





'''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 lowerCamelCase						(     __lowerCamelCase :    str					,       __lowerCamelCase :    Any					,       __lowerCamelCase :    Optional[int]					,       __lowerCamelCase :    Optional[Any]    )						->List[Any]:
    _SCREAMING_SNAKE_CASE									=			original_name.split("""."""    )[0]
    _SCREAMING_SNAKE_CASE									=			key.split("""."""    )
    _SCREAMING_SNAKE_CASE									=			int(key_list[key_list.index(__lowerCamelCase    ) - 2]    )
    _SCREAMING_SNAKE_CASE									=			int(key_list[key_list.index(__lowerCamelCase    ) - 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 lowerCamelCase						(     __lowerCamelCase :    Optional[Any]    )						->Any:
    _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(__lowerCamelCase					,       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(__lowerCamelCase					,       __lowerCamelCase					,       """mlp.fc1"""					,       """output.conv1"""    )
        if "mlp.fc2" in key:
            _SCREAMING_SNAKE_CASE									=			replace_key_with_offset(__lowerCamelCase					,       __lowerCamelCase					,       """mlp.fc2"""					,       """output.conv2"""    )
        if "norm1" in key:
            _SCREAMING_SNAKE_CASE									=			replace_key_with_offset(__lowerCamelCase					,       __lowerCamelCase					,       """norm1"""					,       """before_norm"""    )
        if "norm2" in key:
            _SCREAMING_SNAKE_CASE									=			replace_key_with_offset(__lowerCamelCase					,       __lowerCamelCase					,       """norm2"""					,       """after_norm"""    )
        if "layer_scale_1" in key:
            _SCREAMING_SNAKE_CASE									=			replace_key_with_offset(__lowerCamelCase					,       __lowerCamelCase					,       """layer_scale_1"""					,       """layer_scale_1"""    )
        if "layer_scale_2" in key:
            _SCREAMING_SNAKE_CASE									=			replace_key_with_offset(__lowerCamelCase					,       __lowerCamelCase					,       """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 lowerCamelCase						(     )						->int:
    _SCREAMING_SNAKE_CASE									=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
    _SCREAMING_SNAKE_CASE									=			Image.open(requests.get(__lowerCamelCase					,       stream=__lowerCamelCase    ).raw    )

    return image





@torch.no_grad()
def lowerCamelCase						(     __lowerCamelCase :    int					,       __lowerCamelCase :    Tuple					,       __lowerCamelCase :    int    )						->Optional[Any]:
    _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									=			1000
    _SCREAMING_SNAKE_CASE									=			"""imagenet-1k-id2label.json"""
    _SCREAMING_SNAKE_CASE									=			(1, 1000)

    # set config attributes
    _SCREAMING_SNAKE_CASE									=			json.load(open(hf_hub_download(__lowerCamelCase					,       __lowerCamelCase					,       repo_type="""dataset"""    )					,       """r"""    )    )
    _SCREAMING_SNAKE_CASE									=			{int(__lowerCamelCase    ): 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, 128, 320, 512]
        _SCREAMING_SNAKE_CASE									=			4.0
        _SCREAMING_SNAKE_CASE									=			0.9
    elif size == "s24":
        _SCREAMING_SNAKE_CASE									=			[4, 4, 12, 4]
        _SCREAMING_SNAKE_CASE									=			[64, 128, 320, 512]
        _SCREAMING_SNAKE_CASE									=			4.0
        _SCREAMING_SNAKE_CASE									=			0.9
    elif size == "s36":
        _SCREAMING_SNAKE_CASE									=			[6, 6, 18, 6]
        _SCREAMING_SNAKE_CASE									=			[64, 128, 320, 512]
        _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, 192, 384, 768]
        _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, 192, 384, 768]
        _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=__lowerCamelCase    )

    # Prepare image
    _SCREAMING_SNAKE_CASE									=			prepare_img()
    _SCREAMING_SNAKE_CASE									=			image_processor(images=__lowerCamelCase					,       return_tensors="""pt"""    ).pixel_values

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

    # load original state dict
    _SCREAMING_SNAKE_CASE									=			torch.load(__lowerCamelCase					,       map_location=torch.device("""cpu"""    )    )

    # rename keys
    _SCREAMING_SNAKE_CASE									=			rename_keys(__lowerCamelCase    )

    # create HuggingFace model and load state dict
    _SCREAMING_SNAKE_CASE									=			PoolFormerForImageClassification(__lowerCamelCase    )
    model.load_state_dict(__lowerCamelCase    )
    model.eval()

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

    # forward pass
    _SCREAMING_SNAKE_CASE									=			model(__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			outputs.logits

    # define expected logit slices for different models
    if size == "s12":
        _SCREAMING_SNAKE_CASE									=			torch.tensor([-0.3045, -0.6758, -0.4869]    )
    elif size == "s24":
        _SCREAMING_SNAKE_CASE									=			torch.tensor([0.4402, -0.1374, -0.8045]    )
    elif size == "s36":
        _SCREAMING_SNAKE_CASE									=			torch.tensor([-0.6080, -0.5133, -0.5898]    )
    elif size == "m36":
        _SCREAMING_SNAKE_CASE									=			torch.tensor([0.3952, 0.2263, -1.2668]    )
    elif size == "m48":
        _SCREAMING_SNAKE_CASE									=			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__":
      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 argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
lowercase_					= logging.getLogger(__name__)

if __name__ == "__main__":
      lowercase_					= argparse.ArgumentParser(
          description="""Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"""
      )
      parser.add_argument(
          """--data_file""", type=str, default="""data/dump.bert-base-uncased.pickle""", help="""The binarized dataset."""
      )
      parser.add_argument(
          """--token_counts_dump""", type=str, default="""data/token_counts.bert-base-uncased.pickle""", help="""The dump file."""
      )
      parser.add_argument("""--vocab_size""", default=30_522, type=int)
      lowercase_					= parser.parse_args()

      logger.info(f"""Loading data from {args.data_file}""")
      with open(args.data_file, """rb""") as fp:
            lowercase_					= pickle.load(fp)

      logger.info("""Counting occurrences for MLM.""")
      lowercase_					= Counter()
      for tk_ids in data:
            counter.update(tk_ids)
      lowercase_					= [0] * args.vocab_size
      for k, v in counter.items():
            lowercase_					= v

      logger.info(f"""Dump to {args.token_counts_dump}""")
      with open(args.token_counts_dump, """wb""") as handle:
            pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)



