



import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
a_   :Optional[int]        =      logging.get_logger(__name__)
def        lowercase_							(A : int		,					A : Optional[Any]		,					A : Any		,					A : Any      ):
       snake_case__							:	str   	=	original_name.split('.'      )[0]
       snake_case__							:	List[str]   	=	key.split('.'      )
       snake_case__							:	Tuple   	=	int(key_list[key_list.index(A      ) - 2]      )
       snake_case__							:	Dict   	=	int(key_list[key_list.index(A      ) - 1]      )
       snake_case__							:	Any   	=	orig_block_num - offset

       snake_case__							:	Dict   	=	key.replace(F'''{orig_block_num}.{layer_num}.{original_name}'''		,					F'''block.{new_block_num}.{layer_num}.{new_name}'''      )
       return key
def        lowercase_							(A : Optional[Any]      ):
       snake_case__							:	Dict   	=	OrderedDict()
       snake_case__	,				snake_case__							:	int   	=	0, 0
       for key, value in state_dict.items():
              if key.startswith('network'      ):
                     snake_case__							:	str   	=	key.replace('network'		,					'poolformer.encoder'      )
              if "proj" in key:
                     # Works for the first embedding as well as the internal embedding layers
                     if key.endswith('bias'      ) and "patch_embed" not in key:
                            patch_emb_offset += 1
                     snake_case__							:	Dict   	=	key[: key.find('proj'      )]
                     snake_case__							:	int   	=	key.replace(A		,					F'''patch_embeddings.{total_embed_found}.'''      )
                     snake_case__							:	Dict   	=	key.replace('proj'		,					'projection'      )
                     if key.endswith('bias'      ):
                            total_embed_found += 1
              if "patch_embeddings" in key:
                     snake_case__							:	Dict   	=	'poolformer.encoder.' + key
              if "mlp.fc1" in key:
                     snake_case__							:	Dict   	=	replace_key_with_offset(A		,					A		,					'mlp.fc1'		,					'output.conv1'      )
              if "mlp.fc2" in key:
                     snake_case__							:	List[Any]   	=	replace_key_with_offset(A		,					A		,					'mlp.fc2'		,					'output.conv2'      )
              if "norm1" in key:
                     snake_case__							:	List[str]   	=	replace_key_with_offset(A		,					A		,					'norm1'		,					'before_norm'      )
              if "norm2" in key:
                     snake_case__							:	Dict   	=	replace_key_with_offset(A		,					A		,					'norm2'		,					'after_norm'      )
              if "layer_scale_1" in key:
                     snake_case__							:	Optional[Any]   	=	replace_key_with_offset(A		,					A		,					'layer_scale_1'		,					'layer_scale_1'      )
              if "layer_scale_2" in key:
                     snake_case__							:	Tuple   	=	replace_key_with_offset(A		,					A		,					'layer_scale_2'		,					'layer_scale_2'      )
              if "head" in key:
                     snake_case__							:	Union[str, Any]   	=	key.replace('head'		,					'classifier'      )
              snake_case__							:	int   	=	value
       return new_state_dict
def        lowercase_							():
       snake_case__							:	Dict   	=	'http://images.cocodataset.org/val2017/000000039769.jpg'
       snake_case__							:	List[str]   	=	Image.open(requests.get(A		,					stream=A      ).raw      )

       return image
@torch.no_grad()
def        lowercase_							(A : Union[str, Any]		,					A : Union[str, Any]		,					A : int      ):
       snake_case__							:	Any   	=	PoolFormerConfig()

       # set attributes based on model_name
       snake_case__							:	str   	=	'huggingface/label-files'
       snake_case__							:	List[Any]   	=	model_name[-3:]
       snake_case__							:	Optional[int]   	=	1_0_0_0
       snake_case__							:	Tuple   	=	'imagenet-1k-id2label.json'
       snake_case__							:	Union[str, Any]   	=	(1, 1_0_0_0)

       # set config attributes
       snake_case__							:	Optional[int]   	=	json.load(open(hf_hub_download(A		,					A		,					repo_type='dataset'      )		,					'r'      )      )
       snake_case__							:	Any   	=	{int(A      ): v for k, v in idalabel.items()}
       snake_case__							:	int   	=	idalabel
       snake_case__							:	List[str]   	=	{v: k for k, v in idalabel.items()}
       if size == "s12":
              snake_case__							:	str   	=	[2, 2, 6, 2]
              snake_case__							:	Optional[int]   	=	[6_4, 1_2_8, 3_2_0, 5_1_2]
              snake_case__							:	Union[str, Any]   	=	4.0
              snake_case__							:	str   	=	0.9
       elif size == "s24":
              snake_case__							:	List[str]   	=	[4, 4, 1_2, 4]
              snake_case__							:	Any   	=	[6_4, 1_2_8, 3_2_0, 5_1_2]
              snake_case__							:	List[str]   	=	4.0
              snake_case__							:	Any   	=	0.9
       elif size == "s36":
              snake_case__							:	int   	=	[6, 6, 1_8, 6]
              snake_case__							:	int   	=	[6_4, 1_2_8, 3_2_0, 5_1_2]
              snake_case__							:	Dict   	=	4.0
              snake_case__							:	Dict   	=	1e-6
              snake_case__							:	Any   	=	0.9
       elif size == "m36":
              snake_case__							:	int   	=	[6, 6, 1_8, 6]
              snake_case__							:	Dict   	=	[9_6, 1_9_2, 3_8_4, 7_6_8]
              snake_case__							:	Tuple   	=	4.0
              snake_case__							:	Union[str, Any]   	=	1e-6
              snake_case__							:	str   	=	0.95
       elif size == "m48":
              snake_case__							:	Union[str, Any]   	=	[8, 8, 2_4, 8]
              snake_case__							:	Optional[int]   	=	[9_6, 1_9_2, 3_8_4, 7_6_8]
              snake_case__							:	Optional[Any]   	=	4.0
              snake_case__							:	int   	=	1e-6
              snake_case__							:	Optional[Any]   	=	0.95
       else:
              raise ValueError(F'''Size {size} not supported'''      )

       # load image processor
       snake_case__							:	List[Any]   	=	PoolFormerImageProcessor(crop_pct=A      )

       # Prepare image
       snake_case__							:	Optional[Any]   	=	prepare_img()
       snake_case__							:	Optional[Any]   	=	image_processor(images=A		,					return_tensors='pt'      ).pixel_values

       logger.info(F'''Converting model {model_name}...'''      )

       # load original state dict
       snake_case__							:	Optional[Any]   	=	torch.load(A		,					map_location=torch.device('cpu'      )      )

       # rename keys
       snake_case__							:	Union[str, Any]   	=	rename_keys(A      )

       # create HuggingFace model and load state dict
       snake_case__							:	str   	=	PoolFormerForImageClassification(A      )
       model.load_state_dict(A      )
       model.eval()

       # Define image processor
       snake_case__							:	Union[str, Any]   	=	PoolFormerImageProcessor(crop_pct=A      )
       snake_case__							:	Any   	=	image_processor(images=prepare_img()		,					return_tensors='pt'      ).pixel_values

       # forward pass
       snake_case__							:	List[Any]   	=	model(A      )
       snake_case__							:	List[Any]   	=	outputs.logits

       # define expected logit slices for different models
       if size == "s12":
              snake_case__							:	List[Any]   	=	torch.tensor([-0.3045, -0.6758, -0.4869]      )
       elif size == "s24":
              snake_case__							:	Optional[int]   	=	torch.tensor([0.4402, -0.1374, -0.8045]      )
       elif size == "s36":
              snake_case__							:	Optional[Any]   	=	torch.tensor([-0.6080, -0.5133, -0.5898]      )
       elif size == "m36":
              snake_case__							:	Any   	=	torch.tensor([0.3952, 0.2263, -1.2668]      )
       elif size == "m48":
              snake_case__							:	Optional[int]   	=	torch.tensor([0.1167, -0.0656, -0.3423]      )
       else:
              raise ValueError(F'''Size {size} not supported'''      )

       # verify logits
       assert logits.shape == expected_shape
       assert torch.allclose(logits[0, :3]		,					A		,					atol=1e-2      )

       # finally, save model and image processor
       logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''      )
       Path(A      ).mkdir(exist_ok=A      )
       model.save_pretrained(A      )
       print(F'''Saving image processor to {pytorch_dump_folder_path}'''      )
       image_processor.save_pretrained(A      )


if __name__ == "__main__":
 a_   :Union[str, Any]        =      argparse.ArgumentParser()

 parser.add_argument(
     "--model_name",
     default="poolformer_s12",
     type=str,
     help="Name of the model you'd like to convert.",
 )
 parser.add_argument(
     "--checkpoint_path", default=None, type=str, help="Path to the original PyTorch checkpoint (.pth file)."
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
 )
 a_   :List[Any]        =      parser.parse_args()
 convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)











import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()
def        lowercase_							(A : str		,					A : List[Any]		,					A : Any      ):
       # Initialise PyTorch model
       snake_case__							:	List[Any]   	=	LxmertConfig.from_json_file(A      )
       print(F'''Building PyTorch model from configuration: {config}'''      )
       snake_case__							:	List[str]   	=	LxmertForPreTraining(A      )

       # Load weights from tf checkpoint
       load_tf_weights_in_lxmert(A		,					A		,					A      )

       # Save pytorch-model
       print(F'''Save PyTorch model to {pytorch_dump_path}'''      )
       torch.save(model.state_dict()		,					A      )


if __name__ == "__main__":
 a_   :Union[str, Any]        =      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
 )
 parser.add_argument(
     "--config_file",
     default=None,
     type=str,
     required=True,
     help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 a_   :Optional[int]        =      parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






