


'''simple docstring'''





import os
import unittest
from tempfile import TemporaryDirectory

import torch
import torch.nn as nn

from accelerate.utils import (
    OffloadedWeightsLoader,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
)




class 				UpperCAmelCase			(			nn.Module						):


 def __init__(      self							:	Tuple ):
    super().__init__()
    UpperCAmelCase__	:Optional[Any]      =      nn.Linear(3			,       4 )
    UpperCAmelCase__	:Union[str, Any]      =      nn.BatchNormad(4 )
    UpperCAmelCase__	:str      =      nn.Linear(4			,       5 )





 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any]			,       __lowerCamelCase							:	Any ):
    return self.lineara(self.batchnorm(self.lineara(__lowerCamelCase ) ) )




class 				UpperCAmelCase			(			unittest.TestCase						):


 def        __SCREAMING_SNAKE_CASE					(      self							:	str ):
    UpperCAmelCase__	:List[str]      =      ModelForTest()
    with TemporaryDirectory() as tmp_dir:
       offload_state_dict(__lowerCamelCase			,       model.state_dict() )
       UpperCAmelCase__	:Any      =      os.path.join(__lowerCamelCase			,       '''index.json''' )
       self.assertTrue(os.path.isfile(__lowerCamelCase ) )
       # TODO: add tests on what is inside the index

       for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
          UpperCAmelCase__	:Optional[int]      =      os.path.join(__lowerCamelCase			,       f'''{key}.dat''' )
          self.assertTrue(os.path.isfile(__lowerCamelCase ) )
          # TODO: add tests on the fact weights are properly loaded


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    UpperCAmelCase__	:List[str]      =      [torch.floataa, torch.floataa, torch.bfloataa]

    for dtype in dtypes:
       UpperCAmelCase__	:Optional[Any]      =      torch.randn(2			,       3			,       dtype=__lowerCamelCase )
       with TemporaryDirectory() as tmp_dir:
          UpperCAmelCase__	:int      =      offload_weight(__lowerCamelCase			,       '''weight'''			,       __lowerCamelCase			,       {} )
          UpperCAmelCase__	:Any      =      os.path.join(__lowerCamelCase			,       '''weight.dat''' )
          self.assertTrue(os.path.isfile(__lowerCamelCase ) )
          self.assertDictEqual(__lowerCamelCase			,       {'''weight''': {'''shape''': [2, 3], '''dtype''': str(__lowerCamelCase ).split('''.''' )[1]}} )

          UpperCAmelCase__	:Dict      =      load_offloaded_weight(__lowerCamelCase			,       index['''weight'''] )
          self.assertTrue(torch.equal(__lowerCamelCase			,       __lowerCamelCase ) )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Any ):
    UpperCAmelCase__	:str      =      ModelForTest()
    UpperCAmelCase__	:Dict      =      model.state_dict()
    UpperCAmelCase__	:str      =      {k: v for k, v in state_dict.items() if '''linear2''' not in k}
    UpperCAmelCase__	:Any      =      {k: v for k, v in state_dict.items() if '''linear2''' in k}

    with TemporaryDirectory() as tmp_dir:
       offload_state_dict(__lowerCamelCase			,       __lowerCamelCase )
       UpperCAmelCase__	:Optional[int]      =      OffloadedWeightsLoader(state_dict=__lowerCamelCase			,       save_folder=__lowerCamelCase )

       # Every key is there with the right value
       self.assertEqual(sorted(__lowerCamelCase )			,       sorted(state_dict.keys() ) )
       for key, param in state_dict.items():
          self.assertTrue(torch.allclose(__lowerCamelCase			,       weight_map[key] ) )

    UpperCAmelCase__	:Any      =      {k: v for k, v in state_dict.items() if '''weight''' in k}
    UpperCAmelCase__	:Tuple      =      {k: v for k, v in state_dict.items() if '''weight''' not in k}

    with TemporaryDirectory() as tmp_dir:
       offload_state_dict(__lowerCamelCase			,       __lowerCamelCase )
       UpperCAmelCase__	:List[Any]      =      OffloadedWeightsLoader(state_dict=__lowerCamelCase			,       save_folder=__lowerCamelCase )

       # Every key is there with the right value
       self.assertEqual(sorted(__lowerCamelCase )			,       sorted(state_dict.keys() ) )
       for key, param in state_dict.items():
          self.assertTrue(torch.allclose(__lowerCamelCase			,       weight_map[key] ) )

    with TemporaryDirectory() as tmp_dir:
       offload_state_dict(__lowerCamelCase			,       __lowerCamelCase )
       # Duplicates are removed
       UpperCAmelCase__	:List[str]      =      OffloadedWeightsLoader(state_dict=__lowerCamelCase			,       save_folder=__lowerCamelCase )

       # Every key is there with the right value
       self.assertEqual(sorted(__lowerCamelCase )			,       sorted(state_dict.keys() ) )
       for key, param in state_dict.items():
          self.assertTrue(torch.allclose(__lowerCamelCase			,       weight_map[key] ) )





 def        __SCREAMING_SNAKE_CASE					(      self							:	Any ):
    UpperCAmelCase__	:Union[str, Any]      =      {'''a.1''': 0, '''a.10''': 1, '''a.2''': 2}
    UpperCAmelCase__	:Optional[int]      =      extract_submodules_state_dict(__lowerCamelCase			,       ['''a.1''', '''a.2'''] )
    self.assertDictEqual(__lowerCamelCase			,       {'''a.1''': 0, '''a.2''': 2} )

    UpperCAmelCase__	:List[str]      =      {'''a.1.a''': 0, '''a.10.a''': 1, '''a.2.a''': 2}
    UpperCAmelCase__	:List[Any]      =      extract_submodules_state_dict(__lowerCamelCase			,       ['''a.1''', '''a.2'''] )
    self.assertDictEqual(__lowerCamelCase			,       {'''a.1.a''': 0, '''a.2.a''': 2} )







'''simple docstring'''





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def        a__     (					UpperCamelCase_						:      Tuple						):
   UpperCAmelCase__	:Dict      =      [
       '''encoder.version''',
       '''decoder.version''',
       '''model.encoder.version''',
       '''model.decoder.version''',
       '''decoder.output_projection.weight''',
       '''_float_tensor''',
       '''encoder.embed_positions._float_tensor''',
       '''decoder.embed_positions._float_tensor''',
   ]
   for k in ignore_keys:
      state_dict.pop(UpperCamelCase_,   UpperCamelCase_						)


def        a__     (					UpperCamelCase_						:      Union[str, Any]						):
   UpperCAmelCase__   ,						UpperCAmelCase__	:Optional[Any]      =      emb.weight.shape
   UpperCAmelCase__	:int      =      nn.Linear(UpperCamelCase_,   UpperCamelCase_,   bias=UpperCamelCase_						)
   UpperCAmelCase__	:Tuple      =      emb.weight.data
   return lin_layer


def        a__     (					UpperCamelCase_						:      str						):
   UpperCAmelCase__	:List[str]      =      torch.load(UpperCamelCase_,   map_location='''cpu'''						)
   UpperCAmelCase__	:List[Any]      =      mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
   UpperCAmelCase__	:List[Any]      =      mam_aaa['''model''']
   remove_ignore_keys_(UpperCamelCase_						)
   UpperCAmelCase__	:Tuple      =      state_dict['''encoder.embed_tokens.weight'''].shape[0]

   UpperCAmelCase__	:Union[str, Any]      =      MaMaaaConfig(
       vocab_size=UpperCamelCase_,   max_position_embeddings=1_024,   encoder_layers=args.encoder_layers,   decoder_layers=args.decoder_layers,   encoder_attention_heads=args.encoder_attention_heads,   decoder_attention_heads=args.decoder_attention_heads,   encoder_ffn_dim=args.encoder_ffn_embed_dim,   decoder_ffn_dim=args.decoder_ffn_embed_dim,   d_model=args.encoder_embed_dim,   encoder_layerdrop=args.encoder_layerdrop,   decoder_layerdrop=args.decoder_layerdrop,   dropout=args.dropout,   attention_dropout=args.attention_dropout,   activation_dropout=args.activation_dropout,   activation_function='''relu''',   )

   UpperCAmelCase__	:str      =      state_dict['''decoder.embed_tokens.weight''']
   UpperCAmelCase__	:Tuple      =      MaMaaaForConditionalGeneration(UpperCamelCase_						)
   model.model.load_state_dict(UpperCamelCase_,   strict=UpperCamelCase_						)
   UpperCAmelCase__	:Optional[int]      =      make_linear_from_emb(model.model.shared						)

   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_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)



