


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()
__UpperCamelCase       :       Optional[Any]						       =				logging.get_logger(__name__)

def 			_a  (				SCREAMING_SNAKE_CASE	:				int    , SCREAMING_SNAKE_CASE	:				Union[str, Any]    , SCREAMING_SNAKE_CASE	:				Optional[Any]    , SCREAMING_SNAKE_CASE	:				Union[str, Any]     ):







 """simple docstring"""



 UpperCamelCase__					:    Tuple          =   original_name.split('''.'''     )[0]
 UpperCamelCase__					:    Union[str, Any]          =   key.split('''.'''     )
 UpperCamelCase__					:    int          =   int(key_list[key_list.index(SCREAMING_SNAKE_CASE     ) - 2]     )
 UpperCamelCase__					:    Optional[Any]          =   int(key_list[key_list.index(SCREAMING_SNAKE_CASE     ) - 1]     )
 UpperCamelCase__					:    Tuple          =   orig_block_num - offset

 UpperCamelCase__					:    Optional[Any]          =   key.replace(F"{orig_block_num}.{layer_num}.{original_name}"    , F"block.{new_block_num}.{layer_num}.{new_name}"     )
 return key

def 			_a  (				SCREAMING_SNAKE_CASE	:				Tuple     ):







 """simple docstring"""



 UpperCamelCase__					:    Union[str, Any]          =   OrderedDict()
 UpperCamelCase__       ,				UpperCamelCase__					:    str          =   0, 0
 for key, value in state_dict.items():
  if key.startswith('''network'''     ):
   UpperCamelCase__					:    int          =   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
   UpperCamelCase__					:    Dict          =   key[: key.find('''proj'''     )]
   UpperCamelCase__					:    str          =   key.replace(SCREAMING_SNAKE_CASE    , F"patch_embeddings.{total_embed_found}."     )
   UpperCamelCase__					:    Any          =   key.replace('''proj'''    , '''projection'''     )
   if key.endswith('''bias'''     ):
    total_embed_found += 1
  if "patch_embeddings" in key:
   UpperCamelCase__					:    Optional[Any]          =   '''poolformer.encoder.''' + key
  if "mlp.fc1" in key:
   UpperCamelCase__					:    Optional[Any]          =   replace_key_with_offset(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , '''mlp.fc1'''    , '''output.conv1'''     )
  if "mlp.fc2" in key:
   UpperCamelCase__					:    Dict          =   replace_key_with_offset(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , '''mlp.fc2'''    , '''output.conv2'''     )
  if "norm1" in key:
   UpperCamelCase__					:    Union[str, Any]          =   replace_key_with_offset(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , '''norm1'''    , '''before_norm'''     )
  if "norm2" in key:
   UpperCamelCase__					:    Optional[Any]          =   replace_key_with_offset(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , '''norm2'''    , '''after_norm'''     )
  if "layer_scale_1" in key:
   UpperCamelCase__					:    List[str]          =   replace_key_with_offset(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , '''layer_scale_1'''    , '''layer_scale_1'''     )
  if "layer_scale_2" in key:
   UpperCamelCase__					:    Dict          =   replace_key_with_offset(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , '''layer_scale_2'''    , '''layer_scale_2'''     )
  if "head" in key:
   UpperCamelCase__					:    str          =   key.replace('''head'''    , '''classifier'''     )
  UpperCamelCase__					:    Union[str, Any]          =   value
 return new_state_dict

def 			_a  (				):







 """simple docstring"""



 UpperCamelCase__					:    Optional[int]          =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 UpperCamelCase__					:    List[Any]          =   Image.open(requests.get(SCREAMING_SNAKE_CASE    , stream=SCREAMING_SNAKE_CASE     ).raw     )

 return image

@torch.no_grad()
def 			_a  (				SCREAMING_SNAKE_CASE	:				Union[str, Any]    , SCREAMING_SNAKE_CASE	:				List[str]    , SCREAMING_SNAKE_CASE	:				int     ):







 """simple docstring"""



 UpperCamelCase__					:    Optional[Any]          =   PoolFormerConfig()

 # set attributes based on model_name
 UpperCamelCase__					:    Optional[Any]          =   '''huggingface/label-files'''
 UpperCamelCase__					:    Dict          =   model_name[-3:]
 UpperCamelCase__					:    Optional[int]          =   1000
 UpperCamelCase__					:    int          =   '''imagenet-1k-id2label.json'''
 UpperCamelCase__					:    int          =   (1, 1000)

 # set config attributes
 UpperCamelCase__					:    List[str]          =   json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , repo_type='''dataset'''     )    , '''r'''     )     )
 UpperCamelCase__					:    str          =   {int(SCREAMING_SNAKE_CASE     ): v for k, v in idalabel.items()}
 UpperCamelCase__					:    int          =   idalabel
 UpperCamelCase__					:    int          =   {v: k for k, v in idalabel.items()}
 if size == "s12":
  UpperCamelCase__					:    Union[str, Any]          =   [2, 2, 6, 2]
  UpperCamelCase__					:    Optional[int]          =   [64, 128, 320, 512]
  UpperCamelCase__					:    Optional[int]          =   4.0
  UpperCamelCase__					:    List[str]          =   0.9
 elif size == "s24":
  UpperCamelCase__					:    Union[str, Any]          =   [4, 4, 12, 4]
  UpperCamelCase__					:    str          =   [64, 128, 320, 512]
  UpperCamelCase__					:    Union[str, Any]          =   4.0
  UpperCamelCase__					:    Union[str, Any]          =   0.9
 elif size == "s36":
  UpperCamelCase__					:    int          =   [6, 6, 18, 6]
  UpperCamelCase__					:    Dict          =   [64, 128, 320, 512]
  UpperCamelCase__					:    List[Any]          =   4.0
  UpperCamelCase__					:    Tuple          =   1E-6
  UpperCamelCase__					:    Optional[int]          =   0.9
 elif size == "m36":
  UpperCamelCase__					:    int          =   [6, 6, 18, 6]
  UpperCamelCase__					:    Optional[Any]          =   [96, 192, 384, 768]
  UpperCamelCase__					:    str          =   4.0
  UpperCamelCase__					:    str          =   1E-6
  UpperCamelCase__					:    List[str]          =   0.95
 elif size == "m48":
  UpperCamelCase__					:    Union[str, Any]          =   [8, 8, 24, 8]
  UpperCamelCase__					:    List[Any]          =   [96, 192, 384, 768]
  UpperCamelCase__					:    str          =   4.0
  UpperCamelCase__					:    Any          =   1E-6
  UpperCamelCase__					:    List[str]          =   0.95
 else:
  raise ValueError(F"Size {size} not supported"     )

 # load image processor
 UpperCamelCase__					:    str          =   PoolFormerImageProcessor(crop_pct=SCREAMING_SNAKE_CASE     )

 # Prepare image
 UpperCamelCase__					:    Union[str, Any]          =   prepare_img()
 UpperCamelCase__					:    Union[str, Any]          =   image_processor(images=SCREAMING_SNAKE_CASE    , return_tensors='''pt'''     ).pixel_values

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

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

 # rename keys
 UpperCamelCase__					:    Optional[int]          =   rename_keys(SCREAMING_SNAKE_CASE     )

 # create HuggingFace model and load state dict
 UpperCamelCase__					:    List[Any]          =   PoolFormerForImageClassification(SCREAMING_SNAKE_CASE     )
 model.load_state_dict(SCREAMING_SNAKE_CASE     )
 model.eval()

 # Define image processor
 UpperCamelCase__					:    Optional[Any]          =   PoolFormerImageProcessor(crop_pct=SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    int          =   image_processor(images=prepare_img()    , return_tensors='''pt'''     ).pixel_values

 # forward pass
 UpperCamelCase__					:    Optional[Any]          =   model(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Union[str, Any]          =   outputs.logits

 # define expected logit slices for different models
 if size == "s12":
  UpperCamelCase__					:    str          =   torch.tensor([-0.3045, -0.6758, -0.4869]     )
 elif size == "s24":
  UpperCamelCase__					:    Tuple          =   torch.tensor([0.4402, -0.1374, -0.8045]     )
 elif size == "s36":
  UpperCamelCase__					:    List[Any]          =   torch.tensor([-0.6080, -0.5133, -0.5898]     )
 elif size == "m36":
  UpperCamelCase__					:    List[str]          =   torch.tensor([0.3952, 0.2263, -1.2668]     )
 elif size == "m48":
  UpperCamelCase__					:    Dict          =   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]    , SCREAMING_SNAKE_CASE    , atol=1E-2     )

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


if __name__ == "__main__":
       __UpperCamelCase       :       Tuple						       =				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."
       )
       __UpperCamelCase       :       Optional[Any]						       =				parser.parse_args()
       convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)






import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class       __magic_name__	(     __lowerCAmelCase):
     A:		Optional[Any]			=      ["image_processor", "tokenizer"]
     A:		Optional[Any]			=      "LayoutLMv2ImageProcessor"
     A:		List[str]			=      ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")


     def __init__(      self							:     Dict							,	lowerCamelCase__							:     Union[str, Any]=None							,	lowerCamelCase__							:     List[str]=None							,	**lowerCamelCase__							:     Any			)  ->      Optional[Any]:





      '''simple docstring'''






      if "feature_extractor" in kwargs:
       warnings.warn(
           '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
           ''' instead.'''							,	lowerCamelCase__							,	)
       UpperCamelCase__					:    str          =   kwargs.pop('''feature_extractor'''			)

      UpperCamelCase__					:    List[Any]          =   image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
       raise ValueError('''You need to specify an `image_processor`.'''			)
      if tokenizer is None:
       raise ValueError('''You need to specify a `tokenizer`.'''			)

      super().__init__(lowerCamelCase__							,	lowerCamelCase__			)


     def __call__(      self							:     Optional[Any]							,	lowerCamelCase__							:     Union[str, Any]							,	lowerCamelCase__							:     Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,	lowerCamelCase__							:     Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None							,	lowerCamelCase__							:     Union[List[List[int]], List[List[List[int]]]] = None							,	lowerCamelCase__							:     Optional[Union[List[int], List[List[int]]]] = None							,	lowerCamelCase__							:     bool = True							,	lowerCamelCase__							:     Union[bool, str, PaddingStrategy] = False							,	lowerCamelCase__							:     Union[bool, str, TruncationStrategy] = None							,	lowerCamelCase__							:     Optional[int] = None							,	lowerCamelCase__							:     int = 0							,	lowerCamelCase__							:     Optional[int] = None							,	lowerCamelCase__							:     Optional[bool] = None							,	lowerCamelCase__							:     Optional[bool] = None							,	lowerCamelCase__							:     bool = False							,	lowerCamelCase__							:     bool = False							,	lowerCamelCase__							:     bool = False							,	lowerCamelCase__							:     bool = False							,	lowerCamelCase__							:     bool = True							,	lowerCamelCase__							:     Optional[Union[str, TensorType]] = None							,	**lowerCamelCase__							:     Any							,	)  ->      BatchEncoding:





      '''simple docstring'''






      if self.image_processor.apply_ocr and (boxes is not None):
       raise ValueError(
           '''You cannot provide bounding boxes '''
           '''if you initialized the image processor with apply_ocr set to True.'''			)

      if self.image_processor.apply_ocr and (word_labels is not None):
       raise ValueError(
           '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''			)

      if return_overflowing_tokens is True and return_offsets_mapping is False:
       raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''			)

      # first, apply the image processor
      UpperCamelCase__					:    Optional[Any]          =   self.image_processor(images=lowerCamelCase__							,	return_tensors=lowerCamelCase__			)

      # second, apply the tokenizer
      if text is not None and self.image_processor.apply_ocr and text_pair is None:
       if isinstance(lowerCamelCase__							,	lowerCamelCase__			):
        UpperCamelCase__					:    Optional[int]          =   [text]  # add batch dimension (as the image processor always adds a batch dimension)
       UpperCamelCase__					:    Optional[int]          =   features['''words''']

      UpperCamelCase__					:    Optional[int]          =   self.tokenizer(
          text=text if text is not None else features['''words''']							,	text_pair=text_pair if text_pair is not None else None							,	boxes=boxes if boxes is not None else features['''boxes''']							,	word_labels=lowerCamelCase__							,	add_special_tokens=lowerCamelCase__							,	padding=lowerCamelCase__							,	truncation=lowerCamelCase__							,	max_length=lowerCamelCase__							,	stride=lowerCamelCase__							,	pad_to_multiple_of=lowerCamelCase__							,	return_token_type_ids=lowerCamelCase__							,	return_attention_mask=lowerCamelCase__							,	return_overflowing_tokens=lowerCamelCase__							,	return_special_tokens_mask=lowerCamelCase__							,	return_offsets_mapping=lowerCamelCase__							,	return_length=lowerCamelCase__							,	verbose=lowerCamelCase__							,	return_tensors=lowerCamelCase__							,	**lowerCamelCase__							,	)

      # add pixel values
      UpperCamelCase__					:    Optional[Any]          =   features.pop('''pixel_values'''			)
      if return_overflowing_tokens is True:
       UpperCamelCase__					:    Union[str, Any]          =   self.get_overflowing_images(lowerCamelCase__							,	encoded_inputs['''overflow_to_sample_mapping''']			)
      UpperCamelCase__					:    Tuple          =   images

      return encoded_inputs


     def      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     Any							,	lowerCamelCase__							:     List[Any]			)  ->      str:





      '''simple docstring'''






      UpperCamelCase__					:    int          =   []
      for sample_idx in overflow_to_sample_mapping:
       images_with_overflow.append(images[sample_idx]			)

      if len(lowerCamelCase__			) != len(lowerCamelCase__			):
       raise ValueError(
           '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
           F" {len(lowerCamelCase__			)} and {len(lowerCamelCase__			)}"			)

      return images_with_overflow


     def      UpperCAmelCase__	(      self							:     int							,	*lowerCamelCase__							:     Dict							,	**lowerCamelCase__							:     str			)  ->      str:





      '''simple docstring'''






      return self.tokenizer.batch_decode(*lowerCamelCase__							,	**lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     List[str]							,	*lowerCamelCase__							:     int							,	**lowerCamelCase__							:     str			)  ->      Tuple:





      '''simple docstring'''






      return self.tokenizer.decode(*lowerCamelCase__							,	**lowerCamelCase__			)


     @property
     def      UpperCAmelCase__	(      self							:     int			)  ->      str:





      '''simple docstring'''






      return ["input_ids", "bbox", "attention_mask", "image"]


     @property
     def      UpperCAmelCase__	(      self							:     Dict			)  ->      Tuple:





      '''simple docstring'''






      warnings.warn(
          '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''							,	lowerCamelCase__							,	)
      return self.image_processor_class


     @property
     def      UpperCAmelCase__	(      self							:     Optional[Any]			)  ->      Optional[int]:





      '''simple docstring'''






      warnings.warn(
          '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''							,	lowerCamelCase__							,	)
      return self.image_processor


