




"""simple docstring"""







from string import ascii_uppercase

a      =							{str(ord(c) - 5_5): c for c in ascii_uppercase}





def 							lowercase					(snake_case__   : int      ,       snake_case__   : int )     ->  str:


				'''simple docstring'''






				if isinstance(snake_case__      ,       snake_case__ ):
								raise TypeError("""int() can't convert non-string with explicit base""" )
				if num < 0:
								raise ValueError("""parameter must be positive int""" )
				if isinstance(snake_case__      ,       snake_case__ ):
								raise TypeError("""'str' object cannot be interpreted as an integer""" )
				if isinstance(snake_case__      ,       snake_case__ ):
								raise TypeError("""'float' object cannot be interpreted as an integer""" )
				if base in (0, 1):
								raise ValueError("""base must be >= 2""" )
				if base > 36:
								raise ValueError("""base must be <= 36""" )
				lowerCAmelCase  =				""""""
				lowerCAmelCase  =				0
				lowerCAmelCase  =				0
				while div != 1:
								lowerCAmelCase ,						lowerCAmelCase  =				divmod(snake_case__      ,       snake_case__ )
								if base >= 11 and 9 < mod < 36:
												lowerCAmelCase  =				ALPHABET_VALUES[str(snake_case__ )]
								else:
												lowerCAmelCase  =				str(snake_case__ )
								new_value += actual_value
								lowerCAmelCase  =				num // base
								lowerCAmelCase  =				div
								if div == 0:
												return str(new_value[::-1] )
								elif div == 1:
												new_value += str(snake_case__ )
												return str(new_value[::-1] )

				return new_value[::-1]


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		for base in range(2, 3_7):
				for num in range(1_0_0_0):
						assert int(decimal_to_any(num, base), base) == num, (
						    num,
						    base,
						    decimal_to_any(num, base),
						    int(decimal_to_any(num, base), base),
						)











"""simple docstring"""







import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM





def 							lowercase					(snake_case__   : Union[str, Any] )     ->  List[str]:


				'''simple docstring'''






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





def 							lowercase					(snake_case__   : List[str] )     ->  Optional[int]:


				'''simple docstring'''






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





def 							lowercase					(snake_case__   : str )     ->  Optional[Any]:


				'''simple docstring'''






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

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

				lowerCAmelCase  =				XGLMConfig(
				    vocab_size=snake_case__      ,       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      ,       )

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

				return model


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





