





import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 	UpperCamelCase_							(  UpperCamelCase__					,	unittest.TestCase     ):
							lowerCamelCase_							     =       LongformerTokenizer
							lowerCamelCase_							     =       True
							lowerCamelCase_							     =       LongformerTokenizerFast
							lowerCamelCase_							     =       True


							def   _snake_case	(				self     :int		) -> Tuple:



									"""simple docstring"""



									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									SCREAMING_SNAKE_CASE__								=   [
									    """l""",
									    """o""",
									    """w""",
									    """e""",
									    """r""",
									    """s""",
									    """t""",
									    """i""",
									    """d""",
									    """n""",
									    """\u0120""",
									    """\u0120l""",
									    """\u0120n""",
									    """\u0120lo""",
									    """\u0120low""",
									    """er""",
									    """\u0120lowest""",
									    """\u0120newer""",
									    """\u0120wider""",
									    """<unk>""",
									]
									SCREAMING_SNAKE_CASE__								=   dict(zip(__A				,					range(len(__A		)		)		)		)
									SCREAMING_SNAKE_CASE__								=   ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
									SCREAMING_SNAKE_CASE__								=   {"""unk_token""": """<unk>"""}

									SCREAMING_SNAKE_CASE__								=   os.path.join(self.tmpdirname				,					VOCAB_FILES_NAMES["""vocab_file"""]		)
									SCREAMING_SNAKE_CASE__								=   os.path.join(self.tmpdirname				,					VOCAB_FILES_NAMES["""merges_file"""]		)
									with open(self.vocab_file				,					"""w"""				,					encoding="""utf-8"""		) as fp:
											fp.write(json.dumps(__A		) + """\n"""		)
									with open(self.merges_file				,					"""w"""				,					encoding="""utf-8"""		) as fp:
											fp.write("""\n""".join(__A		)		)


							def   _snake_case	(				self     :str				,					**__A     :Tuple		) -> Optional[Any]:



									"""simple docstring"""



									kwargs.update(self.special_tokens_map		)
									return self.tokenizer_class.from_pretrained(self.tmpdirname				,					**__A		)


							def   _snake_case	(				self     :Union[str, Any]				,					**__A     :str		) -> List[str]:



									"""simple docstring"""



									kwargs.update(self.special_tokens_map		)
									return self.rust_tokenizer_class.from_pretrained(self.tmpdirname				,					**__A		)


							def   _snake_case	(				self     :List[Any]				,					__A     :Optional[Any]		) -> Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   """lower newer"""
									SCREAMING_SNAKE_CASE__								=   """lower newer"""
									return input_text, output_text


							def   _snake_case	(				self     :str		) -> Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.tokenizer_class(self.vocab_file				,					self.merges_file				,					**self.special_tokens_map		)
									SCREAMING_SNAKE_CASE__								=   """lower newer"""
									SCREAMING_SNAKE_CASE__								=   ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
									SCREAMING_SNAKE_CASE__								=   tokenizer.tokenize(__A		)  # , add_prefix_space=True)
									self.assertListEqual(__A				,					__A		)

									SCREAMING_SNAKE_CASE__								=   tokens + [tokenizer.unk_token]
									SCREAMING_SNAKE_CASE__								=   [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(__A		)				,					__A		)


							def   _snake_case	(				self     :int		) -> Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.get_tokenizer()

									self.assertListEqual(tokenizer.encode("""Hello world!"""				,					add_special_tokens=__A		)				,					[0, 3_1414, 232, 328, 2]		)
									self.assertListEqual(
									    tokenizer.encode("""Hello world! cécé herlolip 418"""				,					add_special_tokens=__A		)				,					[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]				,					)


							@slow
							def   _snake_case	(				self     :Any		) -> Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.tokenizer_class.from_pretrained("""allenai/longformer-base-4096"""		)

									SCREAMING_SNAKE_CASE__								=   tokenizer.encode("""sequence builders"""				,					add_special_tokens=__A		)
									SCREAMING_SNAKE_CASE__								=   tokenizer.encode("""multi-sequence build"""				,					add_special_tokens=__A		)

									SCREAMING_SNAKE_CASE__								=   tokenizer.encode(
									    """sequence builders"""				,					add_special_tokens=__A				,					add_prefix_space=__A		)
									SCREAMING_SNAKE_CASE__								=   tokenizer.encode(
									    """sequence builders"""				,					"""multi-sequence build"""				,					add_special_tokens=__A				,					add_prefix_space=__A		)

									SCREAMING_SNAKE_CASE__								=   tokenizer.build_inputs_with_special_tokens(__A		)
									SCREAMING_SNAKE_CASE__								=   tokenizer.build_inputs_with_special_tokens(__A				,					__A		)

									assert encoded_sentence == encoded_text_from_decode
									assert encoded_pair == encoded_pair_from_decode


							def   _snake_case	(				self     :List[str]		) -> Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.get_tokenizer()

									SCREAMING_SNAKE_CASE__								=   """Encode this sequence."""
									SCREAMING_SNAKE_CASE__								=   tokenizer.byte_encoder[""" """.encode("""utf-8"""		)[0]]

									# Testing encoder arguments
									SCREAMING_SNAKE_CASE__								=   tokenizer.encode(__A				,					add_special_tokens=__A				,					add_prefix_space=__A		)
									SCREAMING_SNAKE_CASE__								=   tokenizer.convert_ids_to_tokens(encoded[0]		)[0]
									self.assertNotEqual(__A				,					__A		)

									SCREAMING_SNAKE_CASE__								=   tokenizer.encode(__A				,					add_special_tokens=__A				,					add_prefix_space=__A		)
									SCREAMING_SNAKE_CASE__								=   tokenizer.convert_ids_to_tokens(encoded[0]		)[0]
									self.assertEqual(__A				,					__A		)

									tokenizer.add_special_tokens({"""bos_token""": """<s>"""}		)
									SCREAMING_SNAKE_CASE__								=   tokenizer.encode(__A				,					add_special_tokens=__A		)
									SCREAMING_SNAKE_CASE__								=   tokenizer.convert_ids_to_tokens(encoded[1]		)[0]
									self.assertNotEqual(__A				,					__A		)

									# Testing spaces after special tokens
									SCREAMING_SNAKE_CASE__								=   """<mask>"""
									tokenizer.add_special_tokens(
									    {"""mask_token""": AddedToken(__A				,					lstrip=__A				,					rstrip=__A		)}		)  # mask token has a left space
									SCREAMING_SNAKE_CASE__								=   tokenizer.convert_tokens_to_ids(__A		)

									SCREAMING_SNAKE_CASE__								=   """Encode <mask> sequence"""
									SCREAMING_SNAKE_CASE__								=   """Encode <mask>sequence"""

									SCREAMING_SNAKE_CASE__								=   tokenizer.encode(__A		)
									SCREAMING_SNAKE_CASE__								=   encoded.index(__A		)
									SCREAMING_SNAKE_CASE__								=   tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]		)[0]
									self.assertEqual(__A				,					__A		)

									SCREAMING_SNAKE_CASE__								=   tokenizer.encode(__A		)
									SCREAMING_SNAKE_CASE__								=   encoded.index(__A		)
									SCREAMING_SNAKE_CASE__								=   tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]		)[0]
									self.assertNotEqual(__A				,					__A		)


							def   _snake_case	(				self     :Tuple		) -> Union[str, Any]:



									"""simple docstring"""



									pass


							def   _snake_case	(				self     :List[str]		) -> Optional[int]:



									"""simple docstring"""



									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''		):
													SCREAMING_SNAKE_CASE__								=   self.rust_tokenizer_class.from_pretrained(__A				,					**__A		)
													SCREAMING_SNAKE_CASE__								=   self.tokenizer_class.from_pretrained(__A				,					**__A		)
													SCREAMING_SNAKE_CASE__								=   """A, <mask> AllenNLP sentence."""
													SCREAMING_SNAKE_CASE__								=   tokenizer_r.encode_plus(__A				,					add_special_tokens=__A				,					return_token_type_ids=__A		)
													SCREAMING_SNAKE_CASE__								=   tokenizer_p.encode_plus(__A				,					add_special_tokens=__A				,					return_token_type_ids=__A		)

													# token_type_ids should put 0 everywhere
													self.assertEqual(sum(tokens_r["""token_type_ids"""]		)				,					sum(tokens_p["""token_type_ids"""]		)		)

													# attention_mask should put 1 everywhere, so sum over length should be 1
													self.assertEqual(
													    sum(tokens_r["""attention_mask"""]		) / len(tokens_r["""attention_mask"""]		)				,					sum(tokens_p["""attention_mask"""]		) / len(tokens_p["""attention_mask"""]		)				,					)

													SCREAMING_SNAKE_CASE__								=   tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]		)
													SCREAMING_SNAKE_CASE__								=   tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]		)

													# Rust correctly handles the space before the mask while python doesnt
													self.assertSequenceEqual(tokens_p["""input_ids"""]				,					[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]		)
													self.assertSequenceEqual(tokens_r["""input_ids"""]				,					[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]		)

													self.assertSequenceEqual(
													    __A				,					["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]		)
													self.assertSequenceEqual(
													    __A				,					["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]		)


							def   _snake_case	(				self     :Optional[int]		) -> List[str]:



									"""simple docstring"""



									for trim_offsets, add_prefix_space in itertools.product([True, False]				,					repeat=2		):
											SCREAMING_SNAKE_CASE__								=   self.rust_tokenizer_class.from_pretrained(
											    self.tmpdirname				,					use_fast=__A				,					add_prefix_space=__A				,					trim_offsets=__A		)

											SCREAMING_SNAKE_CASE__								=   json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()		)
											SCREAMING_SNAKE_CASE__								=   json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()		)

											self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]				,					__A		)

											self.assertEqual(post_processor_state["""add_prefix_space"""]				,					__A		)
											self.assertEqual(post_processor_state["""trim_offsets"""]				,					__A		)


							def   _snake_case	(				self     :Optional[int]		) -> Any:



									"""simple docstring"""



									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''		):
													SCREAMING_SNAKE_CASE__								=   """hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
													SCREAMING_SNAKE_CASE__								=   f'''{text_of_1_token} {text_of_1_token}'''

													SCREAMING_SNAKE_CASE__								=   self.rust_tokenizer_class.from_pretrained(
													    __A				,					use_fast=__A				,					add_prefix_space=__A				,					trim_offsets=__A		)
													SCREAMING_SNAKE_CASE__								=   tokenizer_r(__A				,					return_offsets_mapping=__A				,					add_special_tokens=__A		)
													self.assertEqual(encoding.offset_mapping[0]				,					(0, len(__A		))		)
													self.assertEqual(
													    encoding.offset_mapping[1]				,					(len(__A		) + 1, len(__A		) + 1 + len(__A		))				,					)

													SCREAMING_SNAKE_CASE__								=   self.rust_tokenizer_class.from_pretrained(
													    __A				,					use_fast=__A				,					add_prefix_space=__A				,					trim_offsets=__A		)
													SCREAMING_SNAKE_CASE__								=   tokenizer_r(__A				,					return_offsets_mapping=__A				,					add_special_tokens=__A		)
													self.assertEqual(encoding.offset_mapping[0]				,					(0, len(__A		))		)
													self.assertEqual(
													    encoding.offset_mapping[1]				,					(len(__A		) + 1, len(__A		) + 1 + len(__A		))				,					)

													SCREAMING_SNAKE_CASE__								=   self.rust_tokenizer_class.from_pretrained(
													    __A				,					use_fast=__A				,					add_prefix_space=__A				,					trim_offsets=__A		)
													SCREAMING_SNAKE_CASE__								=   tokenizer_r(__A				,					return_offsets_mapping=__A				,					add_special_tokens=__A		)
													self.assertEqual(encoding.offset_mapping[0]				,					(0, len(__A		))		)
													self.assertEqual(
													    encoding.offset_mapping[1]				,					(len(__A		), len(__A		) + 1 + len(__A		))				,					)

													SCREAMING_SNAKE_CASE__								=   self.rust_tokenizer_class.from_pretrained(
													    __A				,					use_fast=__A				,					add_prefix_space=__A				,					trim_offsets=__A		)
													SCREAMING_SNAKE_CASE__								=   tokenizer_r(__A				,					return_offsets_mapping=__A				,					add_special_tokens=__A		)
													self.assertEqual(encoding.offset_mapping[0]				,					(0, len(__A		))		)
													self.assertEqual(
													    encoding.offset_mapping[1]				,					(len(__A		), len(__A		) + 1 + len(__A		))				,					)

													SCREAMING_SNAKE_CASE__								=   f''' {text}'''

													# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
													#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
													# )
													# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
													# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
													# self.assertEqual(
													#     encoding.offset_mapping[1],
													#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
													# )

													SCREAMING_SNAKE_CASE__								=   self.rust_tokenizer_class.from_pretrained(
													    __A				,					use_fast=__A				,					add_prefix_space=__A				,					trim_offsets=__A		)
													SCREAMING_SNAKE_CASE__								=   tokenizer_r(__A				,					return_offsets_mapping=__A				,					add_special_tokens=__A		)
													self.assertEqual(encoding.offset_mapping[0]				,					(1, 1 + len(__A		))		)
													self.assertEqual(
													    encoding.offset_mapping[1]				,					(1 + len(__A		) + 1, 1 + len(__A		) + 1 + len(__A		))				,					)

													SCREAMING_SNAKE_CASE__								=   self.rust_tokenizer_class.from_pretrained(
													    __A				,					use_fast=__A				,					add_prefix_space=__A				,					trim_offsets=__A		)
													SCREAMING_SNAKE_CASE__								=   tokenizer_r(__A				,					return_offsets_mapping=__A				,					add_special_tokens=__A		)
													self.assertEqual(encoding.offset_mapping[0]				,					(0, 1 + len(__A		))		)
													self.assertEqual(
													    encoding.offset_mapping[1]				,					(1 + len(__A		), 1 + len(__A		) + 1 + len(__A		))				,					)

													SCREAMING_SNAKE_CASE__								=   self.rust_tokenizer_class.from_pretrained(
													    __A				,					use_fast=__A				,					add_prefix_space=__A				,					trim_offsets=__A		)
													SCREAMING_SNAKE_CASE__								=   tokenizer_r(__A				,					return_offsets_mapping=__A				,					add_special_tokens=__A		)
													self.assertEqual(encoding.offset_mapping[0]				,					(0, 1 + len(__A		))		)
													self.assertEqual(
													    encoding.offset_mapping[1]				,					(1 + len(__A		), 1 + len(__A		) + 1 + len(__A		))				,					)






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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[Any]       , UpperCamelCase__:				str       , UpperCamelCase__:				Optional[Any]       , UpperCamelCase__:				Union[str, Any]     ):
		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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Any       , UpperCamelCase__:				Optional[Any]       , UpperCamelCase__:				Any       , UpperCamelCase__:				List[str]       , UpperCamelCase__:				Tuple=True     ):
		model.train()
		SCREAMING_SNAKE_CASE__								=   model(UpperCamelCase__     )
		SCREAMING_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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Tuple       , UpperCamelCase__:				List[Any]=False     ):
		set_seed(42     )
		SCREAMING_SNAKE_CASE__								=   RegressionModel()
		SCREAMING_SNAKE_CASE__								=   deepcopy(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   RegressionDataset(length=80     )
		SCREAMING_SNAKE_CASE__								=   DataLoader(UpperCamelCase__       , batch_size=16     )
		model.to(accelerator.device     )
		if sched:
				SCREAMING_SNAKE_CASE__								=   AdamW(params=model.parameters()       , lr=1e-3     )
				SCREAMING_SNAKE_CASE__								=   AdamW(params=ddp_model.parameters()       , lr=1e-3     )
				SCREAMING_SNAKE_CASE__								=   LambdaLR(UpperCamelCase__       , lr_lambda=lambda UpperCamelCase__     : epoch**0.6_5     )
				SCREAMING_SNAKE_CASE__								=   LambdaLR(UpperCamelCase__       , lr_lambda=lambda UpperCamelCase__     : epoch**0.6_5     )
		# Make a copy of `model`
		if sched:
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   accelerator.prepare(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )
		else:
				SCREAMING_SNAKE_CASE__						,    SCREAMING_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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Tuple     ):
		# Test when on a single CPU or GPU that the context manager does nothing
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   get_training_setup(UpperCamelCase__     )
		# Use a single batch
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   next(iter(UpperCamelCase__     )     ).values()
		for iteration in range(3     ):
				# Gather the distributed inputs and targs for the base model
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   accelerator.gather((ddp_input, ddp_target)     )
				SCREAMING_SNAKE_CASE__						,    SCREAMING_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(1_337 + iteration     )
				SCREAMING_SNAKE_CASE__								=   ddp_input[torch.randperm(len(UpperCamelCase__     )     )]






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Optional[Any]     ):
		# Test on distributed setup that context manager behaves properly
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   get_training_setup(UpperCamelCase__     )
		# Use a single batch
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   next(iter(UpperCamelCase__     )     ).values()
		for iteration in range(3     ):
				# Gather the distributed inputs and targs for the base model
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   accelerator.gather((ddp_input, ddp_target)     )
				SCREAMING_SNAKE_CASE__						,    SCREAMING_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(1_337 + iteration     )
				SCREAMING_SNAKE_CASE__								=   ddp_input[torch.randperm(len(UpperCamelCase__     )     )]






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				int=False       , UpperCamelCase__:				Union[str, Any]=False     ):
		SCREAMING_SNAKE_CASE__								=   Accelerator(
		    split_batches=UpperCamelCase__       , dispatch_batches=UpperCamelCase__       , gradient_accumulation_steps=2     )
		# Test that context manager behaves properly
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   get_training_setup(UpperCamelCase__     )
		for iteration, batch in enumerate(UpperCamelCase__     ):
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   batch.values()
				# Gather the distributed inputs and targs for the base model
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   accelerator.gather((ddp_input, ddp_target)     )
				SCREAMING_SNAKE_CASE__						,    SCREAMING_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(1_337 + iteration     )
				SCREAMING_SNAKE_CASE__								=   ddp_input[torch.randperm(len(UpperCamelCase__     )     )]
		GradientState._reset_state()






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Tuple=False       , UpperCamelCase__:				List[str]=False     ):
		SCREAMING_SNAKE_CASE__								=   Accelerator(
		    split_batches=UpperCamelCase__       , dispatch_batches=UpperCamelCase__       , gradient_accumulation_steps=2     )
		# Test that context manager behaves properly
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   get_training_setup(UpperCamelCase__       , UpperCamelCase__     )
		for iteration, batch in enumerate(UpperCamelCase__     ):
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   batch.values()
				# Gather the distributed inputs and targs for the base model
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   accelerator.gather((ddp_input, ddp_target)     )
				SCREAMING_SNAKE_CASE__						,    SCREAMING_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'''
				SCREAMING_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(1_337 + iteration     )
		GradientState._reset_state()






def 					SCREAMING_SNAKE_CASE__				(							):
		SCREAMING_SNAKE_CASE__								=   Accelerator()

		SCREAMING_SNAKE_CASE__								=   RegressionDataset(length=80     )
		SCREAMING_SNAKE_CASE__								=   DataLoader(UpperCamelCase__       , batch_size=16     )
		SCREAMING_SNAKE_CASE__								=   RegressionDataset(length=96     )
		SCREAMING_SNAKE_CASE__								=   DataLoader(UpperCamelCase__       , batch_size=16     )
		SCREAMING_SNAKE_CASE__						,    SCREAMING_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 					SCREAMING_SNAKE_CASE__				(							):
		SCREAMING_SNAKE_CASE__								=   Accelerator()
		SCREAMING_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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Union[str, Any]     ):
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
	main()