




"""simple docstring"""


import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
A_						=				logging.get_logger(__name__)

A_						=				{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
A_						=				[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]


def 					UpperCAmelCase__       (snake_case__							:					str		,   snake_case__							:					Dict		,   snake_case__							:					Any		,   snake_case__							:					str		,   snake_case__							:					str    ):
  """simple docstring"""

  for attribute in key.split("""."""    ):
    _snake_case							:		Optional[Any]								=  getattr(snake_case__		,   snake_case__    )

  if weight_type is not None:
    _snake_case							:		Optional[Any]								=  getattr(snake_case__		,   snake_case__    ).shape
  else:
    _snake_case							:		Optional[Any]								=  hf_pointer.shape

  assert hf_shape == value.shape, (
      F"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
      F" {value.shape} for {full_name}"
  )

  if weight_type == "weight":
    _snake_case							:		int								=  value
  elif weight_type == "weight_g":
    _snake_case							:		str								=  value
  elif weight_type == "weight_v":
    _snake_case							:		Tuple								=  value
  elif weight_type == "bias":
    _snake_case							:		List[str]								=  value
  else:
    _snake_case							:		int								=  value

  logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."    )


def 					UpperCAmelCase__       (snake_case__							:					str		,   snake_case__							:					List[str]    ):
  """simple docstring"""

  _snake_case							:		List[Any]								=  []
  _snake_case							:		Optional[Any]								=  fairseq_model.state_dict()

  _snake_case							:		str								=  hf_model.feature_extractor

  # if encoder has different dim to decoder -> use proj_weight
  _snake_case							:		Optional[Any]								=  None

  for name, value in fairseq_dict.items():
    _snake_case							:		Optional[Any]								=  False
    if "conv_layers" in name:
      load_conv_layer(
          snake_case__		,   snake_case__		,   snake_case__		,   snake_case__		,   hf_model.config.feat_extract_norm == """group"""		,   )
      _snake_case							:		Dict								=  True
    elif name.split("""."""    )[0] == "proj":
      _snake_case							:		Dict								=  fairseq_model.proj
      _snake_case							:		Optional[int]								=  True
    else:
      for key, mapped_key in MAPPING.items():
        if key in name or key.split("""w2v_model."""    )[-1] == name.split("""."""    )[0]:
          _snake_case							:		Dict								=  True
          if "*" in mapped_key:
            _snake_case							:		Optional[int]								=  name.split(snake_case__    )[0].split("""."""    )[-2]
            _snake_case							:		Union[str, Any]								=  mapped_key.replace("""*"""		,   snake_case__    )
          if "weight_g" in name:
            _snake_case							:		str								=  """weight_g"""
          elif "weight_v" in name:
            _snake_case							:		Optional[Any]								=  """weight_v"""
          elif "bias" in name:
            _snake_case							:		Union[str, Any]								=  """bias"""
          elif "weight" in name:
            _snake_case							:		int								=  """weight"""
          else:
            _snake_case							:		Optional[int]								=  None
          set_recursively(snake_case__		,   snake_case__		,   snake_case__		,   snake_case__		,   snake_case__    )
        continue
    if not is_used:
      unused_weights.append(snake_case__    )

  logger.warning(F"Unused weights: {unused_weights}"    )

  return proj_weight


def 					UpperCAmelCase__       (snake_case__							:					Any		,   snake_case__							:					Dict		,   snake_case__							:					Union[str, Any]		,   snake_case__							:					Union[str, Any]		,   snake_case__							:					int    ):
  """simple docstring"""

  _snake_case							:		Any								=  full_name.split("""conv_layers."""    )[-1]
  _snake_case							:		Optional[int]								=  name.split("""."""    )
  _snake_case							:		List[str]								=  int(items[0]    )
  _snake_case							:		Dict								=  int(items[1]    )

  if type_id == 0:
    if "bias" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
          F"{full_name} has size {value.shape}, but"
          F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
      )
      _snake_case							:		Tuple								=  value
      logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."    )
    elif "weight" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
          F"{full_name} has size {value.shape}, but"
          F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
      )
      _snake_case							:		List[Any]								=  value
      logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."    )
  elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
    if "bias" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
          F"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
          " found."
      )
      _snake_case							:		int								=  value
      logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."    )
    elif "weight" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
          F"{full_name} has size {value.shape}, but"
          F" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
      )
      _snake_case							:		List[str]								=  value
      logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."    )
  else:
    unused_weights.append(snake_case__    )


def 					UpperCAmelCase__       (snake_case__							:					Union[str, Any]    ):
  """simple docstring"""

  _snake_case		,					_snake_case							:		Optional[Any]								=  emb.weight.shape
  _snake_case							:		Optional[int]								=  nn.Linear(snake_case__		,   snake_case__		,   bias=snake_case__    )
  _snake_case							:		Union[str, Any]								=  emb.weight.data
  return lin_layer


def 					UpperCAmelCase__       (snake_case__							:					List[Any]    ):
  """simple docstring"""

  with open(snake_case__		,   """r"""		,   encoding="""utf-8"""    ) as f:
    _snake_case							:		Any								=  f.readlines()
    _snake_case							:		Optional[Any]								=  [line.split(""" """    )[0] for line in lines]

  _snake_case							:		str								=  len(snake_case__    )

  _snake_case							:		Tuple								=  {
      """<s>""": 0,
      """<pad>""": 1,
      """</s>""": 2,
      """<unk>""": 3,
  }

  vocab_dict.update(dict(zip(snake_case__		,   range(4		,   num_words + 4    )    )    )    )
  return vocab_dict


@torch.no_grad()
def 					UpperCAmelCase__       (snake_case__							:					int		,   snake_case__							:					List[str]		,   snake_case__							:					int		,   snake_case__							:					Dict		,   snake_case__							:					List[Any]		,   snake_case__							:					str		,   snake_case__							:					Union[str, Any]		,   ):
  """simple docstring"""

  _snake_case							:		Optional[int]								=  WavaVecaConfig.from_pretrained(snake_case__    )
  _snake_case							:		List[str]								=  SpeechaTextaConfig.from_pretrained(
      snake_case__		,   vocab_size=snake_case__		,   decoder_layers=snake_case__		,   do_stable_layer_norm=snake_case__    )

  _snake_case							:		Dict								=  WavaVecaFeatureExtractor(
      feature_size=1		,   sampling_rate=1_60_00		,   padding_value=0		,   do_normalize=snake_case__		,   return_attention_mask=snake_case__		,   )

  _snake_case		,					_snake_case		,					_snake_case							:		Optional[int]								=  fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path]		,   arg_overrides={"""data""": """/""".join(dict_path.split("""/"""    )[:-1]    )}    )
  _snake_case							:		Optional[Any]								=  model[0].eval()

  # set weights for wav2vec2 encoder
  _snake_case							:		Any								=  WavaVecaModel(snake_case__    )
  _snake_case							:		Optional[Any]								=  recursively_load_weights_wavaveca(model.encoder		,   snake_case__    )

  _snake_case							:		Optional[Any]								=  SpeechaTextaForCausalLM(snake_case__    )
  _snake_case		,					_snake_case							:		List[str]								=  hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()		,   strict=snake_case__    )

  # set output linear layer
  unexpected_keys.remove("""embed_out"""    )
  _snake_case							:		Any								=  nn.Parameter(model.decoder.embed_out.detach()    )

  # layer norm is init to identity matrix so leaving it is fine
  logger.warning(F"The following keys are missing when loading the decoder weights: {missing_keys}"    )
  logger.warning(F"The following keys are unexpected when loading the decoder weights: {unexpected_keys}"    )

  _snake_case							:		Any								=  SpeechEncoderDecoderModel(encoder=snake_case__		,   decoder=snake_case__    )
  _snake_case							:		Any								=  False

  # add projection layer
  _snake_case							:		int								=  nn.Parameter(projection_layer.weight    )
  _snake_case							:		Any								=  nn.Parameter(projection_layer.bias    )

  _snake_case							:		Any								=  create_vocab_dict(snake_case__    )

  with open(os.path.join(snake_case__		,   """vocab.json"""    )		,   """w"""    ) as fp:
    json.dump(snake_case__		,   snake_case__    )

  _snake_case							:		Dict								=  SpeechaTextaTokenizer(os.path.join(snake_case__		,   """vocab.json"""    )    )
  tokenizer.save_pretrained(snake_case__    )

  _snake_case							:		str								=  hf_wavavec.config.to_dict()
  _snake_case							:		List[str]								=  tokenizer.pad_token_id
  _snake_case							:		Union[str, Any]								=  tokenizer.bos_token_id
  _snake_case							:		Union[str, Any]								=  tokenizer.eos_token_id
  _snake_case							:		Optional[Any]								=  """speech_to_text_2"""
  _snake_case							:		Optional[int]								=  """wav2vec2"""

  _snake_case							:		Tuple								=  SpeechEncoderDecoderConfig.from_dict(snake_case__    )

  hf_wavavec.save_pretrained(snake_case__    )
  feature_extractor.save_pretrained(snake_case__    )


if __name__ == "__main__":
     A_						=				argparse.ArgumentParser()
     parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
     parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
     parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
     parser.add_argument(
         '''--encoder_config_path''',
         default='''facebook/wav2vec2-large-lv60''',
         type=str,
         help='''Path to hf encoder wav2vec2 checkpoint config''',
     )
     parser.add_argument(
         '''--decoder_config_path''',
         default='''facebook/s2t-small-mustc-en-fr-st''',
         type=str,
         help='''Path to hf decoder s2t checkpoint config''',
     )
     parser.add_argument('''--vocab_size''', default=1_02_24, type=int, help='''Vocab size of decoder''')
     parser.add_argument('''--num_decoder_layers''', default=7, type=int, help='''Number of decoder layers''')

     A_						=				parser.parse_args()
     convert_wavaveca_checkpoint(
         args.checkpoint_path,
         args.pytorch_dump_folder_path,
         args.dict_path,
         encoder_config_path=args.encoder_config_path,
         decoder_config_path=args.decoder_config_path,
         vocab_size=args.vocab_size,
         num_decoder_layers=args.num_decoder_layers,
     )








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()






