


"""simple docstring"""




import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


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






def 							__lowercase       (			):
							a__					=					os.getenv('SM_HP_MP_PARAMETERS' ,   '{}'       )
							try:
														# Parse it and check the field "partitions" is included, it is required for model parallel.
														a__					=					json.loads(SCREAMING_SNAKE_CASE__       )
														if "partitions" not in smp_options:
																					return False
							except json.JSONDecodeError:
														return False

							# Get the sagemaker specific framework parameters from mpi_options variable.
							a__					=					os.getenv('SM_FRAMEWORK_PARAMS' ,   '{}'       )
							try:
														# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
														a__					=					json.loads(SCREAMING_SNAKE_CASE__       )
														if not mpi_options.get('sagemaker_mpi_enabled' ,   SCREAMING_SNAKE_CASE__       ):
																					return False
							except json.JSONDecodeError:
														return False
							# Lastly, check if the `smdistributed` module is present.
							return importlib.util.find_spec('smdistributed'       ) is not None


if is_sagemaker_model_parallel_available():
						import smdistributed.modelparallel.torch as smp

						smp.init()







@dataclass
class 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   str   				=      field(
					    default=''''''     ,						metadata={'''help''': '''Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer'''}     ,						)
					def 			lowerCamelCase__(				self       :Tuple				) ->   str:
												super().__post_init__()
												warnings.warn(
												    '`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use '
												    '`TrainingArguments` instead.'      ,_UpperCAmelCase      ,)
					@cached_property
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   Dict:
												logger.info('PyTorch: setting up devices'				)
												if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
																			logger.warning(
																			    'torch.distributed process group is initialized, but local_rank == -1. '
																			    'In order to use Torch DDP, launch your script with `python -m torch.distributed.launch'				)
												if self.no_cuda:
																			a__					=					torch.device('cpu'				)
																			a__					=					0
												elif is_sagemaker_model_parallel_available():
																			a__					=					smp.local_rank()
																			a__					=					torch.device('cuda'      ,_UpperCAmelCase				)
																			a__					=					1
												elif is_sagemaker_dp_enabled():
																			import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

																			torch.distributed.init_process_group(backend='smddp'      ,timeout=self.ddp_timeout_delta				)
																			a__					=					int(os.getenv('SMDATAPARALLEL_LOCAL_RANK'				)				)
																			a__					=					torch.device('cuda'      ,self.local_rank				)
																			a__					=					1
												elif self.local_rank == -1:
																			# if n_gpu is > 1 we'll use nn.DataParallel.
																			# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
																			# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
																			# trigger an error that a device index is missing. Index 0 takes into account the
																			# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
																			# will use the first GPU in that env, i.e. GPU#1
																			a__					=					torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'				)
																			# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
																			# the default value.
																			a__					=					torch.cuda.device_count()
												else:
																			# Here, we'll use torch.distributed.
																			# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
																			if not torch.distributed.is_initialized():
																										torch.distributed.init_process_group(backend='nccl'      ,timeout=self.ddp_timeout_delta				)
																			a__					=					torch.device('cuda'      ,self.local_rank				)
																			a__					=					1

												if device.type == "cuda":
																			torch.cuda.set_device(_UpperCAmelCase				)

												return device
					@property
					def 			lowerCamelCase__(				self       :List[Any]				) ->   Optional[int]:






												if is_sagemaker_model_parallel_available():
																			return smp.dp_size()

												return super().world_size
					@property
					def 			lowerCamelCase__(				self       :Dict				) ->   Dict:
												return not is_sagemaker_model_parallel_available()



					@property
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   List[str]:
												return False








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)




