


'''simple docstring'''



import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


__A		=logging.get_logger(__name__)







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__=False   ) ->    Dict:
			try:
						import torch  # noqa: F401
			except ImportError:
						logger.error(
						    """Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see"""
						    """ https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
						    """ instructions."""   )
						raise

			if not is_sharded:
						UpperCAmelCase__					:   int       				=       os.path.abspath(UpperCamelCase__   )
						logger.info(f'''Loading PyTorch weights from {pt_path}'''   )

						UpperCAmelCase__					:   List[str]       				=       torch.load(UpperCamelCase__	, map_location="""cpu"""   )
						logger.info(f'''PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()   ):,} parameters.'''   )

						UpperCAmelCase__					:   int       				=       convert_pytorch_state_dict_to_flax(UpperCamelCase__	, UpperCamelCase__   )
			else:
						# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
						UpperCAmelCase__					:   Tuple       				=       convert_pytorch_sharded_state_dict_to_flax(UpperCamelCase__	, UpperCamelCase__   )
			return flax_state_dict







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, ) ->    Any:

			def is_key_or_prefix_key_in_dict(UpperCamelCase__   ) -> bool:
						return len(set(UpperCamelCase__   ) & {key, (model_prefix,) + key}   ) > 0

			# layer norm
			UpperCAmelCase__					:   Optional[int]       				=       pt_tuple_key[:-1] + ("""scale""",)
			if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(UpperCamelCase__   ):
						return renamed_pt_tuple_key, pt_tensor

			# batch norm layer mean
			UpperCAmelCase__					:   Optional[int]       				=       pt_tuple_key[:-1] + ("""mean""",)
			if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(UpperCamelCase__   ):
						return renamed_pt_tuple_key, pt_tensor

			# batch norm layer var
			UpperCAmelCase__					:   List[str]       				=       pt_tuple_key[:-1] + ("""var""",)
			if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(UpperCamelCase__   ):
						return renamed_pt_tuple_key, pt_tensor

			# embedding
			UpperCAmelCase__					:   List[Any]       				=       pt_tuple_key[:-1] + ("""embedding""",)
			if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(UpperCamelCase__   ):
						return renamed_pt_tuple_key, pt_tensor

			# conv layer
			UpperCAmelCase__					:   Dict       				=       pt_tuple_key[:-1] + ("""kernel""",)
			if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(UpperCamelCase__   ):
						UpperCAmelCase__					:   Union[str, Any]       				=       pt_tensor.transpose(2	, 3	, 1	, 0   )
						return renamed_pt_tuple_key, pt_tensor

			# linear layer
			UpperCAmelCase__					:   Tuple       				=       pt_tuple_key[:-1] + ("""kernel""",)
			if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(UpperCamelCase__   ):
						UpperCAmelCase__					:   Union[str, Any]       				=       pt_tensor.T
						return renamed_pt_tuple_key, pt_tensor

			# old PyTorch layer norm weight
			UpperCAmelCase__					:   int       				=       pt_tuple_key[:-1] + ("""weight""",)
			if pt_tuple_key[-1] == "gamma":
						return renamed_pt_tuple_key, pt_tensor

			# old PyTorch layer norm bias
			UpperCAmelCase__					:   Any       				=       pt_tuple_key[:-1] + ("""bias""",)
			if pt_tuple_key[-1] == "beta":
						return renamed_pt_tuple_key, pt_tensor

			# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
			UpperCAmelCase__					:   Dict       				=       None
			if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
						UpperCAmelCase__					:   int       				=       pt_tuple_key[-2] + """_g"""
			elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
						UpperCAmelCase__					:   Dict       				=       pt_tuple_key[-2] + """_v"""
			if name is not None:
						UpperCAmelCase__					:   Dict       				=       pt_tuple_key[:-3] + (name,)
						return renamed_pt_tuple_key, pt_tensor

			return pt_tuple_key, pt_tensor







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__   ) ->    List[Any]:
			# convert pytorch tensor to numpy
			UpperCAmelCase__					:   Union[str, Any]       				=       {k: v.numpy() for k, v in pt_state_dict.items()}

			UpperCAmelCase__					:   Optional[Any]       				=       flax_model.base_model_prefix

			# use params dict if the model contains batch norm layers
			if "params" in flax_model.params:
						UpperCAmelCase__					:   Optional[int]       				=       flax_model.params["""params"""]
			else:
						UpperCAmelCase__					:   Union[str, Any]       				=       flax_model.params
			UpperCAmelCase__					:   List[str]       				=       flatten_dict(UpperCamelCase__   )

			# add batch_stats keys,values to dict
			if "batch_stats" in flax_model.params:
						UpperCAmelCase__					:   Tuple       				=       flatten_dict(flax_model.params["""batch_stats"""]   )
						random_flax_state_dict.update(UpperCamelCase__   )

			UpperCAmelCase__					:   str       				=       {}

			UpperCAmelCase__					:   str       				=       (model_prefix not in flax_model_params) and (
			    model_prefix in {k.split("""."""   )[0] for k in pt_state_dict.keys()}
			)
			UpperCAmelCase__					:   Any       				=       (model_prefix in flax_model_params) and (
			    model_prefix not in {k.split("""."""   )[0] for k in pt_state_dict.keys()}
			)

			# Need to change some parameters name to match Flax names
			for pt_key, pt_tensor in pt_state_dict.items():
						UpperCAmelCase__					:   Tuple       				=       tuple(pt_key.split("""."""   )   )

						# remove base model prefix if necessary
						UpperCAmelCase__					:   List[str]       				=       pt_tuple_key[0] == model_prefix
						if load_model_with_head_into_base_model and has_base_model_prefix:
									UpperCAmelCase__					:   List[Any]       				=       pt_tuple_key[1:]

						# Correctly rename weight parameters
						UpperCAmelCase__					:   Tuple       				=       rename_key_and_reshape_tensor(
						    UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   )

						# add model prefix if necessary
						UpperCAmelCase__					:   Optional[int]       				=       (model_prefix,) + flax_key in random_flax_state_dict
						if load_base_model_into_model_with_head and require_base_model_prefix:
									UpperCAmelCase__					:   Any       				=       (model_prefix,) + flax_key

						if flax_key in random_flax_state_dict:
									if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
												raise ValueError(
												    f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
												    f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''   )

        # add batch stats if the model contains batchnorm layers
						if "batch_stats" in flax_model.params:
									if "mean" in flax_key[-1] or "var" in flax_key[-1]:
												UpperCAmelCase__					:   Optional[int]       				=       jnp.asarray(UpperCamelCase__   )
												continue
									# remove num_batches_tracked key
									if "num_batches_tracked" in flax_key[-1]:
												flax_state_dict.pop(UpperCamelCase__	, UpperCamelCase__   )
												continue

									# also add unexpected weight so that warning is thrown
									UpperCAmelCase__					:   Tuple       				=       jnp.asarray(UpperCamelCase__   )

						else:
									# also add unexpected weight so that warning is thrown
									UpperCAmelCase__					:   Any       				=       jnp.asarray(UpperCamelCase__   )

			return unflatten_dict(UpperCamelCase__   )







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__   ) ->    List[Any]:
			import torch

			# Load the index
			UpperCAmelCase__					:   Union[str, Any]       				=       {}
			for shard_file in shard_filenames:
						# load using msgpack utils
						UpperCAmelCase__					:   List[str]       				=       torch.load(UpperCamelCase__   )
						UpperCAmelCase__					:   List[Any]       				=       {k: v.numpy() for k, v in pt_state_dict.items()}

						UpperCAmelCase__					:   List[Any]       				=       flax_model.base_model_prefix

						# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
						if "batch_stats" in flax_model.params:
									UpperCAmelCase__					:   Union[str, Any]       				=       flax_model.params["""params"""]

									UpperCAmelCase__					:   Any       				=       flatten_dict(UpperCamelCase__   )
									random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""]   )   )
						else:
									UpperCAmelCase__					:   List[str]       				=       flax_model.params
									UpperCAmelCase__					:   List[str]       				=       flatten_dict(UpperCamelCase__   )

						UpperCAmelCase__					:   List[str]       				=       (model_prefix not in flax_model_params) and (
						    model_prefix in {k.split("""."""   )[0] for k in pt_state_dict.keys()}
						)
						UpperCAmelCase__					:   Tuple       				=       (model_prefix in flax_model_params) and (
						    model_prefix not in {k.split("""."""   )[0] for k in pt_state_dict.keys()}
						)
						# Need to change some parameters name to match Flax names
						for pt_key, pt_tensor in pt_state_dict.items():
									UpperCAmelCase__					:   List[Any]       				=       tuple(pt_key.split("""."""   )   )

									# remove base model prefix if necessary
									UpperCAmelCase__					:   Dict       				=       pt_tuple_key[0] == model_prefix
									if load_model_with_head_into_base_model and has_base_model_prefix:
												UpperCAmelCase__					:   Optional[int]       				=       pt_tuple_key[1:]

									# Correctly rename weight parameters
									UpperCAmelCase__					:   Dict       				=       rename_key_and_reshape_tensor(
									    UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   )
									# add model prefix if necessary
									UpperCAmelCase__					:   Optional[Any]       				=       (model_prefix,) + flax_key in random_flax_state_dict
									if load_base_model_into_model_with_head and require_base_model_prefix:
												UpperCAmelCase__					:   Any       				=       (model_prefix,) + flax_key

									if flax_key in random_flax_state_dict:
												if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
															raise ValueError(
															    f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
															    f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''   )

            # add batch stats if the model contains batchnorm layers
									if "batch_stats" in flax_model.params:
												if "mean" in flax_key[-1]:
															UpperCAmelCase__					:   Tuple       				=       jnp.asarray(UpperCamelCase__   )
															continue
												if "var" in flax_key[-1]:
															UpperCAmelCase__					:   Union[str, Any]       				=       jnp.asarray(UpperCamelCase__   )
															continue
												# remove num_batches_tracked key
												if "num_batches_tracked" in flax_key[-1]:
															flax_state_dict.pop(UpperCamelCase__	, UpperCamelCase__   )
															continue

												# also add unexpected weight so that warning is thrown
												UpperCAmelCase__					:   str       				=       jnp.asarray(UpperCamelCase__   )

									else:
												# also add unexpected weight so that warning is thrown
												UpperCAmelCase__					:   int       				=       jnp.asarray(UpperCamelCase__   )
			return unflatten_dict(UpperCamelCase__   )







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__   ) ->    Optional[Any]:
			UpperCAmelCase__					:   List[Any]       				=       os.path.abspath(UpperCamelCase__   )
			logger.info(f'''Loading Flax weights from {flax_checkpoint_path}'''   )

			# import correct flax class
			UpperCAmelCase__					:   Any       				=       getattr(UpperCamelCase__	, """Flax""" + model.__class__.__name__   )

			# load flax weight dict
			with open(UpperCamelCase__	, """rb"""   ) as state_f:
						try:
									UpperCAmelCase__					:   str       				=       from_bytes(UpperCamelCase__	, state_f.read()   )
						except UnpicklingError:
									raise EnvironmentError(f'''Unable to convert {flax_checkpoint_path} to Flax deserializable object. '''   )

			return load_flax_weights_in_pytorch_model(UpperCamelCase__	, UpperCamelCase__   )







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__   ) ->    Dict:

			try:
						import torch  # noqa: F401
			except ImportError:
						logger.error(
						    """Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see"""
						    """ https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
						    """ instructions."""   )
						raise

			# check if we have bf16 weights
			UpperCAmelCase__					:   Optional[Any]       				=       flatten_dict(jax.tree_util.tree_map(lambda UpperCamelCase__   : x.dtype == jnp.bfloataa	, UpperCamelCase__   )   ).values()
			if any(UpperCamelCase__   ):
						# convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
						# and bf16 is not fully supported in PT yet.
						logger.warning(
						    """Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
						    """before loading those in PyTorch model."""   )
						UpperCAmelCase__					:   Union[str, Any]       				=       jax.tree_util.tree_map(
						    lambda UpperCamelCase__   : params.astype(np.floataa   ) if params.dtype == jnp.bfloataa else params	, UpperCamelCase__   )

			UpperCAmelCase__					:   int       				=       flatten_dict(UpperCamelCase__   )
			UpperCAmelCase__					:   Optional[int]       				=       pt_model.state_dict()

			UpperCAmelCase__					:   List[str]       				=       (pt_model.base_model_prefix in flax_state) and (
			    pt_model.base_model_prefix not in {k.split("""."""   )[0] for k in pt_model_dict.keys()}
			)
			UpperCAmelCase__					:   str       				=       (pt_model.base_model_prefix not in flax_state) and (
			    pt_model.base_model_prefix in {k.split("""."""   )[0] for k in pt_model_dict.keys()}
			)

			# keep track of unexpected & missing keys
			UpperCAmelCase__					:   int       				=       []
			UpperCAmelCase__					:   Union[str, Any]       				=       set(pt_model_dict.keys()   )

			for flax_key_tuple, flax_tensor in flax_state_dict.items():
						UpperCAmelCase__					:   Tuple       				=       flax_key_tuple[0] == pt_model.base_model_prefix
						UpperCAmelCase__					:   Tuple       				=       """.""".join((pt_model.base_model_prefix,) + flax_key_tuple   ) in pt_model_dict

						# adapt flax_key to prepare for loading from/to base model only
						if load_model_with_head_into_base_model and has_base_model_prefix:
									UpperCAmelCase__					:   List[str]       				=       flax_key_tuple[1:]
						elif load_base_model_into_model_with_head and require_base_model_prefix:
									UpperCAmelCase__					:   int       				=       (pt_model.base_model_prefix,) + flax_key_tuple

						# rename flax weights to PyTorch format
						if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(UpperCamelCase__   ) not in pt_model_dict:
									# conv layer
									UpperCAmelCase__					:   Any       				=       flax_key_tuple[:-1] + ("""weight""",)
									UpperCAmelCase__					:   List[Any]       				=       jnp.transpose(UpperCamelCase__	, (3, 2, 0, 1)   )
						elif flax_key_tuple[-1] == "kernel" and ".".join(UpperCamelCase__   ) not in pt_model_dict:
									# linear layer
									UpperCAmelCase__					:   Dict       				=       flax_key_tuple[:-1] + ("""weight""",)
									UpperCAmelCase__					:   Dict       				=       flax_tensor.T
						elif flax_key_tuple[-1] in ["scale", "embedding"]:
									UpperCAmelCase__					:   Tuple       				=       flax_key_tuple[:-1] + ("""weight""",)

						# adding batch stats from flax batch norm to pt
						elif "mean" in flax_key_tuple[-1]:
									UpperCAmelCase__					:   str       				=       flax_key_tuple[:-1] + ("""running_mean""",)
						elif "var" in flax_key_tuple[-1]:
									UpperCAmelCase__					:   Optional[Any]       				=       flax_key_tuple[:-1] + ("""running_var""",)

						if "batch_stats" in flax_state:
									UpperCAmelCase__					:   Tuple       				=       """.""".join(flax_key_tuple[1:]   )  # Remove the params/batch_stats header
						else:
									UpperCAmelCase__					:   int       				=       """.""".join(UpperCamelCase__   )

						# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
						UpperCAmelCase__					:   Union[str, Any]       				=       {}
						# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
						for key in pt_model_dict:
									UpperCAmelCase__					:   Any       				=       key.split("""."""   )
									UpperCAmelCase__					:   Optional[int]       				=       None
									if key_components[-3::2] == ["parametrizations", "original0"]:
												UpperCAmelCase__					:   Optional[int]       				=       key_components[-2] + """_g"""
									elif key_components[-3::2] == ["parametrizations", "original1"]:
												UpperCAmelCase__					:   Optional[Any]       				=       key_components[-2] + """_v"""
									if name is not None:
												UpperCAmelCase__					:   Dict       				=       key_components[:-3] + [name]
												UpperCAmelCase__					:   Tuple       				=       """.""".join(UpperCamelCase__   )
												UpperCAmelCase__					:   Any       				=       key

						if flax_key in special_pt_names:
									UpperCAmelCase__					:   List[Any]       				=       special_pt_names[flax_key]

						if flax_key in pt_model_dict:
									if flax_tensor.shape != pt_model_dict[flax_key].shape:
												raise ValueError(
												    f'''Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '''
												    f'''to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'''   )
									else:
												# add weight to pytorch dict
												UpperCAmelCase__					:   Optional[int]       				=       np.asarray(UpperCamelCase__   ) if not isinstance(UpperCamelCase__	, np.ndarray   ) else flax_tensor
												UpperCAmelCase__					:   Any       				=       torch.from_numpy(UpperCamelCase__   )
												# remove from missing keys
												missing_keys.remove(UpperCamelCase__   )
						else:
									# weight is not expected by PyTorch model
									unexpected_keys.append(UpperCamelCase__   )

			pt_model.load_state_dict(UpperCamelCase__   )

			# re-transform missing_keys to list
			UpperCAmelCase__					:   Optional[int]       				=       list(UpperCamelCase__   )

			if len(UpperCamelCase__   ) > 0:
						logger.warning(
						    """Some weights of the Flax model were not used when initializing the PyTorch model"""
						    f''' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'''
						    f''' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'''
						    """ (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"""
						    f''' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'''
						    """ to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"""
						    """ FlaxBertForSequenceClassification model)."""   )
			else:
						logger.warning(f'''All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n'''   )
			if len(UpperCamelCase__   ) > 0:
						logger.warning(
						    f'''Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'''
						    f''' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'''
						    """ use it for predictions and inference."""   )
			else:
						logger.warning(
						    f'''All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n'''
						    """If your task is similar to the task the model of the checkpoint was trained on, """
						    f'''you can already use {pt_model.__class__.__name__} for predictions without further training.'''   )

			return pt_model



'''simple docstring'''



from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class 						_snake_case   (       a__		):
					lowerCAmelCase :Optional[int]						=   ['''image_processor''', '''tokenizer''']
					lowerCAmelCase :Optional[int]						=   '''BridgeTowerImageProcessor'''
					lowerCAmelCase :List[str]						=   ('''RobertaTokenizer''', '''RobertaTokenizerFast''')




					def __init__( self							,							_lowerCamelCase							,							_lowerCamelCase):
								super().__init__(_lowerCamelCase							,							_lowerCamelCase)




					def __call__( self							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = True							,							_lowerCamelCase = False							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = 0							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = False							,							_lowerCamelCase = False							,							_lowerCamelCase = False							,							_lowerCamelCase = False							,							_lowerCamelCase = True							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								UpperCAmelCase__					:   List[str]       				=       self.tokenizer(
								    text=_lowerCamelCase							,							add_special_tokens=_lowerCamelCase							,							padding=_lowerCamelCase							,							truncation=_lowerCamelCase							,							max_length=_lowerCamelCase							,							stride=_lowerCamelCase							,							pad_to_multiple_of=_lowerCamelCase							,							return_token_type_ids=_lowerCamelCase							,							return_attention_mask=_lowerCamelCase							,							return_overflowing_tokens=_lowerCamelCase							,							return_special_tokens_mask=_lowerCamelCase							,							return_offsets_mapping=_lowerCamelCase							,							return_length=_lowerCamelCase							,							verbose=_lowerCamelCase							,							return_tensors=_lowerCamelCase							,							**_lowerCamelCase							,							)
								# add pixel_values + pixel_mask
								UpperCAmelCase__					:   Optional[Any]       				=       self.image_processor(
								    _lowerCamelCase							,							return_tensors=_lowerCamelCase							,							do_normalize=_lowerCamelCase							,							do_center_crop=_lowerCamelCase							,							**_lowerCamelCase)
								encoding.update(_lowerCamelCase)

								return encoding




					def 			snake_case__  ( self							,							*_lowerCamelCase							,							**_lowerCamelCase):
								return self.tokenizer.batch_decode(*_lowerCamelCase							,							**_lowerCamelCase)




					def 			snake_case__  ( self							,							*_lowerCamelCase							,							**_lowerCamelCase):
								return self.tokenizer.decode(*_lowerCamelCase							,							**_lowerCamelCase)




					@property
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[int]       				=       self.tokenizer.model_input_names
								UpperCAmelCase__					:   str       				=       self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))