


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







def 						__lowerCAmelCase		(		lowercase						:						Optional[Any]		,							lowercase						:						List[Any]		,							lowercase						:						List[Any]		,							lowercase						:						Dict				)			->		List[Any]:


    """simple docstring"""





    snake_case					:   Any         =					original_name.split("."				)[0]
    snake_case					:   Optional[int]         =					key.split("."				)
    snake_case					:   Any         =					int(key_list[key_list.index(lowercase				) - 2]				)
    snake_case					:   List[Any]         =					int(key_list[key_list.index(lowercase				) - 1]				)
    snake_case					:   Dict         =					orig_block_num - offset

    snake_case					:   Any         =					key.replace(F'{orig_block_num}.{layer_num}.{original_name}'		,							F'block.{new_block_num}.{layer_num}.{new_name}'				)
    return key







def 						__lowerCAmelCase		(		lowercase						:						Optional[Any]				)			->		Optional[int]:


    """simple docstring"""





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







def 						__lowerCAmelCase		(		)			->		Optional[int]:


    """simple docstring"""





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

    return image







@torch.no_grad()
def 						__lowerCAmelCase		(		lowercase						:						str		,							lowercase						:						Union[str, Any]		,							lowercase						:						List[str]				)			->		Any:


    """simple docstring"""





    snake_case					:   Tuple         =					PoolFormerConfig()

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

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

    # load image processor
    snake_case					:   Optional[Any]         =					PoolFormerImageProcessor(crop_pct=lowercase				)

    # Prepare image
    snake_case					:   Tuple         =					prepare_img()
    snake_case					:   List[str]         =					image_processor(images=lowercase		,							return_tensors="pt"				).pixel_values

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

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

    # rename keys
    snake_case					:   List[Any]         =					rename_keys(lowercase				)

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

    # Define image processor
    snake_case					:   Dict         =					PoolFormerImageProcessor(crop_pct=lowercase				)
    snake_case					:   Tuple         =					image_processor(images=prepare_img()		,							return_tensors="pt"				).pixel_values

    # forward pass
    snake_case					:   str         =					model(lowercase				)
    snake_case					:   Union[str, Any]         =					outputs.logits

    # define expected logit slices for different models
    if size == "s12":
        snake_case					:   List[Any]         =					torch.tensor([-0.3045, -0.6758, -0.4869]				)
    elif size == "s24":
        snake_case					:   Any         =					torch.tensor([0.4402, -0.1374, -0.8045]				)
    elif size == "s36":
        snake_case					:   List[Any]         =					torch.tensor([-0.6080, -0.5133, -0.5898]				)
    elif size == "m36":
        snake_case					:   Union[str, Any]         =					torch.tensor([0.3952, 0.2263, -1.2668]				)
    elif size == "m48":
        snake_case					:   Any         =					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]		,							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__":
  __snake_case         =					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."""
  )
  __snake_case         =					parser.parse_args()
  convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)









"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__snake_case         =					logging.get_logger(__name__)

__snake_case         =					{
    """facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
    """facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
    """facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
    """facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
    """facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
    """facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
    """facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}







class _lowerCAmelCase       (   snake_case_			):
   __UpperCAmelCase				: Union[str, Any]													=						'''xmod'''







   def __init__(			self		,			UpperCamelCase__=3_0522		,			UpperCamelCase__=768		,			UpperCamelCase__=12		,			UpperCamelCase__=12		,			UpperCamelCase__=3072		,			UpperCamelCase__="gelu"		,			UpperCamelCase__=0.1		,			UpperCamelCase__=0.1		,			UpperCamelCase__=512		,			UpperCamelCase__=2		,			UpperCamelCase__=0.02		,			UpperCamelCase__=1e-12		,			UpperCamelCase__=1		,			UpperCamelCase__=0		,			UpperCamelCase__=2		,			UpperCamelCase__="absolute"		,			UpperCamelCase__=True		,			UpperCamelCase__=None		,			UpperCamelCase__=False		,			UpperCamelCase__=2		,			UpperCamelCase__=False		,			UpperCamelCase__=True		,			UpperCamelCase__=True		,			UpperCamelCase__=("en_XX",)		,			UpperCamelCase__=None		,			**UpperCamelCase__		,			)			->	List[str]:

       '''simple docstring'''




       super().__init__(pad_token_id=UpperCamelCase__		,			bos_token_id=UpperCamelCase__		,			eos_token_id=UpperCamelCase__		,			**UpperCamelCase__		)

       snake_case					:   Optional[int]         =					vocab_size
       snake_case					:   Optional[Any]         =					hidden_size
       snake_case					:   Union[str, Any]         =					num_hidden_layers
       snake_case					:   int         =					num_attention_heads
       snake_case					:   Dict         =					hidden_act
       snake_case					:   Union[str, Any]         =					intermediate_size
       snake_case					:   str         =					hidden_dropout_prob
       snake_case					:   List[str]         =					attention_probs_dropout_prob
       snake_case					:   Dict         =					max_position_embeddings
       snake_case					:   Union[str, Any]         =					type_vocab_size
       snake_case					:   Union[str, Any]         =					initializer_range
       snake_case					:   str         =					layer_norm_eps
       snake_case					:   Dict         =					position_embedding_type
       snake_case					:   Any         =					use_cache
       snake_case					:   str         =					classifier_dropout
       snake_case					:   Any         =					pre_norm
       snake_case					:   Union[str, Any]         =					adapter_reduction_factor
       snake_case					:   Optional[int]         =					adapter_layer_norm
       snake_case					:   int         =					adapter_reuse_layer_norm
       snake_case					:   List[Any]         =					ln_before_adapter
       snake_case					:   Tuple         =					list(UpperCamelCase__		)
       snake_case					:   Tuple         =					default_language







class _lowerCAmelCase       (   snake_case_			):







   @property
   def 				lowerCamelCase	(			self		)			->	Mapping[str, Mapping[int, str]]:

       '''simple docstring'''




       if self.task == "multiple-choice":
           snake_case					:   str         =					{0: "batch", 1: "choice", 2: "sequence"}
       else:
           snake_case					:   Tuple         =					{0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
           ]		)





