



'''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()
lowerCAmelCase:   int					=		logging.get_logger(__name__)
def  lowerCamelCase__			(		_A					,     _A					,     _A					,     _A							):
  a		:   str	  =	original_name.split('.'							)[0]
  a		:   int	  =	key.split('.'							)
  a		:   List[str]	  =	int(key_list[key_list.index(lowerCAmelCase_							) - 2]							)
  a		:   int	  =	int(key_list[key_list.index(lowerCAmelCase_							) - 1]							)
  a		:   Any	  =	orig_block_num - offset

  a		:   Optional[Any]	  =	key.replace(f"""{orig_block_num}.{layer_num}.{original_name}"""					,     f"""block.{new_block_num}.{layer_num}.{new_name}"""							)
  return key
def  lowerCamelCase__			(		_A							):
  a		:   Tuple	  =	OrderedDict()
  a  ,      a		:   Optional[int]	  =	0, 0
  for key, value in state_dict.items():
    if key.startswith('network'							):
      a		:   Optional[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
      a		:   Tuple	  =	key[: key.find('proj'							)]
      a		:   Optional[Any]	  =	key.replace(lowerCAmelCase_					,     f"""patch_embeddings.{total_embed_found}."""							)
      a		:   Tuple	  =	key.replace('proj'					,     'projection'							)
      if key.endswith('bias'							):
        total_embed_found += 1
    if "patch_embeddings" in key:
      a		:   int	  =	'poolformer.encoder.' + key
    if "mlp.fc1" in key:
      a		:   Optional[Any]	  =	replace_key_with_offset(lowerCAmelCase_					,     lowerCAmelCase_					,     'mlp.fc1'					,     'output.conv1'							)
    if "mlp.fc2" in key:
      a		:   Union[str, Any]	  =	replace_key_with_offset(lowerCAmelCase_					,     lowerCAmelCase_					,     'mlp.fc2'					,     'output.conv2'							)
    if "norm1" in key:
      a		:   Any	  =	replace_key_with_offset(lowerCAmelCase_					,     lowerCAmelCase_					,     'norm1'					,     'before_norm'							)
    if "norm2" in key:
      a		:   List[str]	  =	replace_key_with_offset(lowerCAmelCase_					,     lowerCAmelCase_					,     'norm2'					,     'after_norm'							)
    if "layer_scale_1" in key:
      a		:   Optional[int]	  =	replace_key_with_offset(lowerCAmelCase_					,     lowerCAmelCase_					,     'layer_scale_1'					,     'layer_scale_1'							)
    if "layer_scale_2" in key:
      a		:   Optional[int]	  =	replace_key_with_offset(lowerCAmelCase_					,     lowerCAmelCase_					,     'layer_scale_2'					,     'layer_scale_2'							)
    if "head" in key:
      a		:   List[Any]	  =	key.replace('head'					,     'classifier'							)
    a		:   int	  =	value
  return new_state_dict
def  lowerCamelCase__			(		):
  a		:   str	  =	'http://images.cocodataset.org/val2017/000000039769.jpg'
  a		:   str	  =	Image.open(requests.get(lowerCAmelCase_					,     stream=lowerCAmelCase_							).raw							)

  return image
@torch.no_grad()
def  lowerCamelCase__			(		_A					,     _A					,     _A							):
  a		:   int	  =	PoolFormerConfig()

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

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

  # load image processor
  a		:   List[str]	  =	PoolFormerImageProcessor(crop_pct=lowerCAmelCase_							)

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

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

  # load original state dict
  a		:   int	  =	torch.load(lowerCAmelCase_					,     map_location=torch.device('cpu'							)							)

  # rename keys
  a		:   List[Any]	  =	rename_keys(lowerCAmelCase_							)

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

  # Define image processor
  a		:   List[Any]	  =	PoolFormerImageProcessor(crop_pct=lowerCAmelCase_							)
  a		:   Optional[Any]	  =	image_processor(images=prepare_img()					,     return_tensors='pt'							).pixel_values

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

  # define expected logit slices for different models
  if size == "s12":
    a		:   int	  =	torch.tensor([-0.3045, -0.6758, -0.4869]							)
  elif size == "s24":
    a		:   str	  =	torch.tensor([0.4402, -0.1374, -0.8045]							)
  elif size == "s36":
    a		:   Any	  =	torch.tensor([-0.6080, -0.5133, -0.5898]							)
  elif size == "m36":
    a		:   List[str]	  =	torch.tensor([0.3952, 0.2263, -1.2668]							)
  elif size == "m48":
    a		:   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]					,     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__":
     lowerCAmelCase:   Optional[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.'
     )
     lowerCAmelCase:   Optional[Any]					=		parser.parse_args()
     convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__a	 =      {"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __a	 =      ["""ReformerTokenizer"""]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __a	 =      ["""ReformerTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __a	 =      [
     """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """ReformerAttention""",
     """ReformerForMaskedLM""",
     """ReformerForQuestionAnswering""",
     """ReformerForSequenceClassification""",
     """ReformerLayer""",
     """ReformerModel""",
     """ReformerModelWithLMHead""",
     """ReformerPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_reformer import ReformerTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_reformer_fast import ReformerTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_reformer import (
      REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      ReformerAttention,
      ReformerForMaskedLM,
      ReformerForQuestionAnswering,
      ReformerForSequenceClassification,
      ReformerLayer,
      ReformerModel,
      ReformerModelWithLMHead,
      ReformerPreTrainedModel,
  )

else:
 import sys

 __a	 =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



