


from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase:	str      							=					logging.get_logger(__name__)

_lowerCAmelCase:	List[Any]      							=					{
    # pcqm4mv1 now deprecated
    'graphormer-base': 'https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json',
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}




class 			lowercase_				(lowercase__      ):
				snake_case  						='graphormer'
				snake_case  						=['past_key_values']





				def __init__(							self					,     lowercase_ = 1					,     lowercase_ = 512 * 9					,     lowercase_ = 512 * 3					,     lowercase_ = 512					,     lowercase_ = 512					,     lowercase_ = 512					,     lowercase_ = 128					,     lowercase_ = 5					,     lowercase_ = 1024					,     lowercase_ = "multi_hop"					,     lowercase_ = 512					,     lowercase_ = False					,     lowercase_ = 12					,     lowercase_ = 768					,     lowercase_ = 768					,     lowercase_ = 32					,     lowercase_ = 0.1					,     lowercase_ = 0.1					,     lowercase_ = 0.0					,     lowercase_ = False					,     lowercase_ = False					,     lowercase_ = False					,     lowercase_ = "gelu"					,     lowercase_ = None					,     lowercase_ = False					,     lowercase_ = 0					,     lowercase_ = False					,     lowercase_ = 0.0					,     lowercase_ = 8					,     lowercase_ = None					,     lowercase_ = None					,     lowercase_ = True					,     lowercase_ = True					,     lowercase_=0					,     lowercase_=1					,     lowercase_=2					,     **lowercase_					,     )     ->   Tuple:
											a__     						=num_classes
											a__     						=num_atoms
											a__     						=num_in_degree
											a__     						=num_out_degree
											a__     						=num_edges
											a__     						=num_spatial
											a__     						=num_edge_dis
											a__     						=edge_type
											a__     						=multi_hop_max_dist
											a__     						=spatial_pos_max
											a__     						=max_nodes
											a__     						=num_hidden_layers
											a__     						=embedding_dim
											a__     						=embedding_dim
											a__     						=ffn_embedding_dim
											a__     						=num_attention_heads
											a__     						=dropout
											a__     						=attention_dropout
											a__     						=layerdrop
											a__     						=encoder_normalize_before
											a__     						=pre_layernorm
											a__     						=apply_graphormer_init
											a__     						=activation_fn
											a__     						=embed_scale
											a__     						=freeze_embeddings
											a__     						=num_trans_layers_to_freeze
											a__     						=share_input_output_embed
											a__     						=traceable
											a__     						=q_noise
											a__     						=qn_block_size

											# These parameters are here for future extensions
											# atm, the model only supports self attention
											a__     						=kdim
											a__     						=vdim
											a__     						=self_attention
											a__     						=bias

											super().__init__(
											    pad_token_id=lowercase_					,     bos_token_id=lowercase_					,     eos_token_id=lowercase_					,     **lowercase_					,     )





import os
import warnings
from pathlib import Path
from typing import Optional, Union

from .dynamic_module_utils import custom_object_save
from .tokenization_utils_base import PreTrainedTokenizerBase
from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging


_lowerCAmelCase:	Any      							=					logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
_lowerCAmelCase:	Dict      							=					direct_transformers_import(Path(__file__).parent)


_lowerCAmelCase:	Optional[Any]      							=					{
    'AutoTokenizer': 'PreTrainedTokenizerBase',
    'AutoFeatureExtractor': 'FeatureExtractionMixin',
    'AutoImageProcessor': 'ImageProcessingMixin',
}




class 			lowercase_				(lowercase__      ):
				snake_case  						=['feature_extractor', 'tokenizer']
				# Names need to be attr_class for attr in attributes
				snake_case  						=None
				snake_case  						=None
				snake_case  						=None





				def __init__(							self					,     *lowercase_					,     **lowercase_)     ->   str:
											# Sanitize args and kwargs
											for key in kwargs:
																		if key not in self.attributes:
																									raise TypeError(F"""Unexpected keyword argument {key}.""")
											for arg, attribute_name in zip(lowercase_					,     self.attributes):
																		if attribute_name in kwargs:
																									raise TypeError(F"""Got multiple values for argument {attribute_name}.""")
																		else:
																									a__     						=arg

											if len(lowercase_) != len(self.attributes):
																		raise ValueError(
																		    F"""This processor requires {len(self.attributes)} arguments: {', '.join(self.attributes)}. Got """
																		    F"""{len(lowercase_)} arguments instead.""")

											# Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
											for attribute_name, arg in kwargs.items():
																		a__     						=getattr(self					,     F"""{attribute_name}_class""")
																		# Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
																		a__     						=AUTO_TO_BASE_CLASS_MAPPING.get(lowercase_					,     lowercase_)
																		if isinstance(lowercase_					,     lowercase_):
																									a__     						=tuple(getattr(lowercase_					,     lowercase_) for n in class_name if n is not None)
																		else:
																									a__     						=getattr(lowercase_					,     lowercase_)

																		if not isinstance(lowercase_					,     lowercase_):
																									raise ValueError(
																									    F"""Received a {type(lowercase_).__name__} for argument {attribute_name}, but a {class_name} was expected.""")

																		setattr(self					,     lowercase_					,     lowercase_)





				def __repr__(							self)     ->   List[str]:
											a__     						=[F"""- {name}: {repr(getattr(self					,     lowercase_))}""" for name in self.attributes]
											a__     						='\n'.join(lowercase_)
											return F"""{self.__class__.__name__}:\n{attributes_repr}"""





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_ = False					,     **lowercase_)     ->   Optional[int]:
											os.makedirs(lowercase_					,     exist_ok=lowercase_)

											if push_to_hub:
																		a__     						=kwargs.pop('commit_message'					,     lowercase_)
																		a__     						=kwargs.pop('repo_id'					,     save_directory.split(os.path.sep)[-1])
																		a__     						=self._create_repo(lowercase_					,     **lowercase_)
																		a__     						=self._get_files_timestamps(lowercase_)
											# If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
											# loaded from the Hub.
											if self._auto_class is not None:
																		a__     						=[getattr(self					,     lowercase_) for attribute_name in self.attributes]
																		a__     						=[(a.init_kwargs if isinstance(lowercase_					,     lowercase_) else a) for a in attrs]
																		custom_object_save(self					,     lowercase_					,     config=lowercase_)

											for attribute_name in self.attributes:
																		a__     						=getattr(self					,     lowercase_)
																		# Include the processor class in the attribute config so this processor can then be reloaded with the
																		# `AutoProcessor` API.
																		if hasattr(lowercase_					,     '_set_processor_class'):
																									attribute._set_processor_class(self.__class__.__name__)
																		attribute.save_pretrained(lowercase_)

											if self._auto_class is not None:
																		# We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
																		for attribute_name in self.attributes:
																									a__     						=getattr(self					,     lowercase_)
																									if isinstance(lowercase_					,     lowercase_):
																																del attribute.init_kwargs["auto_map"]

											if push_to_hub:
																		self._upload_modified_files(
																		    lowercase_					,     lowercase_					,     lowercase_					,     commit_message=lowercase_					,     token=kwargs.get('use_auth_token')					,     )





				@classmethod
				def 	__UpperCamelCase						(							cls					,     lowercase_					,     lowercase_ = None					,     lowercase_ = False					,     lowercase_ = False					,     lowercase_ = None					,     lowercase_ = "main"					,     **lowercase_					,     )     ->   str:
											a__     						=cache_dir
											a__     						=force_download
											a__     						=local_files_only
											a__     						=revision

											a__     						=kwargs.pop('use_auth_token'					,     lowercase_)
											if use_auth_token is not None:
																		warnings.warn(
																		    'The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.'					,     lowercase_)
																		if token is not None:
																									raise ValueError(
																									    '`token` and `use_auth_token` are both specified. Please set only the argument `token`.')
																		a__     						=use_auth_token

											if token is not None:
																		# change to `token` in a follow-up PR
																		a__     						=token

											a__     						=cls._get_arguments_from_pretrained(lowercase_					,     **lowercase_)
											return cls(*lowercase_)





				@classmethod
				def 	__UpperCamelCase						(							cls					,     lowercase_="AutoProcessor")     ->   Any:
											if not isinstance(lowercase_					,     lowercase_):
																		a__     						=auto_class.__name__

											import transformers.models.auto as auto_module

											if not hasattr(lowercase_					,     lowercase_):
																		raise ValueError(F"""{auto_class} is not a valid auto class.""")

											a__     						=auto_class





				@classmethod
				def 	__UpperCamelCase						(							cls					,     lowercase_					,     **lowercase_)     ->   Optional[int]:
											a__     						=[]
											for attribute_name in cls.attributes:
																		a__     						=getattr(cls					,     F"""{attribute_name}_class""")
																		if isinstance(lowercase_					,     lowercase_):
																									a__     						=tuple(getattr(lowercase_					,     lowercase_) if n is not None else None for n in class_name)
																									a__     						=kwargs.get('use_fast'					,     lowercase_)
																									if use_fast and classes[1] is not None:
																																a__     						=classes[1]
																									else:
																																a__     						=classes[0]
																		else:
																									a__     						=getattr(lowercase_					,     lowercase_)

																		args.append(attribute_class.from_pretrained(lowercase_					,     **lowercase_))
											return args





				@property
				def 	__UpperCamelCase						(							self)     ->   List[Any]:
											a__     						=getattr(self					,     self.attributes[0])
											return getattr(lowercase_					,     'model_input_names'					,     lowercase_)







_lowerCAmelCase:	Dict      							=					copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
	_lowerCAmelCase:	Tuple      							=					ProcessorMixin.push_to_hub.__doc__.format(
	    object='processor', object_class='AutoProcessor', object_files='processor files'
	)

