


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()
lowerCAmelCase__  :Optional[int]             =							logging.get_logger(__name__)

lowerCAmelCase__  :Optional[Any]             =							torch.device('''cpu''')

def 	lowerCAmelCase__			(      )  ->		Any:


							'''simple docstring'''





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

def 	lowerCAmelCase__			(      a__:		List[Any]	)  ->		Tuple:


							'''simple docstring'''





							if swiftformer_name == "swiftformer_xs":
														return torch.tensor([-2.17_03e00, 2.11_07e00, -2.08_11e00, 8.86_85e-01, 2.43_60e-01]	)

							elif swiftformer_name == "swiftformer_s":
														return torch.tensor([3.96_36e-01, 2.34_78e-01, -1.69_63e00, -1.73_81e00, -8.63_37e-01]	)

							elif swiftformer_name == "swiftformer_l1":
														return torch.tensor([-4.27_68e-01, -4.74_29e-01, -1.08_97e00, -1.02_48e00, 3.55_23e-02]	)

							elif swiftformer_name == "swiftformer_l3":
														return torch.tensor([-2.53_30e-01, 2.42_11e-01, -6.01_85e-01, -8.27_89e-01, -6.04_46e-02]	)

def 	lowerCAmelCase__			(      a__:		int   ,  a__:		Union[str, Any]   ,  a__:		Optional[Any]	)  ->		Dict:


							'''simple docstring'''





							_UpperCAmelCase					=      dct.pop(a__	)
							_UpperCAmelCase					=      val

def 	lowerCAmelCase__			(      a__:		Union[str, Any]	)  ->		List[str]:


							'''simple docstring'''





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

@torch.no_grad()
def 	lowerCAmelCase__			(      a__:		Optional[int]   ,  a__:		str   ,  a__:		Dict	)  ->		Optional[int]:


							'''simple docstring'''





							_UpperCAmelCase					=      SwiftFormerConfig()

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

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

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

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

							elif swiftformer_name == "swiftformer_l3":
														_UpperCAmelCase					=      [4, 4, 1_2, 6]
														_UpperCAmelCase					=      [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'	):
																					_UpperCAmelCase					=      torch.hub.load_state_dict_from_url(a__   ,  map_location='cpu'   ,  check_hash=a__	)
														else:
																					_UpperCAmelCase					=      torch.load(a__   ,  map_location='cpu'	)
							_UpperCAmelCase					=      checkpoint

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

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

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

							# compare outputs from both models
							_UpperCAmelCase					=      get_expected_output(a__	)
							_UpperCAmelCase					=      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]   ,  a__   ,  atol=1e-3	)

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


if __name__ == "__main__":
		lowerCAmelCase__  :Optional[Any]             =							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.''')

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




import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel

class 		__a				(    pl.LightningModule      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE						)			->	List[Any]:




									"""simple docstring"""





									super().__init__()
									_UpperCAmelCase					=      model
									_UpperCAmelCase					=      2
									_UpperCAmelCase					=      nn.Linear(self.model.config.hidden_size      ,   self.num_labels						)






		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									pass



def 	lowerCAmelCase__			(      a__:		str      ,  a__:		str      ,  a__:		str	)  ->		Tuple:


							'''simple docstring'''





							_UpperCAmelCase					=      LongformerModel.from_pretrained(a__	)
							_UpperCAmelCase					=      LightningModel(a__	)

							_UpperCAmelCase					=      torch.load(a__      ,  map_location=torch.device('cpu'	)	)
							lightning_model.load_state_dict(ckpt['state_dict']	)

							# init longformer question answering model
							_UpperCAmelCase					=      LongformerForQuestionAnswering.from_pretrained(a__	)

							# transfer weights
							longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict()	)
							longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict()	)
							longformer_for_qa.eval()

							# save model
							longformer_for_qa.save_pretrained(a__	)

							print(F'''Conversion successful. Model saved under {pytorch_dump_folder_path}'''	)


if __name__ == "__main__":
		lowerCAmelCase__  :Optional[int]             =							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--longformer_model''',
		    default=None,
		    type=str,
		    required=True,
		    help='''model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.''',
		)
		parser.add_argument(
		    '''--longformer_question_answering_ckpt_path''',
		    default=None,
		    type=str,
		    required=True,
		    help='''Path the official PyTorch Lightning Checkpoint.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		lowerCAmelCase__  :Dict             =							parser.parse_args()
		convert_longformer_qa_checkpoint_to_pytorch(
		    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
		)
