






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					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=None						):


      '''simple docstring'''






      if obj is None:
           return self
      if self.fget is None:
           raise AttributeError("unreadable attribute"						)
      UpperCamelCase__			:   int       				=  "__cached_" + self.fget.__name__
      UpperCamelCase__			:   Optional[int]       				=  getattr(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)
      if cached is None:
           UpperCamelCase__			:   List[Any]       				=  self.fget(SCREAMING_SNAKE_CASE						)
           setattr(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)
      return cached







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	str:
     UpperCamelCase__			:   Tuple       				=  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 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	List[Any]:
     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 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	str:
     return isinstance(__lowerCAmelCase					,    np.ndarray			)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	Any:
     return _is_numpy(__lowerCAmelCase			)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	Optional[Any]:
     import torch

     return isinstance(__lowerCAmelCase					,    torch.Tensor			)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	Any:
     return False if not is_torch_available() else _is_torch(__lowerCAmelCase			)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	Optional[Any]:
     import torch

     return isinstance(__lowerCAmelCase					,    torch.device			)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	Tuple:
     return False if not is_torch_available() else _is_torch_device(__lowerCAmelCase			)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	int:
     import torch

     if isinstance(__lowerCAmelCase					,    __lowerCAmelCase			):
          if hasattr(__lowerCAmelCase					,    __lowerCAmelCase			):
               UpperCamelCase__			:   Any       				=  getattr(__lowerCAmelCase					,    __lowerCAmelCase			)
          else:
               return False
     return isinstance(__lowerCAmelCase					,    torch.dtype			)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	Dict:
     return False if not is_torch_available() else _is_torch_dtype(__lowerCAmelCase			)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	Tuple:
     import tensorflow as tf

     return isinstance(__lowerCAmelCase					,    tf.Tensor			)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	List[str]:
     return False if not is_tf_available() else _is_tensorflow(__lowerCAmelCase			)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	Tuple:
     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 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	Tuple:
     return False if not is_tf_available() else _is_tf_symbolic_tensor(__lowerCAmelCase			)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	Dict:
     import jax.numpy as jnp  # noqa: F811

     return isinstance(__lowerCAmelCase					,    jnp.ndarray			)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	Dict:
     return False if not is_flax_available() else _is_jax(__lowerCAmelCase			)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	Optional[Any]:
     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 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	Optional[Any]:
     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					:   List[Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   Dict       				=  fields(self						)

      # Safety and consistency checks
      if not len(SCREAMING_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__			:   Union[str, Any]       				=  getattr(self							,   class_fields[0].name						)
      UpperCamelCase__			:   Dict       				=  all(getattr(self							,   field.name						) is None for field in class_fields[1:]						)

      if other_fields_are_none and not is_tensor(SCREAMING_SNAKE_CASE						):
           if isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
                UpperCamelCase__			:   Any       				=  first_field.items()
                UpperCamelCase__			:   str       				=  True
           else:
                try:
                     UpperCamelCase__			:   Tuple       				=  iter(SCREAMING_SNAKE_CASE						)
                     UpperCamelCase__			:   Optional[int]       				=  True
                except TypeError:
                     UpperCamelCase__			:   List[str]       				=  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(SCREAMING_SNAKE_CASE						):
                     if (
                         not isinstance(SCREAMING_SNAKE_CASE							,   (list, tuple)						)
                         or not len(SCREAMING_SNAKE_CASE						) == 2
                         or not isinstance(element[0]							,   SCREAMING_SNAKE_CASE						)
                     ):
                          if idx == 0:
                               # If we do not have an iterator of key/values, set it as attribute
                               UpperCamelCase__			:   List[Any]       				=  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__			:   Union[str, Any]       				=  element[1]
           elif first_field is not None:
                UpperCamelCase__			:   List[str]       				=  first_field
      else:
           for field in class_fields:
                UpperCamelCase__			:   Any       				=  getattr(self							,   field.name						)
                if v is not None:
                     UpperCamelCase__			:   str       				=  v







 def __delitem__(		self					:   List[Any]							,   *SCREAMING_SNAKE_CASE					:   List[Any]							,   **SCREAMING_SNAKE_CASE					:   Dict						):


      '''simple docstring'''






      raise Exception(F'You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.'						)







 def 					__lowercase					(		self					:   List[Any]							,   *SCREAMING_SNAKE_CASE					:   Tuple							,   **SCREAMING_SNAKE_CASE					:   Tuple						):


      '''simple docstring'''






      raise Exception(F'You cannot use ``setdefault`` on a {self.__class__.__name__} instance.'						)







 def 					__lowercase					(		self					:   str							,   *SCREAMING_SNAKE_CASE					:   Dict							,   **SCREAMING_SNAKE_CASE					:   Tuple						):


      '''simple docstring'''






      raise Exception(F'You cannot use ``pop`` on a {self.__class__.__name__} instance.'						)







 def 					__lowercase					(		self					:   Tuple							,   *SCREAMING_SNAKE_CASE					:   Dict							,   **SCREAMING_SNAKE_CASE					:   int						):


      '''simple docstring'''






      raise Exception(F'You cannot use ``update`` on a {self.__class__.__name__} instance.'						)







 def __getitem__(		self					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   Dict						):


      '''simple docstring'''






      if isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           UpperCamelCase__			:   str       				=  dict(self.items()						)
           return inner_dict[k]
      else:
           return self.to_tuple()[k]







 def __setattr__(		self					:   int							,   SCREAMING_SNAKE_CASE					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   str						):


      '''simple docstring'''






      if name in self.keys() and value is not None:
           # Don't call self.__setitem__ to avoid recursion errors
           super().__setitem__(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)
      super().__setattr__(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)







 def __setitem__(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   Dict							,   SCREAMING_SNAKE_CASE					:   Tuple						):


      '''simple docstring'''






      super().__setitem__(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)
      # Don't call self.__setattr__ to avoid recursion errors
      super().__setattr__(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   List[Any]						):


      '''simple docstring'''






      return tuple(self[k] for k in self.keys()						)







class 			__a  (    A__       ,   A__       ):







 @classmethod
 def 					__lowercase					(		cls					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   List[Any]						):


      '''simple docstring'''






      raise ValueError(
          F'{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys()						)}'						)







class 			__a  (    A__       ):
 _lowerCAmelCase       :       Dict        = '''longest'''
 _lowerCAmelCase       :       Dict        = '''max_length'''
 _lowerCAmelCase       :       List[str]        = '''do_not_pad'''







class 			__a  (    A__       ):
 _lowerCAmelCase       :       Optional[Any]        = '''pt'''
 _lowerCAmelCase       :       Union[str, Any]        = '''tf'''
 _lowerCAmelCase       :       Dict        = '''np'''
 _lowerCAmelCase       :       str        = '''jax'''







class 			__a  :







 def __init__(		self					:   Tuple							,   SCREAMING_SNAKE_CASE					:   List[ContextManager]						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  context_managers
      UpperCamelCase__			:   List[str]       				=  ExitStack()







 def __enter__(		self					:   Optional[int]						):


      '''simple docstring'''






      for context_manager in self.context_managers:
           self.stack.enter_context(SCREAMING_SNAKE_CASE						)







 def __exit__(		self					:   str							,   *SCREAMING_SNAKE_CASE					:   List[Any]							,   **SCREAMING_SNAKE_CASE					:   Optional[Any]						):


      '''simple docstring'''






      self.stack.__exit__(*SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	Dict:
     UpperCamelCase__			:   Any       				=  infer_framework(__lowerCAmelCase			)
     if framework == "tf":
          UpperCamelCase__			:   Optional[int]       				=  inspect.signature(model_class.call			)  # TensorFlow models
     elif framework == "pt":
          UpperCamelCase__			:   Dict       				=  inspect.signature(model_class.forward			)  # PyTorch models
     else:
          UpperCamelCase__			:   Union[str, Any]       				=  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 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	Union[str, Any]:
     UpperCamelCase__			:   Dict       				=  model_class.__name__
     UpperCamelCase__			:   Tuple       				=  infer_framework(__lowerCAmelCase			)
     if framework == "tf":
          UpperCamelCase__			:   Union[str, Any]       				=  inspect.signature(model_class.call			)  # TensorFlow models
     elif framework == "pt":
          UpperCamelCase__			:   int       				=  inspect.signature(model_class.forward			)  # PyTorch models
     else:
          UpperCamelCase__			:   Any       				=  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 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase = ""					,    __lowerCAmelCase = "."			) ->	str:

     def _flatten_dict(__lowerCAmelCase					,    __lowerCAmelCase=""					,    __lowerCAmelCase="."			):
          for k, v in d.items():
               UpperCamelCase__			:   Tuple       				=  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 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase = False			) ->	int:
     if use_temp_dir:
          with tempfile.TemporaryDirectory() as tmp_dir:
               yield tmp_dir
     else:
          yield working_dir







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase=None			) ->	Optional[Any]:
     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 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase			) ->	Tuple:
     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 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase=None			) ->	Tuple:
     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 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase			) ->	Optional[Any]:
     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 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	Optional[Any]:
     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 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase			) ->	List[Any]:
     for key, value in auto_map.items():
          if isinstance(__lowerCAmelCase					,    (tuple, list)			):
               UpperCamelCase__			:   Union[str, Any]       				=  [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__			:   Any       				=  f'{repo_id}--{value}'

     return auto_map







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	Union[str, Any]:
     for base_class in inspect.getmro(__lowerCAmelCase			):
          UpperCamelCase__			:   Dict       				=  base_class.__module__
          UpperCamelCase__			:   Any       				=  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}.'			)







import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase	:							Optional[int]				=logging.get_logger()





@dataclass
class 			__a  :
 _lowerCAmelCase       :       nn.Module
 _lowerCAmelCase       :       List[nn.Module]        = field(default_factory=A__       )
 _lowerCAmelCase       :       list        = field(default_factory=A__       )







 def 					__lowercase					(		self					:   Dict							,   SCREAMING_SNAKE_CASE					:   str							,   SCREAMING_SNAKE_CASE					:   Tensor							,   SCREAMING_SNAKE_CASE					:   Tensor						):


      '''simple docstring'''






      UpperCamelCase__			:   Dict       				=  len(list(m.modules()						)						) == 1 or isinstance(SCREAMING_SNAKE_CASE							,   nn.Convad						) or isinstance(SCREAMING_SNAKE_CASE							,   nn.BatchNormad						)
      if has_not_submodules:
           self.traced.append(SCREAMING_SNAKE_CASE						)







 def __call__(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Tensor						):


      '''simple docstring'''






      for m in self.module.modules():
           self.handles.append(m.register_forward_hook(self._forward_hook						)						)
      self.module(SCREAMING_SNAKE_CASE						)
      [x.remove() for x in self.handles]
      return self







 @property
 def 					__lowercase					(		self					:   Optional[Any]						):


      '''simple docstring'''






      return list(filter(lambda SCREAMING_SNAKE_CASE						: len(list(x.state_dict().keys()						)						) > 0							,   self.traced						)						)







@dataclass
class 			__a  :
 _lowerCAmelCase       :       nn.Module
 _lowerCAmelCase       :       nn.Module
 _lowerCAmelCase       :       int        = 0
 _lowerCAmelCase       :       List        = field(default_factory=A__       )
 _lowerCAmelCase       :       List        = field(default_factory=A__       )







 def __call__(		self					:   Any							,   SCREAMING_SNAKE_CASE					:   Tensor						):


      '''simple docstring'''






      UpperCamelCase__			:   Union[str, Any]       				=  Tracker(self.dest						)(SCREAMING_SNAKE_CASE						).parametrized
      UpperCamelCase__			:   Any       				=  Tracker(self.src						)(SCREAMING_SNAKE_CASE						).parametrized

      UpperCamelCase__			:   str       				=  list(filter(lambda SCREAMING_SNAKE_CASE						: type(SCREAMING_SNAKE_CASE						) not in self.src_skip							,   SCREAMING_SNAKE_CASE						)						)
      UpperCamelCase__			:   List[Any]       				=  list(filter(lambda SCREAMING_SNAKE_CASE						: type(SCREAMING_SNAKE_CASE						) not in self.dest_skip							,   SCREAMING_SNAKE_CASE						)						)

      if len(SCREAMING_SNAKE_CASE						) != len(SCREAMING_SNAKE_CASE						):
           raise Exception(
               F'Numbers of operations are different. Source module has {len(SCREAMING_SNAKE_CASE						)} operations while'
               F' destination module has {len(SCREAMING_SNAKE_CASE						)}.'						)

      for dest_m, src_m in zip(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           dest_m.load_state_dict(src_m.state_dict()						)
           if self.verbose == 1:
                print(F'Transfered from={src_m} to={dest_m}'						)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase = True			) ->	Optional[int]:
     print(f'Converting {name}...'			)
     with torch.no_grad():
          UpperCamelCase__			:   Union[str, Any]       				=  timm.create_model(__lowerCAmelCase					,    pretrained=__lowerCAmelCase			).eval()
          UpperCamelCase__			:   List[Any]       				=  ResNetForImageClassification(__lowerCAmelCase			).eval()
          UpperCamelCase__			:   Optional[Any]       				=  ModuleTransfer(src=__lowerCAmelCase					,    dest=__lowerCAmelCase			)
          UpperCamelCase__			:   int       				=  torch.randn((1, 3, 224, 224)			)
          module_transfer(__lowerCAmelCase			)

     assert torch.allclose(from_model(__lowerCAmelCase			)					,    our_model(__lowerCAmelCase			).logits			), "The model logits don't match the original one."

     UpperCamelCase__			:   List[Any]       				=  f'resnet{"-".join(name.split("resnet"			)			)}'
     print(__lowerCAmelCase			)

     if push_to_hub:
          our_model.push_to_hub(
              repo_path_or_name=save_directory / checkpoint_name					,    commit_message="Add model"					,    use_temp_dir=__lowerCAmelCase					,    )

          # we can use the convnext one
          UpperCamelCase__			:   List[Any]       				=  AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"			)
          image_processor.push_to_hub(
              repo_path_or_name=save_directory / checkpoint_name					,    commit_message="Add image processor"					,    use_temp_dir=__lowerCAmelCase					,    )

          print(f'Pushed {checkpoint_name}'			)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase = None					,    __lowerCAmelCase = True			) ->	Dict:
     UpperCamelCase__			:   Dict       				=  "imagenet-1k-id2label.json"
     UpperCamelCase__			:   Optional[int]       				=  1000
     UpperCamelCase__			:   Any       				=  (1, num_labels)

     UpperCamelCase__			:   Union[str, Any]       				=  "huggingface/label-files"
     UpperCamelCase__			:   Optional[int]       				=  num_labels
     UpperCamelCase__			:   str       				=  json.load(open(hf_hub_download(__lowerCAmelCase					,    __lowerCAmelCase					,    repo_type="dataset"			)					,    "r"			)			)
     UpperCamelCase__			:   Optional[int]       				=  {int(__lowerCAmelCase			): v for k, v in idalabel.items()}

     UpperCamelCase__			:   Tuple       				=  idalabel
     UpperCamelCase__			:   Union[str, Any]       				=  {v: k for k, v in idalabel.items()}

     UpperCamelCase__			:   Union[str, Any]       				=  partial(__lowerCAmelCase					,    num_labels=__lowerCAmelCase					,    idalabel=__lowerCAmelCase					,    labelaid=__lowerCAmelCase			)

     UpperCamelCase__			:   Union[str, Any]       				=  {
         "resnet18": ImageNetPreTrainedConfig(
             depths=[2, 2, 2, 2]					,    hidden_sizes=[64, 128, 256, 512]					,    layer_type="basic"			),
         "resnet26": ImageNetPreTrainedConfig(
             depths=[2, 2, 2, 2]					,    hidden_sizes=[256, 512, 1024, 2048]					,    layer_type="bottleneck"			),
         "resnet34": ImageNetPreTrainedConfig(
             depths=[3, 4, 6, 3]					,    hidden_sizes=[64, 128, 256, 512]					,    layer_type="basic"			),
         "resnet50": ImageNetPreTrainedConfig(
             depths=[3, 4, 6, 3]					,    hidden_sizes=[256, 512, 1024, 2048]					,    layer_type="bottleneck"			),
         "resnet101": ImageNetPreTrainedConfig(
             depths=[3, 4, 23, 3]					,    hidden_sizes=[256, 512, 1024, 2048]					,    layer_type="bottleneck"			),
         "resnet152": ImageNetPreTrainedConfig(
             depths=[3, 8, 36, 3]					,    hidden_sizes=[256, 512, 1024, 2048]					,    layer_type="bottleneck"			),
     }

     if model_name:
          convert_weight_and_push(__lowerCAmelCase					,    names_to_config[model_name]					,    __lowerCAmelCase					,    __lowerCAmelCase			)
     else:
          for model_name, config in names_to_config.items():
               convert_weight_and_push(__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase			)
     return config, expected_shape


if __name__ == "__main__":
 lowerCamelCase	:							Union[str, Any]				=argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--model_name''',
     default=None,
     type=str,
     help=(
         '''The name of the model you wish to convert, it must be one of the supported resnet* architecture,'''
         ''' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'''
     ),
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''',
     default=None,
     type=Path,
     required=True,
     help='''Path to the output PyTorch model directory.''',
 )
 parser.add_argument(
     '''--push_to_hub''',
     default=True,
     type=bool,
     required=False,
     help='''If True, push model and image processor to the hub.''',
 )

 lowerCamelCase	:							int				=parser.parse_args()
 lowerCamelCase	:							Path				=args.pytorch_dump_folder_path
 pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
 convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)