





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_A					:    Tuple   =  logging.get_logger(__name__)



def        _a						(    UpperCAmelCase     )				->				List[str]:







   """simple docstring"""
   lowerCamelCase__   :						Union[str, Any]					   =							'''huggingface/label-files'''
   lowerCamelCase__   :						Tuple					   =							'''imagenet-1k-id2label.json'''
   lowerCamelCase__   :						Optional[int]					   =							json.load(open(hf_hub_download(__lowerCamelCase			,  __lowerCamelCase			,  repo_type='''dataset'''     )			,  '''r'''     )     )
   lowerCamelCase__   :						Tuple					   =							{int(__lowerCamelCase     ): v for k, v in idalabel.items()}
   lowerCamelCase__   :						Optional[Any]					   =							{v: k for k, v in idalabel.items()}

   lowerCamelCase__   :						Optional[int]					   =							'''std_conv''' if '''bit''' in model_name else False

   # note that when using BiT as backbone for ViT-hybrid checkpoints,
   # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
   # config.conv_layer = "std_conv_same"
   lowerCamelCase__   :						Union[str, Any]					   =							BitConfig(
       conv_layer=__lowerCamelCase			,  num_labels=1000			,  idalabel=__lowerCamelCase			,  labelaid=__lowerCamelCase			,  )

   return config



def        _a						(    UpperCAmelCase     )				->				Any:







   """simple docstring"""
   if "stem.conv" in name:
      lowerCamelCase__   :						Optional[int]					   =							name.replace('''stem.conv'''			,  '''bit.embedder.convolution'''     )
   if "blocks" in name:
      lowerCamelCase__   :						int					   =							name.replace('''blocks'''			,  '''layers'''     )
   if "head.fc" in name:
      lowerCamelCase__   :						Union[str, Any]					   =							name.replace('''head.fc'''			,  '''classifier.1'''     )
   if name.startswith('''norm'''     ):
      lowerCamelCase__   :						Union[str, Any]					   =							'''bit.''' + name
   if "bit" not in name and "classifier" not in name:
      lowerCamelCase__   :						Dict					   =							'''bit.encoder.''' + name

   return name



def        _a						(    )				->				str:







   """simple docstring"""
   lowerCamelCase__   :						Any					   =							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
   lowerCamelCase__   :						Any					   =							Image.open(requests.get(__lowerCamelCase			,  stream=__lowerCamelCase     ).raw     )
   return im



@torch.no_grad()
def        _a						(    UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase=False     )				->				List[Any]:







   """simple docstring"""
   lowerCamelCase__   :						Any					   =							get_config(__lowerCamelCase     )

   # load original model from timm
   lowerCamelCase__   :						Dict					   =							create_model(__lowerCamelCase			,  pretrained=__lowerCamelCase     )
   timm_model.eval()

   # load state_dict of original model
   lowerCamelCase__   :						List[str]					   =							timm_model.state_dict()
   for key in state_dict.copy().keys():
      lowerCamelCase__   :						int					   =							state_dict.pop(__lowerCamelCase     )
      lowerCamelCase__   :						int					   =							val.squeeze() if '''head''' in key else val

   # load HuggingFace model
   lowerCamelCase__   :						Dict					   =							BitForImageClassification(__lowerCamelCase     )
   model.eval()
   model.load_state_dict(__lowerCamelCase     )

   # create image processor
   lowerCamelCase__   :						Optional[int]					   =							create_transform(**resolve_data_config({}			,  model=__lowerCamelCase     )     )
   lowerCamelCase__   :						Optional[Any]					   =							transform.transforms

   lowerCamelCase__   :						Optional[Any]					   =							{
       '''bilinear''': PILImageResampling.BILINEAR,
       '''bicubic''': PILImageResampling.BICUBIC,
       '''nearest''': PILImageResampling.NEAREST,
   }

   lowerCamelCase__   :						Any					   =							BitImageProcessor(
       do_resize=__lowerCamelCase			,  size={'''shortest_edge''': timm_transforms[0].size}			,  resample=pillow_resamplings[timm_transforms[0].interpolation.value]			,  do_center_crop=__lowerCamelCase			,  crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]}			,  do_normalize=__lowerCamelCase			,  image_mean=timm_transforms[-1].mean.tolist()			,  image_std=timm_transforms[-1].std.tolist()			,  )

   lowerCamelCase__   :						str					   =							prepare_img()
   lowerCamelCase__   :						Optional[int]					   =							transform(__lowerCamelCase     ).unsqueeze(0     )
   lowerCamelCase__   :						List[Any]					   =							processor(__lowerCamelCase			,  return_tensors='''pt'''     ).pixel_values

   # verify pixel values
   assert torch.allclose(__lowerCamelCase			,  __lowerCamelCase     )

   # verify logits
   with torch.no_grad():
      lowerCamelCase__   :						str					   =							model(__lowerCamelCase     )
      lowerCamelCase__   :						Optional[Any]					   =							outputs.logits

   print('''Logits:'''			,  logits[0, :3]     )
   print('''Predicted class:'''			,  model.config.idalabel[logits.argmax(-1     ).item()]     )
   lowerCamelCase__   :						Union[str, Any]					   =							timm_model(__lowerCamelCase     )
   assert timm_logits.shape == outputs.logits.shape
   assert torch.allclose(__lowerCamelCase			,  outputs.logits			,  atol=1E-3     )
   print('''Looks ok!'''     )

   if pytorch_dump_folder_path is not None:
      Path(__lowerCamelCase     ).mkdir(exist_ok=__lowerCamelCase     )
      print(f"Saving model {model_name} and processor to {pytorch_dump_folder_path}"     )
      model.save_pretrained(__lowerCamelCase     )
      processor.save_pretrained(__lowerCamelCase     )

   if push_to_hub:
      print(f"Pushing model {model_name} and processor to the hub"     )
      model.push_to_hub(f"ybelkada/{model_name}"     )
      processor.push_to_hub(f"ybelkada/{model_name}"     )


if __name__ == "__main__":
       _A					:    str   =  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--model_name',
           default='resnetv2_50x1_bitm',
           type=str,
           help='Name of the BiT timm model you\'d like to convert.',
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
       )
       parser.add_argument(
           '--push_to_hub',
           action='store_true',
           help='Whether to push the model to the hub.',
       )

       _A					:    Tuple   =  parser.parse_args()
       convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel

class 		__lowerCAmelCase	(	pl.LightningModule ):
							def __init__(						self						:Union[str, Any]		,      __magic_name__						:Optional[int]				):







								'''simple docstring'''







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





							def       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







								pass







def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Optional[int]:
	# load longformer model from model identifier
	a										=							LongformerModel.from_pretrained(__lowerCamelCase    )
	a										=							LightningModel(__lowerCamelCase    )

	a										=							torch.load(__lowerCamelCase							,			map_location=torch.device("""cpu"""    )    )
	lightning_model.load_state_dict(ckpt["""state_dict"""]    )

	# init longformer question answering model
	a										=							LongformerForQuestionAnswering.from_pretrained(__lowerCamelCase    )

	# 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(__lowerCamelCase    )

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


if __name__ == "__main__":
		__UpperCamelCase					:       List[Any]    					=      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."
		)
		__UpperCamelCase					:       List[Any]    					=      parser.parse_args()
		convert_longformer_qa_checkpoint_to_pytorch(
		    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
		)

