






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   :							int						= logging.get_logger(__name__)



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Tuple      ,					__lowercase :      List[Any]      ,					__lowercase :      List[str]      ,					__lowercase :      Any     )    ->   Tuple:





    """simple docstring"""

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

    __A       =						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    (			__lowercase :      int     )    ->   Dict:





    """simple docstring"""

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



def       _SCREAMING_SNAKE_CASE    (			)    ->   List[str]:





    """simple docstring"""

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

    return image



@torch.no_grad()
def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Dict      ,					__lowercase :      int      ,					__lowercase :      Dict     )    ->   List[str]:





    """simple docstring"""

    __A       =						PoolFormerConfig()

    # set attributes based on model_name
    __A       =						"""huggingface/label-files"""
    __A       =						model_name[-3:]
    __A       =						1_0_0_0
    __A       =						"""imagenet-1k-id2label.json"""
    __A       =						(1, 1_0_0_0)

    # set config attributes
    __A       =						json.load(open(hf_hub_download(__lowercase      ,					__lowercase      ,					repo_type="""dataset"""     )      ,					"""r"""     )     )
    __A       =						{int(__lowercase     ): v for k, v in idalabel.items()}
    __A       =						idalabel
    __A       =						{v: k for k, v in idalabel.items()}
    if size == "s12":
        __A       =						[2, 2, 6, 2]
        __A       =						[6_4, 1_2_8, 3_2_0, 5_1_2]
        __A       =						4.0
        __A       =						0.9
    elif size == "s24":
        __A       =						[4, 4, 1_2, 4]
        __A       =						[6_4, 1_2_8, 3_2_0, 5_1_2]
        __A       =						4.0
        __A       =						0.9
    elif size == "s36":
        __A       =						[6, 6, 1_8, 6]
        __A       =						[6_4, 1_2_8, 3_2_0, 5_1_2]
        __A       =						4.0
        __A       =						1E-6
        __A       =						0.9
    elif size == "m36":
        __A       =						[6, 6, 1_8, 6]
        __A       =						[9_6, 1_9_2, 3_8_4, 7_6_8]
        __A       =						4.0
        __A       =						1E-6
        __A       =						0.95
    elif size == "m48":
        __A       =						[8, 8, 2_4, 8]
        __A       =						[9_6, 1_9_2, 3_8_4, 7_6_8]
        __A       =						4.0
        __A       =						1E-6
        __A       =						0.95
    else:
        raise ValueError(f"Size {size} not supported"     )

    # load image processor
    __A       =						PoolFormerImageProcessor(crop_pct=__lowercase     )

    # Prepare image
    __A       =						prepare_img()
    __A       =						image_processor(images=__lowercase      ,					return_tensors="""pt"""     ).pixel_values

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

    # load original state dict
    __A       =						torch.load(__lowercase      ,					map_location=torch.device("""cpu"""     )     )

    # rename keys
    __A       =						rename_keys(__lowercase     )

    # create HuggingFace model and load state dict
    __A       =						PoolFormerForImageClassification(__lowercase     )
    model.load_state_dict(__lowercase     )
    model.eval()

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

    # forward pass
    __A       =						model(__lowercase     )
    __A       =						outputs.logits

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

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


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   :							Optional[int]						= parser.parse_args()
  convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)












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


__a   :							Dict						= logging.get_logger(__name__)

__a   :							Dict						= {
    "bigcode/gpt_bigcode-santacoder": "https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json",
}


class    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					"gpt_bigcode"
     SCREAMING_SNAKE_CASE     =					["past_key_values"]
     SCREAMING_SNAKE_CASE     =					{
         "hidden_size": "n_embd",
         "max_position_embeddings": "n_positions",
         "num_attention_heads": "n_head",
         "num_hidden_layers": "n_layer",
     }




     def __init__(					self							:      List[str]			,						UpperCamelCase_							:      Dict=50_257			,						UpperCamelCase_							:      int=1_024			,						UpperCamelCase_							:      List[Any]=768			,						UpperCamelCase_							:      str=12			,						UpperCamelCase_							:      Union[str, Any]=12			,						UpperCamelCase_							:      Optional[Any]=None			,						UpperCamelCase_							:      Tuple="gelu_pytorch_tanh"			,						UpperCamelCase_							:      Optional[Any]=0.1			,						UpperCamelCase_							:      List[str]=0.1			,						UpperCamelCase_							:      Optional[int]=0.1			,						UpperCamelCase_							:      int=1e-5			,						UpperCamelCase_							:      Tuple=0.02			,						UpperCamelCase_							:      Any=True			,						UpperCamelCase_							:      List[Any]=True			,						UpperCamelCase_							:      Dict=50_256			,						UpperCamelCase_							:      Optional[Any]=50_256			,						UpperCamelCase_							:      int=True			,						UpperCamelCase_							:      List[Any]=True			,						UpperCamelCase_							:      List[Any]=True			,						**UpperCamelCase_							:      int			,						):


         """simple docstring"""



         __A       =						vocab_size
         __A       =						n_positions
         __A       =						n_embd
         __A       =						n_layer
         __A       =						n_head
         __A       =						n_inner
         __A       =						activation_function
         __A       =						resid_pdrop
         __A       =						embd_pdrop
         __A       =						attn_pdrop
         __A       =						layer_norm_epsilon
         __A       =						initializer_range
         __A       =						scale_attn_weights
         __A       =						use_cache
         __A       =						attention_softmax_in_fpaa
         __A       =						scale_attention_softmax_in_fpaa
         __A       =						multi_query

         __A       =						bos_token_id
         __A       =						eos_token_id

         super().__init__(bos_token_id=UpperCamelCase_			,						eos_token_id=UpperCamelCase_			,						**UpperCamelCase_					)




