'''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()
SCREAMING_SNAKE_CASE_:					Optional[int]  =logging.get_logger(__name__)





def      lowerCAmelCase_    ( snake_case_	:			Tuple ,   snake_case_	:			Union[str, Any] ,   snake_case_	:			Any ,   snake_case_	:			Any      )   ->				Tuple:


     '''simple docstring'''




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

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





def      lowerCAmelCase_    ( snake_case_	:			str      )   ->				str:


     '''simple docstring'''




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





def      lowerCAmelCase_    ( )   ->				Any:


     '''simple docstring'''




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

     return image





@torch.no_grad()
def      lowerCAmelCase_    ( snake_case_	:			Optional[Any] ,   snake_case_	:			Any ,   snake_case_	:			Optional[int]      )   ->				Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_	   =       PoolFormerConfig()

     # set attributes based on model_name
     UpperCAmelCase_	   =       "huggingface/label-files"
     UpperCAmelCase_	   =       model_name[-3:]
     UpperCAmelCase_	   =       10_00
     UpperCAmelCase_	   =       "imagenet-1k-id2label.json"
     UpperCAmelCase_	   =       (1, 10_00)

     # set config attributes
     UpperCAmelCase_	   =       json.load(open(hf_hub_download(snake_case_ ,   snake_case_ ,   repo_type="dataset"      ) ,   "r"      )      )
     UpperCAmelCase_	   =       {int(snake_case_      ): v for k, v in idalabel.items()}
     UpperCAmelCase_	   =       idalabel
     UpperCAmelCase_	   =       {v: k for k, v in idalabel.items()}
     if size == "s12":
          UpperCAmelCase_	   =       [2, 2, 6, 2]
          UpperCAmelCase_	   =       [64, 1_28, 3_20, 5_12]
          UpperCAmelCase_	   =       4.0
          UpperCAmelCase_	   =       0.9
     elif size == "s24":
          UpperCAmelCase_	   =       [4, 4, 12, 4]
          UpperCAmelCase_	   =       [64, 1_28, 3_20, 5_12]
          UpperCAmelCase_	   =       4.0
          UpperCAmelCase_	   =       0.9
     elif size == "s36":
          UpperCAmelCase_	   =       [6, 6, 18, 6]
          UpperCAmelCase_	   =       [64, 1_28, 3_20, 5_12]
          UpperCAmelCase_	   =       4.0
          UpperCAmelCase_	   =       1E-6
          UpperCAmelCase_	   =       0.9
     elif size == "m36":
          UpperCAmelCase_	   =       [6, 6, 18, 6]
          UpperCAmelCase_	   =       [96, 1_92, 3_84, 7_68]
          UpperCAmelCase_	   =       4.0
          UpperCAmelCase_	   =       1E-6
          UpperCAmelCase_	   =       0.95
     elif size == "m48":
          UpperCAmelCase_	   =       [8, 8, 24, 8]
          UpperCAmelCase_	   =       [96, 1_92, 3_84, 7_68]
          UpperCAmelCase_	   =       4.0
          UpperCAmelCase_	   =       1E-6
          UpperCAmelCase_	   =       0.95
     else:
          raise ValueError(f"""Size {size} not supported"""      )

     # load image processor
     UpperCAmelCase_	   =       PoolFormerImageProcessor(crop_pct=snake_case_      )

     # Prepare image
     UpperCAmelCase_	   =       prepare_img()
     UpperCAmelCase_	   =       image_processor(images=snake_case_ ,   return_tensors="pt"      ).pixel_values

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

     # load original state dict
     UpperCAmelCase_	   =       torch.load(snake_case_ ,   map_location=torch.device("cpu"      )      )

     # rename keys
     UpperCAmelCase_	   =       rename_keys(snake_case_      )

     # create HuggingFace model and load state dict
     UpperCAmelCase_	   =       PoolFormerForImageClassification(snake_case_      )
     model.load_state_dict(snake_case_      )
     model.eval()

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

     # forward pass
     UpperCAmelCase_	   =       model(snake_case_      )
     UpperCAmelCase_	   =       outputs.logits

     # define expected logit slices for different models
     if size == "s12":
          UpperCAmelCase_	   =       torch.tensor([-0.3045, -0.6758, -0.4869]      )
     elif size == "s24":
          UpperCAmelCase_	   =       torch.tensor([0.4402, -0.1374, -0.8045]      )
     elif size == "s36":
          UpperCAmelCase_	   =       torch.tensor([-0.6080, -0.5133, -0.5898]      )
     elif size == "m36":
          UpperCAmelCase_	   =       torch.tensor([0.3952, 0.2263, -1.2668]      )
     elif size == "m48":
          UpperCAmelCase_	   =       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] ,   snake_case_ ,   atol=1E-2      )

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


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE_:					Optional[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.'
       )
       SCREAMING_SNAKE_CASE_:					Optional[int]  =parser.parse_args()
       convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)





'''simple docstring'''





# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
    import jax
    import jaxlib

SCREAMING_SNAKE_CASE_:					List[str]  =get_logger()

SCREAMING_SNAKE_CASE_:					Optional[dict]  =None




class 	__A				(			TensorFormatter[Mapping, """jax.Array""", Mapping]     ):



    def __init__(self   :   List[Any]       ,      __a   :   Optional[int]=None       ,      __a   :   Any=None       ,      **__a   :   Dict			):
         super().__init__(features=__a			)
         import jax
         from jaxlib.xla_client import Device

         if isinstance(__a       ,      __a			):
              raise ValueError(
                  f"""Expected {device} to be a `str` not {type(__a			)}, as `jaxlib.xla_extension.Device` """
                  "is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
                  "the device with `str()` to get its string identifier that will be internally mapped "
                  "to the actual `jaxlib.xla_extension.Device`."			)
         UpperCAmelCase_	   =       device if isinstance(__a       ,      __a			) else str(jax.devices()[0]			)
         # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
         # with `pickle` nor with `dill`, so we need to use a global variable instead
         global DEVICE_MAPPING
         if DEVICE_MAPPING is None:
              UpperCAmelCase_	   =       self._map_devices_to_str()
         if self.device not in list(DEVICE_MAPPING.keys()			):
              logger.warning(
                  f"""Device with string identifier {self.device} not listed among the available """
                  f"""devices: {list(DEVICE_MAPPING.keys()			)}, so falling back to the default """
                  f"""device: {str(jax.devices()[0]			)}."""			)
              UpperCAmelCase_	   =       str(jax.devices()[0]			)
         UpperCAmelCase_	   =       jnp_array_kwargs



    @staticmethod
    def  _lowercase						():
         import jax

         return {str(__a			): device for device in jax.devices()}



    def  _lowercase						(self   :   str       ,      __a   :   Tuple			):
         import jax
         import jax.numpy as jnp

         if isinstance(__a       ,      __a			) and column:
              if all(
                  isinstance(__a       ,      jax.Array			) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column			):
                   return jnp.stack(__a       ,      axis=0			)
         return column



    def  _lowercase						(self   :   Any       ,      __a   :   Optional[int]			):
         import jax
         import jax.numpy as jnp

         if isinstance(__a       ,      (str, bytes, type(__a			))			):
              return value
         elif isinstance(__a       ,      (np.character, np.ndarray)			) and np.issubdtype(value.dtype       ,      np.character			):
              return value.tolist()

         UpperCAmelCase_	   =       {}

         if isinstance(__a       ,      (np.number, np.ndarray)			) and np.issubdtype(value.dtype       ,      np.integer			):
              # the default int precision depends on the jax config
              # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
              if jax.config.jax_enable_xaa:
                   UpperCAmelCase_	   =       {"dtype": jnp.intaa}
              else:
                   UpperCAmelCase_	   =       {"dtype": jnp.intaa}
         elif isinstance(__a       ,      (np.number, np.ndarray)			) and np.issubdtype(value.dtype       ,      np.floating			):
              UpperCAmelCase_	   =       {"dtype": jnp.floataa}
         elif config.PIL_AVAILABLE and "PIL" in sys.modules:
              import PIL.Image

              if isinstance(__a       ,      PIL.Image.Image			):
                   UpperCAmelCase_	   =       np.asarray(__a			)

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
         global DEVICE_MAPPING
         if DEVICE_MAPPING is None:
              UpperCAmelCase_	   =       self._map_devices_to_str()

         with jax.default_device(DEVICE_MAPPING[self.device]			):
              # calling jnp.array on a np.ndarray does copy the data
              # see https://github.com/google/jax/issues/4486
              return jnp.array(__a       ,      **{**default_dtype, **self.jnp_array_kwargs}			)



    def  _lowercase						(self   :   int       ,      __a   :   Any			):
         import jax

         # support for torch, tf, jax etc.
         if config.TORCH_AVAILABLE and "torch" in sys.modules:
              import torch

              if isinstance(__a       ,      torch.Tensor			):
                   return self._tensorize(data_struct.detach().cpu().numpy()[()]			)
         if hasattr(__a       ,      "__array__"			) and not isinstance(__a       ,      jax.Array			):
              UpperCAmelCase_	   =       data_struct.__array__()
         # support for nested types like struct of list of struct
         if isinstance(__a       ,      np.ndarray			):
              if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
                   return self._consolidate([self.recursive_tensorize(__a			) for substruct in data_struct]			)
         elif isinstance(__a       ,      (list, tuple)			):
              return self._consolidate([self.recursive_tensorize(__a			) for substruct in data_struct]			)
         return self._tensorize(__a			)



    def  _lowercase						(self   :   Union[str, Any]       ,      __a   :   dict			):
         return map_nested(self._recursive_tensorize       ,      __a       ,      map_list=__a			)



    def  _lowercase						(self   :   str       ,      __a   :   pa.Table			):
         UpperCAmelCase_	   =       self.numpy_arrow_extractor().extract_row(__a			)
         UpperCAmelCase_	   =       self.python_features_decoder.decode_row(__a			)
         return self.recursive_tensorize(__a			)



    def  _lowercase						(self   :   Tuple       ,      __a   :   pa.Table			):
         UpperCAmelCase_	   =       self.numpy_arrow_extractor().extract_column(__a			)
         UpperCAmelCase_	   =       self.python_features_decoder.decode_column(__a       ,      pa_table.column_names[0]			)
         UpperCAmelCase_	   =       self.recursive_tensorize(__a			)
         UpperCAmelCase_	   =       self._consolidate(__a			)
         return column






    def  _lowercase						(self   :   str       ,      __a   :   pa.Table			):
         UpperCAmelCase_	   =       self.numpy_arrow_extractor().extract_batch(__a			)
         UpperCAmelCase_	   =       self.python_features_decoder.decode_batch(__a			)
         UpperCAmelCase_	   =       self.recursive_tensorize(__a			)
         for column_name in batch:
              UpperCAmelCase_	   =       self._consolidate(batch[column_name]			)
         return batch




