




import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCAmelCase		:				Optional[int]       			=		get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
		from transformers.models.mbart.modeling_mbart import shift_tokens_right

__lowerCAmelCase		:				str       			=		25_0004
__lowerCAmelCase		:				str       			=		25_0020




@require_sentencepiece
@require_tokenizers
class     snake_case__							(_UpperCamelCase    ,       unittest.TestCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Tuple							 =					MBartTokenizer
				SCREAMING_SNAKE_CASE_       :							Optional[int]							 =					MBartTokenizerFast
				SCREAMING_SNAKE_CASE_       :							int							 =					True
				SCREAMING_SNAKE_CASE_       :							Optional[int]							 =					True





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Optional[int]:
							super().setUp()

							# We have a SentencePiece fixture for testing
							a    =			MBartTokenizer(__lowerCamelCase					,      keep_accents=__lowerCamelCase							)
							tokenizer.save_pretrained(self.tmpdirname							)





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     Any:
							a    =			MBartTokenizer(__lowerCamelCase					,      keep_accents=__lowerCamelCase							)

							a    =			tokenizer.tokenize("This is a test"							)
							self.assertListEqual(__lowerCamelCase					,      ["▁This", "▁is", "▁a", "▁t", "est"]							)

							self.assertListEqual(
							    tokenizer.convert_tokens_to_ids(__lowerCamelCase							)					,      [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]					,      )

							a    =			tokenizer.tokenize("I was born in 92000, and this is falsé."							)
							self.assertListEqual(
							    __lowerCamelCase					,      [
							        SPIECE_UNDERLINE + "I",
							        SPIECE_UNDERLINE + "was",
							        SPIECE_UNDERLINE + "b",
							        "or",
							        "n",
							        SPIECE_UNDERLINE + "in",
							        SPIECE_UNDERLINE + "",
							        "9",
							        "2",
							        "0",
							        "0",
							        "0",
							        ",",
							        SPIECE_UNDERLINE + "and",
							        SPIECE_UNDERLINE + "this",
							        SPIECE_UNDERLINE + "is",
							        SPIECE_UNDERLINE + "f",
							        "al",
							        "s",
							        "é",
							        ".",
							    ]					,      )
							a    =			tokenizer.convert_tokens_to_ids(__lowerCamelCase							)
							self.assertListEqual(
							    __lowerCamelCase					,      [
							        value + tokenizer.fairseq_offset
							        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
							        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
							    ]					,      )

							a    =			tokenizer.convert_ids_to_tokens(__lowerCamelCase							)
							self.assertListEqual(
							    __lowerCamelCase					,      [
							        SPIECE_UNDERLINE + "I",
							        SPIECE_UNDERLINE + "was",
							        SPIECE_UNDERLINE + "b",
							        "or",
							        "n",
							        SPIECE_UNDERLINE + "in",
							        SPIECE_UNDERLINE + "",
							        "<unk>",
							        "2",
							        "0",
							        "0",
							        "0",
							        ",",
							        SPIECE_UNDERLINE + "and",
							        SPIECE_UNDERLINE + "this",
							        SPIECE_UNDERLINE + "is",
							        SPIECE_UNDERLINE + "f",
							        "al",
							        "s",
							        "<unk>",
							        ".",
							    ]					,      )





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Tuple:
							if not self.test_slow_tokenizer:
										# as we don't have a slow version, we can't compare the outputs between slow and fast versions
										return

							a    =			(self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart", {})
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
													a    =			self.rust_tokenizer_class.from_pretrained(__lowerCamelCase					,      **__lowerCamelCase							)
													a    =			self.tokenizer_class.from_pretrained(__lowerCamelCase					,      **__lowerCamelCase							)

													a    =			tempfile.mkdtemp()

													a    =			tokenizer_r.save_pretrained(__lowerCamelCase							)
													a    =			tokenizer_p.save_pretrained(__lowerCamelCase							)

													# Checks it save with the same files + the tokenizer.json file for the fast one
													self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files							)							)
													a    =			tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f							)
													self.assertSequenceEqual(__lowerCamelCase					,      __lowerCamelCase							)

													# Checks everything loads correctly in the same way
													a    =			tokenizer_r.from_pretrained(__lowerCamelCase							)
													a    =			tokenizer_p.from_pretrained(__lowerCamelCase							)

													# Check special tokens are set accordingly on Rust and Python
													for key in tokenizer_pp.special_tokens_map:
																self.assertTrue(hasattr(__lowerCamelCase					,      __lowerCamelCase							)							)
																# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

													shutil.rmtree(__lowerCamelCase							)

													# Save tokenizer rust, legacy_format=True
													a    =			tempfile.mkdtemp()

													a    =			tokenizer_r.save_pretrained(__lowerCamelCase					,      legacy_format=__lowerCamelCase							)
													a    =			tokenizer_p.save_pretrained(__lowerCamelCase							)

													# Checks it save with the same files
													self.assertSequenceEqual(__lowerCamelCase					,      __lowerCamelCase							)

													# Checks everything loads correctly in the same way
													a    =			tokenizer_r.from_pretrained(__lowerCamelCase							)
													a    =			tokenizer_p.from_pretrained(__lowerCamelCase							)

													# Check special tokens are set accordingly on Rust and Python
													for key in tokenizer_pp.special_tokens_map:
																self.assertTrue(hasattr(__lowerCamelCase					,      __lowerCamelCase							)							)

													shutil.rmtree(__lowerCamelCase							)

													# Save tokenizer rust, legacy_format=False
													a    =			tempfile.mkdtemp()

													a    =			tokenizer_r.save_pretrained(__lowerCamelCase					,      legacy_format=__lowerCamelCase							)
													a    =			tokenizer_p.save_pretrained(__lowerCamelCase							)

													# Checks it saved the tokenizer.json file
													self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files							)							)

													# Checks everything loads correctly in the same way
													a    =			tokenizer_r.from_pretrained(__lowerCamelCase							)
													a    =			tokenizer_p.from_pretrained(__lowerCamelCase							)

													# Check special tokens are set accordingly on Rust and Python
													for key in tokenizer_pp.special_tokens_map:
																self.assertTrue(hasattr(__lowerCamelCase					,      __lowerCamelCase							)							)

													shutil.rmtree(__lowerCamelCase							)




@require_torch
@require_sentencepiece
@require_tokenizers
class     snake_case__							(unittest.TestCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							int							 =					"""facebook/mbart-large-en-ro"""
				SCREAMING_SNAKE_CASE_       :							Optional[Any]							 =					[
				    """ UN Chief Says There Is No Military Solution in Syria""",
				    """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
				]
				SCREAMING_SNAKE_CASE_       :							int							 =					[
				    """Şeful ONU declară că nu există o soluţie militară în Siria""",
				    """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
				    """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
				    """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
				]
				SCREAMING_SNAKE_CASE_       :							Dict							 =					[82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2, EN_CODE]





				@classmethod
				def 							__UpperCAmelCase							(      cls				:     Optional[int]							)					->     int:
							a    =			MBartTokenizer.from_pretrained(
							    cls.checkpoint_name					,      src_lang="en_XX"					,      tgt_lang="ro_RO"							)
							a    =			1
							return cls





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     Dict:
							self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"]					,      25_00_01							)
							self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"]					,      25_00_04							)
							self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"]					,      25_00_20							)





				def 							__UpperCAmelCase							(      self				:     str							)					->     Optional[Any]:
							a    =			self.tokenizer.batch_encode_plus(self.src_text							).input_ids[0]
							self.assertListEqual(self.expected_src_tokens					,      __lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Any							)					->     Union[str, Any]:
							self.assertIn(__lowerCamelCase					,      self.tokenizer.all_special_ids							)
							a    =			[RO_CODE, 8_84, 90_19, 96, 9, 9_16, 8_67_92, 36, 1_87_43, 1_55_96, 5, 2]
							a    =			self.tokenizer.decode(__lowerCamelCase					,      skip_special_tokens=__lowerCamelCase							)
							a    =			self.tokenizer.decode(generated_ids[1:]					,      skip_special_tokens=__lowerCamelCase							)
							self.assertEqual(__lowerCamelCase					,      __lowerCamelCase							)
							self.assertNotIn(self.tokenizer.eos_token					,      __lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     Optional[int]:
							a    =			["this is gunna be a long sentence " * 20]
							assert isinstance(src_text[0]					,      __lowerCamelCase							)
							a    =			10
							a    =			self.tokenizer(__lowerCamelCase					,      max_length=__lowerCamelCase					,      truncation=__lowerCamelCase							).input_ids[0]
							self.assertEqual(ids[-2]					,      2							)
							self.assertEqual(ids[-1]					,      __lowerCamelCase							)
							self.assertEqual(len(__lowerCamelCase							)					,      __lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     List[str]:
							self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]							)					,      [25_00_26, 25_00_01]							)





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     int:
							a    =			tempfile.mkdtemp()
							a    =			self.tokenizer.fairseq_tokens_to_ids
							self.tokenizer.save_pretrained(__lowerCamelCase							)
							a    =			MBartTokenizer.from_pretrained(__lowerCamelCase							)
							self.assertDictEqual(new_tok.fairseq_tokens_to_ids					,      __lowerCamelCase							)





				@require_torch
				def 							__UpperCAmelCase							(      self				:     str							)					->     Tuple:
							a    =			self.tokenizer(self.src_text					,      text_target=self.tgt_text					,      padding=__lowerCamelCase					,      return_tensors="pt"							)
							a    =			shift_tokens_right(batch["labels"]					,      self.tokenizer.pad_token_id							)

							# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
							assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
							assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
							assert batch.decoder_input_ids[1][-1] == 2
							assert batch.labels[1][-2:].tolist() == [2, RO_CODE]





				@require_torch
				def 							__UpperCAmelCase							(      self				:     Dict							)					->     Dict:
							a    =			self.tokenizer(
							    self.src_text					,      text_target=self.tgt_text					,      padding=__lowerCamelCase					,      truncation=__lowerCamelCase					,      max_length=len(self.expected_src_tokens							)					,      return_tensors="pt"					,      )

							a    =			shift_tokens_right(batch["labels"]					,      self.tokenizer.pad_token_id							)

							self.assertIsInstance(__lowerCamelCase					,      __lowerCamelCase							)

							self.assertEqual((2, 14)					,      batch.input_ids.shape							)
							self.assertEqual((2, 14)					,      batch.attention_mask.shape							)
							a    =			batch.input_ids.tolist()[0]
							self.assertListEqual(self.expected_src_tokens					,      __lowerCamelCase							)
							self.assertEqual(2					,      batch.decoder_input_ids[0, -1]							)  # EOS
							# Test that special tokens are reset
							self.assertEqual(self.tokenizer.prefix_tokens					,      []							)
							self.assertEqual(self.tokenizer.suffix_tokens					,      [self.tokenizer.eos_token_id, EN_CODE]							)





				def 							__UpperCAmelCase							(      self				:     Any							)					->     List[str]:
							a    =			self.tokenizer(self.src_text					,      padding=__lowerCamelCase					,      truncation=__lowerCamelCase					,      max_length=3					,      return_tensors="pt"							)
							a    =			self.tokenizer(
							    text_target=self.tgt_text					,      padding=__lowerCamelCase					,      truncation=__lowerCamelCase					,      max_length=10					,      return_tensors="pt"							)
							a    =			targets["input_ids"]
							a    =			shift_tokens_right(__lowerCamelCase					,      self.tokenizer.pad_token_id							)

							self.assertEqual(batch.input_ids.shape[1]					,      3							)
							self.assertEqual(batch.decoder_input_ids.shape[1]					,      10							)





				@require_torch
				def 							__UpperCAmelCase							(      self				:     str							)					->     List[str]:
							a    =			self.tokenizer._build_translation_inputs(
							    "A test"					,      return_tensors="pt"					,      src_lang="en_XX"					,      tgt_lang="ar_AR"							)

							self.assertEqual(
							    nested_simplify(__lowerCamelCase							)					,      {
							        # A, test, EOS, en_XX
							        "input_ids": [[62, 30_34, 2, 25_00_04]],
							        "attention_mask": [[1, 1, 1, 1]],
							        # ar_AR
							        "forced_bos_token_id": 25_00_01,
							    }					,      )












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()






