


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   A__ (   nn.Module     ):







    """simple docstring"""

    def __init__(	self   ):
         super().__init__()
         snake_case					=    nn.Linear(3      , 4   )
         snake_case					=    nn.BatchNormad(4   )
         snake_case					=    nn.Linear(4      , 5   )




    def 		a_      (	self      , __snake_case   ):
         return self.lineara(self.batchnorm(self.lineara(__snake_case   )   )   )






class   A__ (   unittest.TestCase     ):







    """simple docstring"""

    def 		a_      (	self   ):
         snake_case					=    ModelForTest()
         with TemporaryDirectory() as tmp_dir:
              offload_state_dict(__snake_case      , model.state_dict()   )
              snake_case					=    os.path.join(__snake_case      , '''index.json'''   )
              self.assertTrue(os.path.isfile(__snake_case   )   )
              # TODO: add tests on what is inside the index

              for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
                   snake_case					=    os.path.join(__snake_case      , F'''{key}.dat'''   )
                   self.assertTrue(os.path.isfile(__snake_case   )   )
                   # TODO: add tests on the fact weights are properly loaded
    def 		a_      (	self   ):
         snake_case					=    [torch.floataa, torch.floataa, torch.bfloataa]

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

                   snake_case					=    load_offloaded_weight(__snake_case      , index['''weight''']   )
                   self.assertTrue(torch.equal(__snake_case      , __snake_case   )   )
    def 		a_      (	self   ):
         snake_case					=    ModelForTest()
         snake_case					=    model.state_dict()
         snake_case					=    {k: v for k, v in state_dict.items() if '''linear2''' not in k}
         snake_case					=    {k: v for k, v in state_dict.items() if '''linear2''' in k}

         with TemporaryDirectory() as tmp_dir:
              offload_state_dict(__snake_case      , __snake_case   )
              snake_case					=    OffloadedWeightsLoader(state_dict=__snake_case      , save_folder=__snake_case   )

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

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

         with TemporaryDirectory() as tmp_dir:
              offload_state_dict(__snake_case      , __snake_case   )
              snake_case					=    OffloadedWeightsLoader(state_dict=__snake_case      , save_folder=__snake_case   )

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

         with TemporaryDirectory() as tmp_dir:
              offload_state_dict(__snake_case      , __snake_case   )
              # Duplicates are removed
              snake_case					=    OffloadedWeightsLoader(state_dict=__snake_case      , save_folder=__snake_case   )

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




    def 		a_      (	self   ):
         snake_case					=    {'''a.1''': 0, '''a.10''': 1, '''a.2''': 2}
         snake_case					=    extract_submodules_state_dict(__snake_case      , ['''a.1''', '''a.2''']   )
         self.assertDictEqual(__snake_case      , {'''a.1''': 0, '''a.2''': 2}   )

         snake_case					=    {'''a.1.a''': 0, '''a.10.a''': 1, '''a.2.a''': 2}
         snake_case					=    extract_submodules_state_dict(__snake_case      , ['''a.1''', '''a.2''']   )
         self.assertDictEqual(__snake_case      , {'''a.1.a''': 0, '''a.2.a''': 2}   )








import re

from filelock import FileLock


try:
 import nltk

 _SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   True
except (ImportError, ModuleNotFoundError):
 _SCREAMING_SNAKE_CASE      :			Optional[Any]      				=   False

if NLTK_AVAILABLE:
 with FileLock(".lock") as lock:
  nltk.download("punkt", quiet=True)
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     re.sub('''<n>'''							,''''''							,UpperCamelCase_    )  # remove pegasus newline char
     assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
     return "\n".join(nltk.sent_tokenize(UpperCamelCase_    )    )




