

import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__=True     ,							UpperCamelCase__="pt"   ):







							'''simple docstring'''






							snake_case_           =					{'add_prefix_space': True} if isinstance(UpperCamelCase__     ,							UpperCamelCase__   ) and not line.startswith(' '   ) else {}
							snake_case_           =					padding_side
							return tokenizer(
							    [line]     ,							max_length=UpperCamelCase__     ,							padding='max_length' if pad_to_max_length else None     ,							truncation=UpperCamelCase__     ,							return_tensors=UpperCamelCase__     ,							add_special_tokens=UpperCamelCase__     ,							**UpperCamelCase__     ,							)





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__=None     ,							):







							'''simple docstring'''






							snake_case_           =					input_ids.ne(UpperCamelCase__   ).any(dim=0   )
							if attention_mask is None:
														return input_ids[:, keep_column_mask]
							else:
														return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])







class 				lowercase       ( lowercase_    ):




				def __init__(      self   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case="train"   ,     snake_case=None   ,     snake_case=None   ,     snake_case=None   ,     snake_case=""   ,     ):
											super().__init__()
											snake_case_           =					Path(snake_case ).joinpath(type_path + '.source' )
											snake_case_           =					Path(snake_case ).joinpath(type_path + '.target' )
											snake_case_           =					self.get_char_lens(self.src_file )
											snake_case_           =					max_source_length
											snake_case_           =					max_target_length
											assert min(self.src_lens ) > 0, F'''found empty line in {self.src_file}'''
											snake_case_           =					tokenizer
											snake_case_           =					prefix
											if n_obs is not None:
																		snake_case_           =					self.src_lens[:n_obs]
											snake_case_           =					src_lang
											snake_case_           =					tgt_lang




				def __len__(      self ):
											return len(self.src_lens )




				def __getitem__(      self   ,     snake_case ):
											snake_case_           =					index + 1  # linecache starts at 1
											snake_case_           =					self.prefix + linecache.getline(str(self.src_file )   ,     snake_case ).rstrip('\n' )
											snake_case_           =					linecache.getline(str(self.tgt_file )   ,     snake_case ).rstrip('\n' )
											assert source_line, F'''empty source line for index {index}'''
											assert tgt_line, F'''empty tgt line for index {index}'''

											# Need to add eos token manually for T5
											if isinstance(self.tokenizer   ,     snake_case ):
																		source_line += self.tokenizer.eos_token
																		tgt_line += self.tokenizer.eos_token

											# Pad source and target to the right
											snake_case_           =					(
											    self.tokenizer.question_encoder if isinstance(self.tokenizer   ,     snake_case ) else self.tokenizer
											)
											snake_case_           =					self.tokenizer.generator if isinstance(self.tokenizer   ,     snake_case ) else self.tokenizer

											snake_case_           =					encode_line(snake_case   ,     snake_case   ,     self.max_source_length   ,     'right' )
											snake_case_           =					encode_line(snake_case   ,     snake_case   ,     self.max_target_length   ,     'right' )

											snake_case_           =					source_inputs['input_ids'].squeeze()
											snake_case_           =					target_inputs['input_ids'].squeeze()
											snake_case_           =					source_inputs['attention_mask'].squeeze()
											return {
											    "input_ids": source_ids,
											    "attention_mask": src_mask,
											    "decoder_input_ids": target_ids,
											}




				@staticmethod
				def 					a							(      snake_case ):
											return [len(snake_case ) for x in Path(snake_case ).open().readlines()]




				def 					a							(      self   ,     snake_case ):
											snake_case_           =					torch.stack([x['input_ids'] for x in batch] )
											snake_case_           =					torch.stack([x['attention_mask'] for x in batch] )
											snake_case_           =					torch.stack([x['decoder_input_ids'] for x in batch] )
											snake_case_           =					(
											    self.tokenizer.generator.pad_token_id
											    if isinstance(self.tokenizer   ,     snake_case )
											    else self.tokenizer.pad_token_id
											)
											snake_case_           =					(
											    self.tokenizer.question_encoder.pad_token_id
											    if isinstance(self.tokenizer   ,     snake_case )
											    else self.tokenizer.pad_token_id
											)
											snake_case_           =					trim_batch(snake_case   ,     snake_case )
											snake_case_       ,					snake_case_           =					trim_batch(snake_case   ,     snake_case   ,     attention_mask=snake_case )
											snake_case_           =					{
											    'input_ids': source_ids,
											    'attention_mask': source_mask,
											    'decoder_input_ids': y,
											}
											return batch



_UpperCAmelCase	:     Any		       = getLogger(__name__)





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							return list(itertools.chain.from_iterable(UpperCamelCase__   )   )





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					get_git_info()
							save_json(UpperCamelCase__     ,							os.path.join(UpperCamelCase__     ,							'git_log.json'   )   )





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__=4     ,							**UpperCamelCase__   ):







							'''simple docstring'''






							with open(UpperCamelCase__     ,							'w'   ) as f:
														json.dump(UpperCamelCase__     ,							UpperCamelCase__     ,							indent=UpperCamelCase__     ,							**UpperCamelCase__   )





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							with open(UpperCamelCase__   ) as f:
														return json.load(UpperCamelCase__   )





def       __lowerCamelCase			(				):







							'''simple docstring'''






							snake_case_           =					git.Repo(search_parent_directories=UpperCamelCase__   )
							snake_case_           =					{
							    'repo_id': str(UpperCamelCase__   ),
							    'repo_sha': str(repo.head.object.hexsha   ),
							    'repo_branch': str(repo.active_branch   ),
							    'hostname': str(socket.gethostname()   ),
							}
							return repo_infos





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							return list(map(UpperCamelCase__     ,							UpperCamelCase__   )   )





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							with open(UpperCamelCase__     ,							'wb'   ) as f:
														return pickle.dump(UpperCamelCase__     ,							UpperCamelCase__   )





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							def remove_articles(UpperCamelCase__   ):
														return re.sub(r'\b(a|an|the)\b'     ,							' '     ,							UpperCamelCase__   )

							def white_space_fix(UpperCamelCase__   ):
														return " ".join(text.split()   )

							def remove_punc(UpperCamelCase__   ):
														snake_case_           =					set(string.punctuation   )
														return "".join(ch for ch in text if ch not in exclude   )

							def lower(UpperCamelCase__   ):
														return text.lower()

							return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase__   )   )   )   )





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					normalize_answer(UpperCamelCase__   ).split()
							snake_case_           =					normalize_answer(UpperCamelCase__   ).split()
							snake_case_           =					Counter(UpperCamelCase__   ) & Counter(UpperCamelCase__   )
							snake_case_           =					sum(common.values()   )
							if num_same == 0:
														return 0
							snake_case_           =					1.0 * num_same / len(UpperCamelCase__   )
							snake_case_           =					1.0 * num_same / len(UpperCamelCase__   )
							snake_case_           =					(2 * precision * recall) / (precision + recall)
							return fa





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							return normalize_answer(UpperCamelCase__   ) == normalize_answer(UpperCamelCase__   )





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							assert len(UpperCamelCase__   ) == len(UpperCamelCase__   )
							snake_case_           =					0
							for hypo, pred in zip(UpperCamelCase__     ,							UpperCamelCase__   ):
														em += exact_match_score(UpperCamelCase__     ,							UpperCamelCase__   )
							if len(UpperCamelCase__   ) > 0:
														em /= len(UpperCamelCase__   )
							return {"em": em}





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							return model_prefix.startswith('rag'   )





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					{p: p for p in extra_params}
							# T5 models don't have `dropout` param, they have `dropout_rate` instead
							snake_case_           =					'dropout_rate'
							for p in extra_params:
														if getattr(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):
																					if not hasattr(UpperCamelCase__     ,							UpperCamelCase__   ) and not hasattr(UpperCamelCase__     ,							equivalent_param[p]   ):
																												logger.info('config doesn\'t have a `{}` attribute'.format(UpperCamelCase__   )   )
																												delattr(UpperCamelCase__     ,							UpperCamelCase__   )
																												continue
																					snake_case_           =					p if hasattr(UpperCamelCase__     ,							UpperCamelCase__   ) else equivalent_param[p]
																					setattr(UpperCamelCase__     ,							UpperCamelCase__     ,							getattr(UpperCamelCase__     ,							UpperCamelCase__   )   )
																					delattr(UpperCamelCase__     ,							UpperCamelCase__   )
							return hparams, config







from copy import deepcopy

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

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed





def       __lowerCamelCase			(				UpperCamelCase__     ,							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       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__=True   ):







							'''simple docstring'''






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





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__=False   ):







							'''simple docstring'''






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





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






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

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

														# Shuffle ddp_input on each iteration
														torch.manual_seed(1337 + iteration   )
														snake_case_           =					ddp_input[torch.randperm(len(UpperCamelCase__   )   )]





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






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

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

        # Shuffle ddp_input on each iteration
														torch.manual_seed(1337 + iteration   )
														snake_case_           =					ddp_input[torch.randperm(len(UpperCamelCase__   )   )]





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







							'''simple docstring'''






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

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

        # Shuffle ddp_input on each iteration
														torch.manual_seed(1337 + iteration   )
														snake_case_           =					ddp_input[torch.randperm(len(UpperCamelCase__   )   )]
							GradientState._reset_state()





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







							'''simple docstring'''






							snake_case_           =					Accelerator(
							    split_batches=UpperCamelCase__     ,							dispatch_batches=UpperCamelCase__     ,							gradient_accumulation_steps=2   )
							# Test that context manager behaves properly
							snake_case_       ,					snake_case_       ,					snake_case_       ,					snake_case_       ,					snake_case_       ,					snake_case_       ,					snake_case_           =					get_training_setup(UpperCamelCase__     ,							UpperCamelCase__   )
							for iteration, batch in enumerate(UpperCamelCase__   ):
														snake_case_       ,					snake_case_           =					batch.values()
														# Gather the distributed inputs and targs for the base model
														snake_case_       ,					snake_case_           =					accelerator.gather((ddp_input, ddp_target)   )
														snake_case_       ,					snake_case_           =					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'''
														snake_case_           =					(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(UpperCamelCase__   ))
														if accelerator.num_processes > 1:
																					check_model_parameters(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   )
														# Shuffle ddp_input on each iteration
														torch.manual_seed(1337 + iteration   )
							GradientState._reset_state()





def       __lowerCamelCase			(				):







							'''simple docstring'''






							snake_case_           =					Accelerator()

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





def       __lowerCamelCase			(				):







							'''simple docstring'''






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

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





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							main()


if __name__ == "__main__":
					main()




