
'''simple docstring'''
from typing import Any, Dict, List, Mapping

import numpy as np
import torch

from ...utils import is_cython_available, requires_backends


if is_cython_available():
     import pyximport

     pyximport.install(setup_args={'include_dirs': np.get_include()})
     from . import algos_graphormer  # noqa E402







def 							A_  (    __SCREAMING_SNAKE_CASE    :	str      ,  __SCREAMING_SNAKE_CASE    :	int = 512			)					->							int:





  """simple docstring"""

  __A		:  int       				=		x.shape[1] if len(x.shape			) > 1 else 1
  __A		:  Tuple       				=		1 + np.arange(0      ,  feature_num * offset      ,  __SCREAMING_SNAKE_CASE      ,  dtype=np.intaa			)
  __A		:  Union[str, Any]       				=		x + feature_offset
  return x







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Union[str, Any]      ,  __SCREAMING_SNAKE_CASE    :	Any=True			)					->							int:





  """simple docstring"""

  requires_backends(__SCREAMING_SNAKE_CASE      ,  ["""cython"""]			)

  if keep_features and "edge_attr" in item.keys():  # edge_attr
    __A		:  Dict       				=		np.asarray(item["""edge_attr"""]      ,  dtype=np.intaa			)
  else:
    __A		:  Optional[Any]       				=		np.ones((len(item["""edge_index"""][0]			), 1)      ,  dtype=np.intaa			)  # same embedding for all

  if keep_features and "node_feat" in item.keys():  # input_nodes
    __A		:  Optional[int]       				=		np.asarray(item["""node_feat"""]      ,  dtype=np.intaa			)
  else:
    __A		:  List[str]       				=		np.ones((item["""num_nodes"""], 1)      ,  dtype=np.intaa			)  # same embedding for all

  __A		:  str       				=		np.asarray(item["""edge_index"""]      ,  dtype=np.intaa			)

  __A		:  Tuple       				=		convert_to_single_emb(__SCREAMING_SNAKE_CASE			) + 1
  __A		:  List[str]       				=		item["""num_nodes"""]

  if len(edge_attr.shape			) == 1:
    __A		:  str       				=		edge_attr[:, None]
  __A		:  List[str]       				=		np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]      ,  dtype=np.intaa			)
  __A		:  Union[str, Any]       				=		convert_to_single_emb(__SCREAMING_SNAKE_CASE			) + 1

  # node adj matrix [num_nodes, num_nodes] bool
  __A		:  int       				=		np.zeros([num_nodes, num_nodes]      ,  dtype=__SCREAMING_SNAKE_CASE			)
  __A		:  Tuple       				=		True

  __A				,       __A		:  str       				=		algos_graphormer.floyd_warshall(__SCREAMING_SNAKE_CASE			)
  __A		:  List[str]       				=		np.amax(__SCREAMING_SNAKE_CASE			)

  __A		:  str       				=		algos_graphormer.gen_edge_input(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE			)
  __A		:  str       				=		np.zeros([num_nodes + 1, num_nodes + 1]      ,  dtype=np.single			)  # with graph token

  # combine
  __A		:  int       				=		input_nodes + 1  # we shift all indices by one for padding
  __A		:  Dict       				=		attn_bias
  __A		:  str       				=		attn_edge_type
  __A		:  Optional[int]       				=		shortest_path_result.astype(np.intaa			) + 1  # we shift all indices by one for padding
  __A		:  Optional[Any]       				=		np.sum(__SCREAMING_SNAKE_CASE      ,  axis=1			).reshape(-1			) + 1  # we shift all indices by one for padding
  __A		:  Optional[Any]       				=		item["""in_degree"""]  # for undirected graph
  __A		:  Tuple       				=		input_edges + 1  # we shift all indices by one for padding
  if "labels" not in item:
    __A		:  Optional[int]       				=		item["""y"""]

  return item







class 			__A					:
 def __init__( self      :     List[Any]					, lowerCamelCase      :     Dict=20					, lowerCamelCase      :     List[str]=False							):



   """simple docstring"""





   if not is_cython_available():
     raise ImportError("""Graphormer preprocessing needs Cython (pyximport)"""							)

   __A		:  str       				=		spatial_pos_max
   __A		:  Optional[Any]       				=		on_the_fly_processing






 def __call__( self      :     Tuple					, lowerCamelCase      :     List[dict]							):



   """simple docstring"""





   if self.on_the_fly_processing:
     __A		:  Optional[Any]       				=		[preprocess_item(lowerCamelCase							) for i in features]

   if not isinstance(features[0]					, lowerCamelCase							):
     __A		:  Optional[int]       				=		[vars(lowerCamelCase							) for f in features]
   __A		:  List[Any]       				=		{}

   __A		:  Dict       				=		max(len(i["""input_nodes"""]							) for i in features							)
   __A		:  Optional[Any]       				=		len(features[0]["""input_nodes"""][0]							)
   __A		:  Tuple       				=		len(features[0]["""attn_edge_type"""][0][0]							)
   __A		:  Any       				=		max(len(i["""input_edges"""][0][0]							) for i in features							)
   __A		:  Tuple       				=		len(features[0]["""input_edges"""][0][0][0]							)
   __A		:  str       				=		len(lowerCamelCase							)

   __A		:  List[str]       				=		torch.zeros(lowerCamelCase					, max_node_num + 1					, max_node_num + 1					, dtype=torch.float							)
   __A		:  Union[str, Any]       				=		torch.zeros(lowerCamelCase					, lowerCamelCase					, lowerCamelCase					, lowerCamelCase					, dtype=torch.long							)
   __A		:  str       				=		torch.zeros(lowerCamelCase					, lowerCamelCase					, lowerCamelCase					, dtype=torch.long							)
   __A		:  Dict       				=		torch.zeros(lowerCamelCase					, lowerCamelCase					, dtype=torch.long							)
   __A		:  Dict       				=		torch.zeros(lowerCamelCase					, lowerCamelCase					, lowerCamelCase					, dtype=torch.long							)
   __A		:  Dict       				=		torch.zeros(
       lowerCamelCase					, lowerCamelCase					, lowerCamelCase					, lowerCamelCase					, lowerCamelCase					, dtype=torch.long							)

   for ix, f in enumerate(lowerCamelCase							):
     for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
       __A		:  Tuple       				=		torch.tensor(f[k]							)

     if len(f["""attn_bias"""][1:, 1:][f["""spatial_pos"""] >= self.spatial_pos_max]							) > 0:
       __A		:  str       				=		float("""-inf"""							)

     __A		:  Optional[Any]       				=		f["""attn_bias"""]
     __A		:  Dict       				=		f[
         """attn_edge_type"""
     ]
     __A		:  Optional[Any]       				=		f["""spatial_pos"""]
     __A		:  int       				=		f["""in_degree"""]
     __A		:  Any       				=		f["""input_nodes"""]
     __A		:  Union[str, Any]       				=		f["""input_edges"""]

   __A		:  Any       				=		batch["""in_degree"""]

   __A		:  Union[str, Any]       				=		features[0]["""labels"""]
   if len(lowerCamelCase							) == 1:  # one task
     if isinstance(sample[0]					, lowerCamelCase							):  # regression
       __A		:  Any       				=		torch.from_numpy(np.concatenate([i["""labels"""] for i in features]							)							)
     else:  # binary classification
       __A		:  Union[str, Any]       				=		torch.from_numpy(np.concatenate([i["""labels"""] for i in features]							)							)
   else:  # multi task classification, left to float to keep the NaNs
     __A		:  List[str]       				=		torch.from_numpy(np.stack([i["""labels"""] for i in features]					, axis=0							)							)

   return batch





'''simple docstring'''
from typing import TYPE_CHECKING

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


A__ :		str					={'configuration_t5': ['T5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'T5Config', 'T5OnnxConfig']}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		Optional[int]					=['T5Tokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		Union[str, Any]					=['T5TokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		Union[str, Any]					=[
         'T5_PRETRAINED_MODEL_ARCHIVE_LIST',
         'T5EncoderModel',
         'T5ForConditionalGeneration',
         'T5Model',
         'T5PreTrainedModel',
         'load_tf_weights_in_t5',
         'T5ForQuestionAnswering',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		List[str]					=[
         'TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFT5EncoderModel',
         'TFT5ForConditionalGeneration',
         'TFT5Model',
         'TFT5PreTrainedModel',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		Union[str, Any]					=[
         'FlaxT5EncoderModel',
         'FlaxT5ForConditionalGeneration',
         'FlaxT5Model',
         'FlaxT5PreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_ta import TaTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_ta_fast import TaTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_ta import (
              T5_PRETRAINED_MODEL_ARCHIVE_LIST,
              TaEncoderModel,
              TaForConditionalGeneration,
              TaForQuestionAnswering,
              TaModel,
              TaPreTrainedModel,
              load_tf_weights_in_ta,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_ta import (
              TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFTaEncoderModel,
              TFTaForConditionalGeneration,
              TFTaModel,
              TFTaPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_ta import (
              FlaxTaEncoderModel,
              FlaxTaForConditionalGeneration,
              FlaxTaModel,
              FlaxTaPreTrainedModel,
          )


else:
     import sys

     A__ :		Dict					=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



