



import unittest

from transformers import LiltConfig, is_torch_available
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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    LiltForQuestionAnswering,
						    LiltForSequenceClassification,
						    LiltForTokenClassification,
						    LiltModel,
						)
						from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST



class 				__snake_case						:




							def __init__(       self     :       Union[str, Any]				,				A_     :       List[Any]				,				A_     :       Dict=1_3				,				A_     :       Any=7				,				A_     :       Tuple=True				,				A_     :       Dict=True				,				A_     :       str=True				,				A_     :       Tuple=True				,				A_     :       int=9_9				,				A_     :       List[Any]=2_4				,				A_     :       str=2				,				A_     :       int=6				,				A_     :       Optional[Any]=3_7				,				A_     :       Dict="gelu"				,				A_     :       Tuple=0.1				,				A_     :       int=0.1				,				A_     :       List[Any]=5_1_2				,				A_     :       List[str]=1_6				,				A_     :       List[Any]=2				,				A_     :       Dict=0.02				,				A_     :       List[Any]=3				,				A_     :       Union[str, Any]=None				,				A_     :       List[Any]=1_0_0_0				,				):
									lowerCAmelCase_			:     int									= parent
									lowerCAmelCase_			:     Optional[int]									= batch_size
									lowerCAmelCase_			:     Dict									= seq_length
									lowerCAmelCase_			:     Dict									= is_training
									lowerCAmelCase_			:     List[str]									= use_input_mask
									lowerCAmelCase_			:     Optional[int]									= use_token_type_ids
									lowerCAmelCase_			:     str									= use_labels
									lowerCAmelCase_			:     List[Any]									= vocab_size
									lowerCAmelCase_			:     Any									= hidden_size
									lowerCAmelCase_			:     Union[str, Any]									= num_hidden_layers
									lowerCAmelCase_			:     Optional[int]									= num_attention_heads
									lowerCAmelCase_			:     int									= intermediate_size
									lowerCAmelCase_			:     Any									= hidden_act
									lowerCAmelCase_			:     Any									= hidden_dropout_prob
									lowerCAmelCase_			:     int									= attention_probs_dropout_prob
									lowerCAmelCase_			:     Union[str, Any]									= max_position_embeddings
									lowerCAmelCase_			:     List[Any]									= type_vocab_size
									lowerCAmelCase_			:     int									= type_sequence_label_size
									lowerCAmelCase_			:     List[str]									= initializer_range
									lowerCAmelCase_			:     List[str]									= num_labels
									lowerCAmelCase_			:     int									= scope
									lowerCAmelCase_			:     Dict									= range_bbox




							def    UpperCAmelCase__			(       self     :       Optional[int]):
									lowerCAmelCase_			:     List[Any]									= ids_tensor([self.batch_size, self.seq_length]				,				self.vocab_size)

									lowerCAmelCase_			:     Tuple									= ids_tensor([self.batch_size, self.seq_length, 4]				,				self.range_bbox)
									# Ensure that bbox is legal
									for i in range(bbox.shape[0]):
											for j in range(bbox.shape[1]):
													if bbox[i, j, 3] < bbox[i, j, 1]:
															lowerCAmelCase_			:     Tuple									= bbox[i, j, 3]
															lowerCAmelCase_			:     str									= bbox[i, j, 1]
															lowerCAmelCase_			:     List[Any]									= t
													if bbox[i, j, 2] < bbox[i, j, 0]:
															lowerCAmelCase_			:     Optional[Any]									= bbox[i, j, 2]
															lowerCAmelCase_			:     Union[str, Any]									= bbox[i, j, 0]
															lowerCAmelCase_			:     str									= t

									lowerCAmelCase_			:     List[Any]									= None
									if self.use_input_mask:
											lowerCAmelCase_			:     Optional[Any]									= ids_tensor([self.batch_size, self.seq_length]				,				vocab_size=2)

									lowerCAmelCase_			:     Tuple									= None
									if self.use_token_type_ids:
											lowerCAmelCase_			:     Tuple									= ids_tensor([self.batch_size, self.seq_length]				,				self.type_vocab_size)

									lowerCAmelCase_			:     Any									= None
									lowerCAmelCase_			:     Optional[int]									= None
									if self.use_labels:
											lowerCAmelCase_			:     str									= ids_tensor([self.batch_size]				,				self.type_sequence_label_size)
											lowerCAmelCase_			:     str									= ids_tensor([self.batch_size, self.seq_length]				,				self.num_labels)

									lowerCAmelCase_			:     Tuple									= self.get_config()

									return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels




							def    UpperCAmelCase__			(       self     :       List[str]):
									return LiltConfig(
									    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				,				initializer_range=self.initializer_range				,				)




							def    UpperCAmelCase__			(       self     :       List[str]				,				A_     :       Optional[int]				,				A_     :       str				,				A_     :       Optional[Any]				,				A_     :       List[str]				,				A_     :       Union[str, Any]				,				A_     :       List[str]				,				A_     :       Any				,				):
									lowerCAmelCase_			:     Optional[Any]									= LiltModel(config=A_)
									model.to(A_)
									model.eval()
									lowerCAmelCase_			:     Union[str, Any]									= model(A_				,				bbox=A_				,				attention_mask=A_				,				token_type_ids=A_)
									lowerCAmelCase_			:     Tuple									= model(A_				,				bbox=A_				,				token_type_ids=A_)
									lowerCAmelCase_			:     Optional[int]									= model(A_				,				bbox=A_)

									self.parent.assertEqual(result.last_hidden_state.shape				,				(self.batch_size, self.seq_length, self.hidden_size))
									self.parent.assertEqual(result.pooler_output.shape				,				(self.batch_size, self.hidden_size))




							def    UpperCAmelCase__			(       self     :       List[str]				,				A_     :       Union[str, Any]				,				A_     :       str				,				A_     :       Any				,				A_     :       Optional[int]				,				A_     :       Optional[int]				,				A_     :       Optional[Any]				,				A_     :       List[Any]				,				):
									lowerCAmelCase_			:     str									= self.num_labels
									lowerCAmelCase_			:     Dict									= LiltForTokenClassification(config=A_)
									model.to(A_)
									model.eval()
									lowerCAmelCase_			:     int									= model(
									    A_				,				bbox=A_				,				attention_mask=A_				,				token_type_ids=A_				,				labels=A_)
									self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.seq_length, self.num_labels))




							def    UpperCAmelCase__			(       self     :       Union[str, Any]				,				A_     :       List[str]				,				A_     :       int				,				A_     :       List[Any]				,				A_     :       List[Any]				,				A_     :       int				,				A_     :       str				,				A_     :       Union[str, Any]				,				):
									lowerCAmelCase_			:     Dict									= LiltForQuestionAnswering(config=A_)
									model.to(A_)
									model.eval()
									lowerCAmelCase_			:     Optional[Any]									= model(
									    A_				,				bbox=A_				,				attention_mask=A_				,				token_type_ids=A_				,				start_positions=A_				,				end_positions=A_				,				)
									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     :       List[str]):
									lowerCAmelCase_			:     Tuple									= self.prepare_config_and_inputs()
									(
									    (
									    lowerCAmelCase_
									)					,  (
									    lowerCAmelCase_
									)					,  (
									    lowerCAmelCase_
									)					,  (
									    lowerCAmelCase_
									)					,  (
									    lowerCAmelCase_
									)					,  (
									    lowerCAmelCase_
									)					,  (
									    lowerCAmelCase_
									)					,  
									)			:     List[Any]									= config_and_inputs
									lowerCAmelCase_			:     Union[str, Any]									= {
									    '''input_ids''': input_ids,
									    '''bbox''': bbox,
									    '''token_type_ids''': token_type_ids,
									    '''attention_mask''': input_mask,
									}
									return config, inputs_dict





@require_torch
class 				__snake_case						(     UpperCamelCase_						,UpperCamelCase_						,UpperCamelCase_						,unittest.TestCase						):
							_a			      = (
							    (
							        LiltModel,
							        LiltForSequenceClassification,
							        LiltForTokenClassification,
							        LiltForQuestionAnswering,
							    )
							    if is_torch_available()
							    else ()
							)
							_a			      = (
							    {
							        '''feature-extraction''': LiltModel,
							        '''question-answering''': LiltForQuestionAnswering,
							        '''text-classification''': LiltForSequenceClassification,
							        '''token-classification''': LiltForTokenClassification,
							        '''zero-shot''': LiltForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							_a			      = False
							_a			      = False




							def    UpperCAmelCase__			(       self     :       Tuple				,				A_     :       Tuple				,				A_     :       List[str]				,				A_     :       Union[str, Any]				,				A_     :       Optional[int]				,				A_     :       Dict):
									return True




							def    UpperCAmelCase__			(       self     :       List[str]):
									lowerCAmelCase_			:     Dict									= LiltModelTester(self)
									lowerCAmelCase_			:     Optional[int]									= ConfigTester(self				,				config_class=A_				,				hidden_size=3_7)




							def    UpperCAmelCase__			(       self     :       List[Any]):
									self.config_tester.run_common_tests()




							def    UpperCAmelCase__			(       self     :       Union[str, Any]):
									lowerCAmelCase_			:     Optional[int]									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*A_)




							def    UpperCAmelCase__			(       self     :       Any):
									lowerCAmelCase_			:     Any									= self.model_tester.prepare_config_and_inputs()
									for type in ["absolute", "relative_key", "relative_key_query"]:
											lowerCAmelCase_			:     int									= type
											self.model_tester.create_and_check_model(*A_)




							def    UpperCAmelCase__			(       self     :       Union[str, Any]):
									lowerCAmelCase_			:     Tuple									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*A_)




							def    UpperCAmelCase__			(       self     :       int):
									lowerCAmelCase_			:     List[str]									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*A_)







							@slow
							def    UpperCAmelCase__			(       self     :       Tuple):
									for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											lowerCAmelCase_			:     Union[str, Any]									= LiltModel.from_pretrained(A_)
											self.assertIsNotNone(A_)





@require_torch
@slow
class 				__snake_case						(     unittest.TestCase						):




							def    UpperCAmelCase__			(       self     :       Tuple):
									lowerCAmelCase_			:     Optional[int]									= LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base''').to(A_)

									lowerCAmelCase_			:     Optional[int]									= torch.tensor([[1, 2]]				,				device=A_)
									lowerCAmelCase_			:     Any									= torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]				,				device=A_)

									# forward pass
									with torch.no_grad():
											lowerCAmelCase_			:     Dict									= model(input_ids=A_				,				bbox=A_)

									lowerCAmelCase_			:     int									= torch.Size([1, 2, 7_6_8])
									lowerCAmelCase_			:     Any									= torch.tensor(
									    [[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]]				,				device=A_				,				)

									self.assertTrue(outputs.last_hidden_state.shape				,				A_)
									self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]				,				A_				,				atol=1e-3))









from copy import deepcopy

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

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed
def 				__lowerCamelCase						( UpperCAmelCase_						:		Any ,  UpperCAmelCase_						:		List[Any] ,  UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		Union[str, Any]						):




      """simple docstring"""





      for param, grad_param in zip(model_a.parameters() ,  model_b.parameters()						):
            if not param.requires_grad:
                  continue
            if not did_step:
                  # Grads should not be in sync
                  assert (
                      torch.allclose(param.grad ,  grad_param.grad						) is False
                  ), F'''Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'''
            else:
                  # Grads should be in sync
                  assert (
                      torch.allclose(param.grad ,  grad_param.grad						) is True
                  ), F'''Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'''
def 				__lowerCamelCase						( UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		Optional[Any] ,  UpperCAmelCase_						:		Optional[Any]=True						):




      """simple docstring"""





      model.train()
      a						:str 							=       model(UpperCAmelCase_						)
      a						:List[str] 							=       F.mse_loss(UpperCAmelCase_ ,  target.to(output.device						)						)
      if not do_backward:
            loss /= accelerator.gradient_accumulation_steps
            loss.backward()
      else:
            accelerator.backward(UpperCAmelCase_						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		int=False						):




      """simple docstring"""





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




      """simple docstring"""





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

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

            # Shuffle ddp_input on each iteration
            torch.manual_seed(1337 + iteration						)
            a						:Union[str, Any] 							=       ddp_input[torch.randperm(len(UpperCAmelCase_						)						)]
def 				__lowerCamelCase						( UpperCAmelCase_						:		Union[str, Any]						):




      """simple docstring"""





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

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

        # Shuffle ddp_input on each iteration
            torch.manual_seed(1337 + iteration						)
            a						:Any 							=       ddp_input[torch.randperm(len(UpperCAmelCase_						)						)]
def 				__lowerCamelCase						( UpperCAmelCase_						:		Union[str, Any]=False ,  UpperCAmelCase_						:		int=False						):




      """simple docstring"""





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

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

        # Shuffle ddp_input on each iteration
            torch.manual_seed(1337 + iteration						)
            a						:List[str] 							=       ddp_input[torch.randperm(len(UpperCAmelCase_						)						)]
      GradientState._reset_state()
def 				__lowerCamelCase						( UpperCAmelCase_						:		Any=False ,  UpperCAmelCase_						:		Optional[int]=False						):




      """simple docstring"""





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




      """simple docstring"""





      a						:Optional[Any] 							=       Accelerator()

      a						:int 							=       RegressionDataset(length=80						)
      a						:List[str] 							=       DataLoader(UpperCAmelCase_ ,  batch_size=16						)
      a						:List[Any] 							=       RegressionDataset(length=96						)
      a						:Any 							=       DataLoader(UpperCAmelCase_ ,  batch_size=16						)
      a							,				a						:Optional[int] 							=       accelerator.prepare(UpperCAmelCase_ ,  UpperCAmelCase_						)
      assert accelerator.gradient_state.active_dataloader is None
      for iteration, _ in enumerate(UpperCAmelCase_						):
            assert id(accelerator.gradient_state.active_dataloader						) == id(UpperCAmelCase_						)
            if iteration < len(UpperCAmelCase_						) - 1:
                  assert not accelerator.gradient_state.end_of_dataloader
                  if iteration == 1:
                        for batch_num, _ in enumerate(UpperCAmelCase_						):
                              assert id(accelerator.gradient_state.active_dataloader						) == id(UpperCAmelCase_						)
                              if batch_num < len(UpperCAmelCase_						) - 1:
                                    assert not accelerator.gradient_state.end_of_dataloader
                              else:
                                    assert accelerator.gradient_state.end_of_dataloader
            else:
                  assert accelerator.gradient_state.end_of_dataloader
      assert accelerator.gradient_state.active_dataloader is None
def 				__lowerCamelCase						( ):




      """simple docstring"""





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

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




      """simple docstring"""





      main()


if __name__ == "__main__":
     main()





