


"""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()
__lowercase    					=      logging.get_logger(__name__)


def      lowercase			(				A_  ,   A_  ,   A_  ,   A_       )->      str:
  '''simple docstring'''







  a						:		Dict      							=			original_name.split("."       )[0]
  a						:		Any      							=			key.split("."       )
  a						:		Optional[Any]      							=			int(key_list[key_list.index(A_       ) - 2]       )
  a						:		Union[str, Any]      							=			int(key_list[key_list.index(A_       ) - 1]       )
  a						:		Optional[Any]      							=			orig_block_num - offset

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


def      lowercase			(				A_       )->      Optional[int]:
  '''simple docstring'''







  a						:		Union[str, Any]      							=			OrderedDict()
  a							,		a						:		str      							=			0, 0
  for key, value in state_dict.items():
    if key.startswith("network"       ):
      a						:		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
      a						:		List[str]      							=			key[: key.find("proj"       )]
      a						:		List[Any]      							=			key.replace(A_  ,   F'''patch_embeddings.{total_embed_found}.'''       )
      a						:		Any      							=			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(A_  ,   A_  ,   "mlp.fc1"  ,   "output.conv1"       )
    if "mlp.fc2" in key:
      a						:		Tuple      							=			replace_key_with_offset(A_  ,   A_  ,   "mlp.fc2"  ,   "output.conv2"       )
    if "norm1" in key:
      a						:		str      							=			replace_key_with_offset(A_  ,   A_  ,   "norm1"  ,   "before_norm"       )
    if "norm2" in key:
      a						:		str      							=			replace_key_with_offset(A_  ,   A_  ,   "norm2"  ,   "after_norm"       )
    if "layer_scale_1" in key:
      a						:		Any      							=			replace_key_with_offset(A_  ,   A_  ,   "layer_scale_1"  ,   "layer_scale_1"       )
    if "layer_scale_2" in key:
      a						:		Optional[Any]      							=			replace_key_with_offset(A_  ,   A_  ,   "layer_scale_2"  ,   "layer_scale_2"       )
    if "head" in key:
      a						:		Tuple      							=			key.replace("head"  ,   "classifier"       )
    a						:		Any      							=			value
  return new_state_dict


def      lowercase			(				)->      Optional[int]:
  '''simple docstring'''







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

  return image


@torch.no_grad()
def      lowercase			(				A_  ,   A_  ,   A_       )->      Optional[int]:
  '''simple docstring'''







  a						:		int      							=			PoolFormerConfig()

  # set attributes based on model_name
  a						:		Dict      							=			"huggingface/label-files"
  a						:		str      							=			model_name[-3:]
  a						:		Tuple      							=			1_000
  a						:		List[Any]      							=			"imagenet-1k-id2label.json"
  a						:		int      							=			(1, 1_000)

  # set config attributes
  a						:		int      							=			json.load(open(hf_hub_download(A_  ,   A_  ,   repo_type="dataset"       )  ,   "r"       )       )
  a						:		Any      							=			{int(A_       ): v for k, v in idalabel.items()}
  a						:		str      							=			idalabel
  a						:		List[Any]      							=			{v: k for k, v in idalabel.items()}
  if size == "s12":
    a						:		Optional[Any]      							=			[2, 2, 6, 2]
    a						:		Any      							=			[64, 128, 320, 512]
    a						:		Any      							=			4.0
    a						:		Optional[int]      							=			0.9
  elif size == "s24":
    a						:		Tuple      							=			[4, 4, 12, 4]
    a						:		Optional[int]      							=			[64, 128, 320, 512]
    a						:		Any      							=			4.0
    a						:		List[Any]      							=			0.9
  elif size == "s36":
    a						:		List[str]      							=			[6, 6, 18, 6]
    a						:		Dict      							=			[64, 128, 320, 512]
    a						:		Optional[Any]      							=			4.0
    a						:		Union[str, Any]      							=			1e-6
    a						:		List[Any]      							=			0.9
  elif size == "m36":
    a						:		Union[str, Any]      							=			[6, 6, 18, 6]
    a						:		Any      							=			[96, 192, 384, 768]
    a						:		Union[str, Any]      							=			4.0
    a						:		Tuple      							=			1e-6
    a						:		Dict      							=			0.9_5
  elif size == "m48":
    a						:		Tuple      							=			[8, 8, 24, 8]
    a						:		Optional[int]      							=			[96, 192, 384, 768]
    a						:		Tuple      							=			4.0
    a						:		Dict      							=			1e-6
    a						:		Any      							=			0.9_5
  else:
    raise ValueError(F'''Size {size} not supported'''       )

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

  # Prepare image
  a						:		List[Any]      							=			prepare_img()
  a						:		Tuple      							=			image_processor(images=A_  ,   return_tensors="pt"       ).pixel_values

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

  # load original state dict
  a						:		Optional[int]      							=			torch.load(A_  ,   map_location=torch.device("cpu"       )       )

  # rename keys
  a						:		Any      							=			rename_keys(A_       )

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

  # Define image processor
  a						:		List[Any]      							=			PoolFormerImageProcessor(crop_pct=A_       )
  a						:		int      							=			image_processor(images=prepare_img()  ,   return_tensors="pt"       ).pixel_values

  # forward pass
  a						:		Optional[int]      							=			model(A_       )
  a						:		Any      							=			outputs.logits

  # define expected logit slices for different models
  if size == "s12":
    a						:		Tuple      							=			torch.tensor([-0.3_0_4_5, -0.6_7_5_8, -0.4_8_6_9]       )
  elif size == "s24":
    a						:		Tuple      							=			torch.tensor([0.4_4_0_2, -0.1_3_7_4, -0.8_0_4_5]       )
  elif size == "s36":
    a						:		Optional[Any]      							=			torch.tensor([-0.6_0_8_0, -0.5_1_3_3, -0.5_8_9_8]       )
  elif size == "m36":
    a						:		List[str]      							=			torch.tensor([0.3_9_5_2, 0.2_2_6_3, -1.2_6_6_8]       )
  elif size == "m48":
    a						:		Dict      							=			torch.tensor([0.1_1_6_7, -0.0_6_5_6, -0.3_4_2_3]       )
  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__":
 __lowercase    					=      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."""
 )
 __lowercase    					=      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


__lowercase    					=      datasets.utils.logging.get_logger(__name__)






@dataclass
class 						_A  (  datasets.BuilderConfig  ):







    """simple docstring"""




    UpperCAmelCase       :							int	=       1_0_0_0_0
    UpperCAmelCase       :							Optional[List[str]]	=       None
    UpperCAmelCase       :							Optional[datasets.Features]	=       None






class 						_A  (  datasets.ArrowBasedBuilder  ):







    """simple docstring"""




    UpperCAmelCase       :							str	=       ParquetConfig






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






    def 	__snake_case							(					self				:    List[Any]				, __UpperCAmelCase				:    str):
      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}''')
      a						:		str      							=			dl_manager.download_and_extract(self.config.data_files)
      if isinstance(__UpperCAmelCase				, (str, list, tuple)):
        a						:		Dict      							=			data_files
        if isinstance(__UpperCAmelCase				, __UpperCAmelCase):
          a						:		str      							=			[files]
        # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
        a						:		List[Any]      							=			[dl_manager.iter_files(__UpperCAmelCase) for file in files]
        return [datasets.SplitGenerator(name=datasets.Split.TRAIN				, gen_kwargs={"files": files})]
      a						:		Dict      							=			[]
      for split_name, files in data_files.items():
        if isinstance(__UpperCAmelCase				, __UpperCAmelCase):
          a						:		Optional[int]      							=			[files]
        # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
        a						:		Tuple      							=			[dl_manager.iter_files(__UpperCAmelCase) 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(__UpperCAmelCase):
            with open(__UpperCAmelCase				, "rb") as f:
              a						:		Tuple      							=			datasets.Features.from_arrow_schema(pq.read_schema(__UpperCAmelCase))
            break
        splits.append(datasets.SplitGenerator(name=__UpperCAmelCase				, gen_kwargs={"files": files}))
      return splits






    def 	__snake_case							(					self				:    List[str]				, __UpperCAmelCase				:    pa.Table):
      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
        a						:		Optional[int]      							=			table_cast(__UpperCAmelCase				, self.info.features.arrow_schema)
      return pa_table






    def 	__snake_case							(					self				:    Union[str, Any]				, __UpperCAmelCase				:    int):
      a						:		Tuple      							=			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(__UpperCAmelCase)):
        with open(__UpperCAmelCase				, "rb") as f:
          a						:		Tuple      							=			pq.ParquetFile(__UpperCAmelCase)
          try:
            for batch_idx, record_batch in enumerate(
                parquet_file.iter_batches(batch_size=self.config.batch_size				, columns=self.config.columns)):
              a						:		Optional[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(__UpperCAmelCase)
          except ValueError as e:
            logger.error(f'''Failed to read file \'{file}\' with error {type(__UpperCAmelCase)}: {e}''')
            raise






