
import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__		):
      UpperCamelCase__     : Dict											=					[
          '''decoder.version''',
          '''decoder.output_projection.weight''',
          '''_float_tensor''',
          '''decoder.embed_positions._float_tensor''',
      ]
      for k in ignore_keys:
            state_dict.pop(UpperCamelCase__			,		UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__		):
      UpperCamelCase__					,UpperCamelCase__     : Any											=					emb.weight.shape
      UpperCamelCase__     : Optional[Any]											=					nn.Linear(UpperCamelCase__			,		UpperCamelCase__			,		bias=UpperCamelCase__		)
      UpperCamelCase__     : List[str]											=					emb.weight.data
      return lin_layer


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__		):
      UpperCamelCase__     : Dict											=					torch.load(UpperCamelCase__			,		map_location='''cpu'''		)
      UpperCamelCase__     : Dict											=					Namespace(**checkpoint['''cfg''']['''model''']		)
      UpperCamelCase__     : Union[str, Any]											=					checkpoint['''model''']
      remove_ignore_keys_(UpperCamelCase__		)
      UpperCamelCase__     : Union[str, Any]											=					state_dict['''decoder.embed_tokens.weight'''].shape[0]

      UpperCamelCase__     : Tuple											=					{key.replace('''decoder'''			,		'''model'''		): val for key, val in state_dict.items()}

      UpperCamelCase__     : Tuple											=					XGLMConfig(
          vocab_size=UpperCamelCase__			,		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__     : Union[str, Any]											=					XGLMForCausalLM(UpperCamelCase__		)
      UpperCamelCase__     : int											=					model.load_state_dict(UpperCamelCase__			,		strict=UpperCamelCase__		)
      print(UpperCamelCase__		)
      UpperCamelCase__     : Any											=					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)




def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__		):
      if index == number_of_items:
            return 0
      UpperCamelCase__     : str											=					0
      UpperCamelCase__     : Optional[Any]											=					0
      UpperCamelCase__     : int											=					knapsack(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		index + 1		)
      if weights[index] <= max_weight:
            UpperCamelCase__     : List[Any]											=					values[index] + knapsack(
                UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		max_weight - weights[index]			,		index + 1		)
      return max(UpperCamelCase__			,		UpperCamelCase__		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


