



import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM







def     UpperCAmelCase_		(					snake_case__					)	->     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(_lowercase    ,			_lowercase					)







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



				"""simple docstring"""





				lowerCAmelCase__  ,   lowerCAmelCase__		  =  emb.weight.shape
				lowerCAmelCase__		  =  nn.Linear(_lowercase    ,			_lowercase    ,			bias=_lowercase					)
				lowerCAmelCase__		  =  emb.weight.data
				return lin_layer







def     UpperCAmelCase_		(					snake_case__					)	->     Optional[int]:



				"""simple docstring"""





				lowerCAmelCase__		  =  torch.load(_lowercase    ,			map_location='cpu'					)
				lowerCAmelCase__		  =  Namespace(**checkpoint['cfg']['model']					)
				lowerCAmelCase__		  =  checkpoint['model']
				remove_ignore_keys_(_lowercase					)
				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=_lowercase    ,			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(_lowercase					)
				lowerCAmelCase__		  =  model.load_state_dict(_lowercase    ,			strict=_lowercase					)
				print(_lowercase					)
				lowerCAmelCase__		  =  make_linear_from_emb(model.model.embed_tokens					)

				return model


if __name__ == "__main__":
	_lowerCAmelCase	:   Union[str, Any]    =   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	:   Tuple    =   parser.parse_args()
	_lowerCAmelCase	:   Optional[Any]    =   convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
	model.save_pretrained(args.pytorch_dump_folder_path)


import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer

def  __lowerCamelCase					( _lowercase						,     _lowercase						,     _lowercase						,     _lowercase=1024			)       ->      Union[str, Any]:
     UpperCamelCase    ,		UpperCamelCase						    =					[], []

     UpperCamelCase						    =					list(zip(_lowercase						,     _lowercase			)			)
     UpperCamelCase    ,		UpperCamelCase						    =					sorted_examples[0]

     def is_too_big(_lowercase			):
          return tok(_lowercase						,     return_tensors='pt'			).input_ids.shape[1] > max_tokens

     for src, tgt in tqdm(sorted_examples[1:]			):
          UpperCamelCase						    =					new_src + ' ' + src
          UpperCamelCase						    =					new_tgt + ' ' + tgt
          if is_too_big(_lowercase			) or is_too_big(_lowercase			):  # cant fit, finalize example
               finished_src.append(_lowercase			)
               finished_tgt.append(_lowercase			)
               UpperCamelCase    ,		UpperCamelCase						    =					src, tgt
          else:  # can fit, keep adding
               UpperCamelCase    ,		UpperCamelCase						    =					cand_src, cand_tgt

    # cleanup
     if new_src:
          assert new_tgt
          finished_src.append(_lowercase			)
          finished_tgt.append(_lowercase			)
     return finished_src, finished_tgt

def  __lowerCamelCase					( _lowercase						,     _lowercase						,     _lowercase						,     _lowercase			)       ->      Union[str, Any]:
     UpperCamelCase						    =					Path(_lowercase			)
     save_path.mkdir(exist_ok=_lowercase			)
     for split in ["train"]:
          UpperCamelCase    ,		UpperCamelCase						    =					data_dir / F'{split}.source', data_dir / F'{split}.target'
          UpperCamelCase						    =					[x.rstrip() for x in Path(_lowercase			).open().readlines()]
          UpperCamelCase						    =					[x.rstrip() for x in Path(_lowercase			).open().readlines()]
          UpperCamelCase    ,		UpperCamelCase						    =					pack_examples(_lowercase						,     _lowercase						,     _lowercase						,     _lowercase			)
          print(F'packed {split} split from {len(_lowercase			)} examples -> {len(_lowercase			)}.'			)
          Path(save_path / F'{split}.source'			).open('w'			).write('\n'.join(_lowercase			)			)
          Path(save_path / F'{split}.target'			).open('w'			).write('\n'.join(_lowercase			)			)
     for split in ["val", "test"]:
          UpperCamelCase    ,		UpperCamelCase						    =					data_dir / F'{split}.source', data_dir / F'{split}.target'
          shutil.copyfile(_lowercase						,     save_path / F'{split}.source'			)
          shutil.copyfile(_lowercase						,     save_path / F'{split}.target'			)

def  __lowerCamelCase					( )       ->      Union[str, Any]:
     UpperCamelCase						    =					argparse.ArgumentParser()
     parser.add_argument('--tok_name'						,     type=_lowercase						,     help='like facebook/bart-large-cnn,t5-base, etc.'			)
     parser.add_argument('--max_seq_len'						,     type=_lowercase						,     default=128			)
     parser.add_argument('--data_dir'						,     type=_lowercase			)
     parser.add_argument('--save_path'						,     type=_lowercase			)
     UpperCamelCase						    =					parser.parse_args()
     UpperCamelCase						    =					AutoTokenizer.from_pretrained(args.tok_name			)
     return pack_data_dir(_lowercase						,     Path(args.data_dir			)						,     args.max_seq_len						,     args.save_path			)


if __name__ == "__main__":
 packer_cli()


