
from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

lowerCamelCase__	=							(
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowerCamelCase__	=							[ord(letter) for letter in string.ascii_lowercase]
lowerCamelCase__	=							{ord(char) for char in VALID_CHARS}

lowerCamelCase__	=							["the", "be", "to", "of", "and", "in", "that", "have"]


def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				str | None:

		"""simple docstring"""






		_UpperCamelCase						    =	""
		_UpperCamelCase						    =	42
		_UpperCamelCase						    =	42
		_UpperCamelCase						    =	42

		for keychar, cipherchar in zip(cycle(lowerCAmelCase )						,  lowerCAmelCase ):
				_UpperCamelCase						    =	cipherchar ^ keychar
				if decodedchar not in VALID_INTS:
						return None
				decoded += chr(lowerCAmelCase )

		return decoded


def 							__A(lowerCAmelCase )		->				list[str]:

		"""simple docstring"""






		_UpperCamelCase						    =	[]
		for key in product(lowerCAmelCase						,  repeat=3 ):
				_UpperCamelCase						    =	try_key(lowerCAmelCase						,  lowerCAmelCase )
				if encoded is not None:
						possibles.append(lowerCAmelCase )
		return possibles


def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				list[str]:

		"""simple docstring"""






		return [possible for possible in possibles if common_word in possible.lower()]


def 							__A(lowerCAmelCase = "p059_cipher.txt" )		->				int:

		"""simple docstring"""






		_UpperCamelCase						    =	42
		_UpperCamelCase						    =	42
		_UpperCamelCase						    =	42
		_UpperCamelCase						    =	42
		_UpperCamelCase						    =	Path(lowerCAmelCase ).parent.joinpath(lowerCAmelCase ).read_text(encoding="""utf-8""" )

		_UpperCamelCase						    =	[int(lowerCAmelCase ) for number in data.strip().split(""",""" )]

		_UpperCamelCase						    =	filter_valid_chars(lowerCAmelCase )
		for common_word in COMMON_WORDS:
				_UpperCamelCase						    =	filter_common_word(lowerCAmelCase						,  lowerCAmelCase )
				if len(lowerCAmelCase ) == 1:
						break

		_UpperCamelCase						    =	possibles[0]
		return sum(ord(lowerCAmelCase ) for char in decoded_text )


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





import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def 							__A(lowerCAmelCase )		->				Any:

		"""simple docstring"""






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


def 							__A(lowerCAmelCase )		->				Optional[int]:

		"""simple docstring"""






		_UpperCamelCase    ,	_UpperCamelCase						    =	emb.weight.shape
		_UpperCamelCase						    =	nn.Linear(lowerCAmelCase						,  lowerCAmelCase						,  bias=lowerCAmelCase )
		_UpperCamelCase						    =	emb.weight.data
		return lin_layer


def 							__A(lowerCAmelCase )		->				Tuple:

		"""simple docstring"""






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

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

		_UpperCamelCase						    =	XGLMConfig(
		    vocab_size=lowerCAmelCase						,  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						,  )

		_UpperCamelCase						    =	XGLMForCausalLM(lowerCAmelCase )
		_UpperCamelCase						    =	model.load_state_dict(lowerCAmelCase						,  strict=lowerCAmelCase )
		print(lowerCAmelCase )
		_UpperCamelCase						    =	make_linear_from_emb(model.model.embed_tokens )

		return model


if __name__ == "__main__":
		lowerCamelCase__	=							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.")
		lowerCamelCase__	=							parser.parse_args()
		lowerCamelCase__	=							convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
		model.save_pretrained(args.pytorch_dump_folder_path)



