

'''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 :       List[str]			   =     logging.get_logger(__name__)







def    a__       (  a__      ,    a__      ,    a__      ,    a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      original_name.split("""."""						)[0]
     __SCREAMING_SNAKE_CASE     =      key.split("""."""						)
     __SCREAMING_SNAKE_CASE     =      int(key_list[key_list.index(a__						) - 2]						)
     __SCREAMING_SNAKE_CASE     =      int(key_list[key_list.index(a__						) - 1]						)
     __SCREAMING_SNAKE_CASE     =      orig_block_num - offset

     __SCREAMING_SNAKE_CASE     =      key.replace(F'{orig_block_num}.{layer_num}.{original_name}'      ,    F'block.{new_block_num}.{layer_num}.{new_name}'						)
     return key







def    a__       (  a__						):




     """simple docstring"""


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







def    a__       (  ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      """http://images.cocodataset.org/val2017/000000039769.jpg"""
     __SCREAMING_SNAKE_CASE     =      Image.open(requests.get(a__      ,    stream=a__						).raw						)

     return image







@torch.no_grad()
def    a__       (  a__      ,    a__      ,    a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      PoolFormerConfig()

     # set attributes based on model_name
     __SCREAMING_SNAKE_CASE     =      """huggingface/label-files"""
     __SCREAMING_SNAKE_CASE     =      model_name[-3:]
     __SCREAMING_SNAKE_CASE     =      10_00
     __SCREAMING_SNAKE_CASE     =      """imagenet-1k-id2label.json"""
     __SCREAMING_SNAKE_CASE     =      (1, 10_00)

     # set config attributes
     __SCREAMING_SNAKE_CASE     =      json.load(open(hf_hub_download(a__      ,    a__      ,    repo_type="""dataset"""						)      ,    """r"""						)						)
     __SCREAMING_SNAKE_CASE     =      {int(a__						): v for k, v in idalabel.items()}
     __SCREAMING_SNAKE_CASE     =      idalabel
     __SCREAMING_SNAKE_CASE     =      {v: k for k, v in idalabel.items()}
     if size == "s12":
          __SCREAMING_SNAKE_CASE     =      [2, 2, 6, 2]
          __SCREAMING_SNAKE_CASE     =      [64, 1_28, 3_20, 5_12]
          __SCREAMING_SNAKE_CASE     =      4.0
          __SCREAMING_SNAKE_CASE     =      0.9
     elif size == "s24":
          __SCREAMING_SNAKE_CASE     =      [4, 4, 12, 4]
          __SCREAMING_SNAKE_CASE     =      [64, 1_28, 3_20, 5_12]
          __SCREAMING_SNAKE_CASE     =      4.0
          __SCREAMING_SNAKE_CASE     =      0.9
     elif size == "s36":
          __SCREAMING_SNAKE_CASE     =      [6, 6, 18, 6]
          __SCREAMING_SNAKE_CASE     =      [64, 1_28, 3_20, 5_12]
          __SCREAMING_SNAKE_CASE     =      4.0
          __SCREAMING_SNAKE_CASE     =      1E-6
          __SCREAMING_SNAKE_CASE     =      0.9
     elif size == "m36":
          __SCREAMING_SNAKE_CASE     =      [6, 6, 18, 6]
          __SCREAMING_SNAKE_CASE     =      [96, 1_92, 3_84, 7_68]
          __SCREAMING_SNAKE_CASE     =      4.0
          __SCREAMING_SNAKE_CASE     =      1E-6
          __SCREAMING_SNAKE_CASE     =      0.95
     elif size == "m48":
          __SCREAMING_SNAKE_CASE     =      [8, 8, 24, 8]
          __SCREAMING_SNAKE_CASE     =      [96, 1_92, 3_84, 7_68]
          __SCREAMING_SNAKE_CASE     =      4.0
          __SCREAMING_SNAKE_CASE     =      1E-6
          __SCREAMING_SNAKE_CASE     =      0.95
     else:
          raise ValueError(F'Size {size} not supported'						)

     # load image processor
     __SCREAMING_SNAKE_CASE     =      PoolFormerImageProcessor(crop_pct=a__						)

     # Prepare image
     __SCREAMING_SNAKE_CASE     =      prepare_img()
     __SCREAMING_SNAKE_CASE     =      image_processor(images=a__      ,    return_tensors="""pt"""						).pixel_values

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

     # load original state dict
     __SCREAMING_SNAKE_CASE     =      torch.load(a__      ,    map_location=torch.device("""cpu"""						)						)

     # rename keys
     __SCREAMING_SNAKE_CASE     =      rename_keys(a__						)

     # create HuggingFace model and load state dict
     __SCREAMING_SNAKE_CASE     =      PoolFormerForImageClassification(a__						)
     model.load_state_dict(a__						)
     model.eval()

     # Define image processor
     __SCREAMING_SNAKE_CASE     =      PoolFormerImageProcessor(crop_pct=a__						)
     __SCREAMING_SNAKE_CASE     =      image_processor(images=prepare_img()      ,    return_tensors="""pt"""						).pixel_values

     # forward pass
     __SCREAMING_SNAKE_CASE     =      model(a__						)
     __SCREAMING_SNAKE_CASE     =      outputs.logits

     # define expected logit slices for different models
     if size == "s12":
          __SCREAMING_SNAKE_CASE     =      torch.tensor([-0.3_045, -0.6_758, -0.4_869]						)
     elif size == "s24":
          __SCREAMING_SNAKE_CASE     =      torch.tensor([0.4_402, -0.1_374, -0.8_045]						)
     elif size == "s36":
          __SCREAMING_SNAKE_CASE     =      torch.tensor([-0.6_080, -0.5_133, -0.5_898]						)
     elif size == "m36":
          __SCREAMING_SNAKE_CASE     =      torch.tensor([0.3_952, 0.2_263, -1.2_668]						)
     elif size == "m48":
          __SCREAMING_SNAKE_CASE     =      torch.tensor([0.1_167, -0.0_656, -0.3_423]						)
     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__":
  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 :       List[Any]			   =     parser.parse_args()
  convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)









'''simple docstring'''



import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


UpperCAmelCase :       str			   =     logging.get_logger(__name__)







class 						lowerCAmelCase__   (      a					):



 """simple docstring"""



 lowerCAmelCase__  					=      "linear"
 lowerCAmelCase__  					=      "cosine"
 lowerCAmelCase__  					=      "cosine_with_restarts"
 lowerCAmelCase__  					=      "polynomial"
 lowerCAmelCase__  					=      "constant"
 lowerCAmelCase__  					=      "constant_with_warmup"
 lowerCAmelCase__  					=      "piecewise_constant"







def    a__       (  a__      ,    a__ = -1						):




     """simple docstring"""


     return LambdaLR(a__      ,    lambda a__						: 1      ,    last_epoch=a__						)







def    a__       (  a__      ,    a__      ,    a__ = -1						):




     """simple docstring"""


     def lr_lambda(a__						):
          if current_step < num_warmup_steps:
               return float(a__						) / float(max(1.0      ,    a__						)						)
          return 1.0

     return LambdaLR(a__      ,    a__      ,    last_epoch=a__						)







def    a__       (  a__      ,    a__      ,    a__ = -1						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      {}
     __SCREAMING_SNAKE_CASE     =      step_rules.split(""","""						)
     for rule_str in rule_list[:-1]:
          __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      rule_str.split(""":"""						)
          __SCREAMING_SNAKE_CASE     =      int(a__						)
          __SCREAMING_SNAKE_CASE     =      float(a__						)
          __SCREAMING_SNAKE_CASE     =      value
     __SCREAMING_SNAKE_CASE     =      float(rule_list[-1]						)

     def create_rules_function(a__      ,    a__						):
          def rule_func(a__						) -> float:
               __SCREAMING_SNAKE_CASE     =      sorted(rules_dict.keys()						)
               for i, sorted_step in enumerate(a__						):
                    if steps < sorted_step:
                         return rules_dict[sorted_steps[i]]
               return last_lr_multiple

          return rule_func

     __SCREAMING_SNAKE_CASE     =      create_rules_function(a__      ,    a__						)

     return LambdaLR(a__      ,    a__      ,    last_epoch=a__						)







def    a__       (  a__      ,    a__      ,    a__      ,    a__=-1						):




     """simple docstring"""


     def lr_lambda(a__						):
          if current_step < num_warmup_steps:
               return float(a__						) / float(max(1      ,    a__						)						)
          return max(
              0.0      ,    float(num_training_steps - current_step						) / float(max(1      ,    num_training_steps - num_warmup_steps						)						)						)

     return LambdaLR(a__      ,    a__      ,    a__						)







def    a__       (  a__      ,    a__      ,    a__      ,    a__ = 0.5      ,    a__ = -1						):




     """simple docstring"""


     def lr_lambda(a__						):
          if current_step < num_warmup_steps:
               return float(a__						) / float(max(1      ,    a__						)						)
          __SCREAMING_SNAKE_CASE     =      float(current_step - num_warmup_steps						) / float(max(1      ,    num_training_steps - num_warmup_steps						)						)
          return max(0.0      ,    0.5 * (1.0 + math.cos(math.pi * float(a__						) * 2.0 * progress						))						)

     return LambdaLR(a__      ,    a__      ,    a__						)







def    a__       (  a__      ,    a__      ,    a__      ,    a__ = 1      ,    a__ = -1						):




     """simple docstring"""


     def lr_lambda(a__						):
          if current_step < num_warmup_steps:
               return float(a__						) / float(max(1      ,    a__						)						)
          __SCREAMING_SNAKE_CASE     =      float(current_step - num_warmup_steps						) / float(max(1      ,    num_training_steps - num_warmup_steps						)						)
          if progress >= 1.0:
               return 0.0
          return max(0.0      ,    0.5 * (1.0 + math.cos(math.pi * ((float(a__						) * progress) % 1.0)						))						)

     return LambdaLR(a__      ,    a__      ,    a__						)







def    a__       (  a__      ,    a__      ,    a__      ,    a__=1E-7      ,    a__=1.0      ,    a__=-1						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      optimizer.defaults["""lr"""]
     if not (lr_init > lr_end):
          raise ValueError(F'lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})'						)

     def lr_lambda(a__						):
          if current_step < num_warmup_steps:
               return float(a__						) / float(max(1      ,    a__						)						)
          elif current_step > num_training_steps:
               return lr_end / lr_init  # as LambdaLR multiplies by lr_init
          else:
               __SCREAMING_SNAKE_CASE     =      lr_init - lr_end
               __SCREAMING_SNAKE_CASE     =      num_training_steps - num_warmup_steps
               __SCREAMING_SNAKE_CASE     =      1 - (current_step - num_warmup_steps) / decay_steps
               __SCREAMING_SNAKE_CASE     =      lr_range * pct_remaining**power + lr_end
               return decay / lr_init  # as LambdaLR multiplies by lr_init

     return LambdaLR(a__      ,    a__      ,    a__						)


UpperCAmelCase :       Optional[Any]			   =     {
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}







def    a__       (  a__      ,    a__      ,    a__ = None      ,    a__ = None      ,    a__ = None      ,    a__ = 1      ,    a__ = 1.0      ,    a__ = -1      ,    ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      SchedulerType(a__						)
     __SCREAMING_SNAKE_CASE     =      TYPE_TO_SCHEDULER_FUNCTION[name]
     if name == SchedulerType.CONSTANT:
          return schedule_func(a__      ,    last_epoch=a__						)

     if name == SchedulerType.PIECEWISE_CONSTANT:
          return schedule_func(a__      ,    step_rules=a__      ,    last_epoch=a__						)

     # All other schedulers require `num_warmup_steps`
     if num_warmup_steps is None:
          raise ValueError(F'{name} requires `num_warmup_steps`, please provide that argument.'						)

     if name == SchedulerType.CONSTANT_WITH_WARMUP:
          return schedule_func(a__      ,    num_warmup_steps=a__      ,    last_epoch=a__						)

     # All other schedulers require `num_training_steps`
     if num_training_steps is None:
          raise ValueError(F'{name} requires `num_training_steps`, please provide that argument.'						)

     if name == SchedulerType.COSINE_WITH_RESTARTS:
          return schedule_func(
              a__      ,    num_warmup_steps=a__      ,    num_training_steps=a__      ,    num_cycles=a__      ,    last_epoch=a__      ,    )

     if name == SchedulerType.POLYNOMIAL:
          return schedule_func(
              a__      ,    num_warmup_steps=a__      ,    num_training_steps=a__      ,    power=a__      ,    last_epoch=a__      ,    )

     return schedule_func(
         a__      ,    num_warmup_steps=a__      ,    num_training_steps=a__      ,    last_epoch=a__						)






