


'''simple docstring'''

import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


__lowercase:		Union[str, Any]    						=  (30, 487)
__lowercase:		Union[str, Any]    						=  (36, 1_024)
__lowercase:		Optional[Any]    						=  (100, 100)
__lowercase:		str    						=  100

__lowercase:		List[str]    						=  datasets.Features(
    {"text": ArrayaD(SHAPE_TEST_1, dtype="float32"), "image": ArrayaD(SHAPE_TEST_2, dtype="float32")}
)

__lowercase						,__lowercase:		Optional[int]    						=  os.path.split(__file__)
__lowercase:		Any    						=  os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))



@get_duration
def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					List[Any]     ,		_UpperCamelCase   :					List[Any]     ,		_UpperCamelCase   :					List[str] )		->       Dict:





   '''simple docstring'''







   with ArrowWriter(features=_UpperCamelCase     ,		path=os.path.join(_UpperCamelCase     ,		"beta.arrow" ) ) as writer:
      for key, record in dummy_data:
         UpperCamelCase__			    =    my_features.encode_example(_UpperCamelCase )
         writer.write(_UpperCamelCase )
      UpperCamelCase__    ,		UpperCamelCase__			    =    writer.finalize()



@get_duration
def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Union[str, Any]     ,		_UpperCamelCase   :					Dict )		->       str:





   '''simple docstring'''







   UpperCamelCase__			    =    datasets.Dataset.from_file(
       filename=os.path.join(_UpperCamelCase     ,		"beta.arrow" )     ,		info=datasets.DatasetInfo(features=_UpperCamelCase ) )
   for _ in dataset:
      pass



@get_duration
def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Union[str, Any]     ,		_UpperCamelCase   :					Optional[int] )		->       List[str]:





   '''simple docstring'''







   UpperCamelCase__			    =    datasets.Dataset.from_file(
       filename=os.path.join(_UpperCamelCase     ,		"beta.arrow" )     ,		info=datasets.DatasetInfo(features=_UpperCamelCase ) )
   dataset.set_format("numpy" )
   for _ in dataset:
      pass



@get_duration
def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Optional[int]     ,		_UpperCamelCase   :					Union[str, Any] )		->       List[str]:





   '''simple docstring'''







   UpperCamelCase__			    =    10
   UpperCamelCase__			    =    datasets.Dataset.from_file(
       filename=os.path.join(_UpperCamelCase     ,		"beta.arrow" )     ,		info=datasets.DatasetInfo(features=_UpperCamelCase ) )
   for i in range(0     ,		len(_UpperCamelCase )     ,		_UpperCamelCase ):
      UpperCamelCase__			    =    dataset[i : i + batch_size]



@get_duration
def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					List[Any]     ,		_UpperCamelCase   :					Any )		->       Dict:





   '''simple docstring'''







   UpperCamelCase__			    =    10
   UpperCamelCase__			    =    datasets.Dataset.from_file(
       filename=os.path.join(_UpperCamelCase     ,		"beta.arrow" )     ,		info=datasets.DatasetInfo(features=_UpperCamelCase ) )
   dataset.set_format("numpy" )
   for i in range(0     ,		len(_UpperCamelCase )     ,		_UpperCamelCase ):
      UpperCamelCase__			    =    dataset[i : i + batch_size]



@get_duration
def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Any     ,		_UpperCamelCase   :					Union[str, Any] )		->       Optional[Any]:





   '''simple docstring'''







   UpperCamelCase__			    =    datasets.Dataset.from_file(
       filename=os.path.join(_UpperCamelCase     ,		"beta.arrow" )     ,		info=datasets.DatasetInfo(features=_UpperCamelCase ) )
   for col in feats:
      UpperCamelCase__			    =    dataset[col]



@get_duration
def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Optional[int]     ,		_UpperCamelCase   :					List[Any] )		->       str:





   '''simple docstring'''







   UpperCamelCase__			    =    datasets.Dataset.from_file(
       filename=os.path.join(_UpperCamelCase     ,		"beta.arrow" )     ,		info=datasets.DatasetInfo(features=_UpperCamelCase ) )
   dataset.set_format("numpy" )
   for col in feats:
      UpperCamelCase__			    =    dataset[col]



def 							SCREAMING_SNAKE_CASE__(		)		->       Dict:





   '''simple docstring'''







   UpperCamelCase__			    =    {}
   UpperCamelCase__			    =    (
       read_unformated,
       read_formatted_as_numpy,
       read_batch_unformated,
       read_batch_formatted_as_numpy,
       read_col_unformated,
       read_col_formatted_as_numpy,
   )
   with tempfile.TemporaryDirectory() as tmp_dir:
      UpperCamelCase__			    =    datasets.Features({"image": ArrayaD(_UpperCamelCase     ,		dtype="float32" )} )
      UpperCamelCase__			    =    generate_examples(features=_UpperCamelCase     ,		num_examples=_UpperCamelCase )
      UpperCamelCase__			    =    write(_UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase )
      for read_func in read_functions:
         UpperCamelCase__			    =    read_func(_UpperCamelCase     ,		_UpperCamelCase )

   with tempfile.TemporaryDirectory() as tmp_dir:
      # don't use fixed length for fair comparison
      # feats = datasets.Features(
      #     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
      # )
      UpperCamelCase__			    =    datasets.Features({"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32" ) ) )} )
      UpperCamelCase__			    =    generate_examples(
          features=_UpperCamelCase     ,		num_examples=_UpperCamelCase     ,		seq_shapes={"image": SPEED_TEST_SHAPE} )
      UpperCamelCase__			    =    write(_UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase )
      for read_func in read_functions:
         UpperCamelCase__			    =    read_func(_UpperCamelCase     ,		_UpperCamelCase )

   with tempfile.TemporaryDirectory() as tmp_dir:
      # don't use fixed length for fair comparison
      # feats = datasets.Features(
      #     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
      # )
      UpperCamelCase__			    =    datasets.Features({"image": datasets.Sequence(datasets.Value("float32" ) )} )
      UpperCamelCase__			    =    generate_examples(
          features=_UpperCamelCase     ,		num_examples=_UpperCamelCase     ,		seq_shapes={"image": [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}     ,		)
      UpperCamelCase__			    =    write(_UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase )
      for read_func in read_functions:
         UpperCamelCase__			    =    read_func(_UpperCamelCase     ,		_UpperCamelCase )

   with open(_UpperCamelCase     ,		"wb" ) as f:
      f.write(json.dumps(_UpperCamelCase ).encode("utf-8" ) )


if __name__ == "__main__":  # useful to run the profiler
       benchmark_array_xd()

"""simple docstring"""




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


snake_case					  =						logging.get_logger(__name__)

snake_case					  =						{
    # 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      (			__magic_name__ ):

				"""simple docstring"""



				UpperCAmelCase_							: Dict		    =							"graphormer"
				UpperCAmelCase_							: Optional[int]		    =							["past_key_values"]


				def __init__(							self						,      lowercase__ = 1						,      lowercase__ = 512 * 9						,      lowercase__ = 512 * 3						,      lowercase__ = 512						,      lowercase__ = 512						,      lowercase__ = 512						,      lowercase__ = 128						,      lowercase__ = 5						,      lowercase__ = 1024						,      lowercase__ = "multi_hop"						,      lowercase__ = 512						,      lowercase__ = False						,      lowercase__ = 12						,      lowercase__ = 768						,      lowercase__ = 768						,      lowercase__ = 32						,      lowercase__ = 0.1						,      lowercase__ = 0.1						,      lowercase__ = 0.0						,      lowercase__ = False						,      lowercase__ = False						,      lowercase__ = False						,      lowercase__ = "gelu"						,      lowercase__ = None						,      lowercase__ = False						,      lowercase__ = 0						,      lowercase__ = False						,      lowercase__ = 0.0						,      lowercase__ = 8						,      lowercase__ = None						,      lowercase__ = None						,      lowercase__ = True						,      lowercase__ = True						,      lowercase__=0						,      lowercase__=1						,      lowercase__=2						,      **lowercase__						,      )       ->       str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      num_classes
									SCREAMING_SNAKE_CASE           =      num_atoms
									SCREAMING_SNAKE_CASE           =      num_in_degree
									SCREAMING_SNAKE_CASE           =      num_out_degree
									SCREAMING_SNAKE_CASE           =      num_edges
									SCREAMING_SNAKE_CASE           =      num_spatial
									SCREAMING_SNAKE_CASE           =      num_edge_dis
									SCREAMING_SNAKE_CASE           =      edge_type
									SCREAMING_SNAKE_CASE           =      multi_hop_max_dist
									SCREAMING_SNAKE_CASE           =      spatial_pos_max
									SCREAMING_SNAKE_CASE           =      max_nodes
									SCREAMING_SNAKE_CASE           =      num_hidden_layers
									SCREAMING_SNAKE_CASE           =      embedding_dim
									SCREAMING_SNAKE_CASE           =      embedding_dim
									SCREAMING_SNAKE_CASE           =      ffn_embedding_dim
									SCREAMING_SNAKE_CASE           =      num_attention_heads
									SCREAMING_SNAKE_CASE           =      dropout
									SCREAMING_SNAKE_CASE           =      attention_dropout
									SCREAMING_SNAKE_CASE           =      layerdrop
									SCREAMING_SNAKE_CASE           =      encoder_normalize_before
									SCREAMING_SNAKE_CASE           =      pre_layernorm
									SCREAMING_SNAKE_CASE           =      apply_graphormer_init
									SCREAMING_SNAKE_CASE           =      activation_fn
									SCREAMING_SNAKE_CASE           =      embed_scale
									SCREAMING_SNAKE_CASE           =      freeze_embeddings
									SCREAMING_SNAKE_CASE           =      num_trans_layers_to_freeze
									SCREAMING_SNAKE_CASE           =      share_input_output_embed
									SCREAMING_SNAKE_CASE           =      traceable
									SCREAMING_SNAKE_CASE           =      q_noise
									SCREAMING_SNAKE_CASE           =      qn_block_size

									# These parameters are here for future extensions
									# atm, the model only supports self attention
									SCREAMING_SNAKE_CASE           =      kdim
									SCREAMING_SNAKE_CASE           =      vdim
									SCREAMING_SNAKE_CASE           =      self_attention
									SCREAMING_SNAKE_CASE           =      bias

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