



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 	lowerCamelCase_    ( _lowercase						):






						'''simple docstring'''







						def __get__(	self      :			Optional[int]	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			Tuple=None )      ->				Dict:
									# See docs.python.org/3/howto/descriptor.html#properties
									if obj is None:
												return self
									if self.fget is None:
												raise AttributeError("unreadable attribute" )
									A							:					Tuple              =						"__cached_" + self.fget.__name__
									A							:					Optional[Any]              =						getattr(A_	,	A_	,	A_ )
									if cached is None:
												A							:					Union[str, Any]              =						self.fget(A_ )
												setattr(A_	,	A_	,	A_ )
									return cached







def 	UpperCAmelCase   (						_lowerCamelCase    ):
			A							:					Dict              =						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 	UpperCAmelCase   (						_lowerCamelCase    ):
			if is_torch_fx_proxy(snake_case__    ):
						return True
			if is_torch_available():
						import torch

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

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

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

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

			return isinstance(snake_case__ ,    np.ndarray    )




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			return isinstance(snake_case__ ,    np.ndarray    )




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			return _is_numpy(snake_case__    )




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			import torch

			return isinstance(snake_case__ ,    torch.Tensor    )




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			return False if not is_torch_available() else _is_torch(snake_case__    )




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			import torch

			return isinstance(snake_case__ ,    torch.device    )




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			return False if not is_torch_available() else _is_torch_device(snake_case__    )




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			import torch

			if isinstance(snake_case__ ,    snake_case__    ):
						if hasattr(snake_case__ ,    snake_case__    ):
									A							:					Dict              =						getattr(snake_case__ ,    snake_case__    )
						else:
									return False
			return isinstance(snake_case__ ,    torch.dtype    )




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			return False if not is_torch_available() else _is_torch_dtype(snake_case__    )




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			import tensorflow as tf

			return isinstance(snake_case__ ,    tf.Tensor    )




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			return False if not is_tf_available() else _is_tensorflow(snake_case__    )




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			import tensorflow as tf

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




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			return False if not is_tf_available() else _is_tf_symbolic_tensor(snake_case__    )




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			import jax.numpy as jnp  # noqa: F811

			return isinstance(snake_case__ ,    jnp.ndarray    )




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			return False if not is_flax_available() else _is_jax(snake_case__    )




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




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

class 	lowerCamelCase_    ( _lowercase						):






						'''simple docstring'''







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				str:
									A							:					Dict              =						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.""" )

									A							:					int              =						getattr(self	,	class_fields[0].name )
									A							:					List[Any]              =						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_ ):
															A							:					List[str]              =						first_field.items()
															A							:					Dict              =						True
												else:
															try:
																		A							:					str              =						iter(A_ )
																		A							:					int              =						True
															except TypeError:
																		A							:					Any              =						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
																								A							:					Optional[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:
																					A							:					Dict              =						element[1]
												elif first_field is not None:
															A							:					Tuple              =						first_field
									else:
												for field in class_fields:
															A							:					Dict              =						getattr(self	,	field.name )
															if v is not None:
																		A							:					List[Any]              =						v







						def __delitem__(	self      :			Optional[int]	,	*__lowerCamelCase      :			Optional[int]	,	**__lowerCamelCase      :			Dict )      ->				Tuple:
									raise Exception(F"""You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.""" )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int	,	*__lowerCamelCase      :			Optional[int]	,	**__lowerCamelCase      :			List[Any] )      ->				Dict:
									raise Exception(F"""You cannot use ``setdefault`` on a {self.__class__.__name__} instance.""" )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any]	,	*__lowerCamelCase      :			Optional[Any]	,	**__lowerCamelCase      :			Dict )      ->				Dict:
									raise Exception(F"""You cannot use ``pop`` on a {self.__class__.__name__} instance.""" )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple	,	*__lowerCamelCase      :			Union[str, Any]	,	**__lowerCamelCase      :			Dict )      ->				Dict:
									raise Exception(F"""You cannot use ``update`` on a {self.__class__.__name__} instance.""" )







						def __getitem__(	self      :			int	,	__lowerCamelCase      :			Any )      ->				str:
									if isinstance(A_	,	A_ ):
												A							:					Optional[int]              =						dict(self.items() )
												return inner_dict[k]
									else:
												return self.to_tuple()[k]







						def __setattr__(	self      :			Optional[Any]	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			Dict )      ->				Dict:
									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      :			int	,	__lowerCamelCase      :			Dict	,	__lowerCamelCase      :			Tuple )      ->				int:
									# Will raise a KeyException if needed
									super().__setitem__(A_	,	A_ )
									# Don't call self.__setattr__ to avoid recursion errors
									super().__setattr__(A_	,	A_ )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				Tuple[Any]:
									return tuple(self[k] for k in self.keys() )







class 	lowerCamelCase_    ( _lowercase						,_lowercase						):






						'''simple docstring'''







						@classmethod
						def 			SCREAMING_SNAKE_CASE__		(	cls      :			Union[str, Any]	,	__lowerCamelCase      :			int )      ->				Tuple:
									raise ValueError(
									    F"""{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys() )}""" )







class 	lowerCamelCase_    ( _lowercase						):






						'''simple docstring'''
						a__   =  '''longest'''
						a__   =  '''max_length'''
						a__   =  '''do_not_pad'''







class 	lowerCamelCase_    ( _lowercase						):






						'''simple docstring'''
						a__   =  '''pt'''
						a__   =  '''tf'''
						a__   =  '''np'''
						a__   =  '''jax'''







class 	lowerCamelCase_    :






						'''simple docstring'''







						def __init__(	self      :			int	,	__lowerCamelCase      :			List[ContextManager] )      ->				Any:
									A							:					Union[str, Any]              =						context_managers
									A							:					Optional[Any]              =						ExitStack()







						def __enter__(	self      :			Tuple )      ->				List[Any]:
									for context_manager in self.context_managers:
												self.stack.enter_context(A_ )







						def __exit__(	self      :			int	,	*__lowerCamelCase      :			str	,	**__lowerCamelCase      :			str )      ->				Any:
									self.stack.__exit__(*A_	,	**A_ )







def 	UpperCAmelCase   (						_lowerCamelCase    ):
			A							:					Dict              =						infer_framework(snake_case__    )
			if framework == "tf":
						A							:					str              =						inspect.signature(model_class.call    )  # TensorFlow models
			elif framework == "pt":
						A							:					Tuple              =						inspect.signature(model_class.forward    )  # PyTorch models
			else:
						A							:					List[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 	UpperCAmelCase   (						_lowerCamelCase    ):
			A							:					str              =						model_class.__name__
			A							:					List[Any]              =						infer_framework(snake_case__    )
			if framework == "tf":
						A							:					Tuple              =						inspect.signature(model_class.call    )  # TensorFlow models
			elif framework == "pt":
						A							:					Optional[int]              =						inspect.signature(model_class.forward    )  # PyTorch models
			else:
						A							:					int              =						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 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase = "" ,    _lowerCamelCase = "."    ):

			def _flatten_dict(_lowerCamelCase ,    _lowerCamelCase="" ,    _lowerCamelCase="."    ):
						for k, v in d.items():
									A							:					Tuple              =						str(snake_case__    ) + delimiter + str(snake_case__    ) if parent_key else k
									if v and isinstance(snake_case__ ,    snake_case__    ):
												yield from flatten_dict(snake_case__ ,    snake_case__ ,    delimiter=snake_case__    ).items()
									else:
												yield key, v

			return dict(_flatten_dict(snake_case__ ,    snake_case__ ,    snake_case__    )    )




@contextmanager
def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase = False    ):
			if use_temp_dir:
						with tempfile.TemporaryDirectory() as tmp_dir:
									yield tmp_dir
			else:
						yield working_dir




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

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




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			if is_numpy_array(snake_case__    ):
						return np.reshape(snake_case__ ,    snake_case__    )
			elif is_torch_tensor(snake_case__    ):
						return array.reshape(*snake_case__    )
			elif is_tf_tensor(snake_case__    ):
						import tensorflow as tf

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




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

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




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

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




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			if is_numpy_array(snake_case__    ):
						return np.size(snake_case__    )
			elif is_torch_tensor(snake_case__    ):
						return array.numel()
			elif is_tf_tensor(snake_case__    ):
						import tensorflow as tf

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




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			for key, value in auto_map.items():
						if isinstance(snake_case__ ,    (tuple, list)    ):
									A							:					Optional[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:
									A							:					Optional[Any]              =						f"""{repo_id}--{value}"""

			return auto_map






def 	UpperCAmelCase   (						_lowerCamelCase    ):
			for base_class in inspect.getmro(snake_case__    ):
						A							:					Dict              =						base_class.__module__
						A							:					int              =						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 huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


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




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			A							:					Optional[Any]              =						RobertaPreLayerNormConfig.from_pretrained(
			    _lowerCamelCase ,    architectures=["RobertaPreLayerNormForMaskedLM"]    )

			# convert state_dict
			A							:					List[Any]              =						torch.load(hf_hub_download(repo_id=_lowerCamelCase ,    filename="pytorch_model.bin"    )    )
			A							:					Union[str, Any]              =						{}
			for tensor_key, tensor_value in original_state_dict.items():
						# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
						if tensor_key.startswith("roberta."    ):
									A							:					int              =						"roberta_prelayernorm." + tensor_key[len("roberta."    ) :]

						# The original implementation contains weights which are not used, remove them from the state_dict
						if tensor_key.endswith(".self.LayerNorm.weight"    ) or tensor_key.endswith(".self.LayerNorm.bias"    ):
									continue

						A							:					Any              =						tensor_value

			A							:					Optional[int]              =						RobertaPreLayerNormForMaskedLM.from_pretrained(
			    pretrained_model_name_or_path=_lowerCamelCase ,    config=_lowerCamelCase ,    state_dict=_lowerCamelCase    )
			model.save_pretrained(_lowerCamelCase    )

			# convert tokenizer
			A							:					Optional[Any]              =						AutoTokenizer.from_pretrained(_lowerCamelCase    )
			tokenizer.save_pretrained(_lowerCamelCase    )


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE           =  argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--checkpoint-repo""",
							    default=None,
							    type=str,
							    required=True,
							    help="""Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.""",
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
							)
							__SCREAMING_SNAKE_CASE           =  parser.parse_args()
							convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)