





import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase							:					Optional[Any]		    =		logging.get_logger(__name__)

def    __snake_case  (    _lowerCAmelCase       :       List[str] , _lowerCAmelCase       :       List[str] , _lowerCAmelCase       :       Optional[int]		)       ->  Optional[Any]:
   A_    : Optional[Any]								=	os.path.abspath(UpperCamelCase__		)
   logger.info(f"Converting TensorFlow checkpoint from {tf_path}"		)
   # Load weights from TF model
   A_    : Optional[Any]								=	tf.train.list_variables(UpperCamelCase__		)
   A_    : List[Any]								=	[]
   A_    : List[Any]								=	[]
   A_    : Any								=	[]
   for full_name, shape in init_vars:
      # logger.info(f"Loading TF weight {name} with shape {shape}")
      A_    : List[Any]								=	full_name.split("/"		)
      if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
         logger.info(f"Skipping non-model layer {full_name}"		)
         continue
      if "optimizer" in full_name:
         logger.info(f"Skipping optimization layer {full_name}"		)
         continue
      if name[0] == "model":
         # ignore initial 'model'
         A_    : Optional[int]								=	name[1:]
      # figure out how many levels deep the name is
      A_    : Dict								=	0
      for _name in name:
         if _name.startswith("layer_with_weights"		):
            depth += 1
         else:
            break
      layer_depth.append(UpperCamelCase__		)
      # read data
      A_    : Any								=	tf.train.load_variable(UpperCamelCase__ , UpperCamelCase__		)
      names.append("/".join(UpperCamelCase__		)		)
      arrays.append(UpperCamelCase__		)
   logger.info(f"Read a total of {len(UpperCamelCase__		):,} layers"		)

   # Sanity check
   if len(set(UpperCamelCase__		)		) != 1:
      raise ValueError(f"Found layer names with different depths (layer depth {list(set(UpperCamelCase__		)		)})"		)
   A_    : int								=	list(set(UpperCamelCase__		)		)[0]
   if layer_depth != 1:
      raise ValueError(
          "The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP"
          " heads."		)

   # convert layers
   logger.info("Converting weights..."		)
   for full_name, array in zip(UpperCamelCase__ , UpperCamelCase__		):
      A_    : List[str]								=	full_name.split("/"		)
      A_    : str								=	model
      A_    : Dict								=	[]
      for i, m_name in enumerate(UpperCamelCase__		):
         if m_name == ".ATTRIBUTES":
            # variable names end with .ATTRIBUTES/VARIABLE_VALUE
            break
         if m_name.startswith("layer_with_weights"		):
            A_    : Tuple								=	int(m_name.split("-"		)[-1]		)
            if layer_num <= 2:
               # embedding layers
               # layer_num 0: word_embeddings
               # layer_num 1: position_embeddings
               # layer_num 2: token_type_embeddings
               continue
            elif layer_num == 3:
               # embedding LayerNorm
               trace.extend(["embeddings", "LayerNorm"]		)
               A_    : List[Any]								=	getattr(UpperCamelCase__ , "embeddings"		)
               A_    : List[str]								=	getattr(UpperCamelCase__ , "LayerNorm"		)
            elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
               # encoder layers
               trace.extend(["encoder", "layer", str(layer_num - 4		)]		)
               A_    : Tuple								=	getattr(UpperCamelCase__ , "encoder"		)
               A_    : Tuple								=	getattr(UpperCamelCase__ , "layer"		)
               A_    : Optional[int]								=	pointer[layer_num - 4]
            elif layer_num == config.num_hidden_layers + 4:
               # pooler layer
               trace.extend(["pooler", "dense"]		)
               A_    : int								=	getattr(UpperCamelCase__ , "pooler"		)
               A_    : Optional[Any]								=	getattr(UpperCamelCase__ , "dense"		)
         elif m_name == "embeddings":
            trace.append("embeddings"		)
            A_    : Dict								=	getattr(UpperCamelCase__ , "embeddings"		)
            if layer_num == 0:
               trace.append("word_embeddings"		)
               A_    : str								=	getattr(UpperCamelCase__ , "word_embeddings"		)
            elif layer_num == 1:
               trace.append("position_embeddings"		)
               A_    : Any								=	getattr(UpperCamelCase__ , "position_embeddings"		)
            elif layer_num == 2:
               trace.append("token_type_embeddings"		)
               A_    : Any								=	getattr(UpperCamelCase__ , "token_type_embeddings"		)
            else:
               raise ValueError(f"Unknown embedding layer with name {full_name}"		)
            trace.append("weight"		)
            A_    : int								=	getattr(UpperCamelCase__ , "weight"		)
         elif m_name == "_attention_layer":
            # self-attention layer
            trace.extend(["attention", "self"]		)
            A_    : Optional[int]								=	getattr(UpperCamelCase__ , "attention"		)
            A_    : Union[str, Any]								=	getattr(UpperCamelCase__ , "self"		)
         elif m_name == "_attention_layer_norm":
            # output attention norm
            trace.extend(["attention", "output", "LayerNorm"]		)
            A_    : List[str]								=	getattr(UpperCamelCase__ , "attention"		)
            A_    : List[Any]								=	getattr(UpperCamelCase__ , "output"		)
            A_    : int								=	getattr(UpperCamelCase__ , "LayerNorm"		)
         elif m_name == "_attention_output_dense":
            # output attention dense
            trace.extend(["attention", "output", "dense"]		)
            A_    : int								=	getattr(UpperCamelCase__ , "attention"		)
            A_    : Tuple								=	getattr(UpperCamelCase__ , "output"		)
            A_    : Tuple								=	getattr(UpperCamelCase__ , "dense"		)
         elif m_name == "_output_dense":
            # output dense
            trace.extend(["output", "dense"]		)
            A_    : Any								=	getattr(UpperCamelCase__ , "output"		)
            A_    : Tuple								=	getattr(UpperCamelCase__ , "dense"		)
         elif m_name == "_output_layer_norm":
            # output dense
            trace.extend(["output", "LayerNorm"]		)
            A_    : int								=	getattr(UpperCamelCase__ , "output"		)
            A_    : List[Any]								=	getattr(UpperCamelCase__ , "LayerNorm"		)
         elif m_name == "_key_dense":
            # attention key
            trace.append("key"		)
            A_    : Union[str, Any]								=	getattr(UpperCamelCase__ , "key"		)
         elif m_name == "_query_dense":
            # attention query
            trace.append("query"		)
            A_    : Optional[Any]								=	getattr(UpperCamelCase__ , "query"		)
         elif m_name == "_value_dense":
            # attention value
            trace.append("value"		)
            A_    : Tuple								=	getattr(UpperCamelCase__ , "value"		)
         elif m_name == "_intermediate_dense":
            # attention intermediate dense
            trace.extend(["intermediate", "dense"]		)
            A_    : int								=	getattr(UpperCamelCase__ , "intermediate"		)
            A_    : Optional[int]								=	getattr(UpperCamelCase__ , "dense"		)
         elif m_name == "_output_layer_norm":
            # output layer norm
            trace.append("output"		)
            A_    : Union[str, Any]								=	getattr(UpperCamelCase__ , "output"		)
         # weights & biases
         elif m_name in ["bias", "beta"]:
            trace.append("bias"		)
            A_    : Optional[int]								=	getattr(UpperCamelCase__ , "bias"		)
         elif m_name in ["kernel", "gamma"]:
            trace.append("weight"		)
            A_    : List[Any]								=	getattr(UpperCamelCase__ , "weight"		)
         else:
            logger.warning(f"Ignored {m_name}"		)
        # for certain layers reshape is necessary
      A_    : Optional[Any]								=	".".join(UpperCamelCase__		)
      if re.match(r"(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)" , UpperCamelCase__		) or re.match(
          r"(\S+)\.attention\.output\.dense\.weight" , UpperCamelCase__		):
         A_    : Any								=	array.reshape(pointer.data.shape		)
      if "kernel" in full_name:
         A_    : List[str]								=	array.transpose()
      if pointer.shape == array.shape:
         A_    : int								=	torch.from_numpy(UpperCamelCase__		)
      else:
         raise ValueError(
             f"Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:"
             f" {array.shape}"		)
      logger.info(f"Successfully set variable {full_name} to PyTorch layer {trace}"		)
   return model

def    __snake_case  (    _lowerCAmelCase       :       Any , _lowerCAmelCase       :       Tuple , _lowerCAmelCase       :       Optional[Any]		)       ->  int:
   logger.info(f"Loading model based on config from {config_path}..."		)
   A_    : List[str]								=	BertConfig.from_json_file(UpperCamelCase__		)
   A_    : Tuple								=	BertModel(UpperCamelCase__		)

   # Load weights from checkpoint
   logger.info(f"Loading weights from checkpoint {tf_checkpoint_path}..."		)
   load_tfa_weights_in_bert(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__		)

   # Save pytorch-model
   logger.info(f"Saving PyTorch model to {pytorch_dump_path}..."		)
   torch.save(model.state_dict() , UpperCamelCase__		)


if __name__ == "__main__":
 _lowerCAmelCase							:					List[Any]		    =		argparse.ArgumentParser()
 parser.add_argument(
     '''--tf_checkpoint_path''', type=str, required=True, help='''Path to the TensorFlow 2.x checkpoint path.'''
 )
 parser.add_argument(
     '''--bert_config_file''',
     type=str,
     required=True,
     help='''The config json file corresponding to the BERT model. This specifies the model architecture.''',
 )
 parser.add_argument(
     '''--pytorch_dump_path''',
     type=str,
     required=True,
     help='''Path to the output PyTorch model (must include filename).''',
 )
 _lowerCAmelCase							:					str		    =		parser.parse_args()
 convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)

"""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      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ ):






							"""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      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__=True ):






							"""simple docstring"""





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






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__=False ):






							"""simple docstring"""





							set_seed(42 )
							A__		   =							RegressionModel()
							A__		   =							deepcopy(UpperCamelCase__ )
							A__		   =							RegressionDataset(length=80 )
							A__		   =							DataLoader(UpperCamelCase__				,			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(UpperCamelCase__				,			lr_lambda=lambda UpperCamelCase__ : epoch**0.6_5 )
														A__		   =							LambdaLR(UpperCamelCase__				,			lr_lambda=lambda UpperCamelCase__ : epoch**0.6_5 )
							# Make a copy of `model`
							if sched:
														A__   ,     A__   ,     A__   ,     A__		   =							accelerator.prepare(UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ )
							else:
														A__   ,     A__		   =							accelerator.prepare(UpperCamelCase__				,			UpperCamelCase__ )
							if sched:
														return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
							return model, ddp_model, dataloader






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							A__   ,     A__   ,     A__		   =							get_training_setup(UpperCamelCase__ )
							# Use a single batch
							A__   ,     A__		   =							next(iter(UpperCamelCase__ ) ).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(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(1_337 + iteration )
														A__		   =							ddp_input[torch.randperm(len(UpperCamelCase__ ) )]






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							A__   ,     A__   ,     A__		   =							get_training_setup(UpperCamelCase__ )
							# Use a single batch
							A__   ,     A__		   =							next(iter(UpperCamelCase__ ) ).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(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(1_337 + iteration )
														A__		   =							ddp_input[torch.randperm(len(UpperCamelCase__ ) )]






def      UpperCAmelCase    (		UpperCamelCase__=False				,			UpperCamelCase__=False ):






							"""simple docstring"""





							A__		   =							Accelerator(
							    split_batches=UpperCamelCase__				,			dispatch_batches=UpperCamelCase__				,			gradient_accumulation_steps=2 )
							# Test that context manager behaves properly
							A__   ,     A__   ,     A__		   =							get_training_setup(UpperCamelCase__ )
							for iteration, batch in enumerate(UpperCamelCase__ ):
														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(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(1_337 + iteration )
														A__		   =							ddp_input[torch.randperm(len(UpperCamelCase__ ) )]
							GradientState._reset_state()






def      UpperCAmelCase    (		UpperCamelCase__=False				,			UpperCamelCase__=False ):






							"""simple docstring"""





							A__		   =							Accelerator(
							    split_batches=UpperCamelCase__				,			dispatch_batches=UpperCamelCase__				,			gradient_accumulation_steps=2 )
							# Test that context manager behaves properly
							A__   ,     A__   ,     A__   ,     A__   ,     A__   ,     A__   ,     A__		   =							get_training_setup(UpperCamelCase__				,			UpperCamelCase__ )
							for iteration, batch in enumerate(UpperCamelCase__ ):
														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(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__		   =							(((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(1_337 + iteration )
							GradientState._reset_state()






def      UpperCAmelCase    (		):






							"""simple docstring"""





							A__		   =							Accelerator()

							A__		   =							RegressionDataset(length=80 )
							A__		   =							DataLoader(UpperCamelCase__				,			batch_size=16 )
							A__		   =							RegressionDataset(length=96 )
							A__		   =							DataLoader(UpperCamelCase__				,			batch_size=16 )
							A__   ,     A__		   =							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      UpperCAmelCase    (		):






							"""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(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      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							main()


if __name__ == "__main__":
				main()





