

"""simple docstring"""





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_a       =							logging.get_logger(__name__)

_a       =							{
    'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
    # See all Marian models at https://huggingface.co/models?filter=marian
}




class 					_lowerCAmelCase  (	_UpperCamelCase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Tuple   		=	"""marian"""
 __UpperCAmelCase	:					Optional[Any]   		=	["""past_key_values"""]
 __UpperCAmelCase	:					Optional[int]   		=	{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}


 def __init__(			self							:			List[Any],					UpperCAmelCase__							:			Union[str, Any]=5_8_1_0_1,					UpperCAmelCase__							:			Optional[Any]=None,					UpperCAmelCase__							:			Dict=1_0_2_4,					UpperCAmelCase__							:			Union[str, Any]=1_2,					UpperCAmelCase__							:			Any=4_0_9_6,					UpperCAmelCase__							:			List[str]=1_6,					UpperCAmelCase__							:			Optional[Any]=1_2,					UpperCAmelCase__							:			int=4_0_9_6,					UpperCAmelCase__							:			Tuple=1_6,					UpperCAmelCase__							:			Optional[int]=0.0,					UpperCAmelCase__							:			List[Any]=0.0,					UpperCAmelCase__							:			Any=True,					UpperCAmelCase__							:			int=True,					UpperCAmelCase__							:			List[Any]="gelu",					UpperCAmelCase__							:			str=1_0_2_4,					UpperCAmelCase__							:			int=0.1,					UpperCAmelCase__							:			int=0.0,					UpperCAmelCase__							:			Dict=0.0,					UpperCAmelCase__							:			Any=0.02,					UpperCAmelCase__							:			List[Any]=5_8_1_0_0,					UpperCAmelCase__							:			List[str]=False,					UpperCAmelCase__							:			Tuple=5_8_1_0_0,					UpperCAmelCase__							:			int=0,					UpperCAmelCase__							:			Optional[Any]=0,					UpperCAmelCase__							:			int=True,					**UpperCAmelCase__							:			Tuple,					):
    __lowercase								=						vocab_size
    __lowercase								=						decoder_vocab_size or vocab_size
    __lowercase								=						max_position_embeddings
    __lowercase								=						d_model
    __lowercase								=						encoder_ffn_dim
    __lowercase								=						encoder_layers
    __lowercase								=						encoder_attention_heads
    __lowercase								=						decoder_ffn_dim
    __lowercase								=						decoder_layers
    __lowercase								=						decoder_attention_heads
    __lowercase								=						dropout
    __lowercase								=						attention_dropout
    __lowercase								=						activation_dropout
    __lowercase								=						activation_function
    __lowercase								=						init_std
    __lowercase								=						encoder_layerdrop
    __lowercase								=						decoder_layerdrop
    __lowercase								=						use_cache
    __lowercase								=						encoder_layers
    __lowercase								=						scale_embedding  # scale factor will be sqrt(d_model) if True
    __lowercase								=						share_encoder_decoder_embeddings
    super().__init__(
        pad_token_id=__lowerCamelCase,					eos_token_id=__lowerCamelCase,					is_encoder_decoder=__lowerCamelCase,					decoder_start_token_id=__lowerCamelCase,					forced_eos_token_id=__lowerCamelCase,					**__lowerCamelCase,					)







class 					_lowerCAmelCase  (	_UpperCamelCase   ):







 """simple docstring"""







 @property
 # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
 def 					_lowercase (			self							:			Tuple	):
    if self.task in ["default", "seq2seq-lm"]:
       __lowercase								=						OrderedDict(
           [
               ("input_ids", {0: "batch", 1: "encoder_sequence"}),
               ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
           ]	)

       if self.use_past:
          __lowercase								=						{0: "batch"}
          __lowercase								=						{0: "batch", 1: "past_decoder_sequence + sequence"}
       else:
          __lowercase								=						{0: "batch", 1: "decoder_sequence"}
          __lowercase								=						{0: "batch", 1: "decoder_sequence"}

       if self.use_past:
          self.fill_with_past_key_values_(__lowerCamelCase,					direction="inputs"	)
    elif self.task == "causal-lm":
       # TODO: figure this case out.
       __lowercase								=						OrderedDict(
           [
               ("input_ids", {0: "batch", 1: "encoder_sequence"}),
               ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
           ]	)
       if self.use_past:
          __lowercase		,__lowercase								=						self.num_layers
          for i in range(__lowerCamelCase	):
             __lowercase								=						{0: "batch", 2: "past_sequence + sequence"}
             __lowercase								=						{0: "batch", 2: "past_sequence + sequence"}
    else:
       __lowercase								=						OrderedDict(
           [
               ("input_ids", {0: "batch", 1: "encoder_sequence"}),
               ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
               ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
               ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
           ]	)

    return common_inputs


 @property
 # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
 def 					_lowercase (			self							:			Union[str, Any]	):
    if self.task in ["default", "seq2seq-lm"]:
       __lowercase								=						super().outputs
    else:
       __lowercase								=						super(__lowerCamelCase,					self	).outputs
       if self.use_past:
          __lowercase		,__lowercase								=						self.num_layers
          for i in range(__lowerCamelCase	):
             __lowercase								=						{0: "batch", 2: "past_sequence + sequence"}
             __lowercase								=						{0: "batch", 2: "past_sequence + sequence"}
    return common_outputs


 def 					_lowercase (			self							:			List[Any],					UpperCAmelCase__							:			PreTrainedTokenizer,					UpperCAmelCase__							:			int = -1,					UpperCAmelCase__							:			int = -1,					UpperCAmelCase__							:			bool = False,					UpperCAmelCase__							:			Optional[TensorType] = None,					):
    __lowercase								=						self._generate_dummy_inputs_for_encoder_and_decoder(
        __lowerCamelCase,					__lowerCamelCase,					__lowerCamelCase,					__lowerCamelCase,					__lowerCamelCase	)

    # Generate decoder inputs
    __lowercase								=						seq_length if not self.use_past else 1
    __lowercase								=						self._generate_dummy_inputs_for_encoder_and_decoder(
        __lowerCamelCase,					__lowerCamelCase,					__lowerCamelCase,					__lowerCamelCase,					__lowerCamelCase	)
    __lowercase								=						{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
    __lowercase								=						dict(**__lowerCamelCase,					**__lowerCamelCase	)

    if self.use_past:
       if not is_torch_available():
          raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."	)
       else:
          import torch
       __lowercase		,__lowercase								=						common_inputs["input_ids"].shape
       __lowercase								=						common_inputs["decoder_input_ids"].shape[1]
       __lowercase		,__lowercase								=						self.num_attention_heads
       __lowercase								=						(
           batch,
           num_encoder_attention_heads,
           encoder_seq_length,
           self._config.hidden_size // num_encoder_attention_heads,
       )
       __lowercase								=						decoder_seq_length + 3
       __lowercase								=						(
           batch,
           num_decoder_attention_heads,
           decoder_past_length,
           self._config.hidden_size // num_decoder_attention_heads,
       )

       __lowercase								=						torch.cat(
           [common_inputs["decoder_attention_mask"], torch.ones(__lowerCamelCase,					__lowerCamelCase	)],					dim=1	)

       __lowercase								=						[]
       # If the number of encoder and decoder layers are present in the model configuration, both are considered
       __lowercase		,__lowercase								=						self.num_layers
       __lowercase								=						min(__lowerCamelCase,					__lowerCamelCase	)
       __lowercase								=						max(__lowerCamelCase,					__lowerCamelCase	) - min_num_layers
       __lowercase								=						"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

       for _ in range(__lowerCamelCase	):
          common_inputs["past_key_values"].append(
              (
                  torch.zeros(__lowerCamelCase	),
                  torch.zeros(__lowerCamelCase	),
                  torch.zeros(__lowerCamelCase	),
                  torch.zeros(__lowerCamelCase	),
              )	)
       # TODO: test this.
       __lowercase								=						encoder_shape if remaining_side_name == "encoder" else decoder_shape
       for _ in range(__lowerCamelCase,					__lowerCamelCase	):
          common_inputs["past_key_values"].append((torch.zeros(__lowerCamelCase	), torch.zeros(__lowerCamelCase	))	)
    return common_inputs


 def 					_lowercase (			self							:			List[Any],					UpperCAmelCase__							:			PreTrainedTokenizer,					UpperCAmelCase__							:			int = -1,					UpperCAmelCase__							:			int = -1,					UpperCAmelCase__							:			bool = False,					UpperCAmelCase__							:			Optional[TensorType] = None,					):
    __lowercase								=						self._generate_dummy_inputs_for_encoder_and_decoder(
        __lowerCamelCase,					__lowerCamelCase,					__lowerCamelCase,					__lowerCamelCase,					__lowerCamelCase	)

    if self.use_past:
       if not is_torch_available():
          raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."	)
       else:
          import torch
       __lowercase		,__lowercase								=						common_inputs["input_ids"].shape
       # Not using the same length for past_key_values
       __lowercase								=						seqlen + 2
       __lowercase		,__lowercase								=						self.num_layers
       __lowercase		,__lowercase								=						self.num_attention_heads
       __lowercase								=						(
           batch,
           num_encoder_attention_heads,
           past_key_values_length,
           self._config.hidden_size // num_encoder_attention_heads,
       )

       __lowercase								=						common_inputs["attention_mask"].dtype
       __lowercase								=						torch.cat(
           [common_inputs["attention_mask"], torch.ones(__lowerCamelCase,					__lowerCamelCase,					dtype=__lowerCamelCase	)],					dim=1	)
       __lowercase								=						[
           (torch.zeros(__lowerCamelCase	), torch.zeros(__lowerCamelCase	)) for _ in range(__lowerCamelCase	)
       ]
    return common_inputs


 def 					_lowercase (			self							:			Optional[int],					UpperCAmelCase__							:			PreTrainedTokenizer,					UpperCAmelCase__							:			int = -1,					UpperCAmelCase__							:			int = -1,					UpperCAmelCase__							:			bool = False,					UpperCAmelCase__							:			Optional[TensorType] = None,					):
    # Copied from OnnxConfig.generate_dummy_inputs
    # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
    # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
    __lowercase								=						compute_effective_axis_dimension(
        __lowerCamelCase,					fixed_dimension=OnnxConfig.default_fixed_batch,					num_token_to_add=0	)

    # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
    __lowercase								=						tokenizer.num_special_tokens_to_add(__lowerCamelCase	)
    __lowercase								=						compute_effective_axis_dimension(
        __lowerCamelCase,					fixed_dimension=OnnxConfig.default_fixed_sequence,					num_token_to_add=__lowerCamelCase	)

    # Generate dummy inputs according to compute batch and sequence
    __lowercase								=						[" ".join([tokenizer.unk_token]	) * seq_length] * batch_size
    __lowercase								=						dict(tokenizer(__lowerCamelCase,					return_tensors=__lowerCamelCase	)	)
    return common_inputs


 def 					_lowercase (			self							:			List[str],					UpperCAmelCase__							:			PreTrainedTokenizer,					UpperCAmelCase__							:			int = -1,					UpperCAmelCase__							:			int = -1,					UpperCAmelCase__							:			bool = False,					UpperCAmelCase__							:			Optional[TensorType] = None,					):
    if self.task in ["default", "seq2seq-lm"]:
       __lowercase								=						self._generate_dummy_inputs_for_default_and_seqaseq_lm(
           __lowerCamelCase,					batch_size=__lowerCamelCase,					seq_length=__lowerCamelCase,					is_pair=__lowerCamelCase,					framework=__lowerCamelCase	)

    else:
       __lowercase								=						self._generate_dummy_inputs_for_causal_lm(
           __lowerCamelCase,					batch_size=__lowerCamelCase,					seq_length=__lowerCamelCase,					is_pair=__lowerCamelCase,					framework=__lowerCamelCase	)

    return common_inputs


 def 					_lowercase (			self							:			Dict,					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			List[Any]	):
    if self.task in ["default", "seq2seq-lm"]:
       __lowercase								=						super()._flatten_past_key_values_(__lowerCamelCase,					__lowerCamelCase,					__lowerCamelCase,					__lowerCamelCase	)
    else:
       __lowercase								=						super(__lowerCamelCase,					self	)._flatten_past_key_values_(
           __lowerCamelCase,					__lowerCamelCase,					__lowerCamelCase,					__lowerCamelCase	)



 @property
 def 					_lowercase (			self							:			Tuple	):
    return 1E-4










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 			__magic_name__				( A		:					Tuple,  A		:					List[Any],  A		:					List[Any],  A		:					Dict   ):






			'''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 			__magic_name__				( A		:					List[Any],  A		:					int,  A		:					Optional[Any],  A		:					Optional[int],  A		:					Any=True   ):






			'''simple docstring'''







			model.train()
			a    =			model(A   )
			a    =			F.mse_loss(A,  target.to(output.device   )   )
			if not do_backward:
						loss /= accelerator.gradient_accumulation_steps
						loss.backward()
			else:
						accelerator.backward(A   )




def 			__magic_name__				( A		:					Any,  A		:					Any=False   ):






			'''simple docstring'''







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




def 			__magic_name__				( A		:					List[Any]   ):






			'''simple docstring'''







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

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




def 			__magic_name__				( A		:					Optional[int]   ):






			'''simple docstring'''







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

						# 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    =			ddp_input[torch.randperm(len(A   )   )]




def 			__magic_name__				( A		:					List[Any]=False,  A		:					List[Any]=False   ):






			'''simple docstring'''







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

						# 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(A   ) - 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    =			ddp_input[torch.randperm(len(A   )   )]
			GradientState._reset_state()




def 			__magic_name__				( A		:					List[Any]=False,  A		:					Any=False   ):






			'''simple docstring'''







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

						if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(A   )):
									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(A   ):
									step_model(A,  A,  A,  A   )
									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    =			(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(A   ))
						if accelerator.num_processes > 1:
									check_model_parameters(A,  A,  A,  A   )
						# Shuffle ddp_input on each iteration
						torch.manual_seed(1337 + iteration   )
			GradientState._reset_state()




def 			__magic_name__				( ):






			'''simple docstring'''







			a    =			Accelerator()

			a    =			RegressionDataset(length=80   )
			a    =			DataLoader(A,  batch_size=16   )
			a    =			RegressionDataset(length=96   )
			a    =			DataLoader(A,  batch_size=16   )
			a    ,		a    =			accelerator.prepare(A,  A   )
			assert accelerator.gradient_state.active_dataloader is None
			for iteration, _ in enumerate(A   ):
						assert id(accelerator.gradient_state.active_dataloader   ) == id(A   )
						if iteration < len(A   ) - 1:
									assert not accelerator.gradient_state.end_of_dataloader
									if iteration == 1:
												for batch_num, _ in enumerate(A   ):
															assert id(accelerator.gradient_state.active_dataloader   ) == id(A   )
															if batch_num < len(A   ) - 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 			__magic_name__				( ):






			'''simple docstring'''







			a    =			Accelerator()
			a    =			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(A   )
			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(A   )
			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(A,  A   )

    # 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(A,  A   )




def 			__magic_name__				( A		:					Optional[int]   ):






			'''simple docstring'''







			main()


if __name__ == "__main__":
		main()






