

import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
					import jax

					from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
					from PIL import Image

					from transformers import BeitImageProcessor







class 				__UpperCAmelCase       ( unittest.TestCase    ):




				def __init__(      self   ,     snake_case   ,     snake_case=100   ,     snake_case=13   ,     snake_case=30   ,     snake_case=2   ,     snake_case=3   ,     snake_case=True   ,     snake_case=True   ,     snake_case=32   ,     snake_case=5   ,     snake_case=4   ,     snake_case=37   ,     snake_case="gelu"   ,     snake_case=0.1   ,     snake_case=0.1   ,     snake_case=10   ,     snake_case=0.02   ,     snake_case=3   ,     ):
											snake_case_           =					parent
											snake_case_           =					vocab_size
											snake_case_           =					batch_size
											snake_case_           =					image_size
											snake_case_           =					patch_size
											snake_case_           =					num_channels
											snake_case_           =					is_training
											snake_case_           =					use_labels
											snake_case_           =					hidden_size
											snake_case_           =					num_hidden_layers
											snake_case_           =					num_attention_heads
											snake_case_           =					intermediate_size
											snake_case_           =					hidden_act
											snake_case_           =					hidden_dropout_prob
											snake_case_           =					attention_probs_dropout_prob
											snake_case_           =					type_sequence_label_size
											snake_case_           =					initializer_range

											# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
											snake_case_           =					(image_size // patch_size) ** 2
											snake_case_           =					num_patches + 1




				def 					a							(      self ):
											snake_case_           =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

											snake_case_           =					None
											if self.use_labels:
																		snake_case_           =					ids_tensor([self.batch_size]   ,     self.type_sequence_label_size )

											snake_case_           =					BeitConfig(
											    vocab_size=self.vocab_size   ,     image_size=self.image_size   ,     patch_size=self.patch_size   ,     num_channels=self.num_channels   ,     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   ,     is_decoder=snake_case   ,     initializer_range=self.initializer_range   ,     )

											return config, pixel_values, labels




				def 					a							(      self   ,     snake_case   ,     snake_case   ,     snake_case ):
											snake_case_           =					FlaxBeitModel(config=snake_case )
											snake_case_           =					model(snake_case )
											self.parent.assertEqual(result.last_hidden_state.shape   ,     (self.batch_size, self.seq_length, self.hidden_size) )




				def 					a							(      self   ,     snake_case   ,     snake_case   ,     snake_case ):
											snake_case_           =					FlaxBeitForMaskedImageModeling(config=snake_case )
											snake_case_           =					model(snake_case )
											self.parent.assertEqual(result.logits.shape   ,     (self.batch_size, self.seq_length - 1, self.vocab_size) )




				def 					a							(      self   ,     snake_case   ,     snake_case   ,     snake_case ):
											snake_case_           =					self.type_sequence_label_size
											snake_case_           =					FlaxBeitForImageClassification(config=snake_case )
											snake_case_           =					model(snake_case )
											self.parent.assertEqual(result.logits.shape   ,     (self.batch_size, self.type_sequence_label_size) )

											# test greyscale images
											snake_case_           =					1
											snake_case_           =					FlaxBeitForImageClassification(snake_case )

											snake_case_           =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
											snake_case_           =					model(snake_case )




				def 					a							(      self ):
											snake_case_           =					self.prepare_config_and_inputs()
											(
											    (
											    snake_case_					
											)       ,					(
											    snake_case_					
											)       ,					(
											    snake_case_					
											)       ,										
											)           =					config_and_inputs
											snake_case_           =					{'pixel_values': pixel_values}
											return config, inputs_dict







@require_flax
class 				__UpperCAmelCase       ( lowercase_			,						unittest.TestCase    ):
				__SCREAMING_SNAKE_CASE      :				Tuple     =    (
				    (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
				)




				def 					a							(      self ):
											snake_case_           =					FlaxBeitModelTester(self )
											snake_case_           =					ConfigTester(self   ,     config_class=snake_case   ,     has_text_modality=snake_case   ,     hidden_size=37 )




				def 					a							(      self ):
											self.config_tester.run_common_tests()




				def 					a							(      self ):
											snake_case_       ,					snake_case_           =					self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		snake_case_           =					model_class(snake_case )
																		snake_case_           =					inspect.signature(model.__call__ )
																		# signature.parameters is an OrderedDict => so arg_names order is deterministic
																		snake_case_           =					[*signature.parameters.keys()]

																		snake_case_           =					['pixel_values']
																		self.assertListEqual(arg_names[:1]   ,     snake_case )




				def 					a							(      self ):
											snake_case_       ,					snake_case_           =					self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__ ):
																									snake_case_           =					self._prepare_for_class(snake_case   ,     snake_case )
																									snake_case_           =					model_class(snake_case )

																									@jax.jit
																									def model_jitted(snake_case   ,     **snake_case ):
																																return model(pixel_values=snake_case   ,     **snake_case )

																									with self.subTest('JIT Enabled' ):
																																snake_case_           =					model_jitted(**snake_case ).to_tuple()

																									with self.subTest('JIT Disabled' ):
																																with jax.disable_jit():
																																							snake_case_           =					model_jitted(**snake_case ).to_tuple()

																									self.assertEqual(len(snake_case )   ,     len(snake_case ) )
																									for jitted_output, output in zip(snake_case   ,     snake_case ):
																																self.assertEqual(jitted_output.shape   ,     output.shape )




				def 					a							(      self ):
											snake_case_           =					self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*snake_case )




				def 					a							(      self ):
											snake_case_           =					self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*snake_case )




				def 					a							(      self ):
											snake_case_           =					self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*snake_case )




				@slow
				def 					a							(      self ):
											for model_class_name in self.all_model_classes:
																		snake_case_           =					model_class_name.from_pretrained('microsoft/beit-base-patch16-224' )
																		snake_case_           =					model(np.ones((1, 3, 224, 224) ) )
																		self.assertIsNotNone(snake_case )





def       __lowerCamelCase			(				):







							'''simple docstring'''






							snake_case_           =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
							return image







@require_vision
@require_flax
class 				__UpperCAmelCase       ( unittest.TestCase    ):




				@cached_property
				def 					a							(      self ):
											return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224' ) if is_vision_available() else None




				@slow
				def 					a							(      self ):
											snake_case_           =					FlaxBeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k' )

											snake_case_           =					self.default_image_processor
											snake_case_           =					prepare_img()
											snake_case_           =					image_processor(images=snake_case   ,     return_tensors='np' ).pixel_values

											# prepare bool_masked_pos
											snake_case_           =					np.ones((1, 196)   ,     dtype=snake_case )

											# forward pass
											snake_case_           =					model(pixel_values=snake_case   ,     bool_masked_pos=snake_case )
											snake_case_           =					outputs.logits

											# verify the logits
											snake_case_           =					(1, 196, 8192)
											self.assertEqual(logits.shape   ,     snake_case )

											snake_case_           =					np.array(
											    [[-3.24_37, 0.50_72, -13.91_74], [-3.24_56, 0.49_48, -13.94_01], [-3.20_33, 0.51_21, -13.85_50]] )

											self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]   ,     snake_case   ,     atol=1e-2 ) )




				@slow
				def 					a							(      self ):
											snake_case_           =					FlaxBeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224' )

											snake_case_           =					self.default_image_processor
											snake_case_           =					prepare_img()
											snake_case_           =					image_processor(images=snake_case   ,     return_tensors='np' )

											# forward pass
											snake_case_           =					model(**snake_case )
											snake_case_           =					outputs.logits

											# verify the logits
											snake_case_           =					(1, 1000)
											self.assertEqual(logits.shape   ,     snake_case )

											snake_case_           =					np.array([-1.23_85, -1.09_87, -1.01_08] )

											self.assertTrue(np.allclose(logits[0, :3]   ,     snake_case   ,     atol=1e-4 ) )

											snake_case_           =					281
											self.assertEqual(logits.argmax(-1 ).item()   ,     snake_case )




				@slow
				def 					a							(      self ):
											snake_case_           =					FlaxBeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k' )

											snake_case_           =					self.default_image_processor
											snake_case_           =					prepare_img()
											snake_case_           =					image_processor(images=snake_case   ,     return_tensors='np' )

											# forward pass
											snake_case_           =					model(**snake_case )
											snake_case_           =					outputs.logits

											# verify the logits
											snake_case_           =					(1, 2_1841)
											self.assertEqual(logits.shape   ,     snake_case )

											snake_case_           =					np.array([1.68_81, -0.27_87, 0.59_01] )

											self.assertTrue(np.allclose(logits[0, :3]   ,     snake_case   ,     atol=1e-4 ) )

											snake_case_           =					2396
											self.assertEqual(logits.argmax(-1 ).item()   ,     snake_case )







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__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''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__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__=True   ):







							'''simple docstring'''






							model.train()
							snake_case_           =					model(UpperCamelCase__   )
							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       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__=False   ):







							'''simple docstring'''






							set_seed(42   )
							snake_case_           =					RegressionModel()
							snake_case_           =					deepcopy(UpperCamelCase__   )
							snake_case_           =					RegressionDataset(length=80   )
							snake_case_           =					DataLoader(UpperCamelCase__     ,							batch_size=16   )
							model.to(accelerator.device   )
							if sched:
														snake_case_           =					AdamW(params=model.parameters()     ,							lr=1E-3   )
														snake_case_           =					AdamW(params=ddp_model.parameters()     ,							lr=1E-3   )
														snake_case_           =					LambdaLR(UpperCamelCase__     ,							lr_lambda=lambda UpperCamelCase__   : epoch**0.65   )
														snake_case_           =					LambdaLR(UpperCamelCase__     ,							lr_lambda=lambda UpperCamelCase__   : epoch**0.65   )
							# Make a copy of `model`
							if sched:
														snake_case_       ,					snake_case_       ,					snake_case_       ,					snake_case_           =					accelerator.prepare(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   )
							else:
														snake_case_       ,					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       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_       ,					snake_case_       ,					snake_case_           =					get_training_setup(UpperCamelCase__   )
							# Use a single batch
							snake_case_       ,					snake_case_           =					next(iter(UpperCamelCase__   )   ).values()
							for iteration in range(3   ):
														# Gather the distributed inputs and targs for the base model
														snake_case_       ,					snake_case_           =					accelerator.gather((ddp_input, ddp_target)   )
														snake_case_       ,					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(1337 + iteration   )
														snake_case_           =					ddp_input[torch.randperm(len(UpperCamelCase__   )   )]





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_       ,					snake_case_       ,					snake_case_           =					get_training_setup(UpperCamelCase__   )
							# Use a single batch
							snake_case_       ,					snake_case_           =					next(iter(UpperCamelCase__   )   ).values()
							for iteration in range(3   ):
														# Gather the distributed inputs and targs for the base model
														snake_case_       ,					snake_case_           =					accelerator.gather((ddp_input, ddp_target)   )
														snake_case_       ,					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(1337 + iteration   )
														snake_case_           =					ddp_input[torch.randperm(len(UpperCamelCase__   )   )]





def       __lowerCamelCase			(				UpperCamelCase__=False     ,							UpperCamelCase__=False   ):







							'''simple docstring'''






							snake_case_           =					Accelerator(
							    split_batches=UpperCamelCase__     ,							dispatch_batches=UpperCamelCase__     ,							gradient_accumulation_steps=2   )
							# Test that context manager behaves properly
							snake_case_       ,					snake_case_       ,					snake_case_           =					get_training_setup(UpperCamelCase__   )
							for iteration, batch in enumerate(UpperCamelCase__   ):
														snake_case_       ,					snake_case_           =					batch.values()
														# Gather the distributed inputs and targs for the base model
														snake_case_       ,					snake_case_           =					accelerator.gather((ddp_input, ddp_target)   )
														snake_case_       ,					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(1337 + iteration   )
														snake_case_           =					ddp_input[torch.randperm(len(UpperCamelCase__   )   )]
							GradientState._reset_state()





def       __lowerCamelCase			(				UpperCamelCase__=False     ,							UpperCamelCase__=False   ):







							'''simple docstring'''






							snake_case_           =					Accelerator(
							    split_batches=UpperCamelCase__     ,							dispatch_batches=UpperCamelCase__     ,							gradient_accumulation_steps=2   )
							# Test that context manager behaves properly
							snake_case_       ,					snake_case_       ,					snake_case_       ,					snake_case_       ,					snake_case_       ,					snake_case_       ,					snake_case_           =					get_training_setup(UpperCamelCase__     ,							UpperCamelCase__   )
							for iteration, batch in enumerate(UpperCamelCase__   ):
														snake_case_       ,					snake_case_           =					batch.values()
														# Gather the distributed inputs and targs for the base model
														snake_case_       ,					snake_case_           =					accelerator.gather((ddp_input, ddp_target)   )
														snake_case_       ,					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'''
														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(1337 + iteration   )
							GradientState._reset_state()





def       __lowerCamelCase			(				):







							'''simple docstring'''






							snake_case_           =					Accelerator()

							snake_case_           =					RegressionDataset(length=80   )
							snake_case_           =					DataLoader(UpperCamelCase__     ,							batch_size=16   )
							snake_case_           =					RegressionDataset(length=96   )
							snake_case_           =					DataLoader(UpperCamelCase__     ,							batch_size=16   )
							snake_case_       ,					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       __lowerCamelCase			(				):







							'''simple docstring'''






							snake_case_           =					Accelerator()
							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       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							main()


if __name__ == "__main__":
					main()




