


'''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      snake_case    (    lowercase			):





  """simple docstring"""




  def __get__(    self			,  UpperCamelCase			,  UpperCamelCase=None     ):







   """simple docstring"""





   if obj is None:
    return self
   if self.fget is None:
    raise AttributeError("unreadable attribute"     )
   lowerCamelCase_      =  "__cached_" + self.fget.__name__
   lowerCamelCase_      =  getattr(__A			,  __A			,  __A     )
   if cached is None:
    lowerCamelCase_      =  self.fget(__A     )
    setattr(__A			,  __A			,  __A     )
   return cached





def __snake_case					(      UpperCAmelCase_						:						Optional[Any]		):
 lowerCamelCase_      =  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 __snake_case					(      UpperCAmelCase_						:						str		):

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

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

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

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

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

 return isinstance(SCREAMING_SNAKE_CASE_					,				np.ndarray		)





def __snake_case					(      UpperCAmelCase_						:						Union[str, Any]		):
 return isinstance(SCREAMING_SNAKE_CASE_					,				np.ndarray		)





def __snake_case					(      UpperCAmelCase_						:						Optional[int]		):
 return _is_numpy(SCREAMING_SNAKE_CASE_		)





def __snake_case					(      UpperCAmelCase_						:						List[str]		):
 import torch

 return isinstance(SCREAMING_SNAKE_CASE_					,				torch.Tensor		)





def __snake_case					(      UpperCAmelCase_						:						List[str]		):
 return False if not is_torch_available() else _is_torch(SCREAMING_SNAKE_CASE_		)





def __snake_case					(      UpperCAmelCase_						:						Optional[Any]		):
 import torch

 return isinstance(SCREAMING_SNAKE_CASE_					,				torch.device		)





def __snake_case					(      UpperCAmelCase_						:						Any		):
 return False if not is_torch_available() else _is_torch_device(SCREAMING_SNAKE_CASE_		)





def __snake_case					(      UpperCAmelCase_						:						Optional[int]		):
 import torch

 if isinstance(SCREAMING_SNAKE_CASE_					,				SCREAMING_SNAKE_CASE_		):
  if hasattr(SCREAMING_SNAKE_CASE_					,				SCREAMING_SNAKE_CASE_		):
   lowerCamelCase_      =  getattr(SCREAMING_SNAKE_CASE_					,				SCREAMING_SNAKE_CASE_		)
  else:
   return False
 return isinstance(SCREAMING_SNAKE_CASE_					,				torch.dtype		)





def __snake_case					(      UpperCAmelCase_						:						Any		):
 return False if not is_torch_available() else _is_torch_dtype(SCREAMING_SNAKE_CASE_		)





def __snake_case					(      UpperCAmelCase_						:						List[str]		):
 import tensorflow as tf

 return isinstance(SCREAMING_SNAKE_CASE_					,				tf.Tensor		)





def __snake_case					(      UpperCAmelCase_						:						str		):
 return False if not is_tf_available() else _is_tensorflow(SCREAMING_SNAKE_CASE_		)





def __snake_case					(      UpperCAmelCase_						:						str		):
 import tensorflow as tf

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





def __snake_case					(      UpperCAmelCase_						:						Union[str, Any]		):
 return False if not is_tf_available() else _is_tf_symbolic_tensor(SCREAMING_SNAKE_CASE_		)





def __snake_case					(      UpperCAmelCase_						:						int		):
 import jax.numpy as jnp  # noqa: F811

 return isinstance(SCREAMING_SNAKE_CASE_					,				jnp.ndarray		)





def __snake_case					(      UpperCAmelCase_						:						int		):
 return False if not is_flax_available() else _is_jax(SCREAMING_SNAKE_CASE_		)





def __snake_case					(      UpperCAmelCase_						:						Dict		):

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





def __snake_case					(      UpperCAmelCase_						:						List[Any]		):

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





class      snake_case    (    lowercase			):





  """simple docstring"""




  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  fields(self     )

   # Safety and consistency checks
   if not len(__A     ):
    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.'''     )

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

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






  def __delitem__(    self			,  *UpperCamelCase			,  **UpperCamelCase     ):







   """simple docstring"""
   raise Exception(f'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.'''     )






  def   snake_case		(    self			,  *UpperCamelCase			,  **UpperCamelCase     ):







   """simple docstring"""
   raise Exception(f'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.'''     )






  def   snake_case		(    self			,  *UpperCamelCase			,  **UpperCamelCase     ):







   """simple docstring"""
   raise Exception(f'''You cannot use ``pop`` on a {self.__class__.__name__} instance.'''     )






  def   snake_case		(    self			,  *UpperCamelCase			,  **UpperCamelCase     ):







   """simple docstring"""
   raise Exception(f'''You cannot use ``update`` on a {self.__class__.__name__} instance.'''     )






  def __getitem__(    self			,  UpperCamelCase     ):







   """simple docstring"""





   if isinstance(__A			,  __A     ):
    lowerCamelCase_      =  dict(self.items()     )
    return inner_dict[k]
   else:
    return self.to_tuple()[k]






  def __setattr__(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""





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






  def __setitem__(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   super().__setitem__(__A			,  __A     )
   # Don't call self.__setattr__ to avoid recursion errors
   super().__setattr__(__A			,  __A     )







  def   snake_case		(    self     ):







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





class      snake_case    (    lowercase			,						lowercase			):





  """simple docstring"""




  @classmethod
  def   snake_case		(    cls			,  UpperCamelCase     ):







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





class      snake_case    (    lowercase			):





  """simple docstring"""




  _lowerCamelCase		    =  "longest"
  _lowerCamelCase		    =  "max_length"
  _lowerCamelCase		    =  "do_not_pad"





class      snake_case    (    lowercase			):





  """simple docstring"""




  _lowerCamelCase		    =  "pt"
  _lowerCamelCase		    =  "tf"
  _lowerCamelCase		    =  "np"
  _lowerCamelCase		    =  "jax"





class      snake_case    :





  """simple docstring"""




  def __init__(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  context_managers
   lowerCamelCase_      =  ExitStack()






  def __enter__(    self     ):







   """simple docstring"""





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







  def __exit__(    self			,  *UpperCamelCase			,  **UpperCamelCase     ):







   """simple docstring"""
   self.stack.__exit__(*__A			,  **__A     )





def __snake_case					(      UpperCAmelCase_						:						Any		):
 lowerCamelCase_      =  infer_framework(SCREAMING_SNAKE_CASE_		)
 if framework == "tf":
  lowerCamelCase_      =  inspect.signature(model_class.call		)  # TensorFlow models
 elif framework == "pt":
  lowerCamelCase_      =  inspect.signature(model_class.forward		)  # PyTorch models
 else:
  lowerCamelCase_      =  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 __snake_case					(      UpperCAmelCase_						:						List[Any]		):
 lowerCamelCase_      =  model_class.__name__
 lowerCamelCase_      =  infer_framework(SCREAMING_SNAKE_CASE_		)
 if framework == "tf":
  lowerCamelCase_      =  inspect.signature(model_class.call		)  # TensorFlow models
 elif framework == "pt":
  lowerCamelCase_      =  inspect.signature(model_class.forward		)  # PyTorch models
 else:
  lowerCamelCase_      =  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 __snake_case					(      UpperCAmelCase_						:						str					,				UpperCAmelCase_						:						Dict = ""					,				UpperCAmelCase_						:						List[Any] = "."		):

 def _flatten_dict(UpperCAmelCase_						:						str					,				UpperCAmelCase_						:						Union[str, Any]=""					,				UpperCAmelCase_						:						Dict="."		):
  for k, v in d.items():
   lowerCamelCase_      =  str(SCREAMING_SNAKE_CASE_		) + delimiter + str(SCREAMING_SNAKE_CASE_		) if parent_key else k
   if v and isinstance(SCREAMING_SNAKE_CASE_					,				SCREAMING_SNAKE_CASE_		):
    yield from flatten_dict(SCREAMING_SNAKE_CASE_					,				SCREAMING_SNAKE_CASE_					,				delimiter=SCREAMING_SNAKE_CASE_		).items()
   else:
    yield key, v

 return dict(_flatten_dict(SCREAMING_SNAKE_CASE_					,				SCREAMING_SNAKE_CASE_					,				SCREAMING_SNAKE_CASE_		)		)





@contextmanager
def __snake_case					(      UpperCAmelCase_						:						List[str]					,				UpperCAmelCase_						:						Optional[int] = False		):

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





def __snake_case					(      UpperCAmelCase_						:						Optional[Any]					,				UpperCAmelCase_						:						Tuple=None		):

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

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





def __snake_case					(      UpperCAmelCase_						:						Dict					,				UpperCAmelCase_						:						str		):

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

  return tf.reshape(SCREAMING_SNAKE_CASE_					,				SCREAMING_SNAKE_CASE_		)
 elif is_jax_tensor(SCREAMING_SNAKE_CASE_		):
  return jnp.reshape(SCREAMING_SNAKE_CASE_					,				SCREAMING_SNAKE_CASE_		)
 else:
  raise ValueError(F'''Type not supported for reshape: {type(SCREAMING_SNAKE_CASE_		)}.'''		)





def __snake_case					(      UpperCAmelCase_						:						Optional[int]					,				UpperCAmelCase_						:						Dict=None		):

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

  return tf.squeeze(SCREAMING_SNAKE_CASE_					,				axis=SCREAMING_SNAKE_CASE_		)
 elif is_jax_tensor(SCREAMING_SNAKE_CASE_		):
  return jnp.squeeze(SCREAMING_SNAKE_CASE_					,				axis=SCREAMING_SNAKE_CASE_		)
 else:
  raise ValueError(F'''Type not supported for squeeze: {type(SCREAMING_SNAKE_CASE_		)}.'''		)





def __snake_case					(      UpperCAmelCase_						:						Tuple					,				UpperCAmelCase_						:						Optional[Any]		):

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

  return tf.expand_dims(SCREAMING_SNAKE_CASE_					,				axis=SCREAMING_SNAKE_CASE_		)
 elif is_jax_tensor(SCREAMING_SNAKE_CASE_		):
  return jnp.expand_dims(SCREAMING_SNAKE_CASE_					,				axis=SCREAMING_SNAKE_CASE_		)
 else:
  raise ValueError(F'''Type not supported for expand_dims: {type(SCREAMING_SNAKE_CASE_		)}.'''		)





def __snake_case					(      UpperCAmelCase_						:						Optional[int]		):

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

  return tf.size(SCREAMING_SNAKE_CASE_		)
 elif is_jax_tensor(SCREAMING_SNAKE_CASE_		):
  return array.size
 else:
  raise ValueError(F'''Type not supported for expand_dims: {type(SCREAMING_SNAKE_CASE_		)}.'''		)





def __snake_case					(      UpperCAmelCase_						:						Optional[int]					,				UpperCAmelCase_						:						Any		):

 for key, value in auto_map.items():
  if isinstance(SCREAMING_SNAKE_CASE_					,				(tuple, list)		):
   lowerCamelCase_      =  [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:
   lowerCamelCase_      =  F'''{repo_id}--{value}'''

 return auto_map







def __snake_case					(      UpperCAmelCase_						:						Any		):

 for base_class in inspect.getmro(SCREAMING_SNAKE_CASE_		):
  lowerCamelCase_      =  base_class.__module__
  lowerCamelCase_      =  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 torch

from transformers import YosoConfig, YosoForMaskedLM






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  if "model" in orig_key:
    lowerCamelCase :					Dict    =				orig_key.replace("model."      ,						""	)
  if "norm1" in orig_key:
    lowerCamelCase :					Union[str, Any]    =				orig_key.replace("norm1"      ,						"attention.output.LayerNorm"	)
  if "norm2" in orig_key:
    lowerCamelCase :					Union[str, Any]    =				orig_key.replace("norm2"      ,						"output.LayerNorm"	)
  if "norm" in orig_key:
    lowerCamelCase :					Optional[Any]    =				orig_key.replace("norm"      ,						"LayerNorm"	)
  if "transformer" in orig_key:
    lowerCamelCase :					int    =				orig_key.split("."	)[0].split("_"	)[-1]
    lowerCamelCase :					Dict    =				orig_key.replace(f"""transformer_{layer_num}"""      ,						f"""encoder.layer.{layer_num}"""	)
  if "mha.attn" in orig_key:
    lowerCamelCase :					List[str]    =				orig_key.replace("mha.attn"      ,						"attention.self"	)
  if "mha" in orig_key:
    lowerCamelCase :					List[Any]    =				orig_key.replace("mha"      ,						"attention"	)
  if "W_q" in orig_key:
    lowerCamelCase :					Optional[int]    =				orig_key.replace("W_q"      ,						"self.query"	)
  if "W_k" in orig_key:
    lowerCamelCase :					List[Any]    =				orig_key.replace("W_k"      ,						"self.key"	)
  if "W_v" in orig_key:
    lowerCamelCase :					Union[str, Any]    =				orig_key.replace("W_v"      ,						"self.value"	)
  if "ff1" in orig_key:
    lowerCamelCase :					Union[str, Any]    =				orig_key.replace("ff1"      ,						"intermediate.dense"	)
  if "ff2" in orig_key:
    lowerCamelCase :					Optional[int]    =				orig_key.replace("ff2"      ,						"output.dense"	)
  if "ff" in orig_key:
    lowerCamelCase :					Optional[int]    =				orig_key.replace("ff"      ,						"output.dense"	)
  if "mlm_class" in orig_key:
    lowerCamelCase :					Dict    =				orig_key.replace("mlm.mlm_class"      ,						"cls.predictions.decoder"	)
  if "mlm" in orig_key:
    lowerCamelCase :					List[Any]    =				orig_key.replace("mlm"      ,						"cls.predictions.transform"	)
  if "cls" not in orig_key:
    lowerCamelCase :					int    =				"yoso." + orig_key

  return orig_key






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  for key in orig_state_dict.copy().keys():
    lowerCamelCase :					List[str]    =				orig_state_dict.pop(SCREAMING_SNAKE_CASE_	)

    if ("pooler" in key) or ("sen_class" in key):
      continue
    else:
      lowerCamelCase :					Dict    =				val

  lowerCamelCase :					Dict    =				orig_state_dict["cls.predictions.decoder.bias"]
  lowerCamelCase :					Dict    =				torch.arange(SCREAMING_SNAKE_CASE_	).expand((1, -1)	) + 2

  return orig_state_dict






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					List[Any]    =				torch.load(SCREAMING_SNAKE_CASE_      ,						map_location="cpu"	)["model_state_dict"]
  lowerCamelCase :					List[str]    =				YosoConfig.from_json_file(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					Any    =				YosoForMaskedLM(SCREAMING_SNAKE_CASE_	)

  lowerCamelCase :					List[Any]    =				convert_checkpoint_helper(config.max_position_embeddings      ,						SCREAMING_SNAKE_CASE_	)

  print(model.load_state_dict(SCREAMING_SNAKE_CASE_	)	)
  model.eval()
  model.save_pretrained(SCREAMING_SNAKE_CASE_	)

  print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""	)


if __name__ == "__main__":
   _snake_case             =     argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to YOSO pytorch checkpoint.'''
   )
   parser.add_argument(
       '''--config_file''',
       default=None,
       type=str,
       required=True,
       help='''The json file for YOSO model config.''',
   )
   parser.add_argument(
       '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   _snake_case             =     parser.parse_args()
   convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)



