





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 							SCREAMING_SNAKE_CASE__       (  )      ->						Any:
		_lowercase							= torch.nn.Linear(2			,							4     )
		_lowercase							= torch.optim.AdamW(model.parameters()			,							lr=1.0     )
		_lowercase							= torch.optim.lr_scheduler.OneCycleLR(SCREAMING_SNAKE_CASE_			,							max_lr=0.01			,							steps_per_epoch=2			,							epochs=1     )
		_lowercase							= DataLoader(TensorDataset(torch.tensor([1, 2, 3]     )     )     )
		_lowercase							= DataLoader(TensorDataset(torch.tensor([4, 5, 6]     )     )     )

		return model, optimizer, scheduler, train_dl, valid_dl

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :List[str]     )      ->						Tuple:
		return (model.weight.abs().sum() + model.bias.abs().sum()).item()

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Any     )      ->						List[str]:
		_lowercase							= torch.nn.Linear(*tuple(model.weight.T.shape     )     ).state_dict()
		model.load_state_dict(SCREAMING_SNAKE_CASE_     )







class 				A_				(      _UpperCAmelCase	):







							"""simple docstring"""





							@require_cuda
							def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					str:
									_lowercase							= Accelerator()
									assert PartialState._shared_state["_cpu"] is False
									assert PartialState._shared_state["device"].type == "cuda"
									with self.assertRaises(lowercase__  ):
											_lowercase							= Accelerator(cpu=lowercase__  )







							def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					List[str]:
									_lowercase							= Accelerator()
									_lowercase							= GradientState()
									assert state.num_steps == 1
									_lowercase							= 4
									assert state.num_steps == 4

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







							def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					Union[str, Any]:
									_lowercase							= Accelerator()
									_lowercase							= create_components()

									(
									    _lowercase		
									)							= 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	:	Tuple  )						->					Optional[Any]:
									_lowercase							= Accelerator()
									_lowercase							= 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	:	List[Any]  )						->					List[Any]:
									PartialState._reset_state()

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

									with patch('torch.cuda.set_device'						,lowercase__  ), patch_environment(ACCELERATE_TORCH_DEVICE='cuda:64'  ):
											_lowercase							= Accelerator()
											self.assertEqual(str(accelerator.state.device  )						,'cuda:64'  )







							def    __UpperCAmelCase (    self	:	int  )						->					int:
									_lowercase							= Accelerator()
									_lowercase							= create_components()
									accelerator.prepare(lowercase__						,lowercase__						,lowercase__						,lowercase__						,lowercase__  )

									_lowercase							= 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	:	Union[str, Any]  )						->					Optional[int]:
									_lowercase							= Accelerator()
									_lowercase							= create_components()
									accelerator.prepare(lowercase__						,lowercase__						,lowercase__						,lowercase__						,lowercase__  )

									_lowercase							= get_signature(lowercase__  )

									# saving hook
									def save_config(__A	:	Optional[int]						,__A	:	Tuple						,__A	:	List[Any]  ):
											_lowercase							= {"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(__A	:	Any						,__A	:	str  ):
											with open(os.path.join(lowercase__						,'data.json'  )						,'r'  ) as f:
													_lowercase							= json.load(lowercase__  )

											_lowercase							= config["class_name"]

									_lowercase							= accelerator.register_save_state_pre_hook(lowercase__  )
									_lowercase							= 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
											_lowercase							= "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
											_lowercase							= "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	:	List[Any]  )						->					Optional[int]:
									_lowercase							= Accelerator()
									_lowercase							= create_components()
									_lowercase							= None

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







							def    __UpperCAmelCase (    self	:	Tuple  )						->					List[Any]:
									_lowercase							= Accelerator()
									_lowercase							= create_components()
									_lowercase							= [1, 2, 3]

									# This should work
									_lowercase							= 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	:	Dict  )						->					Tuple:
									from transformers import AutoModelForCausalLM

									_lowercase							= AutoModelForCausalLM.from_pretrained(
									    'EleutherAI/gpt-neo-125m'						,load_in_abit=lowercase__						,device_map={'': 0}						,)
									_lowercase							= Accelerator()

									# This should work
									_lowercase							= accelerator.prepare(lowercase__  )







							@slow
							@require_bnb
							def    __UpperCAmelCase (    self	:	Tuple  )						->					str:
									from transformers import AutoModelForCausalLM

									_lowercase							= Accelerator()

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

									_lowercase							= 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__  ):
											_lowercase							= accelerator.prepare(lowercase__  )







							@slow
							@require_bnb
							@require_multi_gpu
							def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					Tuple:
									from transformers import AutoModelForCausalLM

									_lowercase							= {"distributed_type": DistributedType.MULTI_GPU}

									with init_empty_weights():
											_lowercase							= AutoModelForCausalLM.from_pretrained(
											    'EleutherAI/gpt-neo-125m'						,)
											model.tie_weights()
											_lowercase							= infer_auto_device_map(lowercase__  )
											_lowercase							= 1

									_lowercase							= AutoModelForCausalLM.from_pretrained(
									    'EleutherAI/gpt-neo-125m'						,load_in_abit=lowercase__						,device_map=lowercase__						,)
									_lowercase							= Accelerator()

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

									PartialState._reset_state()







							@slow
							@require_bnb
							@require_multi_gpu
							def    __UpperCAmelCase (    self	:	Optional[int]  )						->					Optional[Any]:
									from transformers import AutoModelForCausalLM

									with init_empty_weights():
											_lowercase							= AutoModelForCausalLM.from_pretrained(
											    'EleutherAI/gpt-neo-125m'						,)
											_lowercase							= infer_auto_device_map(lowercase__  )
											_lowercase							= 1

									_lowercase							= AutoModelForCausalLM.from_pretrained(
									    'EleutherAI/gpt-neo-125m'						,load_in_abit=lowercase__						,device_map=lowercase__						,)
									_lowercase							= Accelerator()

									# This should work
									_lowercase							= accelerator.prepare(lowercase__  )







							@require_cuda
							def    __UpperCAmelCase (    self	:	List[str]  )						->					List[str]:
									_lowercase							= torch.nn.Linear(10						,10  )
									_lowercase							= torch.optim.SGD(model.parameters()						,lr=0.01  )
									_lowercase							= Accelerator(cpu=lowercase__  )
									_lowercase							= accelerator.prepare(lowercase__  )






import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
      import torch


if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation


snake_case 				=       logging.get_logger(__name__)
snake_case 				=       {"""vocab_file""": """spiece.model"""}

snake_case 				=       {
    """vocab_file""": {
        """AI-Sweden/gpt-sw3-126m""": """https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-350m""": """https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-1.6b""": """https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-6.7b""": """https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-20b""": """https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model""",
    }
}

snake_case 				=       {
    """AI-Sweden/gpt-sw3-126m""": 2_0_4_8,
    """AI-Sweden/gpt-sw3-350m""": 2_0_4_8,
    """AI-Sweden/gpt-sw3-1.6b""": 2_0_4_8,
    """AI-Sweden/gpt-sw3-6.7b""": 2_0_4_8,
    """AI-Sweden/gpt-sw3-20b""": 2_0_4_8,
}





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : Any      =  VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE_      : str      =  PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE_      : List[Any]      =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE_      : Optional[Any]      =  ['''input_ids''', '''attention_mask''']







       def __init__(    self	:	Any						,__A	:	Tuple						,__A	:	Any=False						,__A	:	int=False						,__A	:	List[str]=False						,__A	:	List[str]=None						,__A	:	Dict=None						,__A	:	Dict=None						,__A	:	Union[str, Any]=None						,__A	:	Optional[Dict[str, Any]] = None						,**__A	:	Tuple						,)						->					None:
         _lowercase							= {} if sp_model_kwargs is None else sp_model_kwargs

         _lowercase							= kwargs.get('name_or_path'  )
         if name_or_path is None:
           logger.warning(
               'name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'
               ' you are testing the model, this can safely be ignored'  )
           _lowercase							= 'None'

         # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
         _lowercase							= '<|endoftext|>' if eos_token is None else eos_token
         _lowercase							= '<unk>' if unk_token is None else unk_token
         if "gpt-sw3-7b" in name_or_path:
           _lowercase							= unk_token if pad_token is None else pad_token
           _lowercase							= eos_token if bos_token is None else bos_token
         else:
           _lowercase							= '<pad>' if pad_token is None else pad_token
           _lowercase							= '<s>' if bos_token is None else bos_token

         super().__init__(
             do_lower_case=__A						,remove_space=__A						,keep_accents=__A						,bos_token=__A						,eos_token=__A						,unk_token=__A						,pad_token=__A						,sp_model_kwargs=self.sp_model_kwargs						,**__A						,)

         _lowercase							= do_lower_case
         _lowercase							= remove_space
         _lowercase							= keep_accents
         _lowercase							= vocab_file

         _lowercase							= spm.SentencePieceProcessor(**self.sp_model_kwargs  )
         self.sp_model.Load(__A  )

         # Used for whitespace normalization in input texts
         # fmt : off
         _lowercase							= {' ', ' ', ' ', ' ', ' ', '　', ' ', ' ', ' ', ' ', '￼', ''}
         # fmt : on

         # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
         _lowercase							= re.compile(
             F"""[{"".join(map(__A						,list(range(0						,9  )  ) + list(range(11						,32  )  ) + list(range(127						,160  )  ) + [160, 173, 8203]  )  )}]"""  )







       def __getstate__(    self	:	List[Any]  )						->					List[str]:
         _lowercase							= self.__dict__.copy()
         _lowercase							= None
         return state







       def __setstate__(    self	:	Optional[Any]						,__A	:	Dict  )						->					str:
         _lowercase							= d

         # for backward compatibility
         if not hasattr(self						,'sp_model_kwargs'  ):
           _lowercase							= {}

         _lowercase							= spm.SentencePieceProcessor(**self.sp_model_kwargs  )
         self.sp_model.Load(self.vocab_file  )







       @property
       # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
       def    __UpperCAmelCase (    self	:	List[Any]  )						->					int:
         return len(self.sp_model  )







       def    __UpperCAmelCase (    self	:	Optional[Any]						,__A	:	str  )						->					str:
         _lowercase							= self.non_printing_characters_re.sub(''						,__A  )

         # Normalize whitespaces
         _lowercase							= ''.join([char if char not in self.whitespaces else ' ' for char in text]  )

         # NFC Unicode normalization
         _lowercase							= unicodedata.normalize('NFC'						,__A  )
         return text







       def    __UpperCAmelCase (    self	:	Optional[Any]						,__A	:	str						,**__A	:	Optional[int]  )						->					List[str]:
         _lowercase							= self.preprocess_text(__A  )
         return self.sp_model.encode(__A						,out_type=__A  )







       def    __UpperCAmelCase (    self	:	List[Any]						,__A	:	str  )						->					int:
         return self.sp_model.PieceToId(__A  )







       def    __UpperCAmelCase (    self	:	Any						,__A	:	int  )						->					str:
         return self.sp_model.IdToPiece(__A  )







       @staticmethod
       def    __UpperCAmelCase (    __A	:	str  )						->					str:
         return out_string







       def    __UpperCAmelCase (    self	:	Tuple						,__A	:	List[str]  )						->					str:
         _lowercase							= []
         _lowercase							= ''
         _lowercase							= False
         for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
             # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
             if not prev_is_special:
               out_string += " "

             out_string += self.sp_model.decode(__A  ) + token
             _lowercase							= True
             _lowercase							= []
           else:
             current_sub_tokens.append(__A  )
             _lowercase							= False
         out_string += self.sp_model.decode(__A  )

         return out_string







       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					Dict[str, int]:
         _lowercase							= {self.convert_ids_to_tokens(__A  ): i for i in range(self.vocab_size  )}
         vocab.update(self.added_tokens_encoder  )
         return vocab







       def    __UpperCAmelCase (    self	:	Optional[int]						,__A	:	str						,__A	:	Optional[str] = None  )						->					Tuple[str]:
         if not os.path.isdir(__A  ):
           logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""  )
           return
         _lowercase							= os.path.join(
             __A						,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )

         if os.path.abspath(self.vocab_file  ) != os.path.abspath(__A  ) and os.path.isfile(self.vocab_file  ):
           copyfile(self.vocab_file						,__A  )
         elif not os.path.isfile(self.vocab_file  ):
           with open(__A						,'wb'  ) as fi:
             _lowercase							= self.sp_model.serialized_model_proto()
             fi.write(__A  )

         return (out_vocab_file,)







       def    __UpperCAmelCase (    self	:	str						,__A	:	Union[str, List[str]]						,__A	:	Union[str, bool] = False  )						->					Union[List[int], List[List[int]], "torch.Tensor"]:

         if isinstance(__A						,__A  ):
           _lowercase							= self.preprocess_text(__A  )
           _lowercase							= self.sp_model.encode(__A  )
         else:
           _lowercase							= [self.preprocess_text(__A  ) for t in text]
           _lowercase							= self.sp_model.encode(__A  )

         if return_tensors is True or return_tensors == "pt":
           _lowercase							= torch.tensor(__A  )

         return token_ids







       def    __UpperCAmelCase (    self	:	Optional[Any]						,__A	:	Union[int, List[int]]  )						->					str:
         return self.sp_model.decode(__A  )







       def    __UpperCAmelCase (    self	:	str						,__A	:	"Conversation"  )						->					List[int]:
         _lowercase							= [F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
         _lowercase							= (
             F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(__A  ) + F"""{self.bos_token}Bot:"""
         )
         return self.encode(text=__A  )