




import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	snake_case_						       =	[
	    "decoder.version",
	    "decoder.output_projection.weight",
	    "_float_tensor",
	    "decoder.embed_positions._float_tensor",
	]
	for k in ignore_keys:
		state_dict.pop(_A	,      _A				)


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	snake_case_       , snake_case_						       =	emb.weight.shape
	snake_case_						       =	nn.Linear(_A	,      _A	,      bias=_A				)
	snake_case_						       =	emb.weight.data
	return lin_layer


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	snake_case_						       =	torch.load(_A	,      map_location="cpu"				)
	snake_case_						       =	Namespace(**checkpoint["cfg"]["model"]				)
	snake_case_						       =	checkpoint["model"]
	remove_ignore_keys_(_A				)
	snake_case_						       =	state_dict["decoder.embed_tokens.weight"].shape[0]

	snake_case_						       =	{key.replace("decoder"	,      "model"				): val for key, val in state_dict.items()}

	snake_case_						       =	XGLMConfig(
	    vocab_size=_A	,      max_position_embeddings=args.max_target_positions	,      num_layers=args.decoder_layers	,      attention_heads=args.decoder_attention_heads	,      ffn_dim=args.decoder_ffn_embed_dim	,      d_model=args.decoder_embed_dim	,      layerdrop=args.decoder_layerdrop	,      dropout=args.dropout	,      attention_dropout=args.attention_dropout	,      activation_dropout=args.activation_dropout	,      activation_function="gelu"	,      scale_embedding=not args.no_scale_embedding	,      tie_word_embeddings=args.share_decoder_input_output_embed	,      )

	snake_case_						       =	XGLMForCausalLM(_A				)
	snake_case_						       =	model.load_state_dict(_A	,      strict=_A				)
	print(_A				)
	snake_case_						       =	make_linear_from_emb(model.model.embed_tokens				)

	return model


if __name__ == "__main__":
		lowercase__							:	List[Any]											=		argparse.ArgumentParser()
		# Required parameters
		parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
		parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
		lowercase__							:	List[str]											=		parser.parse_args()
		lowercase__							:	List[Any]											=		convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
		model.save_pretrained(args.pytorch_dump_folder_path)






from __future__ import annotations

from collections.abc import Callable

lowercase__							:	Optional[Any]											=		list[list[float | int]]


def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	snake_case_						       =	len(_A				)
	snake_case_						       =	[[0 for _ in range(size + 1				)] for _ in range(_A				)]
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42

	for row in range(_A				):
		for col in range(_A				):
			snake_case_						       =	matrix[row][col]

		snake_case_						       =	vector[row][0]

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

		for rowa in range(row + 1	,      _A				):
			snake_case_						       =	augmented[rowa][col] / augmented[row][col]
			snake_case_						       =	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				):
			snake_case_						       =	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 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	snake_case_						       =	len(_A				)
	snake_case_						       =	[[0 for _ in range(_A				)] for _ in range(_A				)]
	snake_case_						       =	[[0] for _ in range(_A				)]
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42

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

	snake_case_						       =	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 		lowerCamelCase__       (						_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 		lowerCamelCase__       (						_A = question_function	,      _A = 10				):







	'''simple docstring'''





	snake_case_						       =	[func(_A				) for x_val in range(1	,      order + 1				)]

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

	snake_case_						       =	0
	snake_case_						       =	42
	snake_case_						       =	42

	for poly in polynomials:
		snake_case_						       =	1
		while func(_A				) == poly(_A				):
			x_val += 1

		ret += poly(_A				)

	return ret


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