


import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


snake_case				:    Tuple       				=     logging.get_logger(__name__)







class 		_snake_case  :




 def __init__(		self					,  _lowerCamelCase					,  _lowerCamelCase    ):
       a						:List[Any] 							=       question_encoder
       a						:Any 							=       generator
       a						:List[str] 							=       self.question_encoder




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       if os.path.isfile(_lowerCamelCase    ):
             raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file'''    )
       os.makedirs(_lowerCamelCase					,  exist_ok=_lowerCamelCase    )
       a						:Tuple 							=       os.path.join(_lowerCamelCase					,  '''question_encoder_tokenizer'''    )
       a						:List[str] 							=       os.path.join(_lowerCamelCase					,  '''generator_tokenizer'''    )
       self.question_encoder.save_pretrained(_lowerCamelCase    )
       self.generator.save_pretrained(_lowerCamelCase    )




 @classmethod
 def        SCREAMING_SNAKE_CASE__				(		cls					,  _lowerCamelCase					,  **_lowerCamelCase    ):
       # dynamically import AutoTokenizer
       from ..auto.tokenization_auto import AutoTokenizer

       a						:Optional[int] 							=       kwargs.pop('''config'''					,  _lowerCamelCase    )

       if config is None:
             a						:Any 							=       RagConfig.from_pretrained(_lowerCamelCase    )

       a						:List[Any] 							=       AutoTokenizer.from_pretrained(
           _lowerCamelCase					,  config=config.question_encoder					,  subfolder='''question_encoder_tokenizer'''    )
       a						:str 							=       AutoTokenizer.from_pretrained(
           _lowerCamelCase					,  config=config.generator					,  subfolder='''generator_tokenizer'''    )
       return cls(question_encoder=_lowerCamelCase					,  generator=_lowerCamelCase    )




 def __call__(		self					,  *_lowerCamelCase					,  **_lowerCamelCase    ):
       return self.current_tokenizer(*_lowerCamelCase					,  **_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  *_lowerCamelCase					,  **_lowerCamelCase    ):
       return self.generator.batch_decode(*_lowerCamelCase					,  **_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  *_lowerCamelCase					,  **_lowerCamelCase    ):
       return self.generator.decode(*_lowerCamelCase					,  **_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[Any] 							=       self.question_encoder




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Optional[Any] 							=       self.generator




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  _lowerCamelCase = "longest"					,  _lowerCamelCase = None					,  _lowerCamelCase = True					,  **_lowerCamelCase					,  ):
       warnings.warn(
           '''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
           '''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
           '''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
           '''details'''					,  _lowerCamelCase					,  )
       if max_length is None:
             a						:Any 							=       self.current_tokenizer.model_max_length
       a						:Dict 							=       self(
           _lowerCamelCase					,  add_special_tokens=_lowerCamelCase					,  return_tensors=_lowerCamelCase					,  max_length=_lowerCamelCase					,  padding=_lowerCamelCase					,  truncation=_lowerCamelCase					,  **_lowerCamelCase					,  )
       if tgt_texts is None:
             return model_inputs
       # Process tgt_texts
       if max_target_length is None:
             a						:Union[str, Any] 							=       self.current_tokenizer.model_max_length
       a						:Union[str, Any] 							=       self(
           text_target=_lowerCamelCase					,  add_special_tokens=_lowerCamelCase					,  return_tensors=_lowerCamelCase					,  padding=_lowerCamelCase					,  max_length=_lowerCamelCase					,  truncation=_lowerCamelCase					,  **_lowerCamelCase					,  )
       a						:Optional[int] 							=       labels['''input_ids''']
       return model_inputs









from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed
def 				__lowerCamelCase						( UpperCAmelCase_						:		Any ,  UpperCAmelCase_						:		List[Any] ,  UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		Union[str, Any]						):




      """simple docstring"""





      for param, grad_param in zip(model_a.parameters() ,  model_b.parameters()						):
            if not param.requires_grad:
                  continue
            if not did_step:
                  # Grads should not be in sync
                  assert (
                      torch.allclose(param.grad ,  grad_param.grad						) is False
                  ), F'''Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'''
            else:
                  # Grads should be in sync
                  assert (
                      torch.allclose(param.grad ,  grad_param.grad						) is True
                  ), F'''Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'''
def 				__lowerCamelCase						( UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		Optional[Any] ,  UpperCAmelCase_						:		Optional[Any]=True						):




      """simple docstring"""





      model.train()
      a						:str 							=       model(UpperCAmelCase_						)
      a						:List[str] 							=       F.mse_loss(UpperCAmelCase_ ,  target.to(output.device						)						)
      if not do_backward:
            loss /= accelerator.gradient_accumulation_steps
            loss.backward()
      else:
            accelerator.backward(UpperCAmelCase_						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		int=False						):




      """simple docstring"""





      set_seed(42						)
      a						:List[Any] 							=       RegressionModel()
      a						:Any 							=       deepcopy(UpperCAmelCase_						)
      a						:Tuple 							=       RegressionDataset(length=80						)
      a						:Tuple 							=       DataLoader(UpperCAmelCase_ ,  batch_size=16						)
      model.to(accelerator.device						)
      if sched:
            a						:str 							=       AdamW(params=model.parameters() ,  lr=1E-3						)
            a						:str 							=       AdamW(params=ddp_model.parameters() ,  lr=1E-3						)
            a						:List[str] 							=       LambdaLR(UpperCAmelCase_ ,  lr_lambda=lambda UpperCAmelCase_						: epoch**0.65						)
            a						:List[str] 							=       LambdaLR(UpperCAmelCase_ ,  lr_lambda=lambda UpperCAmelCase_						: epoch**0.65						)
      # Make a copy of `model`
      if sched:
            a							,				a							,				a							,				a						:List[Any] 							=       accelerator.prepare(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)
      else:
            a							,				a						:str 							=       accelerator.prepare(UpperCAmelCase_ ,  UpperCAmelCase_						)
      if sched:
            return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
      return model, ddp_model, dataloader
def 				__lowerCamelCase						( UpperCAmelCase_						:		Union[str, Any]						):




      """simple docstring"""





      a							,				a							,				a						:str 							=       get_training_setup(UpperCAmelCase_						)
      # Use a single batch
      a							,				a						:Dict 							=       next(iter(UpperCAmelCase_						)						).values()
      for iteration in range(3						):
            # Gather the distributed inputs and targs for the base model
            a							,				a						:int 							=       accelerator.gather((ddp_input, ddp_target)						)
            a							,				a						:Union[str, Any] 							=       input.to(accelerator.device						), target.to(accelerator.device						)
            # Perform our initial ground truth step in non "DDP"
            step_model(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)
            # Do "gradient accumulation" (noop)
            if iteration % 2 == 0:
                  # Accumulate grads locally
                  with accelerator.no_sync(UpperCAmelCase_						):
                        step_model(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)
            else:
                  # Sync grads
                  step_model(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)

            # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
            check_model_parameters(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)
            for param, ddp_param in zip(model.parameters() ,  ddp_model.parameters()						):
                  if not param.requires_grad:
                        continue
                  assert torch.allclose(
                      param.grad ,  ddp_param.grad						), F'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''

            # Shuffle ddp_input on each iteration
            torch.manual_seed(1337 + iteration						)
            a						:Union[str, Any] 							=       ddp_input[torch.randperm(len(UpperCAmelCase_						)						)]
def 				__lowerCamelCase						( UpperCAmelCase_						:		Union[str, Any]						):




      """simple docstring"""





      a							,				a							,				a						:List[str] 							=       get_training_setup(UpperCAmelCase_						)
      # Use a single batch
      a							,				a						:List[str] 							=       next(iter(UpperCAmelCase_						)						).values()
      for iteration in range(3						):
            # Gather the distributed inputs and targs for the base model
            a							,				a						:List[Any] 							=       accelerator.gather((ddp_input, ddp_target)						)
            a							,				a						:Union[str, Any] 							=       input.to(accelerator.device						), target.to(accelerator.device						)
            # Perform our initial ground truth step in non "DDP"
            step_model(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)
            # Do "gradient accumulation" (noop)
            if iteration % 2 == 0:
                  # Accumulate grads locally
                  with accelerator.no_sync(UpperCAmelCase_						):
                        step_model(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)
            else:
                  # Sync grads
                  step_model(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)

            # DDP model and model should only be in sync when not (iteration % 2 == 0)
            for param, ddp_param in zip(model.parameters() ,  ddp_model.parameters()						):
                  if not param.requires_grad:
                        continue
                  if iteration % 2 == 0:
                        # Grads should not be in sync
                        assert (
                            torch.allclose(param.grad ,  ddp_param.grad						) is False
                        ), F'''Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''
                  else:
                        # Grads should be in sync
                        assert (
                            torch.allclose(param.grad ,  ddp_param.grad						) is True
                        ), F'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''

        # Shuffle ddp_input on each iteration
            torch.manual_seed(1337 + iteration						)
            a						:Any 							=       ddp_input[torch.randperm(len(UpperCAmelCase_						)						)]
def 				__lowerCamelCase						( UpperCAmelCase_						:		Union[str, Any]=False ,  UpperCAmelCase_						:		int=False						):




      """simple docstring"""





      a						:Optional[int] 							=       Accelerator(
          split_batches=UpperCAmelCase_ ,  dispatch_batches=UpperCAmelCase_ ,  gradient_accumulation_steps=2						)
      # Test that context manager behaves properly
      a							,				a							,				a						:List[str] 							=       get_training_setup(UpperCAmelCase_						)
      for iteration, batch in enumerate(UpperCAmelCase_						):
            a							,				a						:List[Any] 							=       batch.values()
            # Gather the distributed inputs and targs for the base model
            a							,				a						:List[str] 							=       accelerator.gather((ddp_input, ddp_target)						)
            a							,				a						:List[str] 							=       input.to(accelerator.device						), target.to(accelerator.device						)
            # Perform our initial ground truth step in non "DDP"
            step_model(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)
            # Do "gradient accumulation" (noop)
            with accelerator.accumulate(UpperCAmelCase_						):
                  step_model(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)

            # DDP model and model should only be in sync when not (iteration % 2 == 0)
            for param, ddp_param in zip(model.parameters() ,  ddp_model.parameters()						):
                  if not param.requires_grad:
                        continue
                  if ((iteration + 1) % 2 == 0) or (iteration == len(UpperCAmelCase_						) - 1):
                        # Grads should be in sync
                        assert (
                            torch.allclose(param.grad ,  ddp_param.grad						) is True
                        ), F'''Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''
                  else:
                        # Grads should not be in sync
                        assert (
                            torch.allclose(param.grad ,  ddp_param.grad						) is False
                        ), F'''Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''

        # Shuffle ddp_input on each iteration
            torch.manual_seed(1337 + iteration						)
            a						:List[str] 							=       ddp_input[torch.randperm(len(UpperCAmelCase_						)						)]
      GradientState._reset_state()
def 				__lowerCamelCase						( UpperCAmelCase_						:		Any=False ,  UpperCAmelCase_						:		Optional[int]=False						):




      """simple docstring"""





      a						:Optional[Any] 							=       Accelerator(
          split_batches=UpperCAmelCase_ ,  dispatch_batches=UpperCAmelCase_ ,  gradient_accumulation_steps=2						)
      # Test that context manager behaves properly
      a							,				a							,				a							,				a							,				a							,				a							,				a						:Optional[Any] 							=       get_training_setup(UpperCAmelCase_ ,  UpperCAmelCase_						)
      for iteration, batch in enumerate(UpperCAmelCase_						):
            a							,				a						:int 							=       batch.values()
            # Gather the distributed inputs and targs for the base model
            a							,				a						:List[str] 							=       accelerator.gather((ddp_input, ddp_target)						)
            a							,				a						:str 							=       input.to(accelerator.device						), target.to(accelerator.device						)
            # Perform our initial ground truth step in non "DDP"
            model.train()
            ddp_model.train()
            step_model(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)
            opt.step()

            if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(UpperCAmelCase_						)):
                  if split_batches:
                        sched.step()
                  else:
                        for _ in range(accelerator.num_processes						):
                              sched.step()
            opt.zero_grad()
            # Perform gradient accumulation under wrapper
            with accelerator.accumulate(UpperCAmelCase_						):
                  step_model(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)
                  ddp_opt.step()
                  ddp_sched.step()
                  ddp_opt.zero_grad()

            # Learning rates should be the same
            assert (
                opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
            ), F'''Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]['lr']}\nDDP opt: {ddp_opt.param_groups[0]['lr']}\n'''
            a						:Tuple 							=       (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(UpperCAmelCase_						))
            if accelerator.num_processes > 1:
                  check_model_parameters(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)
            # Shuffle ddp_input on each iteration
            torch.manual_seed(1337 + iteration						)
      GradientState._reset_state()
def 				__lowerCamelCase						( ):




      """simple docstring"""





      a						:Optional[Any] 							=       Accelerator()

      a						:int 							=       RegressionDataset(length=80						)
      a						:List[str] 							=       DataLoader(UpperCAmelCase_ ,  batch_size=16						)
      a						:List[Any] 							=       RegressionDataset(length=96						)
      a						:Any 							=       DataLoader(UpperCAmelCase_ ,  batch_size=16						)
      a							,				a						:Optional[int] 							=       accelerator.prepare(UpperCAmelCase_ ,  UpperCAmelCase_						)
      assert accelerator.gradient_state.active_dataloader is None
      for iteration, _ in enumerate(UpperCAmelCase_						):
            assert id(accelerator.gradient_state.active_dataloader						) == id(UpperCAmelCase_						)
            if iteration < len(UpperCAmelCase_						) - 1:
                  assert not accelerator.gradient_state.end_of_dataloader
                  if iteration == 1:
                        for batch_num, _ in enumerate(UpperCAmelCase_						):
                              assert id(accelerator.gradient_state.active_dataloader						) == id(UpperCAmelCase_						)
                              if batch_num < len(UpperCAmelCase_						) - 1:
                                    assert not accelerator.gradient_state.end_of_dataloader
                              else:
                                    assert accelerator.gradient_state.end_of_dataloader
            else:
                  assert accelerator.gradient_state.end_of_dataloader
      assert accelerator.gradient_state.active_dataloader is None
def 				__lowerCamelCase						( ):




      """simple docstring"""





      a						:Optional[int] 							=       Accelerator()
      a						:Optional[int] 							=       accelerator.state
      if state.local_process_index == 0:
            print('''**Test `accumulate` gradient accumulation with dataloader break**'''						)
      test_dataloader_break()
      if state.distributed_type == DistributedType.NO:
            if state.local_process_index == 0:
                  print('''**Test NOOP `no_sync` context manager**'''						)
            test_noop_sync(UpperCAmelCase_						)
      if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
            if state.local_process_index == 0:
                  print('''**Test Distributed `no_sync` context manager**'''						)
            test_distributed_sync(UpperCAmelCase_						)
      if state.distributed_type == DistributedType.MULTI_GPU:
            for split_batch in [True, False]:
                  for dispatch_batches in [True, False]:
                        if state.local_process_index == 0:
                              print(
                                  '''**Test `accumulate` gradient accumulation, ''' ,  F'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**''' ,  )
                        test_gradient_accumulation(UpperCAmelCase_ ,  UpperCAmelCase_						)

    # Currently will break on torch 2.0 +, need to investigate why
      if is_torch_version('''<''' ,  '''2.0'''						) or state.distributed_type == DistributedType.NO:
            if state.local_process_index == 0:
                  print(
                      '''**Test `accumulate` gradient accumulation with optimizer and scheduler, ''' ,  '''`split_batches=False`, `dispatch_batches=False`**''' ,  )
            test_gradient_accumulation_with_opt_and_scheduler()
            if state.distributed_type == DistributedType.MULTI_GPU:
                  for split_batch in [True, False]:
                        for dispatch_batches in [True, False]:
                              if not split_batch and not dispatch_batches:
                                    continue
                              if state.local_process_index == 0:
                                    print(
                                        '''**Test `accumulate` gradient accumulation with optimizer and scheduler, ''' ,  F'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**''' ,  )
                              test_gradient_accumulation_with_opt_and_scheduler(UpperCAmelCase_ ,  UpperCAmelCase_						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		Tuple						):




      """simple docstring"""





      main()


if __name__ == "__main__":
     main()





