




"""simple docstring"""






import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM







def      _snake_case			(				lowercase__		):
				_lowerCamelCase     :			Any    =    [
				    '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      _snake_case			(				lowercase__		):
				_lowerCamelCase, _lowerCamelCase     :			Any    =    emb.weight.shape
				_lowerCamelCase     :			Any    =    nn.Linear(snake_case_							,    snake_case_							,    bias=snake_case_		)
				_lowerCamelCase     :			str    =    emb.weight.data
				return lin_layer







def      _snake_case			(				lowercase__		):
				_lowerCamelCase     :			Optional[Any]    =    torch.load(snake_case_							,    map_location='cpu'		)
				_lowerCamelCase     :			Optional[Any]    =    Namespace(**checkpoint['cfg']['model']		)
				_lowerCamelCase     :			int    =    checkpoint['model']
				remove_ignore_keys_(snake_case_		)
				_lowerCamelCase     :			Optional[Any]    =    state_dict['decoder.embed_tokens.weight'].shape[0]

				_lowerCamelCase     :			Optional[int]    =    {key.replace('decoder'							,    'model'		): val for key, val in state_dict.items()}

				_lowerCamelCase     :			int    =    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     :			Optional[int]    =    XGLMForCausalLM(snake_case_		)
				_lowerCamelCase     :			List[Any]    =    model.load_state_dict(snake_case_							,    strict=snake_case_		)
				print(snake_case_		)
				_lowerCamelCase     :			int    =    make_linear_from_emb(model.model.embed_tokens		)

				return model


if __name__ == "__main__":
			lowercase__     						=						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__     						=						parser.parse_args()
			lowercase__     						=						convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
			model.save_pretrained(args.pytorch_dump_folder_path)
'''simple docstring'''





from __future__ import annotations





def 	lowerCAmelCase_				(					snake_case_       : list					,    snake_case_       : int | None = None					,    snake_case_       : int | None = None			)    ->      None:


     '''simple docstring'''




     if start is None:
          UpperCAmelCase_           =   0

     if end is None:
          UpperCAmelCase_           =   len(snake_case_			) - 1

     if start >= end:
          return

     UpperCAmelCase_           =   (start + end) // 2

     slowsort(snake_case_					,    snake_case_					,    snake_case_			)
     slowsort(snake_case_					,    mid + 1					,    snake_case_			)

     if sequence[end] < sequence[mid]:
          UpperCAmelCase_    , UpperCAmelCase_           =   sequence[mid], sequence[end]

     slowsort(snake_case_					,    snake_case_					,    end - 1			)


if __name__ == "__main__":
    from doctest import testmod

    testmod()




