





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





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       int										=  original_name.split('.'					)[0]
  A_  :       Any										=  key.split('.'					)
  A_  :       List[str]										=  int(key_list[key_list.index(_UpperCAmelCase					) - 2]					)
  A_  :       Optional[int]										=  int(key_list[key_list.index(_UpperCAmelCase					) - 1]					)
  A_  :       str										=  orig_block_num - offset

  A_  :       List[Any]										=  key.replace(f"""{orig_block_num}.{layer_num}.{original_name}""" ,					f"""block.{new_block_num}.{layer_num}.{new_name}"""					)
  return key





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





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





def 						UpperCAmelCase__		(     ):






  """simple docstring"""





  A_  :       Tuple										=  'http://images.cocodataset.org/val2017/000000039769.jpg'
  A_  :       Optional[int]										=  Image.open(requests.get(_UpperCAmelCase ,					stream=_UpperCAmelCase					).raw					)

  return image





@torch.no_grad()
def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       Optional[Any]										=  PoolFormerConfig()

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

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

  # load image processor
  A_  :       Tuple										=  PoolFormerImageProcessor(crop_pct=_UpperCAmelCase					)

  # Prepare image
  A_  :       str										=  prepare_img()
  A_  :       List[Any]										=  image_processor(images=_UpperCAmelCase ,					return_tensors='pt'					).pixel_values

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

  # load original state dict
  A_  :       Any										=  torch.load(_UpperCAmelCase ,					map_location=torch.device('cpu'					)					)

  # rename keys
  A_  :       Tuple										=  rename_keys(_UpperCAmelCase					)

  # create HuggingFace model and load state dict
  A_  :       Tuple										=  PoolFormerForImageClassification(_UpperCAmelCase					)
  model.load_state_dict(_UpperCAmelCase					)
  model.eval()

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

  # forward pass
  A_  :       List[str]										=  model(_UpperCAmelCase					)
  A_  :       Union[str, Any]										=  outputs.logits

  # define expected logit slices for different models
  if size == "s12":
    A_  :       int										=  torch.tensor([-0.3_045, -0.6_758, -0.4_869]					)
  elif size == "s24":
    A_  :       Optional[int]										=  torch.tensor([0.4_402, -0.1_374, -0.8_045]					)
  elif size == "s36":
    A_  :       int										=  torch.tensor([-0.6_080, -0.5_133, -0.5_898]					)
  elif size == "m36":
    A_  :       Optional[Any]										=  torch.tensor([0.3_952, 0.2_263, -1.2_668]					)
  elif size == "m48":
    A_  :       int										=  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] ,					_UpperCAmelCase ,					atol=1E-2					)

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


if __name__ == "__main__":
   lowerCamelCase_     :						str						=					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_     :						Union[str, Any]						=					parser.parse_args()
   convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)






"""simple docstring"""




from typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_     :						Union[str, Any]						=					logging.get_logger(__name__)

lowerCamelCase_     :						Optional[Any]						=					{
    'huggingface/informer-tourism-monthly': (
        'https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json'
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_      : Tuple	    =   """informer"""
      lowercase_      : str	    =   {
          """hidden_size""": """d_model""",
          """num_attention_heads""": """encoder_attention_heads""",
          """num_hidden_layers""": """encoder_layers""",
      }

      def __init__(     self					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = "student_t"					,      snake_case_ = "nll"					,      snake_case_ = 1					,      snake_case_ = None					,      snake_case_ = "mean"					,      snake_case_ = 0					,      snake_case_ = 0					,      snake_case_ = 0					,      snake_case_ = 0					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = 6_4					,      snake_case_ = 3_2					,      snake_case_ = 3_2					,      snake_case_ = 2					,      snake_case_ = 2					,      snake_case_ = 2					,      snake_case_ = 2					,      snake_case_ = True					,      snake_case_ = "gelu"					,      snake_case_ = 0.05					,      snake_case_ = 0.1					,      snake_case_ = 0.1					,      snake_case_ = 0.1					,      snake_case_ = 0.1					,      snake_case_ = 1_0_0					,      snake_case_ = 0.02					,      snake_case_=True					,      snake_case_ = "prob"					,      snake_case_ = 5					,      snake_case_ = True					,      **snake_case_					,      ):

        """simple docstring"""

        A_  :       str										=  prediction_length
        A_  :       List[Any]										=  context_length or prediction_length
        A_  :       str										=  distribution_output
        A_  :       Dict										=  loss
        A_  :       Any										=  input_size
        A_  :       Union[str, Any]										=  num_time_features
        A_  :       Optional[Any]										=  lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
        A_  :       List[Any]										=  scaling
        A_  :       Tuple										=  num_dynamic_real_features
        A_  :       Any										=  num_static_real_features
        A_  :       str										=  num_static_categorical_features

        # set cardinality
        if cardinality and num_static_categorical_features > 0:
          if len(snake_case_  ) != num_static_categorical_features:
            raise ValueError(
                'The cardinality should be a list of the same length as `num_static_categorical_features`'  )
          A_  :       Optional[int]										=  cardinality
        else:
          A_  :       Optional[Any]										=  [0]

        # set embedding_dimension
        if embedding_dimension and num_static_categorical_features > 0:
          if len(snake_case_  ) != num_static_categorical_features:
            raise ValueError(
                'The embedding dimension should be a list of the same length as `num_static_categorical_features`'  )
          A_  :       Any										=  embedding_dimension
        else:
          A_  :       Optional[Any]										=  [min(5_0					,      (cat + 1) // 2  ) for cat in self.cardinality]

        A_  :       int										=  num_parallel_samples

        # Transformer architecture configuration
        A_  :       str										=  input_size * len(self.lags_sequence  ) + self._number_of_features
        A_  :       List[Any]										=  d_model
        A_  :       Dict										=  encoder_attention_heads
        A_  :       Dict										=  decoder_attention_heads
        A_  :       List[Any]										=  encoder_ffn_dim
        A_  :       Union[str, Any]										=  decoder_ffn_dim
        A_  :       int										=  encoder_layers
        A_  :       Any										=  decoder_layers

        A_  :       List[Any]										=  dropout
        A_  :       str										=  attention_dropout
        A_  :       Tuple										=  activation_dropout
        A_  :       List[str]										=  encoder_layerdrop
        A_  :       List[str]										=  decoder_layerdrop

        A_  :       str										=  activation_function
        A_  :       Optional[int]										=  init_std

        A_  :       List[Any]										=  use_cache

        # Informer
        A_  :       Tuple										=  attention_type
        A_  :       List[Any]										=  sampling_factor
        A_  :       Optional[int]										=  distil

        super().__init__(is_encoder_decoder=snake_case_					,      **snake_case_  )

      @property
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        return (
            sum(self.embedding_dimension  )
            + self.num_dynamic_real_features
            + self.num_time_features
            + self.num_static_real_features
            + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
        )