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      lowerCAmelCase						(					nn.Module			):



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





     def 			a__  (					self				,      lowerCAmelCase__       ):
            return self.lineara(self.batchnorm(self.lineara(lowerCAmelCase__       )       )       )





class      lowerCAmelCase						(					unittest.TestCase			):



     def 			a__  (					self       ):
            _A=	ModelForTest()
            with TemporaryDirectory() as tmp_dir:
                   offload_state_dict(lowerCAmelCase__				,      model.state_dict()       )
                   _A=	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"]:
                          _A=	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       ):
            _A=	[torch.floataa, torch.floataa, torch.bfloataa]

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

                          _A=	load_offloaded_weight(lowerCAmelCase__				,      index['weight']       )
                          self.assertTrue(torch.equal(lowerCAmelCase__				,      lowerCAmelCase__       )       )



     def 			a__  (					self       ):
            _A=	ModelForTest()
            _A=	model.state_dict()
            _A=	{k: v for k, v in state_dict.items() if 'linear2' not in k}
            _A=	{k: v for k, v in state_dict.items() if 'linear2' in k}

            with TemporaryDirectory() as tmp_dir:
                   offload_state_dict(lowerCAmelCase__				,      lowerCAmelCase__       )
                   _A=	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]       )       )

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

            with TemporaryDirectory() as tmp_dir:
                   offload_state_dict(lowerCAmelCase__				,      lowerCAmelCase__       )
                   _A=	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
                   _A=	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       ):
            _A=	{'a.1': 0, 'a.10': 1, 'a.2': 2}
            _A=	extract_submodules_state_dict(lowerCAmelCase__				,      ['a.1', 'a.2']       )
            self.assertDictEqual(lowerCAmelCase__				,      {'a.1': 0, 'a.2': 2}       )

            _A=	{'a.1.a': 0, 'a.10.a': 1, 'a.2.a': 2}
            _A=	extract_submodules_state_dict(lowerCAmelCase__				,      ['a.1', 'a.2']       )
            self.assertDictEqual(lowerCAmelCase__				,      {'a.1.a': 0, 'a.2.a': 2}       )
import warnings
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_								=							{
    '''nvidia/segformer-b0-finetuned-ade-512-512''': (
        '''https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json'''
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     Tuple		="""segformer"""



     def __init__(					self				,      lowerCAmelCase__=3				,      lowerCAmelCase__=4				,      lowerCAmelCase__=[2, 2, 2, 2]				,      lowerCAmelCase__=[8, 4, 2, 1]				,      lowerCAmelCase__=[32, 64, 160, 256]				,      lowerCAmelCase__=[7, 3, 3, 3]				,      lowerCAmelCase__=[4, 2, 2, 2]				,      lowerCAmelCase__=[1, 2, 5, 8]				,      lowerCAmelCase__=[4, 4, 4, 4]				,      lowerCAmelCase__="gelu"				,      lowerCAmelCase__=0.0				,      lowerCAmelCase__=0.0				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=0.02				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=1E-6				,      lowerCAmelCase__=256				,      lowerCAmelCase__=255				,      **lowerCAmelCase__				,      ):
            super().__init__(**lowerCAmelCase__       )

            if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
                   warnings.warn(
                       'Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'
                       ' removed, as the behaviour will default to that of reshape_last_stage = True.'				,      lowerCAmelCase__				,      )

            _A=	num_channels
            _A=	num_encoder_blocks
            _A=	depths
            _A=	sr_ratios
            _A=	hidden_sizes
            _A=	patch_sizes
            _A=	strides
            _A=	mlp_ratios
            _A=	num_attention_heads
            _A=	hidden_act
            _A=	hidden_dropout_prob
            _A=	attention_probs_dropout_prob
            _A=	classifier_dropout_prob
            _A=	initializer_range
            _A=	drop_path_rate
            _A=	layer_norm_eps
            _A=	decoder_hidden_size
            _A=	kwargs.get('reshape_last_stage'				,      lowerCAmelCase__       )
            _A=	semantic_loss_ignore_index





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     List[str]		=version.parse("""1.11"""			)



     @property
     def 			a__  (					self       ):
            return OrderedDict(
                [
                    ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
                ]       )



     @property
     def 			a__  (					self       ):
            return 1E-4





     @property
     def 			a__  (					self       ):
            return 12