'''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'''





# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
    import jax
    import jaxlib

SCREAMING_SNAKE_CASE_:					List[str]  =get_logger()

SCREAMING_SNAKE_CASE_:					Optional[dict]  =None




class 	__A				(			TensorFormatter[Mapping, """jax.Array""", Mapping]     ):



    def __init__(self   :   List[Any]       ,      __a   :   Optional[int]=None       ,      __a   :   Any=None       ,      **__a   :   Dict			):
         super().__init__(features=__a			)
         import jax
         from jaxlib.xla_client import Device

         if isinstance(__a       ,      __a			):
              raise ValueError(
                  f"""Expected {device} to be a `str` not {type(__a			)}, as `jaxlib.xla_extension.Device` """
                  "is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
                  "the device with `str()` to get its string identifier that will be internally mapped "
                  "to the actual `jaxlib.xla_extension.Device`."			)
         UpperCAmelCase_	   =       device if isinstance(__a       ,      __a			) else str(jax.devices()[0]			)
         # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
         # with `pickle` nor with `dill`, so we need to use a global variable instead
         global DEVICE_MAPPING
         if DEVICE_MAPPING is None:
              UpperCAmelCase_	   =       self._map_devices_to_str()
         if self.device not in list(DEVICE_MAPPING.keys()			):
              logger.warning(
                  f"""Device with string identifier {self.device} not listed among the available """
                  f"""devices: {list(DEVICE_MAPPING.keys()			)}, so falling back to the default """
                  f"""device: {str(jax.devices()[0]			)}."""			)
              UpperCAmelCase_	   =       str(jax.devices()[0]			)
         UpperCAmelCase_	   =       jnp_array_kwargs



    @staticmethod
    def  _lowercase						():
         import jax

         return {str(__a			): device for device in jax.devices()}



    def  _lowercase						(self   :   str       ,      __a   :   Tuple			):
         import jax
         import jax.numpy as jnp

         if isinstance(__a       ,      __a			) and column:
              if all(
                  isinstance(__a       ,      jax.Array			) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column			):
                   return jnp.stack(__a       ,      axis=0			)
         return column



    def  _lowercase						(self   :   Any       ,      __a   :   Optional[int]			):
         import jax
         import jax.numpy as jnp

         if isinstance(__a       ,      (str, bytes, type(__a			))			):
              return value
         elif isinstance(__a       ,      (np.character, np.ndarray)			) and np.issubdtype(value.dtype       ,      np.character			):
              return value.tolist()

         UpperCAmelCase_	   =       {}

         if isinstance(__a       ,      (np.number, np.ndarray)			) and np.issubdtype(value.dtype       ,      np.integer			):
              # the default int precision depends on the jax config
              # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
              if jax.config.jax_enable_xaa:
                   UpperCAmelCase_	   =       {"dtype": jnp.intaa}
              else:
                   UpperCAmelCase_	   =       {"dtype": jnp.intaa}
         elif isinstance(__a       ,      (np.number, np.ndarray)			) and np.issubdtype(value.dtype       ,      np.floating			):
              UpperCAmelCase_	   =       {"dtype": jnp.floataa}
         elif config.PIL_AVAILABLE and "PIL" in sys.modules:
              import PIL.Image

              if isinstance(__a       ,      PIL.Image.Image			):
                   UpperCAmelCase_	   =       np.asarray(__a			)

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
         global DEVICE_MAPPING
         if DEVICE_MAPPING is None:
              UpperCAmelCase_	   =       self._map_devices_to_str()

         with jax.default_device(DEVICE_MAPPING[self.device]			):
              # calling jnp.array on a np.ndarray does copy the data
              # see https://github.com/google/jax/issues/4486
              return jnp.array(__a       ,      **{**default_dtype, **self.jnp_array_kwargs}			)



    def  _lowercase						(self   :   int       ,      __a   :   Any			):
         import jax

         # support for torch, tf, jax etc.
         if config.TORCH_AVAILABLE and "torch" in sys.modules:
              import torch

              if isinstance(__a       ,      torch.Tensor			):
                   return self._tensorize(data_struct.detach().cpu().numpy()[()]			)
         if hasattr(__a       ,      "__array__"			) and not isinstance(__a       ,      jax.Array			):
              UpperCAmelCase_	   =       data_struct.__array__()
         # support for nested types like struct of list of struct
         if isinstance(__a       ,      np.ndarray			):
              if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
                   return self._consolidate([self.recursive_tensorize(__a			) for substruct in data_struct]			)
         elif isinstance(__a       ,      (list, tuple)			):
              return self._consolidate([self.recursive_tensorize(__a			) for substruct in data_struct]			)
         return self._tensorize(__a			)



    def  _lowercase						(self   :   Union[str, Any]       ,      __a   :   dict			):
         return map_nested(self._recursive_tensorize       ,      __a       ,      map_list=__a			)



    def  _lowercase						(self   :   str       ,      __a   :   pa.Table			):
         UpperCAmelCase_	   =       self.numpy_arrow_extractor().extract_row(__a			)
         UpperCAmelCase_	   =       self.python_features_decoder.decode_row(__a			)
         return self.recursive_tensorize(__a			)



    def  _lowercase						(self   :   Tuple       ,      __a   :   pa.Table			):
         UpperCAmelCase_	   =       self.numpy_arrow_extractor().extract_column(__a			)
         UpperCAmelCase_	   =       self.python_features_decoder.decode_column(__a       ,      pa_table.column_names[0]			)
         UpperCAmelCase_	   =       self.recursive_tensorize(__a			)
         UpperCAmelCase_	   =       self._consolidate(__a			)
         return column






    def  _lowercase						(self   :   str       ,      __a   :   pa.Table			):
         UpperCAmelCase_	   =       self.numpy_arrow_extractor().extract_batch(__a			)
         UpperCAmelCase_	   =       self.python_features_decoder.decode_batch(__a			)
         UpperCAmelCase_	   =       self.recursive_tensorize(__a			)
         for column_name in batch:
              UpperCAmelCase_	   =       self._consolidate(batch[column_name]			)
         return batch




