


'''simple docstring'''


import importlib
import inspect
from functools import wraps
from typing import TYPE_CHECKING, Optional, Union

from .download.streaming_download_manager import (
    xbasename,
    xdirname,
    xet_parse,
    xexists,
    xgetsize,
    xglob,
    xgzip_open,
    xisdir,
    xisfile,
    xjoin,
    xlistdir,
    xnumpy_load,
    xopen,
    xpandas_read_csv,
    xpandas_read_excel,
    xPath,
    xrelpath,
    xsio_loadmat,
    xsplit,
    xsplitext,
    xwalk,
    xxml_dom_minidom_parse,
)
from .utils.logging import get_logger
from .utils.patching import patch_submodule
from .utils.py_utils import get_imports


UpperCAmelCase_		:       Any							       =		get_logger(__name__)


if TYPE_CHECKING:
				from .builder import DatasetBuilder







def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__ = None					):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	List[str]							=				importlib.import_module(_A					)

				# TODO(QL): always update the module to add subsequent new authentication
				if hasattr(_A							,				"""_patched_for_streaming"""					) and module._patched_for_streaming:
								return

				def wrap_auth(SCREAMING_SNAKE_CASE__					):
								@wraps(_A					)
								def wrapper(*SCREAMING_SNAKE_CASE__							,				**SCREAMING_SNAKE_CASE__					):
												return function(*_A							,				use_auth_token=_A							,				**_A					)

								_SCREAMING_SNAKE_CASE			:	Any							=				"""wrap_auth"""
								return wrapper

				# open files in a streaming fashion
				patch_submodule(_A							,				"""open"""							,				wrap_auth(_A					)					).start()
				patch_submodule(_A							,				"""os.listdir"""							,				wrap_auth(_A					)					).start()
				patch_submodule(_A							,				"""os.walk"""							,				wrap_auth(_A					)					).start()
				patch_submodule(_A							,				"""glob.glob"""							,				wrap_auth(_A					)					).start()
				# allow to navigate in remote zip files
				patch_submodule(_A							,				"""os.path.join"""							,				_A					).start()
				patch_submodule(_A							,				"""os.path.dirname"""							,				_A					).start()
				patch_submodule(_A							,				"""os.path.basename"""							,				_A					).start()
				patch_submodule(_A							,				"""os.path.relpath"""							,				_A					).start()
				patch_submodule(_A							,				"""os.path.split"""							,				_A					).start()
				patch_submodule(_A							,				"""os.path.splitext"""							,				_A					).start()
				# allow checks on paths
				patch_submodule(_A							,				"""os.path.exists"""							,				wrap_auth(_A					)					).start()
				patch_submodule(_A							,				"""os.path.isdir"""							,				wrap_auth(_A					)					).start()
				patch_submodule(_A							,				"""os.path.isfile"""							,				wrap_auth(_A					)					).start()
				patch_submodule(_A							,				"""os.path.getsize"""							,				wrap_auth(_A					)					).start()
				patch_submodule(_A							,				"""pathlib.Path"""							,				_A					).start()
				# file readers
				patch_submodule(_A							,				"""gzip.open"""							,				wrap_auth(_A					)					).start()
				patch_submodule(_A							,				"""numpy.load"""							,				wrap_auth(_A					)					).start()
				patch_submodule(_A							,				"""pandas.read_csv"""							,				wrap_auth(_A					)							,				attrs=["""__version__"""]					).start()
				patch_submodule(_A							,				"""pandas.read_excel"""							,				wrap_auth(_A					)							,				attrs=["""__version__"""]					).start()
				patch_submodule(_A							,				"""scipy.io.loadmat"""							,				wrap_auth(_A					)							,				attrs=["""__version__"""]					).start()
				patch_submodule(_A							,				"""xml.etree.ElementTree.parse"""							,				wrap_auth(_A					)					).start()
				patch_submodule(_A							,				"""xml.dom.minidom.parse"""							,				wrap_auth(_A					)					).start()
				_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				True







def       snake_case_       (  SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				extend_module_for_streaming(builder.__module__							,				use_auth_token=builder.use_auth_token					)
				# if needed, we also have to extend additional internal imports (like wmt14 -> wmt_utils)
				if not builder.__module__.startswith("""datasets."""					):  # check that it's not a packaged builder like csv
								for imports in get_imports(inspect.getfile(builder.__class__					)					):
												if imports[0] == "internal":
																_SCREAMING_SNAKE_CASE			:	str							=				imports[1]
																_SCREAMING_SNAKE_CASE			:	Optional[int]							=				""".""".join(builder.__module__.split("""."""					)[:-1] + [internal_import_name]					)
																extend_module_for_streaming(_A							,				use_auth_token=builder.use_auth_token					)

    # builders can inherit from other builders that might use streaming functionality
    # (for example, ImageFolder and AudioFolder inherit from FolderBuilder which implements examples generation)
    # but these parents builders are not patched automatically as they are not instantiated, so we patch them here
				from .builder import DatasetBuilder

				_SCREAMING_SNAKE_CASE			:	Dict							=				[
				    cls.__module__
				    for cls in type(_A					).__mro__[1:]  # make sure it's not the same module we've already patched
				    if issubclass(cls							,				_A					) and cls.__module__ != DatasetBuilder.__module__
				]  # check it's not a standard builder from datasets.builder
				for module in parent_builder_modules:
								extend_module_for_streaming(_A							,				use_auth_token=builder.use_auth_token					)









'''simple docstring'''


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


UpperCAmelCase_		:       Tuple							       =		logging.get_logger(__name__)

UpperCAmelCase_		:       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__  (						_snake_case ):





							'''simple docstring'''


							A_							:				Union[str, Any]				     =  """graphormer"""
							A_							:				Tuple				     =  ["""past_key_values"""]






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

											# These parameters are here for future extensions
											# atm, the model only supports self attention
											_SCREAMING_SNAKE_CASE			:	Any							=				kdim
											_SCREAMING_SNAKE_CASE			:	int							=				vdim
											_SCREAMING_SNAKE_CASE			:	Any							=				self_attention
											_SCREAMING_SNAKE_CASE			:	Tuple							=				bias

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




