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


_SCREAMING_SNAKE_CASE :					Any			=	logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE :					Dict			=	{
    # 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  UpperCamelCase__ ( lowerCAmelCase__			):
      a__   :     Optional[int]								=						'graphormer'
      a__   :     Optional[Any]								=						['past_key_values']






      def __init__(     self					:   Any,		__lowerCamelCase					:   Dict = 1,		__lowerCamelCase					:   int = 5_12 * 9,		__lowerCamelCase					:   List[str] = 5_12 * 3,		__lowerCamelCase					:   Tuple = 5_12,		__lowerCamelCase					:   Tuple = 5_12,		__lowerCamelCase					:   int = 5_12,		__lowerCamelCase					:   Dict = 1_28,		__lowerCamelCase					:   int = 5,		__lowerCamelCase					:   Tuple = 10_24,		__lowerCamelCase					:   Union[str, Any] = "multi_hop",		__lowerCamelCase					:   int = 5_12,		__lowerCamelCase					:   str = False,		__lowerCamelCase					:   int = 12,		__lowerCamelCase					:   Optional[int] = 7_68,		__lowerCamelCase					:   int = 7_68,		__lowerCamelCase					:   int = 32,		__lowerCamelCase					:   Any = 0.1,		__lowerCamelCase					:   Union[str, Any] = 0.1,		__lowerCamelCase					:   Tuple = 0.0,		__lowerCamelCase					:   int = False,		__lowerCamelCase					:   Tuple = False,		__lowerCamelCase					:   int = False,		__lowerCamelCase					:   Optional[Any] = "gelu",		__lowerCamelCase					:   Tuple = None,		__lowerCamelCase					:   Union[str, Any] = False,		__lowerCamelCase					:   int = 0,		__lowerCamelCase					:   Tuple = False,		__lowerCamelCase					:   str = 0.0,		__lowerCamelCase					:   str = 8,		__lowerCamelCase					:   Optional[Any] = None,		__lowerCamelCase					:   List[str] = None,		__lowerCamelCase					:   Tuple = True,		__lowerCamelCase					:   Optional[Any] = True,		__lowerCamelCase					:   Tuple=0,		__lowerCamelCase					:   Tuple=1,		__lowerCamelCase					:   int=2,		**__lowerCamelCase					:   List[Any],		)  -> Union[str, Any]:
           UpperCamelCase__							:  Any								=     num_classes
           UpperCamelCase__							:  Union[str, Any]								=     num_atoms
           UpperCamelCase__							:  Tuple								=     num_in_degree
           UpperCamelCase__							:  List[Any]								=     num_out_degree
           UpperCamelCase__							:  Any								=     num_edges
           UpperCamelCase__							:  List[Any]								=     num_spatial
           UpperCamelCase__							:  str								=     num_edge_dis
           UpperCamelCase__							:  Any								=     edge_type
           UpperCamelCase__							:  int								=     multi_hop_max_dist
           UpperCamelCase__							:  Union[str, Any]								=     spatial_pos_max
           UpperCamelCase__							:  Dict								=     max_nodes
           UpperCamelCase__							:  Optional[int]								=     num_hidden_layers
           UpperCamelCase__							:  Tuple								=     embedding_dim
           UpperCamelCase__							:  int								=     embedding_dim
           UpperCamelCase__							:  Optional[int]								=     ffn_embedding_dim
           UpperCamelCase__							:  List[str]								=     num_attention_heads
           UpperCamelCase__							:  Union[str, Any]								=     dropout
           UpperCamelCase__							:  str								=     attention_dropout
           UpperCamelCase__							:  str								=     layerdrop
           UpperCamelCase__							:  Tuple								=     encoder_normalize_before
           UpperCamelCase__							:  Optional[int]								=     pre_layernorm
           UpperCamelCase__							:  Dict								=     apply_graphormer_init
           UpperCamelCase__							:  Optional[int]								=     activation_fn
           UpperCamelCase__							:  str								=     embed_scale
           UpperCamelCase__							:  List[Any]								=     freeze_embeddings
           UpperCamelCase__							:  List[str]								=     num_trans_layers_to_freeze
           UpperCamelCase__							:  List[Any]								=     share_input_output_embed
           UpperCamelCase__							:  Optional[Any]								=     traceable
           UpperCamelCase__							:  Dict								=     q_noise
           UpperCamelCase__							:  str								=     qn_block_size

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

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








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE :					Optional[Any]			=	{
    """configuration_layoutlm""": ["""LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMConfig""", """LayoutLMOnnxConfig"""],
    """tokenization_layoutlm""": ["""LayoutLMTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					Dict			=	["""LayoutLMTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					List[str]			=	[
       """LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """LayoutLMForMaskedLM""",
       """LayoutLMForSequenceClassification""",
       """LayoutLMForTokenClassification""",
       """LayoutLMForQuestionAnswering""",
       """LayoutLMModel""",
       """LayoutLMPreTrainedModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					str			=	[
       """TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFLayoutLMForMaskedLM""",
       """TFLayoutLMForSequenceClassification""",
       """TFLayoutLMForTokenClassification""",
       """TFLayoutLMForQuestionAnswering""",
       """TFLayoutLMMainLayer""",
       """TFLayoutLMModel""",
       """TFLayoutLMPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
   from .tokenization_layoutlm import LayoutLMTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_layoutlm import (
          LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          LayoutLMForMaskedLM,
          LayoutLMForQuestionAnswering,
          LayoutLMForSequenceClassification,
          LayoutLMForTokenClassification,
          LayoutLMModel,
          LayoutLMPreTrainedModel,
      )
   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_layoutlm import (
          TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFLayoutLMForMaskedLM,
          TFLayoutLMForQuestionAnswering,
          TFLayoutLMForSequenceClassification,
          TFLayoutLMForTokenClassification,
          TFLayoutLMMainLayer,
          TFLayoutLMModel,
          TFLayoutLMPreTrainedModel,
      )

else:
   import sys

   _SCREAMING_SNAKE_CASE :					Any			=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




