






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 ):

  def __init__(    self						:	Dict			):
     """simple docstring"""

     super().__init__()
     __UpperCamelCase	:			str					=					nn.Linear(3   ,				4			)
     __UpperCamelCase	:			List[str]					=					nn.BatchNormad(4			)
     __UpperCamelCase	:			Optional[int]					=					nn.Linear(4   ,				5			)

  def    a					(    self						:	Any   ,				lowerCamelCase__						:	Dict			):
     """simple docstring"""

     return self.lineara(self.batchnorm(self.lineara(lowerCamelCase__			)			)			)







class 		_A						( unittest.TestCase ):

  def    a					(    self						:	Dict			):
     """simple docstring"""

     __UpperCamelCase	:			Tuple					=					ModelForTest()
     with TemporaryDirectory() as tmp_dir:
        offload_state_dict(lowerCamelCase__   ,				model.state_dict()			)
        __UpperCamelCase	:			Tuple					=					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	:			Any					=					os.path.join(lowerCamelCase__   ,				f'{key}.dat'			)
           self.assertTrue(os.path.isfile(lowerCamelCase__			)			)
           # TODO: add tests on the fact weights are properly loaded

  def    a					(    self						:	int			):
     """simple docstring"""

     __UpperCamelCase	:			int					=					[torch.floataa, torch.floataa, torch.bfloataa]

     for dtype in dtypes:
        __UpperCamelCase	:			Tuple					=					torch.randn(2   ,				3   ,				dtype=lowerCamelCase__			)
        with TemporaryDirectory() as tmp_dir:
           __UpperCamelCase	:			List[Any]					=					offload_weight(lowerCamelCase__   ,				"""weight"""   ,				lowerCamelCase__   ,				{}			)
           __UpperCamelCase	:			List[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	:			Optional[Any]					=					load_offloaded_weight(lowerCamelCase__   ,				index["""weight"""]			)
           self.assertTrue(torch.equal(lowerCamelCase__   ,				lowerCamelCase__			)			)

  def    a					(    self						:	Optional[Any]			):
     """simple docstring"""

     __UpperCamelCase	:			int					=					ModelForTest()
     __UpperCamelCase	:			Any					=					model.state_dict()
     __UpperCamelCase	:			List[Any]					=					{k: v for k, v in state_dict.items() if """linear2""" not in k}
     __UpperCamelCase	:			Union[str, 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	:			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]			)			)

     __UpperCamelCase	:			str					=					{k: v for k, v in state_dict.items() if """weight""" in k}
     __UpperCamelCase	:			int					=					{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	:			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    a					(    self						:	Union[str, Any]			):
     """simple docstring"""

     __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	:			Tuple					=					{"""a.1.a""": 0, """a.10.a""": 1, """a.2.a""": 2}
     __UpperCamelCase	:			int					=					extract_submodules_state_dict(lowerCamelCase__   ,				["""a.1""", """a.2"""]			)
     self.assertDictEqual(lowerCamelCase__   ,				{"""a.1.a""": 0, """a.2.a""": 2}			)











import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


UpperCamelCase    							=      logging.get_logger(__name__)







class 		_A						( UpperCAmelCase_ ):

  def __init__(    self						:	str   ,				*lowerCamelCase__						:	Optional[int]   ,				**lowerCamelCase__						:	Any			):
     """simple docstring"""

     warnings.warn(
         """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
         """ use BeitImageProcessor instead."""   ,				lowerCamelCase__   ,				)
     super().__init__(*lowerCamelCase__   ,				**lowerCamelCase__			)



