






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    UpperCamelCase						(	):






						"""simple docstring"""







						__magic_name__      :       Tuple       						= torch.nn.Linear(2,   4			)
						__magic_name__      :       Optional[Any]       						= torch.optim.AdamW(model.parameters(),   lr=1.0			)
						__magic_name__      :       Any       						= torch.optim.lr_scheduler.OneCycleLR(_A,   max_lr=0.01,   steps_per_epoch=2,   epochs=1			)
						__magic_name__      :       List[str]       						= DataLoader(TensorDataset(torch.tensor([1, 2, 3]			)			)			)
						__magic_name__      :       Any       						= DataLoader(TensorDataset(torch.tensor([4, 5, 6]			)			)			)

						return model, optimizer, scheduler, train_dl, valid_dl

def    UpperCamelCase						(	_A			):






						"""simple docstring"""







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

def    UpperCamelCase						(	_A			):






						"""simple docstring"""







						__magic_name__      :       Optional[Any]       						= torch.nn.Linear(*tuple(model.weight.T.shape			)			).state_dict()
						model.load_state_dict(_A			)





class 	snake_case__ (							_lowerCAmelCase				):
	@require_cuda
	def       __magic_name__    (    self			)							->				List[Any]:
							__magic_name__      :       int       						= Accelerator()
							assert PartialState._shared_state["_cpu"] is False
							assert PartialState._shared_state["device"].type == "cuda"
							with self.assertRaises(lowerCAmelCase__			):
													__magic_name__      :       List[Any]       						= Accelerator(cpu=lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Union[str, Any]:
							__magic_name__      :       Dict       						= Accelerator()
							__magic_name__      :       Tuple       						= GradientState()
							assert state.num_steps == 1
							__magic_name__      :       Any       						= 4
							assert state.num_steps == 4

							assert state.sync_gradients is True
							__magic_name__      :       int       						= False
							assert state.sync_gradients is False
							GradientState._reset_state()
	def       __magic_name__    (    self			)							->				Optional[int]:
							__magic_name__      :       Any       						= Accelerator()
							__magic_name__						,__magic_name__						,__magic_name__						,__magic_name__						,__magic_name__      :       Any       						= create_components()

							(
							    (
							    __magic_name__
							)						,(
							    __magic_name__
							)						,(
							    __magic_name__
							)						,(
							    __magic_name__
							)						,(
							    __magic_name__
							)						,
							)      :       str       						= accelerator.prepare(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)

							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       __magic_name__    (    self			)							->				int:
							__magic_name__      :       Tuple       						= Accelerator()
							__magic_name__						,__magic_name__						,__magic_name__						,__magic_name__						,__magic_name__      :       Union[str, Any]       						= create_components()
							accelerator.prepare(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)
							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       __magic_name__    (    self			)							->				Tuple:
							PartialState._reset_state()

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

							with patch("""torch.cuda.set_device"""      ,       lowerCAmelCase__			), patch_environment(ACCELERATE_TORCH_DEVICE="""cuda:64"""			):
													__magic_name__      :       Optional[Any]       						= Accelerator()
													self.assertEqual(str(accelerator.state.device			)      ,       """cuda:64"""			)
	def       __magic_name__    (    self			)							->				Tuple:
							__magic_name__      :       List[str]       						= Accelerator()
							__magic_name__						,__magic_name__						,__magic_name__						,__magic_name__						,__magic_name__      :       List[str]       						= create_components()
							accelerator.prepare(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)

							__magic_name__      :       Union[str, Any]       						= get_signature(lowerCAmelCase__			)

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

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

													# make sure loaded weights match
													accelerator.load_state(lowerCAmelCase__			)
													self.assertTrue(abs(model_signature - get_signature(lowerCAmelCase__			)			) < 1e-3			)
	def       __magic_name__    (    self			)							->				str:
							__magic_name__      :       Dict       						= Accelerator()
							__magic_name__						,__magic_name__						,__magic_name__						,__magic_name__						,__magic_name__      :       Tuple       						= create_components()
							accelerator.prepare(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)

							__magic_name__      :       List[Any]       						= get_signature(lowerCAmelCase__			)

							# saving hook
							def save_config(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			):
													__magic_name__      :       Optional[Any]       						= {"""class_name""": models[0].__class__.__name__}

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

        # loading hook
							def load_config(lowerCAmelCase__      ,       lowerCAmelCase__			):
													with open(os.path.join(lowerCAmelCase__      ,       """data.json"""			)      ,       """r"""			) as f:
																			__magic_name__      :       Optional[Any]       						= json.load(lowerCAmelCase__			)

													__magic_name__      :       Any       						= config["""class_name"""]

							__magic_name__      :       Any       						= accelerator.register_save_state_pre_hook(lowerCAmelCase__			)
							__magic_name__      :       Any       						= accelerator.register_load_state_pre_hook(lowerCAmelCase__			)

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

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

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

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

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

													# random class name to verify correct one is loaded
													__magic_name__      :       Any       						= """random"""

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

													# mode.class_name is NOT loaded from config
													self.assertTrue(model.class_name != model.__class__.__name__			)
	def       __magic_name__    (    self			)							->				int:
							__magic_name__      :       int       						= Accelerator()
							__magic_name__						,__magic_name__						,__magic_name__						,__magic_name__						,__magic_name__      :       str       						= create_components()
							__magic_name__      :       int       						= None

							# This should work
							__magic_name__						,__magic_name__						,__magic_name__						,__magic_name__						,__magic_name__						,__magic_name__      :       List[Any]       						= accelerator.prepare(
							    lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)
							self.assertTrue(dummy_obj is None			)
	def       __magic_name__    (    self			)							->				str:
							__magic_name__      :       List[Any]       						= Accelerator()
							__magic_name__						,__magic_name__						,__magic_name__						,__magic_name__						,__magic_name__      :       List[Any]       						= create_components()
							__magic_name__      :       Dict       						= [1, 2, 3]

							# This should work
							__magic_name__						,__magic_name__						,__magic_name__						,__magic_name__						,__magic_name__						,__magic_name__      :       Dict       						= accelerator.prepare(
							    lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)
							self.assertEqual(
							    getattr(lowerCAmelCase__      ,       """_is_accelerate_prepared"""      ,       lowerCAmelCase__			)      ,       lowerCAmelCase__      ,       """Dummy object should have `_is_accelerate_prepared` set to `True`"""      ,       )
							self.assertEqual(
							    getattr(lowerCAmelCase__      ,       """_is_accelerate_prepared"""      ,       lowerCAmelCase__			)      ,       lowerCAmelCase__      ,       """Model is missing `_is_accelerator_prepared` or is set to `False`"""      ,       )
							self.assertEqual(
							    getattr(lowerCAmelCase__      ,       """_is_accelerate_prepared"""      ,       lowerCAmelCase__			)      ,       lowerCAmelCase__      ,       """Optimizer is missing `_is_accelerator_prepared` or is set to `False`"""      ,       )
							self.assertEqual(
							    getattr(lowerCAmelCase__      ,       """_is_accelerate_prepared"""      ,       lowerCAmelCase__			)      ,       lowerCAmelCase__      ,       """Scheduler is missing `_is_accelerator_prepared` or is set to `False`"""      ,       )
							self.assertEqual(
							    getattr(lowerCAmelCase__      ,       """_is_accelerate_prepared"""      ,       lowerCAmelCase__			)      ,       lowerCAmelCase__      ,       """Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`"""      ,       )
							self.assertEqual(
							    getattr(lowerCAmelCase__      ,       """_is_accelerate_prepared"""      ,       lowerCAmelCase__			)      ,       lowerCAmelCase__      ,       """Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`"""      ,       )
	@slow
	@require_bnb
	def       __magic_name__    (    self			)							->				Tuple:
							from transformers import AutoModelForCausalLM

							__magic_name__      :       List[Any]       						= AutoModelForCausalLM.from_pretrained(
							    """EleutherAI/gpt-neo-125m"""      ,       load_in_abit=lowerCAmelCase__      ,       device_map={"""""": 0}      ,       )
							__magic_name__      :       Union[str, Any]       						= Accelerator()

							# This should work
							__magic_name__      :       Any       						= accelerator.prepare(lowerCAmelCase__			)
	@slow
	@require_bnb
	def       __magic_name__    (    self			)							->				List[Any]:
							from transformers import AutoModelForCausalLM

							__magic_name__      :       Union[str, Any]       						= Accelerator()

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

							__magic_name__      :       str       						= AutoModelForCausalLM.from_pretrained(
							    """EleutherAI/gpt-neo-125m"""      ,       device_map=lowerCAmelCase__      ,       load_in_abit=lowerCAmelCase__      ,       llm_inta_enable_fpaa_cpu_offload=lowerCAmelCase__			)

							# This should not work and get value error
							with self.assertRaises(lowerCAmelCase__			):
													__magic_name__      :       Union[str, Any]       						= accelerator.prepare(lowerCAmelCase__			)
	@slow
	@require_bnb
	@require_multi_gpu
	def       __magic_name__    (    self			)							->				Dict:
							from transformers import AutoModelForCausalLM

							__magic_name__      :       Optional[Any]       						= {"""distributed_type""": DistributedType.MULTI_GPU}

							with init_empty_weights():
													__magic_name__      :       Optional[int]       						= AutoModelForCausalLM.from_pretrained(
													    """EleutherAI/gpt-neo-125m"""      ,       )
													model.tie_weights()
													__magic_name__      :       List[Any]       						= infer_auto_device_map(lowerCAmelCase__			)
													__magic_name__      :       Union[str, Any]       						= 1

							__magic_name__      :       List[Any]       						= AutoModelForCausalLM.from_pretrained(
							    """EleutherAI/gpt-neo-125m"""      ,       load_in_abit=lowerCAmelCase__      ,       device_map=lowerCAmelCase__      ,       )
							__magic_name__      :       int       						= Accelerator()

							# This should not work and get value error
							with self.assertRaises(lowerCAmelCase__			):
													__magic_name__      :       int       						= accelerator.prepare(lowerCAmelCase__			)

							PartialState._reset_state()
	@slow
	@require_bnb
	@require_multi_gpu
	def       __magic_name__    (    self			)							->				Union[str, Any]:
							from transformers import AutoModelForCausalLM

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

							__magic_name__      :       Any       						= AutoModelForCausalLM.from_pretrained(
							    """EleutherAI/gpt-neo-125m"""      ,       load_in_abit=lowerCAmelCase__      ,       device_map=lowerCAmelCase__      ,       )
							__magic_name__      :       Optional[int]       						= Accelerator()

							# This should work
							__magic_name__      :       Optional[int]       						= accelerator.prepare(lowerCAmelCase__			)







	@require_cuda
	def       __magic_name__    (    self			)							->				Tuple:
							__magic_name__      :       Any       						= torch.nn.Linear(10      ,       10			)
							__magic_name__      :       List[str]       						= torch.optim.SGD(model.parameters()      ,       lr=0.0_1			)
							__magic_name__      :       Optional[int]       						= Accelerator(cpu=lowerCAmelCase__			)
							__magic_name__      :       List[str]       						= accelerator.prepare(lowerCAmelCase__			)








import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer





class 	snake_case__ (							tf.keras.layers.Layer				):
	def __init__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = None			)							->				int:
							super().__init__()
							__magic_name__      :       Any       						= pad_token_id
							__magic_name__      :       Any       						= max_length
							__magic_name__      :       List[str]       						= vocab
							__magic_name__      :       List[Any]       						= merges
							__magic_name__      :       int       						= BytePairTokenizer(lowerCAmelCase__      ,       lowerCAmelCase__      ,       sequence_length=lowerCAmelCase__			)
	@classmethod
	def       __magic_name__    (    cls      ,       lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				Any:
							__magic_name__      :       Union[str, Any]       						= [""" """.join(lowerCAmelCase__			) for m in tokenizer.bpe_ranks.keys()]
							__magic_name__      :       Union[str, Any]       						= tokenizer.get_vocab()
							return cls(lowerCAmelCase__      ,       lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)
	@classmethod
	def       __magic_name__    (    cls      ,       lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				List[str]:
							__magic_name__      :       Optional[Any]       						= GPTaTokenizer.from_pretrained(lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)
							return cls.from_tokenizer(lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)
	@classmethod
	def       __magic_name__    (    cls      ,       lowerCAmelCase__			)							->				List[Any]:
							return cls(**lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				int:
							return {
							    "vocab": self.vocab,
							    "merges": self.merges,
							    "max_length": self.max_length,
							    "pad_token_id": self.pad_token_id,
							}







	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				int:
							__magic_name__      :       Dict       						= self.tf_tokenizer(lowerCAmelCase__			)
							__magic_name__      :       Dict       						= tf.ones_like(lowerCAmelCase__			)

							if self.pad_token_id is not None:
													# pad the tokens up to max length
													__magic_name__      :       List[Any]       						= max_length if max_length is not None else self.max_length

													if max_length is not None:
																			__magic_name__						,__magic_name__      :       List[Any]       						= pad_model_inputs(
																			    lowerCAmelCase__      ,       max_seq_length=lowerCAmelCase__      ,       pad_value=self.pad_token_id			)

							return {"attention_mask": attention_mask, "input_ids": input_ids}
