




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

       return model, optimizer, scheduler, train_dl, valid_dl

def      A      (      _SCREAMING_SNAKE_CASE    )	-> Optional[int]:
       return (model.weight.abs().sum() + model.bias.abs().sum()).item()

def      A      (      _SCREAMING_SNAKE_CASE    )	-> List[Any]:
       lowerCamelCase   :      Optional[Any]			 =					torch.nn.Linear(*tuple(model.weight.T.shape    )    ).state_dict()
       model.load_state_dict(_SCREAMING_SNAKE_CASE    )




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



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







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

           assert state.sync_gradients is True
           lowerCamelCase   :      Dict			 =					False
           assert state.sync_gradients is False
           GradientState._reset_state()







    def     _lowercase     (    self     )     ->      Dict:
           lowerCamelCase   :      Tuple			 =					Accelerator()
           lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Optional[int]			 =					create_components()

           (
               (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      
           )   :      List[Any]			 =					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     _lowercase     (    self     )     ->      Union[str, Any]:
           lowerCamelCase   :      str			 =					Accelerator()
           lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      List[str]			 =					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     _lowercase     (    self     )     ->      int:
           PartialState._reset_state()

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

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







    def     _lowercase     (    self     )     ->      Dict:
           lowerCamelCase   :      Union[str, Any]			 =					Accelerator()
           lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Union[str, Any]			 =					create_components()
           accelerator.prepare(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )

           lowerCamelCase   :      List[str]			 =					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     _lowercase     (    self     )     ->      List[str]:
           lowerCamelCase   :      List[str]			 =					Accelerator()
           lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      List[str]			 =					create_components()
           accelerator.prepare(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )

           lowerCamelCase   :      Dict			 =					get_signature(UpperCamelCase__     )

           # saving hook
           def save_config(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ):
                  lowerCamelCase   :      Union[str, Any]			 =					{"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__     ,  UpperCamelCase__     ):
                  with open(os.path.join(UpperCamelCase__     ,  "data.json"     )     ,  "r"     ) as f:
                         lowerCamelCase   :      str			 =					json.load(UpperCamelCase__     )

                  lowerCamelCase   :      int			 =					config["class_name"]

           lowerCamelCase   :      Any			 =					accelerator.register_save_state_pre_hook(UpperCamelCase__     )
           lowerCamelCase   :      Dict			 =					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
                  lowerCamelCase   :      Optional[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
                  lowerCamelCase   :      List[Any]			 =					"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     _lowercase     (    self     )     ->      int:
           lowerCamelCase   :      Optional[Any]			 =					Accelerator()
           lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Dict			 =					create_components()
           lowerCamelCase   :      Optional[int]			 =					None

           # This should work
           lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Union[str, Any]			 =					accelerator.prepare(
               UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )
           self.assertTrue(dummy_obj is None     )







    def     _lowercase     (    self     )     ->      List[Any]:
           lowerCamelCase   :      List[str]			 =					Accelerator()
           lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Dict			 =					create_components()
           lowerCamelCase   :      Optional[int]			 =					[1, 2, 3]

           # This should work
           lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Any			 =					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     _lowercase     (    self     )     ->      int:
           from transformers import AutoModelForCausalLM

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

           # This should work
           lowerCamelCase   :      Tuple			 =					accelerator.prepare(UpperCamelCase__     )







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

           lowerCamelCase   :      Tuple			 =					Accelerator()

           with init_empty_weights():
                  lowerCamelCase   :      Tuple			 =					AutoModelForCausalLM.from_pretrained(
                      "EleutherAI/gpt-neo-125m"     ,  )
                  model.tie_weights()
                  lowerCamelCase   :      List[Any]			 =					infer_auto_device_map(UpperCamelCase__     )
                  lowerCamelCase   :      Optional[int]			 =					"cpu"

           lowerCamelCase   :      Tuple			 =					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__     ):
                  lowerCamelCase   :      List[str]			 =					accelerator.prepare(UpperCamelCase__     )







    @slow
    @require_bnb
    @require_multi_gpu
    def     _lowercase     (    self     )     ->      int:
           from transformers import AutoModelForCausalLM

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

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

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

           # This should not work and get value error
           with self.assertRaises(UpperCamelCase__     ):
                  lowerCamelCase   :      Union[str, Any]			 =					accelerator.prepare(UpperCamelCase__     )

           PartialState._reset_state()







    @slow
    @require_bnb
    @require_multi_gpu
    def     _lowercase     (    self     )     ->      str:
           from transformers import AutoModelForCausalLM

           with init_empty_weights():
                  lowerCamelCase   :      Union[str, Any]			 =					AutoModelForCausalLM.from_pretrained(
                      "EleutherAI/gpt-neo-125m"     ,  )
                  lowerCamelCase   :      int			 =					infer_auto_device_map(UpperCamelCase__     )
                  lowerCamelCase   :      Any			 =					1

           lowerCamelCase   :      Tuple			 =					AutoModelForCausalLM.from_pretrained(
               "EleutherAI/gpt-neo-125m"     ,  load_in_abit=UpperCamelCase__     ,  device_map=UpperCamelCase__     ,  )
           lowerCamelCase   :      str			 =					Accelerator()

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







    @require_cuda
    def     _lowercase     (    self     )     ->      Dict:
           lowerCamelCase   :      str			 =					torch.nn.Linear(10     ,  10     )
           lowerCamelCase   :      int			 =					torch.optim.SGD(model.parameters()     ,  lr=0.01     )
           lowerCamelCase   :      int			 =					Accelerator(cpu=UpperCamelCase__     )
           lowerCamelCase   :      Union[str, Any]			 =					accelerator.prepare(UpperCamelCase__     )












import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,)	-> List[str]:
       if config_name_or_path is None:
              lowerCamelCase   :      Any			 =					"facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"

       if generator_tokenizer_name_or_path is None:
              lowerCamelCase   :      Dict			 =					generator_name_or_path

       if question_encoder_tokenizer_name_or_path is None:
              lowerCamelCase   :      Any			 =					question_encoder_name_or_path

       lowerCamelCase   :      str			 =					RagTokenForGeneration if model_type == "rag_token" else RagSequenceForGeneration

       # Save model.
       lowerCamelCase   :      List[Any]			 =					RagConfig.from_pretrained(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Union[str, Any]			 =					AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Optional[int]			 =					AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE    )

       lowerCamelCase   :      Optional[Any]			 =					gen_config
       lowerCamelCase   :      Optional[Any]			 =					question_encoder_config

       lowerCamelCase   :      List[Any]			 =					model_class.from_pretrained_question_encoder_generator(
           _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,config=_SCREAMING_SNAKE_CASE    )
       rag_model.save_pretrained(_SCREAMING_SNAKE_CASE    )

       # Sanity check.
       model_class.from_pretrained(_SCREAMING_SNAKE_CASE    )

       # Save tokenizers.
       lowerCamelCase   :      List[str]			 =					AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE    )
       gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/"    )
       lowerCamelCase   :      int			 =					AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE    )
       question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/"    )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      Any  =   argparse.ArgumentParser()
       parser.add_argument(
           '--model_type',
           choices=['rag_sequence', 'rag_token'],
           required=True,
           type=str,
           help='RAG model type: rag_sequence, rag_token',
       )
       parser.add_argument('--dest', type=str, required=True, help='Path to the output checkpoint directory.')
       parser.add_argument('--generator_name_or_path', type=str, required=True, help='Generator model identifier')
       parser.add_argument(
           '--question_encoder_name_or_path', type=str, required=True, help='Question encoder model identifier'
       )

       parser.add_argument(
           '--generator_tokenizer_name_or_path',
           type=str,
           help='Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``',
       )
       parser.add_argument(
           '--question_encoder_tokenizer_name_or_path',
           type=str,
           help='Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``',
       )
       parser.add_argument(
           '--config_name_or_path',
           type=str,
           help=(
               'Identifier of the model config to use, if not provided, resolves to a base config for a given'
               ' ``model_type``'
           ),
       )

       SCREAMING_SNAKE_CASE__       :      Union[str, Any]  =   parser.parse_args()

       SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   Path(args.dest)
       dest_dir.mkdir(exist_ok=True)

       consolidate(
           args.model_type,
           args.generator_name_or_path,
           args.question_encoder_name_or_path,
           dest_dir,
           args.config_name_or_path,
           args.generator_tokenizer_name_or_path,
           args.question_encoder_tokenizer_name_or_path,
       )






