





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     lowercase__       (	nn.Module				):

   '''simple docstring'''


   def __init__(    self							) ->      Tuple:






       """simple docstring"""
       super().__init__()
       UpperCamelCase__							:				List[Any]          =      nn.Linear(3,		4							)
       UpperCamelCase__							:				Dict          =      nn.BatchNormad(4							)
       UpperCamelCase__							:				List[Any]          =      nn.Linear(4,		5							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Optional[int]:






       """simple docstring"""
       return self.lineara(self.batchnorm(self.lineara(__magic_name__							)							)							)






class     lowercase__       (	unittest.TestCase				):

   '''simple docstring'''


   def       UpperCamelCase__  (    self							) ->      Any:






       """simple docstring"""
       UpperCamelCase__							:				str          =      ModelForTest()
       with TemporaryDirectory() as tmp_dir:
           offload_state_dict(__magic_name__,		model.state_dict()							)
           UpperCamelCase__							:				Any          =      os.path.join(__magic_name__,		'''index.json'''							)
           self.assertTrue(os.path.isfile(__magic_name__							)							)
           # TODO: add tests on what is inside the index

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




   def       UpperCamelCase__  (    self							) ->      str:






       """simple docstring"""
       UpperCamelCase__							:				int          =      [torch.floataa, torch.floataa, torch.bfloataa]

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

               UpperCamelCase__							:				Optional[int]          =      load_offloaded_weight(__magic_name__,		index['''weight''']							)
               self.assertTrue(torch.equal(__magic_name__,		__magic_name__							)							)




   def       UpperCamelCase__  (    self							) ->      List[str]:






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

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

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

       UpperCamelCase__							:				Dict          =      {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(__magic_name__,		__magic_name__							)
           UpperCamelCase__							:				List[Any]          =      OffloadedWeightsLoader(state_dict=__magic_name__,		save_folder=__magic_name__							)

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

       with TemporaryDirectory() as tmp_dir:
           offload_state_dict(__magic_name__,		__magic_name__							)
           # Duplicates are removed
           UpperCamelCase__							:				List[Any]          =      OffloadedWeightsLoader(state_dict=__magic_name__,		save_folder=__magic_name__							)

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




   def       UpperCamelCase__  (    self							) ->      Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:				Tuple          =      {'''a.1''': 0, '''a.10''': 1, '''a.2''': 2}
       UpperCamelCase__							:				Tuple          =      extract_submodules_state_dict(__magic_name__,		['''a.1''', '''a.2''']							)
       self.assertDictEqual(__magic_name__,		{'''a.1''': 0, '''a.2''': 2}							)

       UpperCamelCase__							:				Optional[int]          =      {'''a.1.a''': 0, '''a.10.a''': 1, '''a.2.a''': 2}
       UpperCamelCase__							:				Optional[Any]          =      extract_submodules_state_dict(__magic_name__,		['''a.1''', '''a.2''']							)
       self.assertDictEqual(__magic_name__,		{'''a.1.a''': 0, '''a.2.a''': 2}							)








from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_     							= logging.get_logger(__name__)

UpperCAmelCase_     							= {
    'google/mobilenet_v2_1.4_224': 'https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json',
    'google/mobilenet_v2_1.0_224': 'https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json',
    'google/mobilenet_v2_0.75_160': 'https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json',
    'google/mobilenet_v2_0.35_96': 'https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							List[str]   		=		"mobilenet_v2"




   def __init__(    self,		__magic_name__=3,		__magic_name__=224,		__magic_name__=1.0,		__magic_name__=8,		__magic_name__=8,		__magic_name__=6,		__magic_name__=32,		__magic_name__=True,		__magic_name__=True,		__magic_name__="relu6",		__magic_name__=True,		__magic_name__=0.8,		__magic_name__=0.02,		__magic_name__=0.001,		__magic_name__=255,		**__magic_name__,		) ->      List[Any]:






       """simple docstring"""
       super().__init__(**__magic_name__							)

       if depth_multiplier <= 0:
           raise ValueError('''depth_multiplier must be greater than zero.'''							)

       UpperCamelCase__							:				Union[str, Any]          =      num_channels
       UpperCamelCase__							:				int          =      image_size
       UpperCamelCase__							:				int          =      depth_multiplier
       UpperCamelCase__							:				Tuple          =      depth_divisible_by
       UpperCamelCase__							:				List[str]          =      min_depth
       UpperCamelCase__							:				Optional[int]          =      expand_ratio
       UpperCamelCase__							:				Optional[int]          =      output_stride
       UpperCamelCase__							:				Tuple          =      first_layer_is_expansion
       UpperCamelCase__							:				Union[str, Any]          =      finegrained_output
       UpperCamelCase__							:				str          =      hidden_act
       UpperCamelCase__							:				Optional[Any]          =      tf_padding
       UpperCamelCase__							:				Optional[int]          =      classifier_dropout_prob
       UpperCamelCase__							:				int          =      initializer_range
       UpperCamelCase__							:				Union[str, Any]          =      layer_norm_eps
       UpperCamelCase__							:				Tuple          =      semantic_loss_ignore_index






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							Union[str, Any]   		=		version.parse("1.11"				)




   @property
   def       UpperCamelCase__  (    self							) ->      Mapping[str, Mapping[int, str]]:






       """simple docstring"""
       return OrderedDict([('''pixel_values''', {0: '''batch'''})]							)




   @property
   def       UpperCamelCase__  (    self							) ->      Mapping[str, Mapping[int, str]]:






       """simple docstring"""
       if self.task == "image-classification":
           return OrderedDict([('''logits''', {0: '''batch'''})]							)
       else:
           return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]							)




   @property
   def       UpperCamelCase__  (    self							) ->      float:






       """simple docstring"""
       return 1E-4

