



'''simple docstring'''





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											=		logging.get_logger(__name__)

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			) -> Optional[Any]:
  __lowerCamelCase			:			Optional[Any]       			=					original_name.split('.'			)[0]
  __lowerCamelCase			:			Union[str, Any]       			=					key.split('.'			)
  __lowerCamelCase			:			Optional[int]       			=					int(key_list[key_list.index(_lowerCAmelCase			) - 2]			)
  __lowerCamelCase			:			Optional[int]       			=					int(key_list[key_list.index(_lowerCAmelCase			) - 1]			)
  __lowerCamelCase			:			int       			=					orig_block_num - offset

  __lowerCamelCase			:			Optional[int]       			=					key.replace(F'{orig_block_num}.{layer_num}.{original_name}'			,F'block.{new_block_num}.{layer_num}.{new_name}'			)
  return key

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

def 			a_	(				) -> Any:
  __lowerCamelCase			:			int       			=					'http://images.cocodataset.org/val2017/000000039769.jpg'
  __lowerCamelCase			:			List[str]       			=					Image.open(requests.get(_lowerCAmelCase			,stream=_lowerCAmelCase			).raw			)

  return image

@torch.no_grad()
def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			) -> Optional[Any]:
  __lowerCamelCase			:			Optional[Any]       			=					PoolFormerConfig()

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

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

  # load image processor
  __lowerCamelCase			:			str       			=					PoolFormerImageProcessor(crop_pct=_lowerCAmelCase			)

  # Prepare image
  __lowerCamelCase			:			str       			=					prepare_img()
  __lowerCamelCase			:			Optional[int]       			=					image_processor(images=_lowerCAmelCase			,return_tensors='pt'			).pixel_values

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

  # load original state dict
  __lowerCamelCase			:			Tuple       			=					torch.load(_lowerCAmelCase			,map_location=torch.device('cpu'			)			)

  # rename keys
  __lowerCamelCase			:			Optional[int]       			=					rename_keys(_lowerCAmelCase			)

  # create HuggingFace model and load state dict
  __lowerCamelCase			:			int       			=					PoolFormerForImageClassification(_lowerCAmelCase			)
  model.load_state_dict(_lowerCAmelCase			)
  model.eval()

  # Define image processor
  __lowerCamelCase			:			int       			=					PoolFormerImageProcessor(crop_pct=_lowerCAmelCase			)
  __lowerCamelCase			:			Any       			=					image_processor(images=prepare_img()			,return_tensors='pt'			).pixel_values

  # forward pass
  __lowerCamelCase			:			Tuple       			=					model(_lowerCAmelCase			)
  __lowerCamelCase			:			List[str]       			=					outputs.logits

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

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


if __name__ == "__main__":
   _UpperCamelCase											=		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											=		parser.parse_args()
   convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)











'''simple docstring'''





import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_UpperCamelCase											=		logging.get_logger(__name__)

_UpperCamelCase											=		{'vocab_file': 'vocab.json'}

_UpperCamelCase											=		{
    'vocab_file': {
        'mgp-str': 'https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json',
    }
}

_UpperCamelCase											=		{'mgp-str': 27}






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   =VOCAB_FILES_NAMES
  a_				   =PRETRAINED_VOCAB_FILES_MAP
  a_				   =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




  def __init__(					self :      Optional[Any]     ,      _a :      Optional[Any]     ,      _a :      Any="[GO]"     ,      _a :      Optional[Any]="[GO]"     ,      _a :      Optional[int]="[s]"     ,      _a :      Optional[int]="[GO]"     ,      **_a :      Union[str, Any]  )   ->   Optional[int]:
    super().__init__(
        unk_token=_a     ,      bos_token=_a     ,      eos_token=_a     ,      pad_token=_a     ,      **_a     ,      )

    with open(_a     ,      encoding='utf-8'  ) as vocab_handle:
      __lowerCamelCase			:			List[str]       			=					json.load(_a  )
    __lowerCamelCase			:			Tuple       			=					{v: k for k, v in self.vocab.items()}




  @property
  def 		_lowercase	(					self :      Union[str, Any]  )   ->   List[str]:
    return len(self.vocab  )




  def 		_lowercase	(					self :      Union[str, Any]  )   ->   Tuple:
    return dict(self.vocab     ,      **self.added_tokens_encoder  )




  def 		_lowercase	(					self :      Dict     ,      _a :      Tuple  )   ->   int:
    __lowerCamelCase			:			Optional[Any]       			=					[]
    for s in text:
      char_tokens.extend(_a  )
    return char_tokens




  def 		_lowercase	(					self :      Optional[Any]     ,      _a :      Any  )   ->   Dict:
    return self.vocab.get(_a     ,      self.vocab.get(self.unk_token  )  )




  def 		_lowercase	(					self :      List[Any]     ,      _a :      Dict  )   ->   Optional[int]:
    return self.decoder.get(_a  )




  def 		_lowercase	(					self :      Union[str, Any]     ,      _a :      str     ,      _a :      Optional[str] = None  )   ->   Tuple[str]:
    if not os.path.isdir(_a  ):
      logger.error('Vocabulary path ({}) should be a directory'.format(_a  )  )
      return
    __lowerCamelCase			:			List[Any]       			=					os.path.join(
        _a     ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )

    with open(_a     ,      'w'     ,      encoding='utf-8'  ) as f:
      f.write(json.dumps(self.vocab     ,      indent=2     ,      sort_keys=_a     ,      ensure_ascii=_a  ) + '\n'  )

    return (vocab_file,)






