





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 (  )	->			List[str]:
     lowerCAmelCase_					:       Union[str, Any]    =		torch.nn.Linear(2					,   4			)
     lowerCAmelCase_					:       Optional[Any]    =		torch.optim.AdamW(model.parameters()					,   lr=1.0			)
     lowerCAmelCase_					:       int    =		torch.optim.lr_scheduler.OneCycleLR(_a					,   max_lr=0.01					,   steps_per_epoch=2					,   epochs=1			)
     lowerCAmelCase_					:       str    =		DataLoader(TensorDataset(torch.tensor([1, 2, 3]			)			)			)
     lowerCAmelCase_					:       Dict    =		DataLoader(TensorDataset(torch.tensor([4, 5, 6]			)			)			)

     return model, optimizer, scheduler, train_dl, valid_dl






def 							_lowerCAmelCase (  _a   :					Any			)	->			Tuple:
     return (model.weight.abs().sum() + model.bias.abs().sum()).item()






def 							_lowerCAmelCase (  _a   :					Optional[int]			)	->			Any:
     lowerCAmelCase_					:       Optional[Any]    =		torch.nn.Linear(*tuple(model.weight.T.shape			)			).state_dict()
     model.load_state_dict(_a			)







class      lowercase__    (							__A  ):







  @require_cuda
  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       List[Any]    =		Accelerator()
       assert PartialState._shared_state["_cpu"] is False
       assert PartialState._shared_state["device"].type == "cuda"
       with self.assertRaises(_lowercase						):
            lowerCAmelCase_					:       Union[str, Any]    =		Accelerator(cpu=_lowercase						)







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       List[Any]    =		Accelerator()
       lowerCAmelCase_					:       int    =		GradientState()
       assert state.num_steps == 1
       lowerCAmelCase_					:       Dict    =		4
       assert state.num_steps == 4

       assert state.sync_gradients is True
       lowerCAmelCase_					:       str    =		False
       assert state.sync_gradients is False
       GradientState._reset_state()







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Optional[Any]    =		Accelerator()
       lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_					:       Optional[Any]    =		create_components()

       (
           (
           lowerCAmelCase_
       )   ,  (
           lowerCAmelCase_
       )   ,  (
           lowerCAmelCase_
       )   ,  (
           lowerCAmelCase_
       )   ,  (
           lowerCAmelCase_
       )   ,  
       )					:       Optional[Any]    =		accelerator.prepare(_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase						)

       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 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Union[str, Any]    =		Accelerator()
       lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_					:       Tuple    =		create_components()
       accelerator.prepare(_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase						)
       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 	UpperCAmelCase__		(	self						):
       PartialState._reset_state()

       # Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
       def noop(*_lowercase    ,						**_lowercase						):
            pass

       with patch("""torch.cuda.set_device"""    ,						_lowercase						), patch_environment(ACCELERATE_TORCH_DEVICE="""cuda:64"""						):
            lowerCAmelCase_					:       List[str]    =		Accelerator()
            self.assertEqual(str(accelerator.state.device						)    ,						"""cuda:64"""						)







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Dict    =		Accelerator()
       lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_					:       str    =		create_components()
       accelerator.prepare(_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase						)

       lowerCAmelCase_					:       List[Any]    =		get_signature(_lowercase						)

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

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

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







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Tuple    =		Accelerator()
       lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_					:       Any    =		create_components()
       accelerator.prepare(_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase						)

       lowerCAmelCase_					:       Tuple    =		get_signature(_lowercase						)

       # saving hook
       def save_config(_lowercase    ,						_lowercase    ,						_lowercase						):
            lowerCAmelCase_					:       int    =		{"""class_name""": models[0].__class__.__name__}

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

        # loading hook
       def load_config(_lowercase    ,						_lowercase						):
            with open(os.path.join(_lowercase    ,						"""data.json"""						)    ,						"""r"""						) as f:
                 lowerCAmelCase_					:       Dict    =		json.load(_lowercase						)

            lowerCAmelCase_					:       Dict    =		config["""class_name"""]

       lowerCAmelCase_					:       Optional[Any]    =		accelerator.register_save_state_pre_hook(_lowercase						)
       lowerCAmelCase_					:       Tuple    =		accelerator.register_load_state_pre_hook(_lowercase						)

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

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

            # random class name to verify correct one is loaded
            lowerCAmelCase_					:       List[str]    =		"""random"""

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

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

            # random class name to verify correct one is loaded
            lowerCAmelCase_					:       Dict    =		"""random"""

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

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







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       List[str]    =		Accelerator()
       lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_					:       str    =		create_components()
       lowerCAmelCase_					:       Tuple    =		None

       # This should work
       lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_					:       List[str]    =		accelerator.prepare(
           _lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase						)
       self.assertTrue(dummy_obj is None						)







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Any    =		Accelerator()
       lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_					:       Optional[Any]    =		create_components()
       lowerCAmelCase_					:       int    =		[1, 2, 3]

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







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

       lowerCAmelCase_					:       str    =		AutoModelForCausalLM.from_pretrained(
           """EleutherAI/gpt-neo-125m"""    ,						load_in_abit=_lowercase    ,						device_map={"""""": 0}    ,						)
       lowerCAmelCase_					:       Optional[int]    =		Accelerator()

       # This should work
       lowerCAmelCase_					:       List[Any]    =		accelerator.prepare(_lowercase						)







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

       lowerCAmelCase_					:       Optional[int]    =		Accelerator()

       with init_empty_weights():
            lowerCAmelCase_					:       Optional[Any]    =		AutoModelForCausalLM.from_pretrained(
                """EleutherAI/gpt-neo-125m"""    ,						)
            model.tie_weights()
            lowerCAmelCase_					:       Dict    =		infer_auto_device_map(_lowercase						)
            lowerCAmelCase_					:       Any    =		"""cpu"""

       lowerCAmelCase_					:       Any    =		AutoModelForCausalLM.from_pretrained(
           """EleutherAI/gpt-neo-125m"""    ,						device_map=_lowercase    ,						load_in_abit=_lowercase    ,						llm_inta_enable_fpaa_cpu_offload=_lowercase						)

       # This should not work and get value error
       with self.assertRaises(_lowercase						):
            lowerCAmelCase_					:       Optional[Any]    =		accelerator.prepare(_lowercase						)







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

       lowerCAmelCase_					:       Any    =		{"""distributed_type""": DistributedType.MULTI_GPU}

       with init_empty_weights():
            lowerCAmelCase_					:       Optional[Any]    =		AutoModelForCausalLM.from_pretrained(
                """EleutherAI/gpt-neo-125m"""    ,						)
            model.tie_weights()
            lowerCAmelCase_					:       Dict    =		infer_auto_device_map(_lowercase						)
            lowerCAmelCase_					:       Optional[Any]    =		1

       lowerCAmelCase_					:       List[Any]    =		AutoModelForCausalLM.from_pretrained(
           """EleutherAI/gpt-neo-125m"""    ,						load_in_abit=_lowercase    ,						device_map=_lowercase    ,						)
       lowerCAmelCase_					:       Optional[Any]    =		Accelerator()

       # This should not work and get value error
       with self.assertRaises(_lowercase						):
            lowerCAmelCase_					:       Dict    =		accelerator.prepare(_lowercase						)

       PartialState._reset_state()







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

       with init_empty_weights():
            lowerCAmelCase_					:       Optional[int]    =		AutoModelForCausalLM.from_pretrained(
                """EleutherAI/gpt-neo-125m"""    ,						)
            lowerCAmelCase_					:       Any    =		infer_auto_device_map(_lowercase						)
            lowerCAmelCase_					:       Optional[int]    =		1

       lowerCAmelCase_					:       List[str]    =		AutoModelForCausalLM.from_pretrained(
           """EleutherAI/gpt-neo-125m"""    ,						load_in_abit=_lowercase    ,						device_map=_lowercase    ,						)
       lowerCAmelCase_					:       int    =		Accelerator()

       # This should work
       lowerCAmelCase_					:       Union[str, Any]    =		accelerator.prepare(_lowercase						)







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







from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


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

UpperCAmelCase_					:   int 							=						{
    """google/bit-50""": """https://huggingface.co/google/bit-50/resolve/main/config.json""",
}





class      lowercase__    (							__A     ,					__A  ):
  __UpperCamelCase	     =  """bit"""
  __UpperCamelCase	     =  ["""preactivation""", """bottleneck"""]
  __UpperCamelCase	     =  ["""SAME""", """VALID"""]







  def __init__(	self    ,						_lowercase=3    ,						_lowercase=64    ,						_lowercase=[256, 512, 1_024, 2_048]    ,						_lowercase=[3, 4, 6, 3]    ,						_lowercase="preactivation"    ,						_lowercase="relu"    ,						_lowercase=None    ,						_lowercase=32    ,						_lowercase=0.0    ,						_lowercase=False    ,						_lowercase=32    ,						_lowercase=1    ,						_lowercase=None    ,						_lowercase=None    ,						**_lowercase    ,						):
       super().__init__(**_lowercase						)
       if layer_type not in self.layer_types:
            raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types						)}'						)
       if global_padding is not None:
            if global_padding.upper() in self.supported_padding:
                 lowerCAmelCase_					:       int    =		global_padding.upper()
            else:
                 raise ValueError(F'Padding strategy {global_padding} not supported'						)
       lowerCAmelCase_					:       Optional[Any]    =		num_channels
       lowerCAmelCase_					:       List[str]    =		embedding_size
       lowerCAmelCase_					:       Dict    =		hidden_sizes
       lowerCAmelCase_					:       Optional[int]    =		depths
       lowerCAmelCase_					:       List[Any]    =		layer_type
       lowerCAmelCase_					:       Union[str, Any]    =		hidden_act
       lowerCAmelCase_					:       Optional[int]    =		global_padding
       lowerCAmelCase_					:       Optional[Any]    =		num_groups
       lowerCAmelCase_					:       Optional[Any]    =		drop_path_rate
       lowerCAmelCase_					:       Tuple    =		embedding_dynamic_padding
       lowerCAmelCase_					:       Any    =		output_stride
       lowerCAmelCase_					:       List[Any]    =		width_factor

       lowerCAmelCase_					:       Union[str, Any]    =		["""stem"""] + [F'stage{idx}' for idx in range(1    ,						len(_lowercase						) + 1						)]
       lowerCAmelCase_   ,  lowerCAmelCase_					:       Tuple    =		get_aligned_output_features_output_indices(
           out_features=_lowercase    ,						out_indices=_lowercase    ,						stage_names=self.stage_names						)
