"""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()
_a      :		Tuple           =   logging.get_logger(__name__)





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Optional[Any]			,_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       Dict			,_lowerCamelCase						:       List[str]	)       ->      int:
  _lowerCAmelCase						:       str  			=     original_name.split("""."""	)[0]
  _lowerCAmelCase						:       Optional[int]  			=     key.split("""."""	)
  _lowerCAmelCase						:       Any  			=     int(key_list[key_list.index(_lowerCamelCase	) - 2]	)
  _lowerCAmelCase						:       Tuple  			=     int(key_list[key_list.index(_lowerCamelCase	) - 1]	)
  _lowerCAmelCase						:       Optional[int]  			=     orig_block_num - offset

  _lowerCAmelCase						:       Tuple  			=     key.replace(f"{orig_block_num}.{layer_num}.{original_name}"			,f"block.{new_block_num}.{layer_num}.{new_name}"	)
  return key





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





def 							SCREAMING_SNAKE_CASE     (			)       ->      Dict:
  _lowerCAmelCase						:       Any  			=     """http://images.cocodataset.org/val2017/000000039769.jpg"""
  _lowerCAmelCase						:       List[str]  			=     Image.open(requests.get(_lowerCamelCase			,stream=_lowerCamelCase	).raw	)

  return image





@torch.no_grad()
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Tuple			,_lowerCamelCase						:       Any			,_lowerCamelCase						:       str	)       ->      Dict:
  _lowerCAmelCase						:       int  			=     PoolFormerConfig()

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

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

  # load image processor
  _lowerCAmelCase						:       List[str]  			=     PoolFormerImageProcessor(crop_pct=_lowerCamelCase	)

  # Prepare image
  _lowerCAmelCase						:       int  			=     prepare_img()
  _lowerCAmelCase						:       str  			=     image_processor(images=_lowerCamelCase			,return_tensors="""pt"""	).pixel_values

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

  # load original state dict
  _lowerCAmelCase						:       List[Any]  			=     torch.load(_lowerCamelCase			,map_location=torch.device("""cpu"""	)	)

  # rename keys
  _lowerCAmelCase						:       str  			=     rename_keys(_lowerCamelCase	)

  # create HuggingFace model and load state dict
  _lowerCAmelCase						:       Optional[int]  			=     PoolFormerForImageClassification(_lowerCamelCase	)
  model.load_state_dict(_lowerCamelCase	)
  model.eval()

  # Define image processor
  _lowerCAmelCase						:       str  			=     PoolFormerImageProcessor(crop_pct=_lowerCamelCase	)
  _lowerCAmelCase						:       Optional[Any]  			=     image_processor(images=prepare_img()			,return_tensors="""pt"""	).pixel_values

  # forward pass
  _lowerCAmelCase						:       Union[str, Any]  			=     model(_lowerCamelCase	)
  _lowerCAmelCase						:       Dict  			=     outputs.logits

  # define expected logit slices for different models
  if size == "s12":
    _lowerCAmelCase						:       str  			=     torch.tensor([-0.30_45, -0.67_58, -0.48_69]	)
  elif size == "s24":
    _lowerCAmelCase						:       Optional[Any]  			=     torch.tensor([0.44_02, -0.13_74, -0.80_45]	)
  elif size == "s36":
    _lowerCAmelCase						:       Union[str, Any]  			=     torch.tensor([-0.60_80, -0.51_33, -0.58_98]	)
  elif size == "m36":
    _lowerCAmelCase						:       str  			=     torch.tensor([0.39_52, 0.22_63, -1.26_68]	)
  elif size == "m48":
    _lowerCAmelCase						:       Optional[int]  			=     torch.tensor([0.11_67, -0.06_56, -0.34_23]	)
  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__":
     _a      :		int           =   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.'
     )
     _a      :		Tuple           =   parser.parse_args()
     convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)





"""simple docstring"""




import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


_a      :		Dict           =   datasets.utils.logging.get_logger(__name__)
@dataclass
class 						__A							( datasets.BuilderConfig		):
     _UpperCamelCase							:			int							=		10_000
     _UpperCamelCase							:			Optional[List[str]]							=		None
     _UpperCamelCase							:			Optional[datasets.Features]							=		None







class 						__A							( datasets.ArrowBasedBuilder		):
     _UpperCamelCase							:			List[str]							=		ParquetConfig






     def     __A  (		self  ):
       return datasets.DatasetInfo(features=self.config.features  )






     def     __A  (		self				,     a__  ):
       if not self.config.data_files:
         raise ValueError(F"At least one data file must be specified, but got data_files={self.config.data_files}"  )
       _lowerCAmelCase						:       Optional[Any]  			=     dl_manager.download_and_extract(self.config.data_files  )
       if isinstance(a__				,     (str, list, tuple)  ):
         _lowerCAmelCase						:       Any  			=     data_files
         if isinstance(a__				,     a__  ):
           _lowerCAmelCase						:       Tuple  			=     [files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         _lowerCAmelCase						:       Any  			=     [dl_manager.iter_files(a__  ) for file in files]
         return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,     gen_kwargs={"""files""": files}  )]
       _lowerCAmelCase						:       Optional[Any]  			=     []
       for split_name, files in data_files.items():
         if isinstance(a__				,     a__  ):
           _lowerCAmelCase						:       Dict  			=     [files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         _lowerCAmelCase						:       Tuple  			=     [dl_manager.iter_files(a__  ) for file in files]
         # Infer features is they are stoed in the arrow schema
         if self.info.features is None:
           for file in itertools.chain.from_iterable(a__  ):
             with open(a__				,     """rb"""  ) as f:
               _lowerCAmelCase						:       Optional[Any]  			=     datasets.Features.from_arrow_schema(pq.read_schema(a__  )  )
             break
         splits.append(datasets.SplitGenerator(name=a__				,     gen_kwargs={"""files""": files}  )  )
       return splits






     def     __A  (		self				,     a__  ):
       if self.info.features is not None:
         # more expensive cast to support nested features with keys in a different order
         # allows str <-> int/float or str to Audio for example
         _lowerCAmelCase						:       Optional[int]  			=     table_cast(a__				,     self.info.features.arrow_schema  )
       return pa_table






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Optional[int]  			=     self.info.features.arrow_schema if self.info.features is not None else None
       if self.info.features is not None and self.config.columns is not None:
         if sorted(field.name for field in schema  ) != sorted(self.config.columns  ):
           raise ValueError(
               F"Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"  )
       for file_idx, file in enumerate(itertools.chain.from_iterable(a__  )  ):
         with open(a__				,     """rb"""  ) as f:
           _lowerCAmelCase						:       Tuple  			=     pq.ParquetFile(a__  )
           try:
             for batch_idx, record_batch in enumerate(
                 parquet_file.iter_batches(batch_size=self.config.batch_size				,     columns=self.config.columns  )  ):
               _lowerCAmelCase						:       Any  			=     pa.Table.from_batches([record_batch]  )
               # Uncomment for debugging (will print the Arrow table size and elements)
               # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
               # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
               yield F"{file_idx}_{batch_idx}", self._cast_table(a__  )
           except ValueError as e:
             logger.error(F"Failed to read file '{file}' with error {type(a__  )}: {e}"  )
             raise




