"""simple docstring"""
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


SCREAMING_SNAKE_CASE__      =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      =					{
    "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_      (     lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Union[str, Any]     					=  """segformer"""

				def __init__(			self						,      lowerCAmelCase=3						,      lowerCAmelCase=4						,      lowerCAmelCase=[2, 2, 2, 2]						,      lowerCAmelCase=[8, 4, 2, 1]						,      lowerCAmelCase=[32, 64, 1_60, 2_56]						,      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=2_56						,      lowerCAmelCase=2_55						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										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						,      )

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





class 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      int     					=  version.parse("""1.11"""   )

				@property
				def 						snake_case	(			self      ):






										"""simple docstring"""

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

				@property
				def 						snake_case	(			self      ):






										"""simple docstring"""

										return 1E-4

				@property
				def 						snake_case	(			self      ):






										"""simple docstring"""

										return 12



"""simple docstring"""
import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu



@require_cpu
class 					lowerCAmelCase_      (     unittest.TestCase   ):



				"""simple docstring"""

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     torch.nn.Linear(10						,      10      )
										snake_case        =     torch.optim.SGD(model.parameters()						,      0.1      )
										snake_case        =     Accelerator()
										snake_case        =     accelerator.prepare(lowerCAmelCase      )
										try:
																pickle.loads(pickle.dumps(lowerCAmelCase      )      )
										except Exception as e:
																self.fail(F"""Accelerated optimizer pickling failed with {e}"""      )
										AcceleratorState._reset_state()


