'''simple docstring'''





import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple

import numpy as np

from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy


if is_flax_available():
    import jax.numpy as jnp




class        __A      ( _a	):



    def __get__(self    :    str   ,			__a    :    Union[str, Any]   ,			__a    :    Tuple=None ):
         # See docs.python.org/3/howto/descriptor.html#properties
         if obj is None:
              return self
         if self.fget is None:
              raise AttributeError("unreadable attribute" )
         UpperCAmelCase_           =   """__cached_""" + self.fget.__name__
         UpperCAmelCase_           =   getattr(snake_case_   ,			snake_case_   ,			snake_case_ )
         if cached is None:
              UpperCAmelCase_           =   self.fget(snake_case_ )
              setattr(snake_case_   ,			snake_case_   ,			snake_case_ )
         return cached





def 	lowerCAmelCase_				(					snake_case_       : Optional[int]			)    ->      Tuple:


     '''simple docstring'''




     UpperCAmelCase_           =   val.lower()
     if val in {"y", "yes", "t", "true", "on", "1"}:
          return 1
     if val in {"n", "no", "f", "false", "off", "0"}:
          return 0
     raise ValueError(f"""invalid truth value {val!r}"""			)





def 	lowerCAmelCase_				(					snake_case_       : int			)    ->      Any:


     '''simple docstring'''




     if is_torch_fx_proxy(_lowerCAmelCase			):
          return True
     if is_torch_available():
          import torch

          if isinstance(_lowerCAmelCase					,    torch.Tensor			):
               return True
     if is_tf_available():
          import tensorflow as tf

          if isinstance(_lowerCAmelCase					,    tf.Tensor			):
               return True

     if is_flax_available():
          import jax.numpy as jnp
          from jax.core import Tracer

          if isinstance(_lowerCAmelCase					,    (jnp.ndarray, Tracer)			):
               return True

     return isinstance(_lowerCAmelCase					,    np.ndarray			)





def 	lowerCAmelCase_				(					snake_case_       : Tuple			)    ->      Optional[Any]:


     '''simple docstring'''




     return isinstance(_lowerCAmelCase					,    np.ndarray			)





def 	lowerCAmelCase_				(					snake_case_       : Optional[Any]			)    ->      int:


     '''simple docstring'''




     return _is_numpy(_lowerCAmelCase			)





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


     '''simple docstring'''




     import torch

     return isinstance(_lowerCAmelCase					,    torch.Tensor			)





def 	lowerCAmelCase_				(					snake_case_       : Optional[Any]			)    ->      str:


     '''simple docstring'''




     return False if not is_torch_available() else _is_torch(_lowerCAmelCase			)





def 	lowerCAmelCase_				(					snake_case_       : Any			)    ->      List[str]:


     '''simple docstring'''




     import torch

     return isinstance(_lowerCAmelCase					,    torch.device			)





def 	lowerCAmelCase_				(					snake_case_       : Any			)    ->      List[Any]:


     '''simple docstring'''




     return False if not is_torch_available() else _is_torch_device(_lowerCAmelCase			)





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


     '''simple docstring'''




     import torch

     if isinstance(_lowerCAmelCase					,    _lowerCAmelCase			):
          if hasattr(_lowerCAmelCase					,    _lowerCAmelCase			):
               UpperCAmelCase_           =   getattr(_lowerCAmelCase					,    _lowerCAmelCase			)
          else:
               return False
     return isinstance(_lowerCAmelCase					,    torch.dtype			)





def 	lowerCAmelCase_				(					snake_case_       : Tuple			)    ->      List[Any]:


     '''simple docstring'''




     return False if not is_torch_available() else _is_torch_dtype(_lowerCAmelCase			)





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


     '''simple docstring'''




     import tensorflow as tf

     return isinstance(_lowerCAmelCase					,    tf.Tensor			)





def 	lowerCAmelCase_				(					snake_case_       : int			)    ->      Any:


     '''simple docstring'''




     return False if not is_tf_available() else _is_tensorflow(_lowerCAmelCase			)





def 	lowerCAmelCase_				(					snake_case_       : List[Any]			)    ->      Optional[int]:


     '''simple docstring'''




     import tensorflow as tf

     # the `is_symbolic_tensor` predicate is only available starting with TF 2.14
     if hasattr(_lowerCAmelCase					,    "is_symbolic_tensor"			):
          return tf.is_symbolic_tensor(_lowerCAmelCase			)
     return type(_lowerCAmelCase			) == tf.Tensor





def 	lowerCAmelCase_				(					snake_case_       : int			)    ->      Optional[Any]:


     '''simple docstring'''




     return False if not is_tf_available() else _is_tf_symbolic_tensor(_lowerCAmelCase			)





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


     '''simple docstring'''




     import jax.numpy as jnp  # noqa: F811

     return isinstance(_lowerCAmelCase					,    jnp.ndarray			)





def 	lowerCAmelCase_				(					snake_case_       : int			)    ->      List[str]:


     '''simple docstring'''




     return False if not is_flax_available() else _is_jax(_lowerCAmelCase			)





def 	lowerCAmelCase_				(					snake_case_       : str			)    ->      Any:


     '''simple docstring'''




     if isinstance(_lowerCAmelCase					,    (dict, UserDict)			):
          return {k: to_py_obj(_lowerCAmelCase			) for k, v in obj.items()}
     elif isinstance(_lowerCAmelCase					,    (list, tuple)			):
          return [to_py_obj(_lowerCAmelCase			) for o in obj]
     elif is_tf_tensor(_lowerCAmelCase			):
          return obj.numpy().tolist()
     elif is_torch_tensor(_lowerCAmelCase			):
          return obj.detach().cpu().tolist()
     elif is_jax_tensor(_lowerCAmelCase			):
          return np.asarray(_lowerCAmelCase			).tolist()
     elif isinstance(_lowerCAmelCase					,    (np.ndarray, np.number)			):  # tolist also works on 0d np arrays
          return obj.tolist()
     else:
          return obj





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


     '''simple docstring'''




     if isinstance(_lowerCAmelCase					,    (dict, UserDict)			):
          return {k: to_numpy(_lowerCAmelCase			) for k, v in obj.items()}
     elif isinstance(_lowerCAmelCase					,    (list, tuple)			):
          return np.array(_lowerCAmelCase			)
     elif is_tf_tensor(_lowerCAmelCase			):
          return obj.numpy()
     elif is_torch_tensor(_lowerCAmelCase			):
          return obj.detach().cpu().numpy()
     elif is_jax_tensor(_lowerCAmelCase			):
          return np.asarray(_lowerCAmelCase			)
     else:
          return obj




class        __A      ( _a	):



    def 					_lowercase						(self    :    Union[str, Any] ):
         UpperCAmelCase_           =   fields(self )

         # Safety and consistency checks
         if not len(snake_case_ ):
              raise ValueError(f"""{self.__class__.__name__} has no fields.""" )
         if not all(field.default is None for field in class_fields[1:] ):
              raise ValueError(f"""{self.__class__.__name__} should not have more than one required field.""" )

         UpperCAmelCase_           =   getattr(self   ,			class_fields[0].name )
         UpperCAmelCase_           =   all(getattr(self   ,			field.name ) is None for field in class_fields[1:] )

         if other_fields_are_none and not is_tensor(snake_case_ ):
              if isinstance(snake_case_   ,			snake_case_ ):
                   UpperCAmelCase_           =   first_field.items()
                   UpperCAmelCase_           =   True
              else:
                   try:
                        UpperCAmelCase_           =   iter(snake_case_ )
                        UpperCAmelCase_           =   True
                   except TypeError:
                        UpperCAmelCase_           =   False

            # if we provided an iterator as first field and the iterator is a (key, value) iterator
            # set the associated fields
              if first_field_iterator:
                   for idx, element in enumerate(snake_case_ ):
                        if (
                            not isinstance(snake_case_   ,			(list, tuple) )
                            or not len(snake_case_ ) == 2
                            or not isinstance(element[0]   ,			snake_case_ )
                        ):
                             if idx == 0:
                                  # If we do not have an iterator of key/values, set it as attribute
                                  UpperCAmelCase_           =   first_field
                             else:
                                  # If we have a mixed iterator, raise an error
                                  raise ValueError(
                                      f"""Cannot set key/value for {element}. It needs to be a tuple (key, value).""" )
                             break
                        setattr(self   ,			element[0]   ,			element[1] )
                        if element[1] is not None:
                             UpperCAmelCase_           =   element[1]
              elif first_field is not None:
                   UpperCAmelCase_           =   first_field
         else:
              for field in class_fields:
                   UpperCAmelCase_           =   getattr(self   ,			field.name )
                   if v is not None:
                        UpperCAmelCase_           =   v



    def __delitem__(self    :    Union[str, Any]   ,			*__a    :    Tuple   ,			**__a    :    int ):
         raise Exception(f"""You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.""" )



    def 					_lowercase						(self    :    List[str]   ,			*__a    :    List[Any]   ,			**__a    :    Optional[Any] ):
         raise Exception(f"""You cannot use ``setdefault`` on a {self.__class__.__name__} instance.""" )



    def 					_lowercase						(self    :    Dict   ,			*__a    :    List[Any]   ,			**__a    :    str ):
         raise Exception(f"""You cannot use ``pop`` on a {self.__class__.__name__} instance.""" )



    def 					_lowercase						(self    :    List[str]   ,			*__a    :    Tuple   ,			**__a    :    Tuple ):
         raise Exception(f"""You cannot use ``update`` on a {self.__class__.__name__} instance.""" )



    def __getitem__(self    :    Tuple   ,			__a    :    Optional[Any] ):
         if isinstance(snake_case_   ,			snake_case_ ):
              UpperCAmelCase_           =   dict(self.items() )
              return inner_dict[k]
         else:
              return self.to_tuple()[k]



    def __setattr__(self    :    List[str]   ,			__a    :    Tuple   ,			__a    :    Union[str, Any] ):
         if name in self.keys() and value is not None:
              # Don't call self.__setitem__ to avoid recursion errors
              super().__setitem__(snake_case_   ,			snake_case_ )
         super().__setattr__(snake_case_   ,			snake_case_ )



    def __setitem__(self    :    str   ,			__a    :    Union[str, Any]   ,			__a    :    int ):
         # Will raise a KeyException if needed
         super().__setitem__(snake_case_   ,			snake_case_ )
         # Don't call self.__setattr__ to avoid recursion errors
         super().__setattr__(snake_case_   ,			snake_case_ )






    def 					_lowercase						(self    :    int ):
         return tuple(self[k] for k in self.keys() )




class        __A      ( _a		,	_a	):



    @classmethod
    def 					_lowercase						(cls    :    int   ,			__a    :    List[Any] ):
         raise ValueError(
             f"""{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys() )}""" )




class        __A      ( _a	):
    a__       :						Optional[Any]          =  """longest"""
    a__       :						List[Any]          =  """max_length"""
    a__       :						Union[str, Any]          =  """do_not_pad"""




class        __A      ( _a	):
    a__       :						Dict          =  """pt"""
    a__       :						Any          =  """tf"""
    a__       :						List[Any]          =  """np"""
    a__       :						Dict          =  """jax"""




class        __A      :



    def __init__(self    :    int   ,			__a    :    List[ContextManager] ):
         UpperCAmelCase_           =   context_managers
         UpperCAmelCase_           =   ExitStack()



    def __enter__(self    :    Union[str, Any] ):
         for context_manager in self.context_managers:
              self.stack.enter_context(snake_case_ )






    def __exit__(self    :    Optional[int]   ,			*__a    :    List[Any]   ,			**__a    :    Any ):
         self.stack.__exit__(*snake_case_   ,			**snake_case_ )





def 	lowerCAmelCase_				(					snake_case_       : Tuple			)    ->      List[str]:


     '''simple docstring'''




     UpperCAmelCase_           =   infer_framework(_lowerCAmelCase			)
     if framework == "tf":
          UpperCAmelCase_           =   inspect.signature(model_class.call			)  # TensorFlow models
     elif framework == "pt":
          UpperCAmelCase_           =   inspect.signature(model_class.forward			)  # PyTorch models
     else:
          UpperCAmelCase_           =   inspect.signature(model_class.__call__			)  # Flax models

     for p in signature.parameters:
          if p == "return_loss" and signature.parameters[p].default is True:
               return True

     return False





def 	lowerCAmelCase_				(					snake_case_       : Optional[Any]			)    ->      Dict:


     '''simple docstring'''




     UpperCAmelCase_           =   model_class.__name__
     UpperCAmelCase_           =   infer_framework(_lowerCAmelCase			)
     if framework == "tf":
          UpperCAmelCase_           =   inspect.signature(model_class.call			)  # TensorFlow models
     elif framework == "pt":
          UpperCAmelCase_           =   inspect.signature(model_class.forward			)  # PyTorch models
     else:
          UpperCAmelCase_           =   inspect.signature(model_class.__call__			)  # Flax models

     if "QuestionAnswering" in model_name:
          return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
     else:
          return [p for p in signature.parameters if "label" in p]





def 	lowerCAmelCase_				(					snake_case_       : Optional[Any]					,    snake_case_       : Union[str, Any] = ""					,    snake_case_       : Union[str, Any] = "."			)    ->      List[str]:


     '''simple docstring'''




     def _flatten_dict(snake_case_       : str					,    snake_case_       : Optional[int]=""					,    snake_case_       : Any="."			):
          for k, v in d.items():
               UpperCAmelCase_           =   str(_lowerCAmelCase			) + delimiter + str(_lowerCAmelCase			) if parent_key else k
               if v and isinstance(_lowerCAmelCase					,    _lowerCAmelCase			):
                    yield from flatten_dict(_lowerCAmelCase					,    _lowerCAmelCase					,    delimiter=_lowerCAmelCase			).items()
               else:
                    yield key, v

     return dict(_flatten_dict(_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase			)			)





@contextmanager
def 	lowerCAmelCase_				(					snake_case_       : Dict					,    snake_case_       : Tuple = False			)    ->      Tuple:


     '''simple docstring'''




     if use_temp_dir:
          with tempfile.TemporaryDirectory() as tmp_dir:
               yield tmp_dir
     else:
          yield working_dir





def 	lowerCAmelCase_				(					snake_case_       : Dict					,    snake_case_       : List[str]=None			)    ->      Optional[Any]:


     '''simple docstring'''




     if is_numpy_array(_lowerCAmelCase			):
          return np.transpose(_lowerCAmelCase					,    axes=_lowerCAmelCase			)
     elif is_torch_tensor(_lowerCAmelCase			):
          return array.T if axes is None else array.permute(*_lowerCAmelCase			)
     elif is_tf_tensor(_lowerCAmelCase			):
          import tensorflow as tf

          return tf.transpose(_lowerCAmelCase					,    perm=_lowerCAmelCase			)
     elif is_jax_tensor(_lowerCAmelCase			):
          return jnp.transpose(_lowerCAmelCase					,    axes=_lowerCAmelCase			)
     else:
          raise ValueError(f"""Type not supported for transpose: {type(_lowerCAmelCase			)}."""			)





def 	lowerCAmelCase_				(					snake_case_       : int					,    snake_case_       : Optional[Any]			)    ->      Tuple:


     '''simple docstring'''




     if is_numpy_array(_lowerCAmelCase			):
          return np.reshape(_lowerCAmelCase					,    _lowerCAmelCase			)
     elif is_torch_tensor(_lowerCAmelCase			):
          return array.reshape(*_lowerCAmelCase			)
     elif is_tf_tensor(_lowerCAmelCase			):
          import tensorflow as tf

          return tf.reshape(_lowerCAmelCase					,    _lowerCAmelCase			)
     elif is_jax_tensor(_lowerCAmelCase			):
          return jnp.reshape(_lowerCAmelCase					,    _lowerCAmelCase			)
     else:
          raise ValueError(f"""Type not supported for reshape: {type(_lowerCAmelCase			)}."""			)





def 	lowerCAmelCase_				(					snake_case_       : Any					,    snake_case_       : Tuple=None			)    ->      Dict:


     '''simple docstring'''




     if is_numpy_array(_lowerCAmelCase			):
          return np.squeeze(_lowerCAmelCase					,    axis=_lowerCAmelCase			)
     elif is_torch_tensor(_lowerCAmelCase			):
          return array.squeeze() if axis is None else array.squeeze(dim=_lowerCAmelCase			)
     elif is_tf_tensor(_lowerCAmelCase			):
          import tensorflow as tf

          return tf.squeeze(_lowerCAmelCase					,    axis=_lowerCAmelCase			)
     elif is_jax_tensor(_lowerCAmelCase			):
          return jnp.squeeze(_lowerCAmelCase					,    axis=_lowerCAmelCase			)
     else:
          raise ValueError(f"""Type not supported for squeeze: {type(_lowerCAmelCase			)}."""			)





def 	lowerCAmelCase_				(					snake_case_       : Optional[int]					,    snake_case_       : Any			)    ->      Tuple:


     '''simple docstring'''




     if is_numpy_array(_lowerCAmelCase			):
          return np.expand_dims(_lowerCAmelCase					,    _lowerCAmelCase			)
     elif is_torch_tensor(_lowerCAmelCase			):
          return array.unsqueeze(dim=_lowerCAmelCase			)
     elif is_tf_tensor(_lowerCAmelCase			):
          import tensorflow as tf

          return tf.expand_dims(_lowerCAmelCase					,    axis=_lowerCAmelCase			)
     elif is_jax_tensor(_lowerCAmelCase			):
          return jnp.expand_dims(_lowerCAmelCase					,    axis=_lowerCAmelCase			)
     else:
          raise ValueError(f"""Type not supported for expand_dims: {type(_lowerCAmelCase			)}."""			)





def 	lowerCAmelCase_				(					snake_case_       : int			)    ->      Optional[Any]:


     '''simple docstring'''




     if is_numpy_array(_lowerCAmelCase			):
          return np.size(_lowerCAmelCase			)
     elif is_torch_tensor(_lowerCAmelCase			):
          return array.numel()
     elif is_tf_tensor(_lowerCAmelCase			):
          import tensorflow as tf

          return tf.size(_lowerCAmelCase			)
     elif is_jax_tensor(_lowerCAmelCase			):
          return array.size
     else:
          raise ValueError(f"""Type not supported for expand_dims: {type(_lowerCAmelCase			)}."""			)





def 	lowerCAmelCase_				(					snake_case_       : Optional[int]					,    snake_case_       : Union[str, Any]			)    ->      Dict:


     '''simple docstring'''




     for key, value in auto_map.items():
          if isinstance(_lowerCAmelCase					,    (tuple, list)			):
               UpperCAmelCase_           =   [f"""{repo_id}--{v}""" if (v is not None and """--""" not in v) else v for v in value]
          elif value is not None and "--" not in value:
               UpperCAmelCase_           =   f"""{repo_id}--{value}"""

     return auto_map






def 	lowerCAmelCase_				(					snake_case_       : List[str]			)    ->      int:


     '''simple docstring'''




     for base_class in inspect.getmro(_lowerCAmelCase			):
          UpperCAmelCase_           =   base_class.__module__
          UpperCAmelCase_           =   base_class.__name__
          if module.startswith("tensorflow"			) or module.startswith("keras"			) or name == "TFPreTrainedModel":
               return "tf"
          elif module.startswith("torch"			) or name == "PreTrainedModel":
               return "pt"
          elif module.startswith("flax"			) or module.startswith("jax"			) or name == "FlaxPreTrainedModel":
               return "flax"
     else:
          raise TypeError(f"""Could not infer framework from class {model_class}."""			)









'''simple docstring'''





import argparse
import json
from pathlib import Path

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

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__a          =		logging.get_logger(__name__)





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase=False		)		-> str:
      snake_case__  :    Union[str, Any]    						=      []
      for i in range(config.num_hidden_layers		):
            # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
            rename_keys.append((f"blocks.{i}.norm1.weight", f"deit.encoder.layer.{i}.layernorm_before.weight")		)
            rename_keys.append((f"blocks.{i}.norm1.bias", f"deit.encoder.layer.{i}.layernorm_before.bias")		)
            rename_keys.append((f"blocks.{i}.attn.proj.weight", f"deit.encoder.layer.{i}.attention.output.dense.weight")		)
            rename_keys.append((f"blocks.{i}.attn.proj.bias", f"deit.encoder.layer.{i}.attention.output.dense.bias")		)
            rename_keys.append((f"blocks.{i}.norm2.weight", f"deit.encoder.layer.{i}.layernorm_after.weight")		)
            rename_keys.append((f"blocks.{i}.norm2.bias", f"deit.encoder.layer.{i}.layernorm_after.bias")		)
            rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"deit.encoder.layer.{i}.intermediate.dense.weight")		)
            rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"deit.encoder.layer.{i}.intermediate.dense.bias")		)
            rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"deit.encoder.layer.{i}.output.dense.weight")		)
            rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"deit.encoder.layer.{i}.output.dense.bias")		)

      # projection layer + position embeddings
      rename_keys.extend(
          [
              ("""cls_token""", """deit.embeddings.cls_token"""),
              ("""dist_token""", """deit.embeddings.distillation_token"""),
              ("""patch_embed.proj.weight""", """deit.embeddings.patch_embeddings.projection.weight"""),
              ("""patch_embed.proj.bias""", """deit.embeddings.patch_embeddings.projection.bias"""),
              ("""pos_embed""", """deit.embeddings.position_embeddings"""),
          ]		)

      if base_model:
            # layernorm + pooler
            rename_keys.extend(
                [
                    ("""norm.weight""", """layernorm.weight"""),
                    ("""norm.bias""", """layernorm.bias"""),
                    ("""pre_logits.fc.weight""", """pooler.dense.weight"""),
                    ("""pre_logits.fc.bias""", """pooler.dense.bias"""),
                ]		)

            # if just the base model, we should remove "deit" from all keys that start with "deit"
            snake_case__  :    List[Any]    						=      [(pair[0], pair[1][4:]) if pair[1].startswith("""deit"""		) else pair for pair in rename_keys]
      else:
            # layernorm + classification heads
            rename_keys.extend(
                [
                    ("""norm.weight""", """deit.layernorm.weight"""),
                    ("""norm.bias""", """deit.layernorm.bias"""),
                    ("""head.weight""", """cls_classifier.weight"""),
                    ("""head.bias""", """cls_classifier.bias"""),
                    ("""head_dist.weight""", """distillation_classifier.weight"""),
                    ("""head_dist.bias""", """distillation_classifier.bias"""),
                ]		)

      return rename_keys





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase=False		)		-> Union[str, Any]:
      for i in range(config.num_hidden_layers		):
            if base_model:
                  snake_case__  :    Tuple    						=      """"""
            else:
                  snake_case__  :    Dict    						=      """deit."""
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            snake_case__  :    Optional[Any]    						=      state_dict.pop(f"blocks.{i}.attn.qkv.weight"		)
            snake_case__  :    Tuple    						=      state_dict.pop(f"blocks.{i}.attn.qkv.bias"		)
            # next, add query, keys and values (in that order) to the state dict
            snake_case__  :    Any    						=      in_proj_weight[
                : config.hidden_size, :
            ]
            snake_case__  :    Optional[int]    						=      in_proj_bias[: config.hidden_size]
            snake_case__  :    Any    						=      in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            snake_case__  :    str    						=      in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            snake_case__  :    List[str]    						=      in_proj_weight[
                -config.hidden_size :, :
            ]
            snake_case__  :    Tuple    						=      in_proj_bias[-config.hidden_size :]





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> int:
      snake_case__  :    str    						=      dct.pop(_lowerCAmelCase		)
      snake_case__  :    Tuple    						=      val





def 						__snake_case(				)		-> Tuple:
      snake_case__  :    Union[str, Any]    						=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
      snake_case__  :    Optional[int]    						=      Image.open(requests.get(_lowerCAmelCase     ,   stream=_lowerCAmelCase		).raw		)
      return im





@torch.no_grad()
def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> str:
      snake_case__  :    Optional[int]    						=      DeiTConfig()
      # all deit models have fine-tuned heads
      snake_case__  :    Union[str, Any]    						=      False
      # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
      snake_case__  :    int    						=      1_000
      snake_case__  :    Any    						=      """huggingface/label-files"""
      snake_case__  :    Optional[Any]    						=      """imagenet-1k-id2label.json"""
      snake_case__  :    Tuple    						=      json.load(open(hf_hub_download(_lowerCAmelCase     ,   _lowerCAmelCase     ,   repo_type="""dataset"""		)     ,   """r"""		)		)
      snake_case__  :    List[Any]    						=      {int(_lowerCAmelCase		): v for k, v in idalabel.items()}
      snake_case__  :    List[Any]    						=      idalabel
      snake_case__  :    List[str]    						=      {v: k for k, v in idalabel.items()}
      snake_case__  :    Tuple    						=      int(deit_name[-6:-4]		)
      snake_case__  :    Optional[Any]    						=      int(deit_name[-3:]		)
      # size of the architecture
      if deit_name[9:].startswith("""tiny"""		):
            snake_case__  :    Tuple    						=      192
            snake_case__  :    Union[str, Any]    						=      768
            snake_case__  :    Tuple    						=      12
            snake_case__  :    Union[str, Any]    						=      3
      elif deit_name[9:].startswith("""small"""		):
            snake_case__  :    str    						=      384
            snake_case__  :    Any    						=      1_536
            snake_case__  :    str    						=      12
            snake_case__  :    int    						=      6
      if deit_name[9:].startswith("""base"""		):
            pass
      elif deit_name[4:].startswith("""large"""		):
            snake_case__  :    Union[str, Any]    						=      1_024
            snake_case__  :    Any    						=      4_096
            snake_case__  :    List[Any]    						=      24
            snake_case__  :    Tuple    						=      16

      # load original model from timm
      snake_case__  :    List[Any]    						=      timm.create_model(_lowerCAmelCase     ,   pretrained=_lowerCAmelCase		)
      timm_model.eval()

      # load state_dict of original model, remove and rename some keys
      snake_case__  :    Optional[Any]    						=      timm_model.state_dict()
      snake_case__  :    Optional[int]    						=      create_rename_keys(_lowerCAmelCase     ,   _lowerCAmelCase		)
      for src, dest in rename_keys:
            rename_key(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)
      read_in_q_k_v(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)

      # load HuggingFace model
      snake_case__  :    Optional[Any]    						=      DeiTForImageClassificationWithTeacher(_lowerCAmelCase		).eval()
      model.load_state_dict(_lowerCAmelCase		)

      # Check outputs on an image, prepared by DeiTImageProcessor
      snake_case__  :    List[Any]    						=      int(
          (256 / 224) * config.image_size		)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
      snake_case__  :    Optional[Any]    						=      DeiTImageProcessor(size=_lowerCAmelCase     ,   crop_size=config.image_size		)
      snake_case__  :    str    						=      image_processor(images=prepare_img()     ,   return_tensors="""pt"""		)
      snake_case__  :    Optional[Any]    						=      encoding["""pixel_values"""]
      snake_case__  :    Tuple    						=      model(_lowerCAmelCase		)

      snake_case__  :    Optional[int]    						=      timm_model(_lowerCAmelCase		)
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(_lowerCAmelCase     ,   outputs.logits     ,   atol=1e-3		)

      Path(_lowerCAmelCase		).mkdir(exist_ok=_lowerCAmelCase		)
      print(f"Saving model {deit_name} to {pytorch_dump_folder_path}"		)
      model.save_pretrained(_lowerCAmelCase		)
      print(f"Saving image processor to {pytorch_dump_folder_path}"		)
      image_processor.save_pretrained(_lowerCAmelCase		)


if __name__ == "__main__":
       __a          =		argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--deit_name",
           default="vit_deit_base_distilled_patch16_224",
           type=str,
           help="Name of the DeiT 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."
       )

       __a          =		parser.parse_args()
       convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)
