


"""simple docstring"""







import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 						_A  (  _a   ,unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase	:							List[Any]     =	RobertaTokenizer
    UpperCAmelCase	:							str     =	RobertaTokenizerFast
    UpperCAmelCase	:							Tuple     =	True
    UpperCAmelCase	:							List[str]     =	{"""cls_token""": """<s>"""}






    def      __snake_case							( self    :   Optional[int]):
      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      a						:		Tuple											=      [
          "l",
          "o",
          "w",
          "e",
          "r",
          "s",
          "t",
          "i",
          "d",
          "n",
          "\u0120",
          "\u0120l",
          "\u0120n",
          "\u0120lo",
          "\u0120low",
          "er",
          "\u0120lowest",
          "\u0120newer",
          "\u0120wider",
          "<unk>",
      ]
      a						:		List[str]											=      dict(zip(__UpperCAmelCase    ,			range(len(__UpperCAmelCase))))
      a						:		str											=      ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
      a						:		List[str]											=      {"unk_token": "<unk>"}

      a						:		Optional[int]											=      os.path.join(self.tmpdirname    ,			VOCAB_FILES_NAMES["vocab_file"])
      a						:		int											=      os.path.join(self.tmpdirname    ,			VOCAB_FILES_NAMES["merges_file"])
      with open(self.vocab_file    ,			"w"    ,			encoding="utf-8") as fp:
        fp.write(json.dumps(__UpperCAmelCase) + "\n")
      with open(self.merges_file    ,			"w"    ,			encoding="utf-8") as fp:
        fp.write("\n".join(__UpperCAmelCase))






    def      __snake_case							( self    :   int    ,			**__UpperCAmelCase    :   List[Any]):
      kwargs.update(self.special_tokens_map)
      return self.tokenizer_class.from_pretrained(self.tmpdirname    ,			**__UpperCAmelCase)






    def      __snake_case							( self    :   Dict    ,			**__UpperCAmelCase    :   Tuple):
      kwargs.update(self.special_tokens_map)
      return RobertaTokenizerFast.from_pretrained(self.tmpdirname    ,			**__UpperCAmelCase)






    def      __snake_case							( self    :   List[str]    ,			__UpperCAmelCase    :   Tuple):
      a						:		Any											=      "lower newer"
      a						:		Dict											=      "lower newer"
      return input_text, output_text






    def      __snake_case							( self    :   Optional[int]):
      a						:		Optional[int]											=      self.tokenizer_class(self.vocab_file    ,			self.merges_file    ,			**self.special_tokens_map)
      a						:		Union[str, Any]											=      "lower newer"
      a						:		Optional[Any]											=      ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
      a						:		int											=      tokenizer.tokenize(__UpperCAmelCase)  # , add_prefix_space=True)
      self.assertListEqual(__UpperCAmelCase    ,			__UpperCAmelCase)

      a						:		Dict											=      tokens + [tokenizer.unk_token]
      a						:		Tuple											=      [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase)    ,			__UpperCAmelCase)






    def      __snake_case							( self    :   Union[str, Any]):
      a						:		int											=      self.get_tokenizer()

      self.assertListEqual(tokenizer.encode("Hello world!"    ,			add_special_tokens=__UpperCAmelCase)    ,			[0, 31414, 232, 328, 2])
      self.assertListEqual(
          tokenizer.encode("Hello world! cécé herlolip 418"    ,			add_special_tokens=__UpperCAmelCase)    ,			[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]    ,			)






    @slow
    def      __snake_case							( self    :   Union[str, Any]):
      a						:		Union[str, Any]											=      self.tokenizer_class.from_pretrained("roberta-base")

      a						:		List[str]											=      tokenizer.encode("sequence builders"    ,			add_special_tokens=__UpperCAmelCase)
      a						:		Optional[Any]											=      tokenizer.encode("multi-sequence build"    ,			add_special_tokens=__UpperCAmelCase)

      a						:		List[str]											=      tokenizer.encode(
          "sequence builders"    ,			add_special_tokens=__UpperCAmelCase    ,			add_prefix_space=__UpperCAmelCase)
      a						:		List[Any]											=      tokenizer.encode(
          "sequence builders"    ,			"multi-sequence build"    ,			add_special_tokens=__UpperCAmelCase    ,			add_prefix_space=__UpperCAmelCase)

      a						:		int											=      tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase)
      a						:		Dict											=      tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase    ,			__UpperCAmelCase)

      assert encoded_sentence == encoded_text_from_decode
      assert encoded_pair == encoded_pair_from_decode






    def      __snake_case							( self    :   Optional[int]):
      a						:		Optional[int]											=      self.get_tokenizer()

      a						:		Dict											=      "Encode this sequence."
      a						:		Dict											=      tokenizer.byte_encoder[" ".encode("utf-8")[0]]

      # Testing encoder arguments
      a						:		Union[str, Any]											=      tokenizer.encode(__UpperCAmelCase    ,			add_special_tokens=__UpperCAmelCase    ,			add_prefix_space=__UpperCAmelCase)
      a						:		Optional[int]											=      tokenizer.convert_ids_to_tokens(encoded[0])[0]
      self.assertNotEqual(__UpperCAmelCase    ,			__UpperCAmelCase)

      a						:		Dict											=      tokenizer.encode(__UpperCAmelCase    ,			add_special_tokens=__UpperCAmelCase    ,			add_prefix_space=__UpperCAmelCase)
      a						:		int											=      tokenizer.convert_ids_to_tokens(encoded[0])[0]
      self.assertEqual(__UpperCAmelCase    ,			__UpperCAmelCase)

      tokenizer.add_special_tokens({"bos_token": "<s>"})
      a						:		Optional[Any]											=      tokenizer.encode(__UpperCAmelCase    ,			add_special_tokens=__UpperCAmelCase)
      a						:		Tuple											=      tokenizer.convert_ids_to_tokens(encoded[1])[0]
      self.assertNotEqual(__UpperCAmelCase    ,			__UpperCAmelCase)

      # Testing spaces after special tokens
      a						:		Union[str, Any]											=      "<mask>"
      tokenizer.add_special_tokens(
          {"mask_token": AddedToken(__UpperCAmelCase    ,			lstrip=__UpperCAmelCase    ,			rstrip=__UpperCAmelCase)})  # mask token has a left space
      a						:		List[str]											=      tokenizer.convert_tokens_to_ids(__UpperCAmelCase)

      a						:		Optional[Any]											=      "Encode <mask> sequence"
      a						:		List[str]											=      "Encode <mask>sequence"

      a						:		str											=      tokenizer.encode(__UpperCAmelCase)
      a						:		List[str]											=      encoded.index(__UpperCAmelCase)
      a						:		Optional[Any]											=      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0]
      self.assertEqual(__UpperCAmelCase    ,			__UpperCAmelCase)

      a						:		int											=      tokenizer.encode(__UpperCAmelCase)
      a						:		Dict											=      encoded.index(__UpperCAmelCase)
      a						:		Any											=      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0]
      self.assertNotEqual(__UpperCAmelCase    ,			__UpperCAmelCase)






    def      __snake_case							( self    :   Dict):
      pass






    def      __snake_case							( self    :   Dict):
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''):
          a						:		Tuple											=      self.rust_tokenizer_class.from_pretrained(__UpperCAmelCase    ,			**__UpperCAmelCase)
          a						:		int											=      self.tokenizer_class.from_pretrained(__UpperCAmelCase    ,			**__UpperCAmelCase)
          a						:		str											=      "A, <mask> AllenNLP sentence."
          a						:		List[Any]											=      tokenizer_r.encode_plus(__UpperCAmelCase    ,			add_special_tokens=__UpperCAmelCase    ,			return_token_type_ids=__UpperCAmelCase)
          a						:		Optional[Any]											=      tokenizer_p.encode_plus(__UpperCAmelCase    ,			add_special_tokens=__UpperCAmelCase    ,			return_token_type_ids=__UpperCAmelCase)

          # token_type_ids should put 0 everywhere
          self.assertEqual(sum(tokens_r["token_type_ids"])    ,			sum(tokens_p["token_type_ids"]))

          # attention_mask should put 1 everywhere, so sum over length should be 1
          self.assertEqual(
              sum(tokens_r["attention_mask"]) / len(tokens_r["attention_mask"])    ,			sum(tokens_p["attention_mask"]) / len(tokens_p["attention_mask"])    ,			)

          a						:		Dict											=      tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
          a						:		List[Any]											=      tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"])

          # Rust correctly handles the space before the mask while python doesnt
          self.assertSequenceEqual(tokens_p["input_ids"]    ,			[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2])
          self.assertSequenceEqual(tokens_r["input_ids"]    ,			[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2])

          self.assertSequenceEqual(
              __UpperCAmelCase    ,			["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])
          self.assertSequenceEqual(
              __UpperCAmelCase    ,			["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])






    def      __snake_case							( self    :   int):
      for trim_offsets, add_prefix_space in itertools.product([True, False]    ,			repeat=2):
        a						:		Dict											=      self.rust_tokenizer_class.from_pretrained(
            self.tmpdirname    ,			use_fast=__UpperCAmelCase    ,			add_prefix_space=__UpperCAmelCase    ,			trim_offsets=__UpperCAmelCase)

        a						:		str											=      json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__())
        a						:		Optional[int]											=      json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__())

        self.assertEqual(pre_tokenizer_state["add_prefix_space"]    ,			__UpperCAmelCase)

        self.assertEqual(post_processor_state["add_prefix_space"]    ,			__UpperCAmelCase)
        self.assertEqual(post_processor_state["trim_offsets"]    ,			__UpperCAmelCase)






    def      __snake_case							( self    :   Optional[int]):
      # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
      # `trim_offsets`
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''):
          a						:		Optional[int]											=      "hello"  # `hello` is a token in the vocabulary of `pretrained_name`
          a						:		str											=      f'''{text_of_1_token} {text_of_1_token}'''

          a						:		int											=      self.rust_tokenizer_class.from_pretrained(
              __UpperCAmelCase    ,			use_fast=__UpperCAmelCase    ,			add_prefix_space=__UpperCAmelCase    ,			trim_offsets=__UpperCAmelCase)
          a						:		Dict											=      tokenizer_r(__UpperCAmelCase    ,			return_offsets_mapping=__UpperCAmelCase    ,			add_special_tokens=__UpperCAmelCase)
          self.assertEqual(encoding.offset_mapping[0]    ,			(0, len(__UpperCAmelCase)))
          self.assertEqual(
              encoding.offset_mapping[1]    ,			(len(__UpperCAmelCase) + 1, len(__UpperCAmelCase) + 1 + len(__UpperCAmelCase))    ,			)

          a						:		Optional[int]											=      self.rust_tokenizer_class.from_pretrained(
              __UpperCAmelCase    ,			use_fast=__UpperCAmelCase    ,			add_prefix_space=__UpperCAmelCase    ,			trim_offsets=__UpperCAmelCase)
          a						:		Union[str, Any]											=      tokenizer_r(__UpperCAmelCase    ,			return_offsets_mapping=__UpperCAmelCase    ,			add_special_tokens=__UpperCAmelCase)
          self.assertEqual(encoding.offset_mapping[0]    ,			(0, len(__UpperCAmelCase)))
          self.assertEqual(
              encoding.offset_mapping[1]    ,			(len(__UpperCAmelCase) + 1, len(__UpperCAmelCase) + 1 + len(__UpperCAmelCase))    ,			)

          a						:		List[Any]											=      self.rust_tokenizer_class.from_pretrained(
              __UpperCAmelCase    ,			use_fast=__UpperCAmelCase    ,			add_prefix_space=__UpperCAmelCase    ,			trim_offsets=__UpperCAmelCase)
          a						:		List[str]											=      tokenizer_r(__UpperCAmelCase    ,			return_offsets_mapping=__UpperCAmelCase    ,			add_special_tokens=__UpperCAmelCase)
          self.assertEqual(encoding.offset_mapping[0]    ,			(0, len(__UpperCAmelCase)))
          self.assertEqual(
              encoding.offset_mapping[1]    ,			(len(__UpperCAmelCase), len(__UpperCAmelCase) + 1 + len(__UpperCAmelCase))    ,			)

          a						:		Union[str, Any]											=      self.rust_tokenizer_class.from_pretrained(
              __UpperCAmelCase    ,			use_fast=__UpperCAmelCase    ,			add_prefix_space=__UpperCAmelCase    ,			trim_offsets=__UpperCAmelCase)
          a						:		Union[str, Any]											=      tokenizer_r(__UpperCAmelCase    ,			return_offsets_mapping=__UpperCAmelCase    ,			add_special_tokens=__UpperCAmelCase)
          self.assertEqual(encoding.offset_mapping[0]    ,			(0, len(__UpperCAmelCase)))
          self.assertEqual(
              encoding.offset_mapping[1]    ,			(len(__UpperCAmelCase), len(__UpperCAmelCase) + 1 + len(__UpperCAmelCase))    ,			)

          a						:		Tuple											=      f''' {text}'''

          # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
          #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
          # )
          # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
          # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
          # self.assertEqual(
          #     encoding.offset_mapping[1],
          #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
          # )

          a						:		Dict											=      self.rust_tokenizer_class.from_pretrained(
              __UpperCAmelCase    ,			use_fast=__UpperCAmelCase    ,			add_prefix_space=__UpperCAmelCase    ,			trim_offsets=__UpperCAmelCase)
          a						:		str											=      tokenizer_r(__UpperCAmelCase    ,			return_offsets_mapping=__UpperCAmelCase    ,			add_special_tokens=__UpperCAmelCase)
          self.assertEqual(encoding.offset_mapping[0]    ,			(1, 1 + len(__UpperCAmelCase)))
          self.assertEqual(
              encoding.offset_mapping[1]    ,			(1 + len(__UpperCAmelCase) + 1, 1 + len(__UpperCAmelCase) + 1 + len(__UpperCAmelCase))    ,			)

          a						:		int											=      self.rust_tokenizer_class.from_pretrained(
              __UpperCAmelCase    ,			use_fast=__UpperCAmelCase    ,			add_prefix_space=__UpperCAmelCase    ,			trim_offsets=__UpperCAmelCase)
          a						:		List[str]											=      tokenizer_r(__UpperCAmelCase    ,			return_offsets_mapping=__UpperCAmelCase    ,			add_special_tokens=__UpperCAmelCase)
          self.assertEqual(encoding.offset_mapping[0]    ,			(0, 1 + len(__UpperCAmelCase)))
          self.assertEqual(
              encoding.offset_mapping[1]    ,			(1 + len(__UpperCAmelCase), 1 + len(__UpperCAmelCase) + 1 + len(__UpperCAmelCase))    ,			)

          a						:		Any											=      self.rust_tokenizer_class.from_pretrained(
              __UpperCAmelCase    ,			use_fast=__UpperCAmelCase    ,			add_prefix_space=__UpperCAmelCase    ,			trim_offsets=__UpperCAmelCase)
          a						:		List[Any]											=      tokenizer_r(__UpperCAmelCase    ,			return_offsets_mapping=__UpperCAmelCase    ,			add_special_tokens=__UpperCAmelCase)
          self.assertEqual(encoding.offset_mapping[0]    ,			(0, 1 + len(__UpperCAmelCase)))
          self.assertEqual(
              encoding.offset_mapping[1]    ,			(1 + len(__UpperCAmelCase), 1 + len(__UpperCAmelCase) + 1 + len(__UpperCAmelCase))    ,			)










"""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 					lowercase      (    )->				Union[str, Any]:
  '''simple docstring'''







  a						:		Union[str, Any]											=      torch.nn.Linear(2  ,       4	)
  a						:		Tuple											=      torch.optim.AdamW(model.parameters()  ,       lr=1.0	)
  a						:		Union[str, Any]											=      torch.optim.lr_scheduler.OneCycleLR(A_  ,       max_lr=0.0_1  ,       steps_per_epoch=2  ,       epochs=1	)
  a						:		List[str]											=      DataLoader(TensorDataset(torch.tensor([1, 2, 3]	)	)	)
  a						:		int											=      DataLoader(TensorDataset(torch.tensor([4, 5, 6]	)	)	)

  return model, optimizer, scheduler, train_dl, valid_dl


def 					lowercase      (    A_	)->				List[Any]:
  '''simple docstring'''







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


def 					lowercase      (    A_	)->				Tuple:
  '''simple docstring'''







  a						:		Optional[int]											=      torch.nn.Linear(*tuple(model.weight.T.shape	)	).state_dict()
  model.load_state_dict(A_	)







class 						_A  (  _a  ):







    """simple docstring"""




    @require_cuda
    def      __snake_case							( self    :   Any):
      a						:		List[str]											=      Accelerator()
      assert PartialState._shared_state["_cpu"] is False
      assert PartialState._shared_state["device"].type == "cuda"
      with self.assertRaises(__UpperCAmelCase):
        a						:		Any											=      Accelerator(cpu=__UpperCAmelCase)






    def      __snake_case							( self    :   List[Any]):
      a						:		str											=      Accelerator()
      a						:		Optional[Any]											=      GradientState()
      assert state.num_steps == 1
      a						:		Dict											=      4
      assert state.num_steps == 4

      assert state.sync_gradients is True
      a						:		Optional[int]											=      False
      assert state.sync_gradients is False
      GradientState._reset_state()






    def      __snake_case							( self    :   str):
      a						:		int											=      Accelerator()
      a							,		a							,		a							,		a							,		a						:		Tuple											=      create_components()

      (
          (
          a
      )							,		(
          a
      )							,		(
          a
      )							,		(
          a
      )							,		(
          a
      )							,		
      )						:		Tuple											=      accelerator.prepare(__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)

      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      __snake_case							( self    :   Dict):
      a						:		Dict											=      Accelerator()
      a							,		a							,		a							,		a							,		a						:		Any											=      create_components()
      accelerator.prepare(__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)
      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      __snake_case							( self    :   int):
      PartialState._reset_state()

      # Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
      def noop(*__UpperCAmelCase    :   Union[str, Any]    ,			**__UpperCAmelCase    :   Any):
        pass

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






    def      __snake_case							( self    :   List[str]):
      a						:		Tuple											=      Accelerator()
      a							,		a							,		a							,		a							,		a						:		Optional[Any]											=      create_components()
      accelerator.prepare(__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)

      a						:		Dict											=      get_signature(__UpperCAmelCase)

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

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

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






    def      __snake_case							( self    :   Optional[int]):
      a						:		str											=      Accelerator()
      a							,		a							,		a							,		a							,		a						:		Dict											=      create_components()
      accelerator.prepare(__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)

      a						:		Union[str, Any]											=      get_signature(__UpperCAmelCase)

      # saving hook
      def save_config(__UpperCAmelCase    :   Tuple    ,			__UpperCAmelCase    :   int    ,			__UpperCAmelCase    :   Union[str, Any]):
        a						:		Tuple											=      {"class_name": models[0].__class__.__name__}

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

        # loading hook
      def load_config(__UpperCAmelCase    :   Dict    ,			__UpperCAmelCase    :   Union[str, Any]):
        with open(os.path.join(__UpperCAmelCase    ,			"data.json")    ,			"r") as f:
          a						:		Optional[Any]											=      json.load(__UpperCAmelCase)

        a						:		Tuple											=      config["class_name"]

      a						:		Optional[int]											=      accelerator.register_save_state_pre_hook(__UpperCAmelCase)
      a						:		Tuple											=      accelerator.register_load_state_pre_hook(__UpperCAmelCase)

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

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

        # random class name to verify correct one is loaded
        a						:		int											=      "random"

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

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

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

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

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






    def      __snake_case							( self    :   Optional[Any]):
      a						:		List[str]											=      Accelerator()
      a							,		a							,		a							,		a							,		a						:		int											=      create_components()
      a						:		Tuple											=      None

      # This should work
      a							,		a							,		a							,		a							,		a							,		a						:		Any											=      accelerator.prepare(
          __UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)
      self.assertTrue(dummy_obj is None)






    def      __snake_case							( self    :   List[str]):
      a						:		str											=      Accelerator()
      a							,		a							,		a							,		a							,		a						:		List[Any]											=      create_components()
      a						:		Union[str, Any]											=      [1, 2, 3]

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






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

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

      # This should work
      a						:		List[Any]											=      accelerator.prepare(__UpperCAmelCase)






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

      a						:		Dict											=      Accelerator()

      with init_empty_weights():
        a						:		Any											=      AutoModelForCausalLM.from_pretrained(
            "EleutherAI/gpt-neo-125m"    ,			)
        model.tie_weights()
        a						:		Union[str, Any]											=      infer_auto_device_map(__UpperCAmelCase)
        a						:		str											=      "cpu"

      a						:		int											=      AutoModelForCausalLM.from_pretrained(
          "EleutherAI/gpt-neo-125m"    ,			device_map=__UpperCAmelCase    ,			load_in_abit=__UpperCAmelCase    ,			llm_inta_enable_fpaa_cpu_offload=__UpperCAmelCase)

      # This should not work and get value error
      with self.assertRaises(__UpperCAmelCase):
        a						:		Optional[int]											=      accelerator.prepare(__UpperCAmelCase)






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

      a						:		Union[str, Any]											=      {"distributed_type": DistributedType.MULTI_GPU}

      with init_empty_weights():
        a						:		List[str]											=      AutoModelForCausalLM.from_pretrained(
            "EleutherAI/gpt-neo-125m"    ,			)
        model.tie_weights()
        a						:		Any											=      infer_auto_device_map(__UpperCAmelCase)
        a						:		Dict											=      1

      a						:		Any											=      AutoModelForCausalLM.from_pretrained(
          "EleutherAI/gpt-neo-125m"    ,			load_in_abit=__UpperCAmelCase    ,			device_map=__UpperCAmelCase    ,			)
      a						:		int											=      Accelerator()

      # This should not work and get value error
      with self.assertRaises(__UpperCAmelCase):
        a						:		Optional[int]											=      accelerator.prepare(__UpperCAmelCase)

      PartialState._reset_state()






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

      with init_empty_weights():
        a						:		List[str]											=      AutoModelForCausalLM.from_pretrained(
            "EleutherAI/gpt-neo-125m"    ,			)
        a						:		Tuple											=      infer_auto_device_map(__UpperCAmelCase)
        a						:		str											=      1

      a						:		Optional[int]											=      AutoModelForCausalLM.from_pretrained(
          "EleutherAI/gpt-neo-125m"    ,			load_in_abit=__UpperCAmelCase    ,			device_map=__UpperCAmelCase    ,			)
      a						:		str											=      Accelerator()

      # This should work
      a						:		Any											=      accelerator.prepare(__UpperCAmelCase)






    @require_cuda
    def      __snake_case							( self    :   List[Any]):
      a						:		Tuple											=      torch.nn.Linear(10    ,			10)
      a						:		int											=      torch.optim.SGD(model.parameters()    ,			lr=0.01)
      a						:		Optional[Any]											=      Accelerator(cpu=__UpperCAmelCase)
      a						:		List[str]											=      accelerator.prepare(__UpperCAmelCase)






