
"""simple docstring"""




from typing import TYPE_CHECKING

from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available


_A      :	Dict       		=	{
    """configuration_utils""": ["""GenerationConfig"""],
    """streamers""": ["""TextIteratorStreamer""", """TextStreamer"""],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A      :	List[Any]       		=	[
       """Constraint""",
       """ConstraintListState""",
       """DisjunctiveConstraint""",
       """PhrasalConstraint""",
   ]
   _A      :	List[str]       		=	[
       """BeamHypotheses""",
       """BeamScorer""",
       """BeamSearchScorer""",
       """ConstrainedBeamSearchScorer""",
   ]
   _A      :	Any       		=	[
       """EpsilonLogitsWarper""",
       """EtaLogitsWarper""",
       """ForcedBOSTokenLogitsProcessor""",
       """ForcedEOSTokenLogitsProcessor""",
       """HammingDiversityLogitsProcessor""",
       """InfNanRemoveLogitsProcessor""",
       """LogitsProcessor""",
       """LogitsProcessorList""",
       """LogitsWarper""",
       """MinLengthLogitsProcessor""",
       """MinNewTokensLengthLogitsProcessor""",
       """NoBadWordsLogitsProcessor""",
       """NoRepeatNGramLogitsProcessor""",
       """PrefixConstrainedLogitsProcessor""",
       """RepetitionPenaltyLogitsProcessor""",
       """SequenceBiasLogitsProcessor""",
       """EncoderRepetitionPenaltyLogitsProcessor""",
       """TemperatureLogitsWarper""",
       """TopKLogitsWarper""",
       """TopPLogitsWarper""",
       """TypicalLogitsWarper""",
       """EncoderNoRepeatNGramLogitsProcessor""",
       """ExponentialDecayLengthPenalty""",
       """LogitNormalization""",
   ]
   _A      :	Union[str, Any]       		=	[
       """MaxNewTokensCriteria""",
       """MaxLengthCriteria""",
       """MaxTimeCriteria""",
       """StoppingCriteria""",
       """StoppingCriteriaList""",
       """validate_stopping_criteria""",
   ]
   _A      :	Any       		=	[
       """GenerationMixin""",
       """top_k_top_p_filtering""",
       """GreedySearchEncoderDecoderOutput""",
       """GreedySearchDecoderOnlyOutput""",
       """SampleEncoderDecoderOutput""",
       """SampleDecoderOnlyOutput""",
       """BeamSearchEncoderDecoderOutput""",
       """BeamSearchDecoderOnlyOutput""",
       """BeamSampleEncoderDecoderOutput""",
       """BeamSampleDecoderOnlyOutput""",
       """ContrastiveSearchEncoderDecoderOutput""",
       """ContrastiveSearchDecoderOnlyOutput""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A      :	Union[str, Any]       		=	[
       """TFForcedBOSTokenLogitsProcessor""",
       """TFForcedEOSTokenLogitsProcessor""",
       """TFLogitsProcessor""",
       """TFLogitsProcessorList""",
       """TFLogitsWarper""",
       """TFMinLengthLogitsProcessor""",
       """TFNoBadWordsLogitsProcessor""",
       """TFNoRepeatNGramLogitsProcessor""",
       """TFRepetitionPenaltyLogitsProcessor""",
       """TFTemperatureLogitsWarper""",
       """TFTopKLogitsWarper""",
       """TFTopPLogitsWarper""",
       """TFForceTokensLogitsProcessor""",
       """TFSuppressTokensAtBeginLogitsProcessor""",
       """TFSuppressTokensLogitsProcessor""",
   ]
   _A      :	Any       		=	[
       """TFGenerationMixin""",
       """tf_top_k_top_p_filtering""",
       """TFGreedySearchDecoderOnlyOutput""",
       """TFGreedySearchEncoderDecoderOutput""",
       """TFSampleEncoderDecoderOutput""",
       """TFSampleDecoderOnlyOutput""",
       """TFBeamSearchEncoderDecoderOutput""",
       """TFBeamSearchDecoderOnlyOutput""",
       """TFBeamSampleEncoderDecoderOutput""",
       """TFBeamSampleDecoderOnlyOutput""",
       """TFContrastiveSearchEncoderDecoderOutput""",
       """TFContrastiveSearchDecoderOnlyOutput""",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A      :	int       		=	[
       """FlaxForcedBOSTokenLogitsProcessor""",
       """FlaxForcedEOSTokenLogitsProcessor""",
       """FlaxLogitsProcessor""",
       """FlaxLogitsProcessorList""",
       """FlaxLogitsWarper""",
       """FlaxMinLengthLogitsProcessor""",
       """FlaxTemperatureLogitsWarper""",
       """FlaxTopKLogitsWarper""",
       """FlaxTopPLogitsWarper""",
   ]
   _A      :	List[str]       		=	[
       """FlaxGenerationMixin""",
       """FlaxGreedySearchOutput""",
       """FlaxSampleOutput""",
       """FlaxBeamSearchOutput""",
   ]

if TYPE_CHECKING:
   from .configuration_utils import GenerationConfig
   from .streamers import TextIteratorStreamer, TextStreamer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
      from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
      from .logits_process import (
          EncoderNoRepeatNGramLogitsProcessor,
          EncoderRepetitionPenaltyLogitsProcessor,
          EpsilonLogitsWarper,
          EtaLogitsWarper,
          ExponentialDecayLengthPenalty,
          ForcedBOSTokenLogitsProcessor,
          ForcedEOSTokenLogitsProcessor,
          HammingDiversityLogitsProcessor,
          InfNanRemoveLogitsProcessor,
          LogitNormalization,
          LogitsProcessor,
          LogitsProcessorList,
          LogitsWarper,
          MinLengthLogitsProcessor,
          MinNewTokensLengthLogitsProcessor,
          NoBadWordsLogitsProcessor,
          NoRepeatNGramLogitsProcessor,
          PrefixConstrainedLogitsProcessor,
          RepetitionPenaltyLogitsProcessor,
          SequenceBiasLogitsProcessor,
          TemperatureLogitsWarper,
          TopKLogitsWarper,
          TopPLogitsWarper,
          TypicalLogitsWarper,
      )
      from .stopping_criteria import (
          MaxLengthCriteria,
          MaxNewTokensCriteria,
          MaxTimeCriteria,
          StoppingCriteria,
          StoppingCriteriaList,
          validate_stopping_criteria,
      )
      from .utils import (
          BeamSampleDecoderOnlyOutput,
          BeamSampleEncoderDecoderOutput,
          BeamSearchDecoderOnlyOutput,
          BeamSearchEncoderDecoderOutput,
          ContrastiveSearchDecoderOnlyOutput,
          ContrastiveSearchEncoderDecoderOutput,
          GenerationMixin,
          GreedySearchDecoderOnlyOutput,
          GreedySearchEncoderDecoderOutput,
          SampleDecoderOnlyOutput,
          SampleEncoderDecoderOutput,
          top_k_top_p_filtering,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tf_logits_process import (
          TFForcedBOSTokenLogitsProcessor,
          TFForcedEOSTokenLogitsProcessor,
          TFForceTokensLogitsProcessor,
          TFLogitsProcessor,
          TFLogitsProcessorList,
          TFLogitsWarper,
          TFMinLengthLogitsProcessor,
          TFNoBadWordsLogitsProcessor,
          TFNoRepeatNGramLogitsProcessor,
          TFRepetitionPenaltyLogitsProcessor,
          TFSuppressTokensAtBeginLogitsProcessor,
          TFSuppressTokensLogitsProcessor,
          TFTemperatureLogitsWarper,
          TFTopKLogitsWarper,
          TFTopPLogitsWarper,
      )
      from .tf_utils import (
          TFBeamSampleDecoderOnlyOutput,
          TFBeamSampleEncoderDecoderOutput,
          TFBeamSearchDecoderOnlyOutput,
          TFBeamSearchEncoderDecoderOutput,
          TFContrastiveSearchDecoderOnlyOutput,
          TFContrastiveSearchEncoderDecoderOutput,
          TFGenerationMixin,
          TFGreedySearchDecoderOnlyOutput,
          TFGreedySearchEncoderDecoderOutput,
          TFSampleDecoderOnlyOutput,
          TFSampleEncoderDecoderOutput,
          tf_top_k_top_p_filtering,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .flax_logits_process import (
          FlaxForcedBOSTokenLogitsProcessor,
          FlaxForcedEOSTokenLogitsProcessor,
          FlaxLogitsProcessor,
          FlaxLogitsProcessorList,
          FlaxLogitsWarper,
          FlaxMinLengthLogitsProcessor,
          FlaxTemperatureLogitsWarper,
          FlaxTopKLogitsWarper,
          FlaxTopPLogitsWarper,
      )
      from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
   import sys

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




"""simple docstring"""




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


_A      :	List[Any]       		=	logging.get_logger(__name__)

_A      :	Optional[int]       		=	{
    # 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 		a__						(				a_						):
   __lowerCAmelCase						=    """graphormer"""
   __lowerCAmelCase						=    ["""past_key_values"""]
   def __init__(      self     ,   _a = 1     ,   _a = 512 * 9     ,   _a = 512 * 3     ,   _a = 512     ,   _a = 512     ,   _a = 512     ,   _a = 128     ,   _a = 5     ,   _a = 1_024     ,   _a = "multi_hop"     ,   _a = 512     ,   _a = False     ,   _a = 12     ,   _a = 768     ,   _a = 768     ,   _a = 32     ,   _a = 0.1     ,   _a = 0.1     ,   _a = 0.0     ,   _a = False     ,   _a = False     ,   _a = False     ,   _a = "gelu"     ,   _a = None     ,   _a = False     ,   _a = 0     ,   _a = False     ,   _a = 0.0     ,   _a = 8     ,   _a = None     ,   _a = None     ,   _a = True     ,   _a = True     ,   _a=0     ,   _a=1     ,   _a=2     ,   **_a     ,   ):
          lowercase   :    Any			=							num_classes
          lowercase   :    Optional[Any]			=							num_atoms
          lowercase   :    Optional[Any]			=							num_in_degree
          lowercase   :    Any			=							num_out_degree
          lowercase   :    Optional[int]			=							num_edges
          lowercase   :    Any			=							num_spatial
          lowercase   :    Any			=							num_edge_dis
          lowercase   :    str			=							edge_type
          lowercase   :    Union[str, Any]			=							multi_hop_max_dist
          lowercase   :    List[str]			=							spatial_pos_max
          lowercase   :    Dict			=							max_nodes
          lowercase   :    str			=							num_hidden_layers
          lowercase   :    Union[str, Any]			=							embedding_dim
          lowercase   :    Optional[int]			=							embedding_dim
          lowercase   :    int			=							ffn_embedding_dim
          lowercase   :    List[Any]			=							num_attention_heads
          lowercase   :    Optional[int]			=							dropout
          lowercase   :    Dict			=							attention_dropout
          lowercase   :    int			=							layerdrop
          lowercase   :    int			=							encoder_normalize_before
          lowercase   :    List[str]			=							pre_layernorm
          lowercase   :    Union[str, Any]			=							apply_graphormer_init
          lowercase   :    Tuple			=							activation_fn
          lowercase   :    Tuple			=							embed_scale
          lowercase   :    List[Any]			=							freeze_embeddings
          lowercase   :    List[Any]			=							num_trans_layers_to_freeze
          lowercase   :    str			=							share_input_output_embed
          lowercase   :    List[str]			=							traceable
          lowercase   :    str			=							q_noise
          lowercase   :    List[str]			=							qn_block_size

          # These parameters are here for future extensions
          # atm, the model only supports self attention
          lowercase   :    Optional[int]			=							kdim
          lowercase   :    str			=							vdim
          lowercase   :    List[Any]			=							self_attention
          lowercase   :    Any			=							bias

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


