'''simple docstring'''





import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM





def 	lowerCAmelCase_				(					snake_case_       : Union[str, Any]			)    ->      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(snake_case_					,    snake_case_			)





def 	lowerCAmelCase_				(					snake_case_       : List[Any]			)    ->      int:


     '''simple docstring'''




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





def 	lowerCAmelCase_				(					snake_case_       : int			)    ->      str:


     '''simple docstring'''




     UpperCAmelCase_           =   torch.load(snake_case_					,    map_location="cpu"			)
     UpperCAmelCase_           =   Namespace(**checkpoint["cfg"]["model"]			)
     UpperCAmelCase_           =   checkpoint["model"]
     remove_ignore_keys_(snake_case_			)
     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=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					,    )

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

     return model


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:     str  =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.')
    SCREAMING_SNAKE_CASE_:     int  =parser.parse_args()
    SCREAMING_SNAKE_CASE_:     Union[str, Any]  =convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
    model.save_pretrained(args.pytorch_dump_folder_path)





'''simple docstring'''





import math





def 	lowerCAmelCase_				(					)    ->      None:


     '''simple docstring'''




     UpperCAmelCase_           =   input("Enter message: "			)
     UpperCAmelCase_           =   int(input(f"""Enter key [2-{len(snake_case_			) - 1}]: """			)			)
     UpperCAmelCase_           =   input("Encryption/Decryption [e/d]: "			)

     if mode.lower().startswith("e"			):
          UpperCAmelCase_           =   encrypt_message(snake_case_					,    snake_case_			)
     elif mode.lower().startswith("d"			):
          UpperCAmelCase_           =   decrypt_message(snake_case_					,    snake_case_			)

     # Append pipe symbol (vertical bar) to identify spaces at the end.
     print(f"""Output:\n{text + "|"}"""			)





def 	lowerCAmelCase_				(					snake_case_       : int					,    snake_case_       : str			)    ->      str:


     '''simple docstring'''




     UpperCAmelCase_           =   [""] * key
     for col in range(snake_case_			):
          UpperCAmelCase_           =   col
          while pointer < len(snake_case_			):
               cipher_text[col] += message[pointer]
               pointer += key
     return "".join(snake_case_			)





def 	lowerCAmelCase_				(					snake_case_       : int					,    snake_case_       : str			)    ->      str:


     '''simple docstring'''




     UpperCAmelCase_           =   math.ceil(len(snake_case_			) / key			)
     UpperCAmelCase_           =   key
     UpperCAmelCase_           =   (num_cols * num_rows) - len(snake_case_			)
     UpperCAmelCase_           =   [""] * num_cols
     UpperCAmelCase_           =   0
     UpperCAmelCase_           =   0

     for symbol in message:
          plain_text[col] += symbol
          col += 1

          if (
              (col == num_cols)
              or (col == num_cols - 1)
              and (row >= num_rows - num_shaded_boxes)
          ):
               UpperCAmelCase_           =   0
               row += 1

     return "".join(snake_case_			)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()




