"""simple docstring"""





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 				a_     (      _lowerCAmelCase  : str   , _lowerCAmelCase  : Tuple   , _lowerCAmelCase  : str   , _lowerCAmelCase  : 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 				a_     (      _lowerCAmelCase  : int   , _lowerCAmelCase  : Union[str, Any]   , _lowerCAmelCase  : Union[str, Any]   , _lowerCAmelCase  : List[str]   , _lowerCAmelCase  : Dict=True     ):





       '''simple docstring'''

       model.train()
       lowercase__			:		Optional[int]       =	model(_lowerCAmelCase     )
       lowercase__			:		int       =	F.mse_loss(_lowerCAmelCase   , target.to(output.device     )     )
       if not do_backward:
              loss /= accelerator.gradient_accumulation_steps
              loss.backward()
       else:
              accelerator.backward(_lowerCAmelCase     )





def 				a_     (      _lowerCAmelCase  : Union[str, Any]   , _lowerCAmelCase  : str=False     ):





       '''simple docstring'''

       set_seed(42     )
       lowercase__			:		Union[str, Any]       =	RegressionModel()
       lowercase__			:		Dict       =	deepcopy(_lowerCAmelCase     )
       lowercase__			:		Dict       =	RegressionDataset(length=80     )
       lowercase__			:		int       =	DataLoader(_lowerCAmelCase   , batch_size=16     )
       model.to(accelerator.device     )
       if sched:
              lowercase__			:		int       =	AdamW(params=model.parameters()   , lr=1E-3     )
              lowercase__			:		Dict       =	AdamW(params=ddp_model.parameters()   , lr=1E-3     )
              lowercase__			:		str       =	LambdaLR(_lowerCAmelCase   , lr_lambda=lambda _lowerCAmelCase     : epoch**0.6_5     )
              lowercase__			:		Optional[Any]       =	LambdaLR(_lowerCAmelCase   , lr_lambda=lambda _lowerCAmelCase     : epoch**0.6_5     )
       # Make a copy of `model`
       if sched:
              lowercase__    ,      lowercase__    ,      lowercase__    ,      lowercase__			:		Optional[int]       =	accelerator.prepare(_lowerCAmelCase   , _lowerCAmelCase   , _lowerCAmelCase   , _lowerCAmelCase     )
       else:
              lowercase__    ,      lowercase__			:		str       =	accelerator.prepare(_lowerCAmelCase   , _lowerCAmelCase     )
       if sched:
              return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
       return model, ddp_model, dataloader





def 				a_     (      _lowerCAmelCase  : Optional[Any]     ):





       '''simple docstring'''

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

              # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
              check_model_parameters(_lowerCAmelCase   , _lowerCAmelCase   , _lowerCAmelCase   , _lowerCAmelCase     )
              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     )
              lowercase__			:		Tuple       =	ddp_input[torch.randperm(len(_lowerCAmelCase     )     )]





def 				a_     (      _lowerCAmelCase  : Any     ):





       '''simple docstring'''

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

              # 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     )
              lowercase__			:		List[Any]       =	ddp_input[torch.randperm(len(_lowerCAmelCase     )     )]





def 				a_     (      _lowerCAmelCase  : List[str]=False   , _lowerCAmelCase  : Optional[int]=False     ):





       '''simple docstring'''

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

              # 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(_lowerCAmelCase     ) - 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     )
              lowercase__			:		int       =	ddp_input[torch.randperm(len(_lowerCAmelCase     )     )]
       GradientState._reset_state()





def 				a_     (      _lowerCAmelCase  : Union[str, Any]=False   , _lowerCAmelCase  : Optional[int]=False     ):





       '''simple docstring'''

       lowercase__			:		List[Any]       =	Accelerator(
           split_batches=_lowerCAmelCase   , dispatch_batches=_lowerCAmelCase   , gradient_accumulation_steps=2     )
       # Test that context manager behaves properly
       lowercase__    ,      lowercase__    ,      lowercase__    ,      lowercase__    ,      lowercase__    ,      lowercase__    ,      lowercase__			:		List[str]       =	get_training_setup(_lowerCAmelCase   , _lowerCAmelCase     )
       for iteration, batch in enumerate(_lowerCAmelCase     ):
              lowercase__    ,      lowercase__			:		str       =	batch.values()
              # Gather the distributed inputs and targs for the base model
              lowercase__    ,      lowercase__			:		List[str]       =	accelerator.gather((ddp_input, ddp_target)     )
              lowercase__    ,      lowercase__			:		List[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(_lowerCAmelCase   , _lowerCAmelCase   , _lowerCAmelCase   , _lowerCAmelCase   , _lowerCAmelCase     )
              opt.step()

              if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(_lowerCAmelCase     )):
                     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(_lowerCAmelCase     ):
                     step_model(_lowerCAmelCase   , _lowerCAmelCase   , _lowerCAmelCase   , _lowerCAmelCase     )
                     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"""
              lowercase__			:		List[str]       =	(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(_lowerCAmelCase     ))
              if accelerator.num_processes > 1:
                     check_model_parameters(_lowerCAmelCase   , _lowerCAmelCase   , _lowerCAmelCase   , _lowerCAmelCase     )
              # Shuffle ddp_input on each iteration
              torch.manual_seed(1337 + iteration     )
       GradientState._reset_state()





def 				a_     (      ):





       '''simple docstring'''

       lowercase__			:		List[str]       =	Accelerator()

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





       '''simple docstring'''

       lowercase__			:		List[str]       =	Accelerator()
       lowercase__			:		str       =	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(_lowerCAmelCase     )
       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(_lowerCAmelCase     )
       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(_lowerCAmelCase   , _lowerCAmelCase     )

    # 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(_lowerCAmelCase   , _lowerCAmelCase     )





def 				a_     (      _lowerCAmelCase  : str     ):





       '''simple docstring'''

       main()


if __name__ == "__main__":
       main()








'''simple docstring'''
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Any					=["image_processor", "tokenizer"]
 SCREAMING_SNAKE_CASE_	:					List[Any]					="BlipImageProcessor"
 SCREAMING_SNAKE_CASE_	:					Optional[int]					=("BertTokenizer", "BertTokenizerFast")






 def __init__(       self       :							Dict							,	__A       :							Optional[int]							,	__A       :							List[Any]	):
        __UpperCamelCase       =  False
        super().__init__(__A							,	__A	)
        __UpperCamelCase       =  self.image_processor






 def __call__(       self       :							List[Any]							,	__A       :							ImageInput = None							,	__A       :							Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,	__A       :							bool = True							,	__A       :							Union[bool, str, PaddingStrategy] = False							,	__A       :							Union[bool, str, TruncationStrategy] = None							,	__A       :							Optional[int] = None							,	__A       :							int = 0							,	__A       :							Optional[int] = None							,	__A       :							Optional[bool] = None							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							bool = True							,	__A       :							Optional[Union[str, TensorType]] = None							,	**__A       :							List[Any]							,	):
        if images is None and text is None:
               raise ValueError('You have to specify either images or text.'	)

        # Get only text
        if images is None:
               __UpperCamelCase       =  self.tokenizer
               __UpperCamelCase       =  self.tokenizer(
                   text=__A							,	add_special_tokens=__A							,	padding=__A							,	truncation=__A							,	max_length=__A							,	stride=__A							,	pad_to_multiple_of=__A							,	return_attention_mask=__A							,	return_overflowing_tokens=__A							,	return_special_tokens_mask=__A							,	return_offsets_mapping=__A							,	return_token_type_ids=__A							,	return_length=__A							,	verbose=__A							,	return_tensors=__A							,	**__A							,	)
               return text_encoding

        # add pixel_values
        __UpperCamelCase       =  self.image_processor(__A							,	return_tensors=__A	)

        if text is not None:
               __UpperCamelCase       =  self.tokenizer(
                   text=__A							,	add_special_tokens=__A							,	padding=__A							,	truncation=__A							,	max_length=__A							,	stride=__A							,	pad_to_multiple_of=__A							,	return_attention_mask=__A							,	return_overflowing_tokens=__A							,	return_special_tokens_mask=__A							,	return_offsets_mapping=__A							,	return_token_type_ids=__A							,	return_length=__A							,	verbose=__A							,	return_tensors=__A							,	**__A							,	)
        else:
               __UpperCamelCase       =  None

        if text_encoding is not None:
               encoding_image_processor.update(__A	)

        return encoding_image_processor






 def        _lowerCamelCase					(       self       :							List[Any]							,	*__A       :							Dict							,	**__A       :							Optional[int]	):
        return self.tokenizer.batch_decode(*__A							,	**__A	)






 def        _lowerCamelCase					(       self       :							List[Any]							,	*__A       :							List[str]							,	**__A       :							Dict	):
        return self.tokenizer.decode(*__A							,	**__A	)






 @property
 def        _lowerCamelCase					(       self       :							Tuple	):
        __UpperCamelCase       =  self.tokenizer.model_input_names
        __UpperCamelCase       =  self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)
