"""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       (			_lowercase							, _lowercase = 512						):







					"""simple docstring"""





					lowerCamelCase__		:   Optional[Any]  				=					x.shape[1] if len(x.shape						) > 1 else 1
					lowerCamelCase__		:   Dict  				=					1 + np.arange(0							, feature_num * offset							, lowerCamelCase_							, dtype=np.intaa						)
					lowerCamelCase__		:   List[Any]  				=					x + feature_offset
					return x



def 				__a       (			_lowercase							, _lowercase=True						):







					"""simple docstring"""





					requires_backends(lowerCamelCase_							, ['''cython''']						)

					if keep_features and "edge_attr" in item.keys():  # edge_attr
										lowerCamelCase__		:   Union[str, Any]  				=					np.asarray(item['''edge_attr''']							, dtype=np.intaa						)
					else:
										lowerCamelCase__		:   List[str]  				=					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
										lowerCamelCase__		:   Any  				=					np.asarray(item['''node_feat''']							, dtype=np.intaa						)
					else:
										lowerCamelCase__		:   Dict  				=					np.ones((item['''num_nodes'''], 1)							, dtype=np.intaa						)  # same embedding for all

					lowerCamelCase__		:   Optional[int]  				=					np.asarray(item['''edge_index''']							, dtype=np.intaa						)

					lowerCamelCase__		:   int  				=					convert_to_single_emb(lowerCamelCase_						) + 1
					lowerCamelCase__		:   str  				=					item['''num_nodes''']

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

					# node adj matrix [num_nodes, num_nodes] bool
					lowerCamelCase__		:   List[Any]  				=					np.zeros([num_nodes, num_nodes]							, dtype=lowerCamelCase_						)
					lowerCamelCase__		:   List[str]  				=					True

					lowerCamelCase__				,						lowerCamelCase__		:   List[Any]  				=					algos_graphormer.floyd_warshall(lowerCamelCase_						)
					lowerCamelCase__		:   Optional[int]  				=					np.amax(lowerCamelCase_						)

					lowerCamelCase__		:   List[Any]  				=					algos_graphormer.gen_edge_input(lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_						)
					lowerCamelCase__		:   int  				=					np.zeros([num_nodes + 1, num_nodes + 1]							, dtype=np.single						)  # with graph token

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

					return item





class        __SCREAMING_SNAKE_CASE     :







				def __init__(    self    :Optional[int]      ,__UpperCAmelCase    :Tuple=20      ,__UpperCAmelCase    :Dict=False  )      ->					Dict:




									"""simple docstring"""



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

									lowerCamelCase__		:   Any  				=					spatial_pos_max
									lowerCamelCase__		:   Union[str, Any]  				=					on_the_fly_processing







				def __call__(    self    :Any      ,__UpperCAmelCase    :Union[str, Any]  )      ->					Any:




									"""simple docstring"""



									if self.on_the_fly_processing:
														lowerCamelCase__		:   str  				=					[preprocess_item(UpperCAmelCase__  ) for i in features]

									if not isinstance(features[0]      ,UpperCAmelCase__  ):
														lowerCamelCase__		:   List[Any]  				=					[vars(UpperCAmelCase__  ) for f in features]
									lowerCamelCase__		:   str  				=					{}

									lowerCamelCase__		:   int  				=					max(len(i['''input_nodes''']  ) for i in features  )
									lowerCamelCase__		:   List[str]  				=					len(features[0]['''input_nodes'''][0]  )
									lowerCamelCase__		:   Dict  				=					len(features[0]['''attn_edge_type'''][0][0]  )
									lowerCamelCase__		:   Any  				=					max(len(i['''input_edges'''][0][0]  ) for i in features  )
									lowerCamelCase__		:   Dict  				=					len(features[0]['''input_edges'''][0][0][0]  )
									lowerCamelCase__		:   str  				=					len(UpperCAmelCase__  )

									lowerCamelCase__		:   List[str]  				=					torch.zeros(UpperCAmelCase__      ,max_node_num + 1      ,max_node_num + 1      ,dtype=torch.float  )
									lowerCamelCase__		:   List[Any]  				=					torch.zeros(UpperCAmelCase__      ,UpperCAmelCase__      ,UpperCAmelCase__      ,UpperCAmelCase__      ,dtype=torch.long  )
									lowerCamelCase__		:   Optional[int]  				=					torch.zeros(UpperCAmelCase__      ,UpperCAmelCase__      ,UpperCAmelCase__      ,dtype=torch.long  )
									lowerCamelCase__		:   Any  				=					torch.zeros(UpperCAmelCase__      ,UpperCAmelCase__      ,dtype=torch.long  )
									lowerCamelCase__		:   Tuple  				=					torch.zeros(UpperCAmelCase__      ,UpperCAmelCase__      ,UpperCAmelCase__      ,dtype=torch.long  )
									lowerCamelCase__		:   str  				=					torch.zeros(
									    UpperCAmelCase__      ,UpperCAmelCase__      ,UpperCAmelCase__      ,UpperCAmelCase__      ,UpperCAmelCase__      ,dtype=torch.long  )

									for ix, f in enumerate(UpperCAmelCase__  ):
														for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
																			lowerCamelCase__		:   Any  				=					torch.tensor(f[k]  )

														if len(f['''attn_bias'''][1:, 1:][f['''spatial_pos'''] >= self.spatial_pos_max]  ) > 0:
																			lowerCamelCase__		:   Any  				=					float('''-inf'''  )

														lowerCamelCase__		:   str  				=					f['''attn_bias''']
														lowerCamelCase__		:   str  				=					f[
														    '''attn_edge_type'''
														]
														lowerCamelCase__		:   int  				=					f['''spatial_pos''']
														lowerCamelCase__		:   Optional[int]  				=					f['''in_degree''']
														lowerCamelCase__		:   Tuple  				=					f['''input_nodes''']
														lowerCamelCase__		:   Tuple  				=					f['''input_edges''']

									lowerCamelCase__		:   List[Any]  				=					batch['''in_degree''']

									lowerCamelCase__		:   List[str]  				=					features[0]['''labels''']
									if len(UpperCAmelCase__  ) == 1:  # one task
														if isinstance(sample[0]      ,UpperCAmelCase__  ):  # regression
																			lowerCamelCase__		:   int  				=					torch.from_numpy(np.concatenate([i['''labels'''] for i in features]  )  )
														else:  # binary classification
																			lowerCamelCase__		:   Optional[int]  				=					torch.from_numpy(np.concatenate([i['''labels'''] for i in features]  )  )
									else:  # multi task classification, left to float to keep the NaNs
														lowerCamelCase__		:   int  				=					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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCamelCase  =	{
    'configuration_open_llama': ['OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenLlamaConfig'],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase  =	['LlamaTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase  =	['LlamaTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase  =	[
	    'OpenLlamaForCausalLM',
	    'OpenLlamaModel',
	    'OpenLlamaPreTrainedModel',
	    'OpenLlamaForSequenceClassification',
	]


if TYPE_CHECKING:
	from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from transformers import LlamaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from transformers import LlamaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_open_llama import (
		    OpenLlamaForCausalLM,
		    OpenLlamaForSequenceClassification,
		    OpenLlamaModel,
		    OpenLlamaPreTrainedModel,
		)


else:
	import sys

	_lowerCamelCase  =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
