


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 							__lowercase       (			__lowerCAmelCase							:    Union[str, Any] ,   __lowerCAmelCase							:    Any ,   __lowerCAmelCase							:    Dict ,   __lowerCAmelCase							:    str       ):
							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 							__lowercase       (			__lowerCAmelCase							:    Optional[Any] ,   __lowerCAmelCase							:    Any ,   __lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    int ,   __lowerCAmelCase							:    Tuple=True       ):
							model.train()
							a__					=					model(__lowerCAmelCase       )
							a__					=					F.mse_loss(__lowerCAmelCase ,   target.to(output.device       )       )
							if not do_backward:
														loss /= accelerator.gradient_accumulation_steps
														loss.backward()
							else:
														accelerator.backward(__lowerCAmelCase       )






def 							__lowercase       (			__lowerCAmelCase							:    Optional[Any] ,   __lowerCAmelCase							:    Union[str, Any]=False       ):
							set_seed(4_2       )
							a__					=					RegressionModel()
							a__					=					deepcopy(__lowerCAmelCase       )
							a__					=					RegressionDataset(length=8_0       )
							a__					=					DataLoader(__lowerCAmelCase ,   batch_size=1_6       )
							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(__lowerCAmelCase ,   lr_lambda=lambda __lowerCAmelCase       : epoch**0.65       )
														a__					=					LambdaLR(__lowerCAmelCase ,   lr_lambda=lambda __lowerCAmelCase       : epoch**0.65       )
							# Make a copy of `model`
							if sched:
														a__				, a__				, a__				, a__					=					accelerator.prepare(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )
							else:
														a__				, a__					=					accelerator.prepare(__lowerCAmelCase ,   __lowerCAmelCase       )
							if sched:
														return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
							return model, ddp_model, dataloader






def 							__lowercase       (			__lowerCAmelCase							:    List[Any]       ):
							# Test when on a single CPU or GPU that the context manager does nothing
							a__				, a__				, a__					=					get_training_setup(__lowerCAmelCase       )
							# Use a single batch
							a__				, a__					=					next(iter(__lowerCAmelCase       )       ).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(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )
														# Do "gradient accumulation" (noop)
														if iteration % 2 == 0:
																					# Accumulate grads locally
																					with accelerator.no_sync(__lowerCAmelCase       ):
																												step_model(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )
														else:
																					# Sync grads
																					step_model(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )

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

														# Shuffle ddp_input on each iteration
														torch.manual_seed(1_3_3_7 + iteration       )
														a__					=					ddp_input[torch.randperm(len(__lowerCAmelCase       )       )]






def 							__lowercase       (			__lowerCAmelCase							:    Optional[int]       ):
							# Test on distributed setup that context manager behaves properly
							a__				, a__				, a__					=					get_training_setup(__lowerCAmelCase       )
							# Use a single batch
							a__				, a__					=					next(iter(__lowerCAmelCase       )       ).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(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )
														# Do "gradient accumulation" (noop)
														if iteration % 2 == 0:
																					# Accumulate grads locally
																					with accelerator.no_sync(__lowerCAmelCase       ):
																												step_model(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )
														else:
																					# Sync grads
																					step_model(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )

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

        # Shuffle ddp_input on each iteration
														torch.manual_seed(1_3_3_7 + iteration       )
														a__					=					ddp_input[torch.randperm(len(__lowerCAmelCase       )       )]






def 							__lowercase       (			__lowerCAmelCase							:    Any=False ,   __lowerCAmelCase							:    Optional[Any]=False       ):
							a__					=					Accelerator(
							    split_batches=__lowerCAmelCase ,   dispatch_batches=__lowerCAmelCase ,   gradient_accumulation_steps=2       )
							# Test that context manager behaves properly
							a__				, a__				, a__					=					get_training_setup(__lowerCAmelCase       )
							for iteration, batch in enumerate(__lowerCAmelCase       ):
														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(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )
														# Do "gradient accumulation" (noop)
														with accelerator.accumulate(__lowerCAmelCase       ):
																					step_model(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )

														# 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(__lowerCAmelCase       ) - 1):
																												# Grads should be in sync
																												assert (
																												    torch.allclose(param.grad ,   ddp_param.grad       ) is True
																												), F'Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'
																					else:
																												# Grads should not be in sync
																												assert (
																												    torch.allclose(param.grad ,   ddp_param.grad       ) is False
																												), F'Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'

        # Shuffle ddp_input on each iteration
														torch.manual_seed(1_3_3_7 + iteration       )
														a__					=					ddp_input[torch.randperm(len(__lowerCAmelCase       )       )]
							GradientState._reset_state()






def 							__lowercase       (			__lowerCAmelCase							:    Union[str, Any]=False ,   __lowerCAmelCase							:    List[Any]=False       ):
							a__					=					Accelerator(
							    split_batches=__lowerCAmelCase ,   dispatch_batches=__lowerCAmelCase ,   gradient_accumulation_steps=2       )
							# Test that context manager behaves properly
							a__				, a__				, a__				, a__				, a__				, a__				, a__					=					get_training_setup(__lowerCAmelCase ,   __lowerCAmelCase       )
							for iteration, batch in enumerate(__lowerCAmelCase       ):
														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(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )
														opt.step()

														if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(__lowerCAmelCase       )):
																					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(__lowerCAmelCase       ):
																					step_model(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )
																					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(__lowerCAmelCase       ))
														if accelerator.num_processes > 1:
																					check_model_parameters(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )
														# Shuffle ddp_input on each iteration
														torch.manual_seed(1_3_3_7 + iteration       )
							GradientState._reset_state()






def 							__lowercase       (			):
							a__					=					Accelerator()

							a__					=					RegressionDataset(length=8_0       )
							a__					=					DataLoader(__lowerCAmelCase ,   batch_size=1_6       )
							a__					=					RegressionDataset(length=9_6       )
							a__					=					DataLoader(__lowerCAmelCase ,   batch_size=1_6       )
							a__				, a__					=					accelerator.prepare(__lowerCAmelCase ,   __lowerCAmelCase       )
							assert accelerator.gradient_state.active_dataloader is None
							for iteration, _ in enumerate(__lowerCAmelCase       ):
														assert id(accelerator.gradient_state.active_dataloader       ) == id(__lowerCAmelCase       )
														if iteration < len(__lowerCAmelCase       ) - 1:
																					assert not accelerator.gradient_state.end_of_dataloader
																					if iteration == 1:
																												for batch_num, _ in enumerate(__lowerCAmelCase       ):
																																			assert id(accelerator.gradient_state.active_dataloader       ) == id(__lowerCAmelCase       )
																																			if batch_num < len(__lowerCAmelCase       ) - 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 							__lowercase       (			):
							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(__lowerCAmelCase       )
							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(__lowerCAmelCase       )
							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(__lowerCAmelCase ,   __lowerCAmelCase       )

    # 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(__lowerCAmelCase ,   __lowerCAmelCase       )






def 							__lowercase       (			__lowerCAmelCase							:    Any       ):
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
						main()








import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
snake_case							:	Union[str, Any]					      =				logging.get_logger(__name__)

snake_case							:	Tuple					      =				torch.device('''cpu''')






def 							__lowercase       (			):
							a__					=					'http://images.cocodataset.org/val2017/000000039769.jpg'
							a__					=					Image.open(requests.get(__lowerCAmelCase ,   stream=__lowerCAmelCase       ).raw       )
							return im






def 							__lowercase       (			__lowerCAmelCase							:    List[str]       ):
							if swiftformer_name == "swiftformer_xs":
														return torch.tensor([-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0, 8.8_6_8_5E-0_1, 2.4_3_6_0E-0_1]       )

							elif swiftformer_name == "swiftformer_s":
														return torch.tensor([3.9_6_3_6E-0_1, 2.3_4_7_8E-0_1, -1.6_9_6_3E0_0, -1.7_3_8_1E0_0, -8.6_3_3_7E-0_1]       )

							elif swiftformer_name == "swiftformer_l1":
														return torch.tensor([-4.2_7_6_8E-0_1, -4.7_4_2_9E-0_1, -1.0_8_9_7E0_0, -1.0_2_4_8E0_0, 3.5_5_2_3E-0_2]       )

							elif swiftformer_name == "swiftformer_l3":
														return torch.tensor([-2.5_3_3_0E-0_1, 2.4_2_1_1E-0_1, -6.0_1_8_5E-0_1, -8.2_7_8_9E-0_1, -6.0_4_4_6E-0_2]       )






def 							__lowercase       (			__lowerCAmelCase							:    List[Any] ,   __lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    str       ):
							a__					=					dct.pop(__lowerCAmelCase       )
							a__					=					val






def 							__lowercase       (			__lowerCAmelCase							:    int       ):
							a__					=					[]
							for k in state_dict.keys():
														a__					=					k
														if ".pwconv" in k:
																					a__					=					k_new.replace('.pwconv' ,   '.point_wise_conv'       )
														if ".dwconv" in k:
																					a__					=					k_new.replace('.dwconv' ,   '.depth_wise_conv'       )
														if ".Proj." in k:
																					a__					=					k_new.replace('.Proj.' ,   '.proj.'       )
														if "patch_embed" in k_new:
																					a__					=					k_new.replace('patch_embed' ,   'swiftformer.patch_embed.patch_embedding'       )
														if "network" in k_new:
																					a__					=					k_new.split('.'       )
																					if ls[2].isdigit():
																												a__					=					'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]       )
																					else:
																												a__					=					k_new.replace('network' ,   'swiftformer.encoder.network'       )
														rename_keys.append((k, k_new)       )
							return rename_keys






@torch.no_grad()
def 							__lowercase       (			__lowerCAmelCase							:    Optional[Any] ,   __lowerCAmelCase							:    str ,   __lowerCAmelCase							:    str       ):
							a__					=					SwiftFormerConfig()

							# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
							a__					=					1_0_0_0
							a__					=					'huggingface/label-files'
							a__					=					'imagenet-1k-id2label.json'
							a__					=					json.load(open(hf_hub_download(__lowerCAmelCase ,   __lowerCAmelCase ,   repo_type='dataset'       ) ,   'r'       )       )
							a__					=					{int(__lowerCAmelCase       ): v for k, v in idalabel.items()}
							a__					=					idalabel
							a__					=					{v: k for k, v in idalabel.items()}

							# size of the architecture
							if swiftformer_name == "swiftformer_xs":
														a__					=					[3, 3, 6, 4]
														a__					=					[4_8, 5_6, 1_1_2, 2_2_0]

							elif swiftformer_name == "swiftformer_s":
														a__					=					[3, 3, 9, 6]
														a__					=					[4_8, 6_4, 1_6_8, 2_2_4]

							elif swiftformer_name == "swiftformer_l1":
														a__					=					[4, 3, 1_0, 5]
														a__					=					[4_8, 9_6, 1_9_2, 3_8_4]

							elif swiftformer_name == "swiftformer_l3":
														a__					=					[4, 4, 1_2, 6]
														a__					=					[6_4, 1_2_8, 3_2_0, 5_1_2]

							# load state_dict of original model, remove and rename some keys
							if original_ckpt:
														if original_ckpt.startswith('https'       ):
																					a__					=					torch.hub.load_state_dict_from_url(__lowerCAmelCase ,   map_location='cpu' ,   check_hash=__lowerCAmelCase       )
														else:
																					a__					=					torch.load(__lowerCAmelCase ,   map_location='cpu'       )
							a__					=					checkpoint

							a__					=					create_rename_keys(__lowerCAmelCase       )
							for rename_key_src, rename_key_dest in rename_keys:
														rename_key(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )

							# load HuggingFace model
							a__					=					SwiftFormerForImageClassification(__lowerCAmelCase       ).eval()
							hf_model.load_state_dict(__lowerCAmelCase       )

							# prepare test inputs
							a__					=					prepare_img()
							a__					=					ViTImageProcessor.from_pretrained('preprocessor_config'       )
							a__					=					processor(images=__lowerCAmelCase ,   return_tensors='pt'       )

							# compare outputs from both models
							a__					=					get_expected_output(__lowerCAmelCase       )
							a__					=					hf_model(inputs['pixel_values']       ).logits

							assert hf_logits.shape == torch.Size([1, 1_0_0_0]       )
							assert torch.allclose(hf_logits[0, 0:5] ,   __lowerCAmelCase ,   atol=1E-3       )

							Path(__lowerCAmelCase       ).mkdir(exist_ok=__lowerCAmelCase       )
							print(F'Saving model {swiftformer_name} to {pytorch_dump_folder_path}'       )
							hf_model.save_pretrained(__lowerCAmelCase       )


if __name__ == "__main__":
						snake_case							:	int					      =				argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--swiftformer_name''',
						    default='''swiftformer_xs''',
						    choices=['''swiftformer_xs''', '''swiftformer_s''', '''swiftformer_l1''', '''swiftformer_l3'''],
						    type=str,
						    help='''Name of the SwiftFormer model you\'d like to convert.''',
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''',
						    default='''./converted_outputs/''',
						    type=str,
						    help='''Path to the output PyTorch model directory.''',
						)
						parser.add_argument('''--original_ckpt''', default=None, type=str, help='''Path to the original model checkpoint.''')

						snake_case							:	Optional[int]					      =				parser.parse_args()
						convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)




