




import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXModel,
		)




class     snake_case__							:
				"""simple docstring"""



				def __init__(      self				:     Union[str, Any]					,      __lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     Optional[Any]=13					,      __lowerCamelCase				:     Union[str, Any]=7					,      __lowerCamelCase				:     List[str]=True					,      __lowerCamelCase				:     List[Any]=True					,      __lowerCamelCase				:     Tuple=True					,      __lowerCamelCase				:     Optional[int]=True					,      __lowerCamelCase				:     str=99					,      __lowerCamelCase				:     Optional[int]=64					,      __lowerCamelCase				:     Optional[int]=5					,      __lowerCamelCase				:     List[Any]=4					,      __lowerCamelCase				:     Optional[int]=37					,      __lowerCamelCase				:     List[str]="gelu"					,      __lowerCamelCase				:     Dict=0.1					,      __lowerCamelCase				:     List[Any]=0.1					,      __lowerCamelCase				:     List[str]=5_12					,      __lowerCamelCase				:     Any=16					,      __lowerCamelCase				:     Tuple=2					,      __lowerCamelCase				:     Optional[Any]=0.02					,      __lowerCamelCase				:     List[Any]=3					,      __lowerCamelCase				:     Optional[Any]=4					,      __lowerCamelCase				:     str=None					,      )					->     int:
							a    =			parent
							a    =			batch_size
							a    =			seq_length
							a    =			is_training
							a    =			use_input_mask
							a    =			use_token_type_ids
							a    =			use_labels
							a    =			vocab_size
							a    =			hidden_size
							a    =			num_hidden_layers
							a    =			num_attention_heads
							a    =			intermediate_size
							a    =			hidden_act
							a    =			hidden_dropout_prob
							a    =			attention_probs_dropout_prob
							a    =			max_position_embeddings
							a    =			type_vocab_size
							a    =			type_sequence_label_size
							a    =			initializer_range
							a    =			num_labels
							a    =			num_choices
							a    =			scope
							a    =			vocab_size - 1





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Optional[Any]:
							a    =			ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size							)

							a    =			None
							if self.use_input_mask:
										a    =			random_attention_mask([self.batch_size, self.seq_length]							)

							a    =			None
							if self.use_labels:
										a    =			ids_tensor([self.batch_size, self.seq_length]					,      self.num_labels							)

							a    =			self.get_config()

							return config, input_ids, input_mask, token_labels





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     str:
							return GPTNeoXConfig(
							    vocab_size=self.vocab_size					,      hidden_size=self.hidden_size					,      num_hidden_layers=self.num_hidden_layers					,      num_attention_heads=self.num_attention_heads					,      intermediate_size=self.intermediate_size					,      hidden_act=self.hidden_act					,      hidden_dropout_prob=self.hidden_dropout_prob					,      attention_probs_dropout_prob=self.attention_probs_dropout_prob					,      max_position_embeddings=self.max_position_embeddings					,      type_vocab_size=self.type_vocab_size					,      is_decoder=__lowerCamelCase					,      initializer_range=self.initializer_range					,      pad_token_id=self.pad_token_id					,      )





				def 							__UpperCAmelCase							(      self				:     str							)					->     Any:
							a    ,		a    ,		a    ,		a    =			self.prepare_config_and_inputs()

							a    =			True

							return config, input_ids, input_mask, token_labels





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]					,      __lowerCamelCase				:     Any					,      __lowerCamelCase				:     List[Any]					,      __lowerCamelCase				:     Optional[Any]							)					->     Optional[int]:
							a    =			GPTNeoXModel(config=__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a    =			model(__lowerCamelCase					,      attention_mask=__lowerCamelCase							)
							a    =			model(__lowerCamelCase							)
							self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size)							)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     str							)					->     Dict:
							a    =			True
							a    =			GPTNeoXModel(__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a    =			model(__lowerCamelCase					,      attention_mask=__lowerCamelCase							)
							self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size)							)





				def 							__UpperCAmelCase							(      self				:     Optional[int]					,      __lowerCamelCase				:     Tuple					,      __lowerCamelCase				:     Optional[Any]					,      __lowerCamelCase				:     List[str]					,      __lowerCamelCase				:     Dict							)					->     str:
							a    =			GPTNeoXForCausalLM(config=__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a    =			model(__lowerCamelCase					,      attention_mask=__lowerCamelCase					,      labels=__lowerCamelCase							)
							self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.vocab_size)							)





				def 							__UpperCAmelCase							(      self				:     Optional[int]					,      __lowerCamelCase				:     Dict					,      __lowerCamelCase				:     Any					,      __lowerCamelCase				:     Optional[int]					,      __lowerCamelCase				:     Union[str, Any]							)					->     str:
							a    =			self.num_labels
							a    =			GPTNeoXForQuestionAnswering(__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a    =			model(__lowerCamelCase					,      attention_mask=__lowerCamelCase							)
							self.parent.assertEqual(result.start_logits.shape					,      (self.batch_size, self.seq_length)							)
							self.parent.assertEqual(result.end_logits.shape					,      (self.batch_size, self.seq_length)							)





				def 							__UpperCAmelCase							(      self				:     Optional[int]					,      __lowerCamelCase				:     List[str]					,      __lowerCamelCase				:     Optional[int]					,      __lowerCamelCase				:     List[Any]					,      __lowerCamelCase				:     Optional[Any]							)					->     Union[str, Any]:
							a    =			self.num_labels
							a    =			GPTNeoXForSequenceClassification(__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a    =			ids_tensor([self.batch_size]					,      self.type_sequence_label_size							)
							a    =			model(__lowerCamelCase					,      attention_mask=__lowerCamelCase					,      labels=__lowerCamelCase							)
							self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.num_labels)							)





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]					,      __lowerCamelCase				:     List[str]					,      __lowerCamelCase				:     Any					,      __lowerCamelCase				:     Tuple					,      __lowerCamelCase				:     Dict							)					->     Dict:
							a    =			self.num_labels
							a    =			GPTNeoXForTokenClassification(__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a    =			model(__lowerCamelCase					,      attention_mask=__lowerCamelCase					,      labels=__lowerCamelCase							)
							self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.num_labels)							)





				def 							__UpperCAmelCase							(      self				:     int					,      __lowerCamelCase				:     Optional[int]					,      __lowerCamelCase				:     Tuple					,      __lowerCamelCase				:     Dict							)					->     List[Any]:
							a    =			True
							a    =			GPTNeoXForCausalLM(config=__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()

							# first forward pass
							a    =			model(__lowerCamelCase					,      attention_mask=__lowerCamelCase					,      use_cache=__lowerCamelCase							)
							a    =			outputs.past_key_values

							# create hypothetical multiple next token and extent to next_input_ids
							a    =			ids_tensor((self.batch_size, 3)					,      config.vocab_size							)
							a    =			ids_tensor((self.batch_size, 3)					,      vocab_size=2							)

							# append to next input_ids and
							a    =			torch.cat([input_ids, next_tokens]					,      dim=-1							)
							a    =			torch.cat([input_mask, next_mask]					,      dim=-1							)

							a    =			model(__lowerCamelCase					,      attention_mask=__lowerCamelCase					,      output_hidden_states=__lowerCamelCase							)
							a    =			output_from_no_past["hidden_states"][0]
							a    =			model(
							    __lowerCamelCase					,      attention_mask=__lowerCamelCase					,      past_key_values=__lowerCamelCase					,      output_hidden_states=__lowerCamelCase					,      )["hidden_states"][0]

							# select random slice
							a    =			ids_tensor((1,)					,      output_from_past.shape[-1]							).item()
							a    =			output_from_no_past[:, -3:, random_slice_idx].detach()
							a    =			output_from_past[:, :, random_slice_idx].detach()

							self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]							)

							# test that outputs are equal for slice
							self.parent.assertTrue(torch.allclose(__lowerCamelCase					,      __lowerCamelCase					,      atol=1e-3							)							)





				def 							__UpperCAmelCase							(      self				:     Dict							)					->     List[Any]:
							a    =			self.prepare_config_and_inputs()
							a    ,		a    ,		a    ,		a    =			config_and_inputs
							a    =			{"input_ids": input_ids, "attention_mask": input_mask}
							return config, inputs_dict




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



				SCREAMING_SNAKE_CASE_       :							Union[str, Any]							 =					(
				    (
				        GPTNeoXModel,
				        GPTNeoXForCausalLM,
				        GPTNeoXForQuestionAnswering,
				        GPTNeoXForSequenceClassification,
				        GPTNeoXForTokenClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE_       :							List[Any]							 =					(GPTNeoXForCausalLM,) if is_torch_available() else ()
				SCREAMING_SNAKE_CASE_       :							List[str]							 =					(
				    {
				        """feature-extraction""": GPTNeoXModel,
				        """question-answering""": GPTNeoXForQuestionAnswering,
				        """text-classification""": GPTNeoXForSequenceClassification,
				        """text-generation""": GPTNeoXForCausalLM,
				        """token-classification""": GPTNeoXForTokenClassification,
				        """zero-shot""": GPTNeoXForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				SCREAMING_SNAKE_CASE_       :							int							 =					False
				SCREAMING_SNAKE_CASE_       :							Optional[Any]							 =					False
				SCREAMING_SNAKE_CASE_       :							int							 =					False
				SCREAMING_SNAKE_CASE_       :							List[Any]							 =					False





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Optional[int]:
							a    =			GPTNeoXModelTester(self							)
							a    =			ConfigTester(self					,      config_class=__lowerCamelCase					,      hidden_size=64					,      num_attention_heads=8							)





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     Union[str, Any]:
							self.config_tester.run_common_tests()





				def 							__UpperCAmelCase							(      self				:     Dict							)					->     Dict:
							a    ,		a    ,		a    ,		a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Tuple:
							a    ,		a    ,		a    ,		a    =			self.model_tester.prepare_config_and_inputs_for_decoder()
							self.model_tester.create_and_check_model_as_decoder(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     int:
							# This regression test was failing with PyTorch < 1.3
							a    ,		a    ,		a    ,		a    =			self.model_tester.prepare_config_and_inputs_for_decoder()

							a    =			None

							self.model_tester.create_and_check_model_as_decoder(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     List[Any]:
							a    ,		a    ,		a    ,		a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_decoder_model_past_large_inputs(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     str							)					->     List[Any]:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_causal_lm(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     Optional[Any]:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     str							)					->     Union[str, Any]:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     int							)					->     str:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase							)





				@unittest.skip(reason="Feed forward chunking is not implemented"							)
				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     Any:
							pass





				@parameterized.expand([("linear",), ("dynamic",)]							)
				def 							__UpperCAmelCase							(      self				:     Union[str, Any]					,      __lowerCamelCase				:     Union[str, Any]							)					->     str:
							a    ,		a    =			self.model_tester.prepare_config_and_inputs_for_common()
							a    =			ids_tensor([1, 10]					,      config.vocab_size							)
							a    =			ids_tensor([1, int(config.max_position_embeddings * 1.5							)]					,      config.vocab_size							)

							set_seed(42							)  # Fixed seed at init time so the two models get the same random weights
							a    =			GPTNeoXModel(__lowerCamelCase							)
							original_model.to(__lowerCamelCase							)
							original_model.eval()
							a    =			original_model(__lowerCamelCase							).last_hidden_state
							a    =			original_model(__lowerCamelCase							).last_hidden_state

							set_seed(42							)  # Fixed seed at init time so the two models get the same random weights
							a    =			{"type": scaling_type, "factor": 10.0}
							a    =			GPTNeoXModel(__lowerCamelCase							)
							scaled_model.to(__lowerCamelCase							)
							scaled_model.eval()
							a    =			scaled_model(__lowerCamelCase							).last_hidden_state
							a    =			scaled_model(__lowerCamelCase							).last_hidden_state

							# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
							# maximum sequence length, so the outputs for the short input should match.
							if scaling_type == "dynamic":
										self.assertTrue(torch.allclose(__lowerCamelCase					,      __lowerCamelCase					,      atol=1e-5							)							)
							else:
										self.assertFalse(torch.allclose(__lowerCamelCase					,      __lowerCamelCase					,      atol=1e-5							)							)

							# The output should be different for long inputs
							self.assertFalse(torch.allclose(__lowerCamelCase					,      __lowerCamelCase					,      atol=1e-5							)							)




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



				@slow
				def 							__UpperCAmelCase							(      self				:     Any							)					->     List[Any]:
							a    =			AutoTokenizer.from_pretrained("EleutherAI/pythia-410m-deduped"							)
							for checkpointing in [True, False]:
										a    =			GPTNeoXForCausalLM.from_pretrained("EleutherAI/pythia-410m-deduped"							)

										if checkpointing:
													model.gradient_checkpointing_enable()
										else:
													model.gradient_checkpointing_disable()
										model.to(__lowerCamelCase							)

										a    =			tokenizer("My favorite food is"					,      return_tensors="pt"							).to(__lowerCamelCase							)
										# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
										# See: https://github.com/huggingface/transformers/pull/24193
										a    =			"My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"

										a    =			model.generate(**__lowerCamelCase					,      do_sample=__lowerCamelCase					,      max_new_tokens=20							)
										a    =			tokenizer.batch_decode(__lowerCamelCase							)[0]

										self.assertEqual(__lowerCamelCase					,      __lowerCamelCase							)












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()






