

"""simple docstring"""





import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_a       =							logging.get_logger(__name__)


_a       =							{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all BART models at https://huggingface.co/models?filter=bart
_a       =							{
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
}

_a       =							{
    'facebook/bart-base': 10_24,
    'facebook/bart-large': 10_24,
    'facebook/bart-large-mnli': 10_24,
    'facebook/bart-large-cnn': 10_24,
    'facebook/bart-large-xsum': 10_24,
    'yjernite/bart_eli5': 10_24,
}




@lru_cache()
def   _A  (  )	->						List[Any]:
   '''simple docstring'''


   __lowercase								=						(
       list(range(ord("!"),			ord("~") + 1)) + list(range(ord("¡"),			ord("¬") + 1)) + list(range(ord("®"),			ord("ÿ") + 1))
   )
   __lowercase								=						bs[:]
   __lowercase								=						0
   for b in range(2**8):
      if b not in bs:
         bs.append(UpperCamelCase_)
         cs.append(2**8 + n)
         n += 1
   __lowercase								=						[chr(UpperCamelCase_) for n in cs]
   return dict(zip(UpperCamelCase_,			UpperCamelCase_))




def   _A  (  UpperCamelCase_     :       Dict)	->						Tuple:
   '''simple docstring'''


   __lowercase								=						set()
   __lowercase								=						word[0]
   for char in word[1:]:
      pairs.add((prev_char, char))
      __lowercase								=						char
   return pairs




class 					_lowerCAmelCase  (	_UpperCamelCase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Optional[Any]   		=	VOCAB_FILES_NAMES
 __UpperCAmelCase	:					int   		=	PRETRAINED_VOCAB_FILES_MAP
 __UpperCAmelCase	:					Dict   		=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __UpperCAmelCase	:					Optional[Any]   		=	["""input_ids""", """attention_mask"""]


 def __init__(			self							:			Any,					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			List[Any],					UpperCAmelCase__							:			Optional[int]="replace",					UpperCAmelCase__							:			List[str]="<s>",					UpperCAmelCase__							:			Union[str, Any]="</s>",					UpperCAmelCase__							:			Dict="</s>",					UpperCAmelCase__							:			Optional[int]="<s>",					UpperCAmelCase__							:			List[str]="<unk>",					UpperCAmelCase__							:			str="<pad>",					UpperCAmelCase__							:			Tuple="<mask>",					UpperCAmelCase__							:			Tuple=False,					**UpperCAmelCase__							:			Optional[Any],					):
    __lowercase								=						AddedToken(__lowerCamelCase,					lstrip=__lowerCamelCase,					rstrip=__lowerCamelCase	) if isinstance(__lowerCamelCase,					__lowerCamelCase	) else bos_token
    __lowercase								=						AddedToken(__lowerCamelCase,					lstrip=__lowerCamelCase,					rstrip=__lowerCamelCase	) if isinstance(__lowerCamelCase,					__lowerCamelCase	) else eos_token
    __lowercase								=						AddedToken(__lowerCamelCase,					lstrip=__lowerCamelCase,					rstrip=__lowerCamelCase	) if isinstance(__lowerCamelCase,					__lowerCamelCase	) else sep_token
    __lowercase								=						AddedToken(__lowerCamelCase,					lstrip=__lowerCamelCase,					rstrip=__lowerCamelCase	) if isinstance(__lowerCamelCase,					__lowerCamelCase	) else cls_token
    __lowercase								=						AddedToken(__lowerCamelCase,					lstrip=__lowerCamelCase,					rstrip=__lowerCamelCase	) if isinstance(__lowerCamelCase,					__lowerCamelCase	) else unk_token
    __lowercase								=						AddedToken(__lowerCamelCase,					lstrip=__lowerCamelCase,					rstrip=__lowerCamelCase	) if isinstance(__lowerCamelCase,					__lowerCamelCase	) else pad_token

    # Mask token behave like a normal word, i.e. include the space before it
    __lowercase								=						AddedToken(__lowerCamelCase,					lstrip=__lowerCamelCase,					rstrip=__lowerCamelCase	) if isinstance(__lowerCamelCase,					__lowerCamelCase	) else mask_token

    super().__init__(
        errors=__lowerCamelCase,					bos_token=__lowerCamelCase,					eos_token=__lowerCamelCase,					unk_token=__lowerCamelCase,					sep_token=__lowerCamelCase,					cls_token=__lowerCamelCase,					pad_token=__lowerCamelCase,					mask_token=__lowerCamelCase,					add_prefix_space=__lowerCamelCase,					**__lowerCamelCase,					)

    with open(__lowerCamelCase,					encoding="utf-8"	) as vocab_handle:
       __lowercase								=						json.load(__lowerCamelCase	)
    __lowercase								=						{v: k for k, v in self.encoder.items()}
    __lowercase								=						errors  # how to handle errors in decoding
    __lowercase								=						bytes_to_unicode()
    __lowercase								=						{v: k for k, v in self.byte_encoder.items()}
    with open(__lowerCamelCase,					encoding="utf-8"	) as merges_handle:
       __lowercase								=						merges_handle.read().split("\n"	)[1:-1]
    __lowercase								=						[tuple(merge.split()	) for merge in bpe_merges]
    __lowercase								=						dict(zip(__lowerCamelCase,					range(len(__lowerCamelCase	)	)	)	)
    __lowercase								=						{}
    __lowercase								=						add_prefix_space

    # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
    __lowercase								=						re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"	)


 @property
 def 					_lowercase (			self							:			Tuple	):
    return len(self.encoder	)


 def 					_lowercase (			self							:			Optional[int]	):
    return dict(self.encoder,					**self.added_tokens_encoder	)


 def 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			Any	):
    if token in self.cache:
       return self.cache[token]
    __lowercase								=						tuple(__lowerCamelCase	)
    __lowercase								=						get_pairs(__lowerCamelCase	)

    if not pairs:
       return token

    while True:
       __lowercase								=						min(__lowerCamelCase,					key=lambda UpperCAmelCase__	: self.bpe_ranks.get(__lowerCamelCase,					float("inf"	)	)	)
       if bigram not in self.bpe_ranks:
          break
       __lowercase		,__lowercase								=						bigram
       __lowercase								=						[]
       __lowercase								=						0
       while i < len(__lowerCamelCase	):
          try:
             __lowercase								=						word.index(__lowerCamelCase,					__lowerCamelCase	)
          except ValueError:
             new_word.extend(word[i:]	)
             break
          else:
             new_word.extend(word[i:j]	)
             __lowercase								=						j

          if word[i] == first and i < len(__lowerCamelCase	) - 1 and word[i + 1] == second:
             new_word.append(first + second	)
             i += 2
          else:
             new_word.append(word[i]	)
             i += 1
       __lowercase								=						tuple(__lowerCamelCase	)
       __lowercase								=						new_word
       if len(__lowerCamelCase	) == 1:
          break
       else:
          __lowercase								=						get_pairs(__lowerCamelCase	)
    __lowercase								=						" ".join(__lowerCamelCase	)
    __lowercase								=						word
    return word


 def 					_lowercase (			self							:			Tuple,					UpperCAmelCase__							:			List[str]	):
    __lowercase								=						[]
    for token in re.findall(self.pat,					__lowerCamelCase	):
       __lowercase								=						"".join(
           self.byte_encoder[b] for b in token.encode("utf-8"	)	)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
       bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__lowerCamelCase	).split(" "	)	)
    return bpe_tokens


 def 					_lowercase (			self							:			Dict,					UpperCAmelCase__							:			List[str]	):
    return self.encoder.get(__lowerCamelCase,					self.encoder.get(self.unk_token	)	)


 def 					_lowercase (			self							:			Optional[int],					UpperCAmelCase__							:			Optional[int]	):
    return self.decoder.get(__lowerCamelCase	)


 def 					_lowercase (			self							:			str,					UpperCAmelCase__							:			int	):
    __lowercase								=						"".join(__lowerCamelCase	)
    __lowercase								=						bytearray([self.byte_decoder[c] for c in text]	).decode("utf-8",					errors=self.errors	)
    return text


 def 					_lowercase (			self							:			Tuple,					UpperCAmelCase__							:			str,					UpperCAmelCase__							:			Optional[str] = None	):
    if not os.path.isdir(__lowerCamelCase	):
       logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""	)
       return
    __lowercase								=						os.path.join(
        __lowerCamelCase,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)
    __lowercase								=						os.path.join(
        __lowerCamelCase,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]	)

    with open(__lowerCamelCase,					"w",					encoding="utf-8"	) as f:
       f.write(json.dumps(self.encoder,					indent=2,					sort_keys=__lowerCamelCase,					ensure_ascii=__lowerCamelCase	) + "\n"	)

    __lowercase								=						0
    with open(__lowerCamelCase,					"w",					encoding="utf-8"	) as writer:
       writer.write("#version: 0.2\n"	)
       for bpe_tokens, token_index in sorted(self.bpe_ranks.items(),					key=lambda UpperCAmelCase__	: kv[1]	):
          if index != token_index:
             logger.warning(
                 F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                 " Please check that the tokenizer is not corrupted!"	)
             __lowercase								=						token_index
          writer.write(" ".join(__lowerCamelCase	) + "\n"	)
          index += 1

    return vocab_file, merge_file


 def 					_lowercase (			self							:			int,					UpperCAmelCase__							:			List[int],					UpperCAmelCase__							:			Optional[List[int]] = None	):
    if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    __lowercase								=						[self.cls_token_id]
    __lowercase								=						[self.sep_token_id]
    return cls + token_ids_a + sep + sep + token_ids_a + sep


 def 					_lowercase (			self							:			List[Any],					UpperCAmelCase__							:			List[int],					UpperCAmelCase__							:			Optional[List[int]] = None,					UpperCAmelCase__							:			bool = False	):
    if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=__lowerCamelCase,					token_ids_a=__lowerCamelCase,					already_has_special_tokens=__lowerCamelCase	)

    if token_ids_a is None:
       return [1] + ([0] * len(__lowerCamelCase	)) + [1]
    return [1] + ([0] * len(__lowerCamelCase	)) + [1, 1] + ([0] * len(__lowerCamelCase	)) + [1]


 def 					_lowercase (			self							:			int,					UpperCAmelCase__							:			List[int],					UpperCAmelCase__							:			Optional[List[int]] = None	):
    __lowercase								=						[self.sep_token_id]
    __lowercase								=						[self.cls_token_id]

    if token_ids_a is None:
       return len(cls + token_ids_a + sep	) * [0]
    return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]



 def 					_lowercase (			self							:			Any,					UpperCAmelCase__							:			str,					UpperCAmelCase__							:			Union[str, Any]=False,					**UpperCAmelCase__							:			Tuple	):
    __lowercase								=						kwargs.pop("add_prefix_space",					self.add_prefix_space	)
    if (is_split_into_words or add_prefix_space) and (len(__lowerCamelCase	) > 0 and not text[0].isspace()):
       __lowercase								=						" " + text
    return (text, kwargs)










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()






