "albert":
													try:
																		from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
																		    convert_tf_checkpoint_to_pytorch,
																		)
													except ImportError:
																		raise ImportError(_snake_case	)

													convert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,   self._config       ,   self._pytorch_dump_output	)
								elif self._model_type == "bert":
													try:
																		from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
																		    convert_tf_checkpoint_to_pytorch,
																		)
													except ImportError:
																		raise ImportError(_snake_case	)

													convert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,   self._config       ,   self._pytorch_dump_output	)
								elif self._model_type == "funnel":
													try:
																		from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
																		    convert_tf_checkpoint_to_pytorch,
																		)
													except ImportError:
																		raise ImportError(_snake_case	)

													convert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,   self._config       ,   self._pytorch_dump_output	)
								elif self._model_type == "t5":
													try:
																		from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
													except ImportError:
																		raise ImportError(_snake_case	)

													convert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,   self._config       ,   self._pytorch_dump_output	)
								elif self._model_type == "gpt":
													from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
													    convert_openai_checkpoint_to_pytorch,
													)

													convert_openai_checkpoint_to_pytorch(self._tf_checkpoint       ,   self._config       ,   self._pytorch_dump_output	)
								elif self._model_type == "transfo_xl":
													try:
																		from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
																		    convert_transfo_xl_checkpoint_to_pytorch,
																		)
													except ImportError:
																		raise ImportError(_snake_case	)

													if "ckpt" in self._tf_checkpoint.lower():
																		A_				 =						self._tf_checkpoint
																		A_				 =						""
													else:
																		A_				 =						self._tf_checkpoint
																		A_				 =						""
													convert_transfo_xl_checkpoint_to_pytorch(
													    _snake_case       ,   self._config       ,   self._pytorch_dump_output       ,   _snake_case	)
								elif self._model_type == "gpt2":
													try:
																		from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
																		    convert_gpta_checkpoint_to_pytorch,
																		)
													except ImportError:
																		raise ImportError(_snake_case	)

													convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint       ,   self._config       ,   self._pytorch_dump_output	)
								elif self._model_type == "xlnet":
													try:
																		from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
																		    convert_xlnet_checkpoint_to_pytorch,
																		)
													except ImportError:
																		raise ImportError(_snake_case	)

													convert_xlnet_checkpoint_to_pytorch(
													    self._tf_checkpoint       ,   self._config       ,   self._pytorch_dump_output       ,   self._finetuning_task_name	)
								elif self._model_type == "xlm":
													from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
													    convert_xlm_checkpoint_to_pytorch,
													)

													convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint       ,   self._pytorch_dump_output	)
								elif self._model_type == "lxmert":
													from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
													    convert_lxmert_checkpoint_to_pytorch,
													)

													convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint       ,   self._pytorch_dump_output	)
								elif self._model_type == "rembert":
													from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
													    convert_rembert_tf_checkpoint_to_pytorch,
													)

													convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,   self._config       ,   self._pytorch_dump_output	)
								else:
													raise ValueError(
													    "--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]"	)




