

# 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

__UpperCAmelCase								=			get_logger()

__UpperCAmelCase								=			None



class SCREAMING_SNAKE_CASE     (  TensorFormatter[Mapping, "jax.Array", Mapping]			):
  """simple docstring"""






  def __init__(      self			:   List[Any]	,							lowerCAmelCase			:   Union[str, Any]=None	,							lowerCAmelCase			:   int=None	,							**lowerCAmelCase			:   Dict   )							->     int:






     """simple docstring"""







     super().__init__(features=lowerCAmelCase   )
     import jax
     from jaxlib.xla_client import Device

     if isinstance(lowerCAmelCase	,							lowerCAmelCase   ):
        raise ValueError(
            f'''Expected {device} to be a `str` not {type(lowerCAmelCase   )}, 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`."""   )
     __lowerCAmelCase  :      int    =						device if isinstance(lowerCAmelCase	,							lowerCAmelCase   ) 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:
        __lowerCAmelCase  :      Any    =						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]   )}.'''   )
        __lowerCAmelCase  :      List[Any]    =						str(jax.devices()[0]   )
     __lowerCAmelCase  :      str    =						jnp_array_kwargs







  @staticmethod
  def 	SCREAMING_SNAKE_CASE   (      )							->     Dict[str, "jaxlib.xla_extension.Device"]:






     """simple docstring"""







     import jax

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







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict	,							lowerCAmelCase			:   List[str]   )							->     Dict:






     """simple docstring"""







     import jax
     import jax.numpy as jnp

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







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple	,							lowerCAmelCase			:   Union[str, Any]   )							->     List[str]:






     """simple docstring"""







     import jax
     import jax.numpy as jnp

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

     __lowerCAmelCase  :      int    =						{}

     if isinstance(lowerCAmelCase	,							(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:
           __lowerCAmelCase  :      Optional[int]    =						{"""dtype""": jnp.intaa}
        else:
           __lowerCAmelCase  :      Optional[Any]    =						{"""dtype""": jnp.intaa}
     elif isinstance(lowerCAmelCase	,							(np.number, np.ndarray)   ) and np.issubdtype(value.dtype	,							np.floating   ):
        __lowerCAmelCase  :      List[str]    =						{"""dtype""": jnp.floataa}
     elif config.PIL_AVAILABLE and "PIL" in sys.modules:
        import PIL.Image

        if isinstance(lowerCAmelCase	,							PIL.Image.Image   ):
           __lowerCAmelCase  :      Any    =						np.asarray(lowerCAmelCase   )

        # 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:
        __lowerCAmelCase  :      List[Any]    =						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(lowerCAmelCase	,							**{**default_dtype, **self.jnp_array_kwargs}   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any	,							lowerCAmelCase			:   Optional[int]   )							->     Union[str, Any]:






     """simple docstring"""







     import jax

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

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







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]	,							lowerCAmelCase			:   dict   )							->     int:






     """simple docstring"""







     return map_nested(self._recursive_tensorize	,							lowerCAmelCase	,							map_list=lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							lowerCAmelCase			:   pa.Table   )							->     Mapping:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						self.numpy_arrow_extractor().extract_row(lowerCAmelCase   )
     __lowerCAmelCase  :      int    =						self.python_features_decoder.decode_row(lowerCAmelCase   )
     return self.recursive_tensorize(lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							lowerCAmelCase			:   pa.Table   )							->     "jax.Array":






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.numpy_arrow_extractor().extract_column(lowerCAmelCase   )
     __lowerCAmelCase  :      int    =						self.python_features_decoder.decode_column(lowerCAmelCase	,							pa_table.column_names[0]   )
     __lowerCAmelCase  :      Optional[Any]    =						self.recursive_tensorize(lowerCAmelCase   )
     __lowerCAmelCase  :      List[Any]    =						self._consolidate(lowerCAmelCase   )
     return column







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]	,							lowerCAmelCase			:   pa.Table   )							->     Mapping:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						self.numpy_arrow_extractor().extract_batch(lowerCAmelCase   )
     __lowerCAmelCase  :      List[Any]    =						self.python_features_decoder.decode_batch(lowerCAmelCase   )
     __lowerCAmelCase  :      List[Any]    =						self.recursive_tensorize(lowerCAmelCase   )
     for column_name in batch:
        __lowerCAmelCase  :      Dict    =						self._consolidate(batch[column_name]   )
     return batch





import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification





def      snake_case_				(__A					:						Optional[Any]					)      ->     Tuple:
   __lowerCAmelCase  :      Optional[int]    =						SwinConfig()
   __lowerCAmelCase  :      List[Any]    =						swin_name.split("""_"""					)

   __lowerCAmelCase  :      Dict    =						name_split[1]
   __lowerCAmelCase  :      Optional[Any]    =						int(name_split[4]					)
   __lowerCAmelCase  :      List[Any]    =						int(name_split[3][-1]					)

   if model_size == "tiny":
      __lowerCAmelCase  :      List[Any]    =						9_6
      __lowerCAmelCase  :      List[Any]    =						(2, 2, 6, 2)
      __lowerCAmelCase  :      Optional[Any]    =						(3, 6, 1_2, 2_4)
   elif model_size == "small":
      __lowerCAmelCase  :      List[Any]    =						9_6
      __lowerCAmelCase  :      Optional[int]    =						(2, 2, 1_8, 2)
      __lowerCAmelCase  :      Optional[int]    =						(3, 6, 1_2, 2_4)
   elif model_size == "base":
      __lowerCAmelCase  :      List[Any]    =						1_2_8
      __lowerCAmelCase  :      Tuple    =						(2, 2, 1_8, 2)
      __lowerCAmelCase  :      int    =						(4, 8, 1_6, 3_2)
   else:
      __lowerCAmelCase  :      List[Any]    =						1_9_2
      __lowerCAmelCase  :      List[str]    =						(2, 2, 1_8, 2)
      __lowerCAmelCase  :      int    =						(6, 1_2, 2_4, 4_8)

   if "in22k" in swin_name:
      __lowerCAmelCase  :      Dict    =						2_1_8_4_1
   else:
      __lowerCAmelCase  :      Optional[Any]    =						1_0_0_0
      __lowerCAmelCase  :      Union[str, Any]    =						"""huggingface/label-files"""
      __lowerCAmelCase  :      Any    =						"""imagenet-1k-id2label.json"""
      __lowerCAmelCase  :      Any    =						json.load(open(hf_hub_download(__A ,       __A ,       repo_type="""dataset"""					) ,       """r"""					)					)
      __lowerCAmelCase  :      int    =						{int(__A					): v for k, v in idalabel.items()}
      __lowerCAmelCase  :      str    =						idalabel
      __lowerCAmelCase  :      int    =						{v: k for k, v in idalabel.items()}

   __lowerCAmelCase  :      Optional[Any]    =						img_size
   __lowerCAmelCase  :      Optional[Any]    =						num_classes
   __lowerCAmelCase  :      Tuple    =						embed_dim
   __lowerCAmelCase  :      Union[str, Any]    =						depths
   __lowerCAmelCase  :      Optional[Any]    =						num_heads
   __lowerCAmelCase  :      Tuple    =						window_size

   return config





def      snake_case_				(__A					:						int					)      ->     Optional[Any]:
   if "patch_embed.proj" in name:
      __lowerCAmelCase  :      Optional[Any]    =						name.replace("""patch_embed.proj""" ,       """embeddings.patch_embeddings.projection"""					)
   if "patch_embed.norm" in name:
      __lowerCAmelCase  :      List[Any]    =						name.replace("""patch_embed.norm""" ,       """embeddings.norm"""					)
   if "layers" in name:
      __lowerCAmelCase  :      int    =						"""encoder.""" + name
   if "attn.proj" in name:
      __lowerCAmelCase  :      Tuple    =						name.replace("""attn.proj""" ,       """attention.output.dense"""					)
   if "attn" in name:
      __lowerCAmelCase  :      Optional[Any]    =						name.replace("""attn""" ,       """attention.self"""					)
   if "norm1" in name:
      __lowerCAmelCase  :      Dict    =						name.replace("""norm1""" ,       """layernorm_before"""					)
   if "norm2" in name:
      __lowerCAmelCase  :      Dict    =						name.replace("""norm2""" ,       """layernorm_after"""					)
   if "mlp.fc1" in name:
      __lowerCAmelCase  :      List[str]    =						name.replace("""mlp.fc1""" ,       """intermediate.dense"""					)
   if "mlp.fc2" in name:
      __lowerCAmelCase  :      str    =						name.replace("""mlp.fc2""" ,       """output.dense"""					)

   if name == "norm.weight":
      __lowerCAmelCase  :      Dict    =						"""layernorm.weight"""
   if name == "norm.bias":
      __lowerCAmelCase  :      Optional[int]    =						"""layernorm.bias"""

   if "head" in name:
      __lowerCAmelCase  :      int    =						name.replace("""head""" ,       """classifier"""					)
   else:
      __lowerCAmelCase  :      List[str]    =						"""swin.""" + name

   return name





def      snake_case_				(__A					:						List[Any] ,       __A					:						str					)      ->     int:
   for key in orig_state_dict.copy().keys():
      __lowerCAmelCase  :      Tuple    =						orig_state_dict.pop(__A					)

      if "mask" in key:
         continue
      elif "qkv" in key:
         __lowerCAmelCase  :      Any    =						key.split("""."""					)
         __lowerCAmelCase  :      Union[str, Any]    =						int(key_split[1]					)
         __lowerCAmelCase  :      Optional[Any]    =						int(key_split[3]					)
         __lowerCAmelCase  :      Optional[int]    =						model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

         if "weight" in key:
            __lowerCAmelCase  :      List[str]    =						val[:dim, :]
            __lowerCAmelCase  :      List[Any]    =						val[
                dim : dim * 2, :
            ]
            __lowerCAmelCase  :      str    =						val[-dim:, :]
         else:
            __lowerCAmelCase  :      str    =						val[
                :dim
            ]
            __lowerCAmelCase  :      int    =						val[
                dim : dim * 2
            ]
            __lowerCAmelCase  :      int    =						val[
                -dim:
            ]
      else:
         __lowerCAmelCase  :      Tuple    =						val

   return orig_state_dict





def      snake_case_				(__A					:						Union[str, Any] ,       __A					:						int					)      ->     Any:
   __lowerCAmelCase  :      List[Any]    =						timm.create_model(__A ,       pretrained=__A					)
   timm_model.eval()

   __lowerCAmelCase  :      str    =						get_swin_config(__A					)
   __lowerCAmelCase  :      Any    =						SwinForImageClassification(__A					)
   model.eval()

   __lowerCAmelCase  :      str    =						convert_state_dict(timm_model.state_dict() ,       __A					)
   model.load_state_dict(__A					)

   __lowerCAmelCase  :      str    =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""

   __lowerCAmelCase  :      Any    =						AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_""" ,       """-"""					)					)					)
   __lowerCAmelCase  :      List[Any]    =						Image.open(requests.get(__A ,       stream=__A					).raw					)
   __lowerCAmelCase  :      List[str]    =						image_processor(images=__A ,       return_tensors="""pt"""					)

   __lowerCAmelCase  :      Tuple    =						timm_model(inputs["""pixel_values"""]					)
   __lowerCAmelCase  :      Dict    =						model(**__A					).logits

   assert torch.allclose(__A ,       __A ,       atol=1e-3					)

   print(f'''Saving model {swin_name} to {pytorch_dump_folder_path}'''					)
   model.save_pretrained(__A					)

   print(f'''Saving image processor to {pytorch_dump_folder_path}'''					)
   image_processor.save_pretrained(__A					)


if __name__ == "__main__":
   __UpperCAmelCase								=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--swin_name""",
       default="""swin_tiny_patch4_window7_224""",
       type=str,
       help="""Name of the Swin timm model you'd like to convert.""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
   )

   __UpperCAmelCase								=			parser.parse_args()
   convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)


