'''simple docstring'''





import json
import os
import tempfile
from unittest.mock import patch

import torch
from torch.utils.data import DataLoader, TensorDataset

from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, PartialState
from accelerate.test_utils import require_bnb, require_multi_gpu, slow
from accelerate.test_utils.testing import AccelerateTestCase, require_cuda
from accelerate.utils import patch_environment





def      lowerCAmelCase_    ( )   ->				Dict:


     '''simple docstring'''




     UpperCAmelCase_	   =       torch.nn.Linear(2 ,   4      )
     UpperCAmelCase_	   =       torch.optim.AdamW(model.parameters() ,   lr=1.0      )
     UpperCAmelCase_	   =       torch.optim.lr_scheduler.OneCycleLR(snake_case_ ,   max_lr=0.01 ,   steps_per_epoch=2 ,   epochs=1      )
     UpperCAmelCase_	   =       DataLoader(TensorDataset(torch.tensor([1, 2, 3]      )      )      )
     UpperCAmelCase_	   =       DataLoader(TensorDataset(torch.tensor([4, 5, 6]      )      )      )

     return model, optimizer, scheduler, train_dl, valid_dl





def      lowerCAmelCase_    ( snake_case_	:			str      )   ->				Tuple:


     '''simple docstring'''




     return (model.weight.abs().sum() + model.bias.abs().sum()).item()





def      lowerCAmelCase_    ( snake_case_	:			Tuple      )   ->				Dict:


     '''simple docstring'''




     UpperCAmelCase_	   =       torch.nn.Linear(*tuple(model.weight.T.shape      )      ).state_dict()
     model.load_state_dict(snake_case_      )






class 	__A				(			UpperCamelCase__     ):



    @require_cuda
    def  _lowercase						(self   :   int			):
         UpperCAmelCase_	   =       Accelerator()
         assert PartialState._shared_state["_cpu"] is False
         assert PartialState._shared_state["device"].type == "cuda"
         with self.assertRaises(__a			):
              UpperCAmelCase_	   =       Accelerator(cpu=__a			)



    def  _lowercase						(self   :   Tuple			):
         UpperCAmelCase_	   =       Accelerator()
         UpperCAmelCase_	   =       GradientState()
         assert state.num_steps == 1
         UpperCAmelCase_	   =       4
         assert state.num_steps == 4

         assert state.sync_gradients is True
         UpperCAmelCase_	   =       False
         assert state.sync_gradients is False
         GradientState._reset_state()



    def  _lowercase						(self   :   Dict			):
         UpperCAmelCase_	   =       Accelerator()
         UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_	   =       create_components()

         (
             (
             UpperCAmelCase_
         )   ,			(
             UpperCAmelCase_
         )   ,			(
             UpperCAmelCase_
         )   ,			(
             UpperCAmelCase_
         )   ,			(
             UpperCAmelCase_
         )   ,			
         )	   =       accelerator.prepare(__a       ,      __a       ,      __a       ,      __a       ,      __a			)

         self.assertTrue(prepared_model in accelerator._models			)
         self.assertTrue(prepared_optimizer in accelerator._optimizers			)
         self.assertTrue(prepared_scheduler in accelerator._schedulers			)
         self.assertTrue(prepared_train_dl in accelerator._dataloaders			)
         self.assertTrue(prepared_valid_dl in accelerator._dataloaders			)



    def  _lowercase						(self   :   Any			):
         UpperCAmelCase_	   =       Accelerator()
         UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_	   =       create_components()
         accelerator.prepare(__a       ,      __a       ,      __a       ,      __a       ,      __a			)
         accelerator.free_memory()

         self.assertTrue(len(accelerator._models			) == 0			)
         self.assertTrue(len(accelerator._optimizers			) == 0			)
         self.assertTrue(len(accelerator._schedulers			) == 0			)
         self.assertTrue(len(accelerator._dataloaders			) == 0			)



    def  _lowercase						(self   :   str			):
         PartialState._reset_state()

         # Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
         def noop(*__a   :   Dict       ,      **__a   :   Optional[int]			):
              pass

         with patch("torch.cuda.set_device"       ,      __a			), patch_environment(ACCELERATE_TORCH_DEVICE="cuda:64"			):
              UpperCAmelCase_	   =       Accelerator()
              self.assertEqual(str(accelerator.state.device			)       ,      "cuda:64"			)



    def  _lowercase						(self   :   Dict			):
         UpperCAmelCase_	   =       Accelerator()
         UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_	   =       create_components()
         accelerator.prepare(__a       ,      __a       ,      __a       ,      __a       ,      __a			)

         UpperCAmelCase_	   =       get_signature(__a			)

         with tempfile.TemporaryDirectory() as tmpdirname:
              accelerator.save_state(__a			)

              # make sure random weights don't match
              load_random_weights(__a			)
              self.assertTrue(abs(model_signature - get_signature(__a			)			) > 1E-3			)

              # make sure loaded weights match
              accelerator.load_state(__a			)
              self.assertTrue(abs(model_signature - get_signature(__a			)			) < 1E-3			)



    def  _lowercase						(self   :   Dict			):
         UpperCAmelCase_	   =       Accelerator()
         UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_	   =       create_components()
         accelerator.prepare(__a       ,      __a       ,      __a       ,      __a       ,      __a			)

         UpperCAmelCase_	   =       get_signature(__a			)

         # saving hook
         def save_config(__a   :   List[str]       ,      __a   :   str       ,      __a   :   List[str]			):
              UpperCAmelCase_	   =       {"class_name": models[0].__class__.__name__}

              with open(os.path.join(__a       ,      "data.json"			)       ,      "w"			) as f:
                   json.dump(__a       ,      __a			)

        # loading hook
         def load_config(__a   :   str       ,      __a   :   List[str]			):
              with open(os.path.join(__a       ,      "data.json"			)       ,      "r"			) as f:
                   UpperCAmelCase_	   =       json.load(__a			)

              UpperCAmelCase_	   =       config["class_name"]

         UpperCAmelCase_	   =       accelerator.register_save_state_pre_hook(__a			)
         UpperCAmelCase_	   =       accelerator.register_load_state_pre_hook(__a			)

         with tempfile.TemporaryDirectory() as tmpdirname:
              accelerator.save_state(__a			)

              # make sure random weights don't match with hooks
              load_random_weights(__a			)
              self.assertTrue(abs(model_signature - get_signature(__a			)			) > 1E-3			)

              # random class name to verify correct one is loaded
              UpperCAmelCase_	   =       "random"

              # make sure loaded weights match with hooks
              accelerator.load_state(__a			)
              self.assertTrue(abs(model_signature - get_signature(__a			)			) < 1E-3			)

              # mode.class_name is loaded from config
              self.assertTrue(model.class_name == model.__class__.__name__			)

         # remove hooks
         save_hook.remove()
         load_hook.remove()

         with tempfile.TemporaryDirectory() as tmpdirname:
              accelerator.save_state(__a			)

              # make sure random weights don't match with hooks removed
              load_random_weights(__a			)
              self.assertTrue(abs(model_signature - get_signature(__a			)			) > 1E-3			)

              # random class name to verify correct one is loaded
              UpperCAmelCase_	   =       "random"

              # make sure loaded weights match with hooks removed
              accelerator.load_state(__a			)
              self.assertTrue(abs(model_signature - get_signature(__a			)			) < 1E-3			)

              # mode.class_name is NOT loaded from config
              self.assertTrue(model.class_name != model.__class__.__name__			)



    def  _lowercase						(self   :   Dict			):
         UpperCAmelCase_	   =       Accelerator()
         UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_	   =       create_components()
         UpperCAmelCase_	   =       None

         # This should work
         UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_	   =       accelerator.prepare(
             __a       ,      __a       ,      __a       ,      __a       ,      __a       ,      __a			)
         self.assertTrue(dummy_obj is None			)



    def  _lowercase						(self   :   Tuple			):
         UpperCAmelCase_	   =       Accelerator()
         UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_	   =       create_components()
         UpperCAmelCase_	   =       [1, 2, 3]

         # This should work
         UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_	   =       accelerator.prepare(
             __a       ,      __a       ,      __a       ,      __a       ,      __a       ,      __a			)
         self.assertEqual(
             getattr(__a       ,      "_is_accelerate_prepared"       ,      __a			)       ,      __a       ,      "Dummy object should have `_is_accelerate_prepared` set to `True`"       ,      )
         self.assertEqual(
             getattr(__a       ,      "_is_accelerate_prepared"       ,      __a			)       ,      __a       ,      "Model is missing `_is_accelerator_prepared` or is set to `False`"       ,      )
         self.assertEqual(
             getattr(__a       ,      "_is_accelerate_prepared"       ,      __a			)       ,      __a       ,      "Optimizer is missing `_is_accelerator_prepared` or is set to `False`"       ,      )
         self.assertEqual(
             getattr(__a       ,      "_is_accelerate_prepared"       ,      __a			)       ,      __a       ,      "Scheduler is missing `_is_accelerator_prepared` or is set to `False`"       ,      )
         self.assertEqual(
             getattr(__a       ,      "_is_accelerate_prepared"       ,      __a			)       ,      __a       ,      "Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`"       ,      )
         self.assertEqual(
             getattr(__a       ,      "_is_accelerate_prepared"       ,      __a			)       ,      __a       ,      "Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`"       ,      )



    @slow
    @require_bnb
    def  _lowercase						(self   :   List[Any]			):
         from transformers import AutoModelForCausalLM

         UpperCAmelCase_	   =       AutoModelForCausalLM.from_pretrained(
             "EleutherAI/gpt-neo-125m"       ,      load_in_abit=__a       ,      device_map={"": 0}       ,      )
         UpperCAmelCase_	   =       Accelerator()

         # This should work
         UpperCAmelCase_	   =       accelerator.prepare(__a			)



    @slow
    @require_bnb
    def  _lowercase						(self   :   Any			):
         from transformers import AutoModelForCausalLM

         UpperCAmelCase_	   =       Accelerator()

         with init_empty_weights():
              UpperCAmelCase_	   =       AutoModelForCausalLM.from_pretrained(
                  "EleutherAI/gpt-neo-125m"       ,      )
              model.tie_weights()
              UpperCAmelCase_	   =       infer_auto_device_map(__a			)
              UpperCAmelCase_	   =       "cpu"

         UpperCAmelCase_	   =       AutoModelForCausalLM.from_pretrained(
             "EleutherAI/gpt-neo-125m"       ,      device_map=__a       ,      load_in_abit=__a       ,      llm_inta_enable_fpaa_cpu_offload=__a			)

         # This should not work and get value error
         with self.assertRaises(__a			):
              UpperCAmelCase_	   =       accelerator.prepare(__a			)



    @slow
    @require_bnb
    @require_multi_gpu
    def  _lowercase						(self   :   Tuple			):
         from transformers import AutoModelForCausalLM

         UpperCAmelCase_	   =       {"distributed_type": DistributedType.MULTI_GPU}

         with init_empty_weights():
              UpperCAmelCase_	   =       AutoModelForCausalLM.from_pretrained(
                  "EleutherAI/gpt-neo-125m"       ,      )
              model.tie_weights()
              UpperCAmelCase_	   =       infer_auto_device_map(__a			)
              UpperCAmelCase_	   =       1

         UpperCAmelCase_	   =       AutoModelForCausalLM.from_pretrained(
             "EleutherAI/gpt-neo-125m"       ,      load_in_abit=__a       ,      device_map=__a       ,      )
         UpperCAmelCase_	   =       Accelerator()

         # This should not work and get value error
         with self.assertRaises(__a			):
              UpperCAmelCase_	   =       accelerator.prepare(__a			)

         PartialState._reset_state()



    @slow
    @require_bnb
    @require_multi_gpu
    def  _lowercase						(self   :   Optional[int]			):
         from transformers import AutoModelForCausalLM

         with init_empty_weights():
              UpperCAmelCase_	   =       AutoModelForCausalLM.from_pretrained(
                  "EleutherAI/gpt-neo-125m"       ,      )
              UpperCAmelCase_	   =       infer_auto_device_map(__a			)
              UpperCAmelCase_	   =       1

         UpperCAmelCase_	   =       AutoModelForCausalLM.from_pretrained(
             "EleutherAI/gpt-neo-125m"       ,      load_in_abit=__a       ,      device_map=__a       ,      )
         UpperCAmelCase_	   =       Accelerator()

         # This should work
         UpperCAmelCase_	   =       accelerator.prepare(__a			)






    @require_cuda
    def  _lowercase						(self   :   Union[str, Any]			):
         UpperCAmelCase_	   =       torch.nn.Linear(10       ,      10			)
         UpperCAmelCase_	   =       torch.optim.SGD(model.parameters()       ,      lr=0.01			)
         UpperCAmelCase_	   =       Accelerator(cpu=__a			)
         UpperCAmelCase_	   =       accelerator.prepare(__a			)





'''simple docstring'''





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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


SCREAMING_SNAKE_CASE_:					List[str]  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:					Tuple  ={
    'microsoft/resnet-50': 'https://huggingface.co/microsoft/resnet-50/blob/main/config.json',
}




class 	__A				(			UpperCamelCase__		,     UpperCamelCase__     ):
    a__	:						Optional[Any]	       =  """resnet"""
    a__	:						Tuple	       =  ["""basic""", """bottleneck"""]



    def __init__(self   :   List[Any]       ,      __a   :   Any=3       ,      __a   :   Dict=64       ,      __a   :   Union[str, Any]=[256, 512, 1024, 2048]       ,      __a   :   str=[3, 4, 6, 3]       ,      __a   :   Optional[Any]="bottleneck"       ,      __a   :   Tuple="relu"       ,      __a   :   int=False       ,      __a   :   Optional[int]=None       ,      __a   :   str=None       ,      **__a   :   Dict       ,      ):
         super().__init__(**__a			)
         if layer_type not in self.layer_types:
              raise ValueError(f"""layer_type={layer_type} is not one of {",".join(self.layer_types			)}"""			)
         UpperCAmelCase_	   =       num_channels
         UpperCAmelCase_	   =       embedding_size
         UpperCAmelCase_	   =       hidden_sizes
         UpperCAmelCase_	   =       depths
         UpperCAmelCase_	   =       layer_type
         UpperCAmelCase_	   =       hidden_act
         UpperCAmelCase_	   =       downsample_in_first_stage
         UpperCAmelCase_	   =       ["stem"] + [f"""stage{idx}""" for idx in range(1       ,      len(__a			) + 1			)]
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       get_aligned_output_features_output_indices(
             out_features=__a       ,      out_indices=__a       ,      stage_names=self.stage_names			)




class 	__A				(			UpperCamelCase__     ):
    a__	:						int	       =  version.parse("""1.11"""     )



    @property
    def  _lowercase						(self   :   Optional[int]			):
         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
             ]			)






    @property
    def  _lowercase						(self   :   str			):
         return 1E-3




