
"""simple docstring"""




import argparse
import os
import sys
import urllib.request
import zipfile


snake_case					  =						['CoLA', 'SST', 'MRPC', 'QQP', 'STS', 'MNLI', 'SNLI', 'QNLI', 'RTE', 'WNLI', 'diagnostic']
snake_case					  =						{
    'CoLA': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FCoLA.zip?alt=media&token=46d5e637-3411-4188-bc44-5809b5bfb5f4',
    'SST': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8',
    'MRPC': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2Fmrpc_dev_ids.tsv?alt=media&token=ec5c0836-31d5-48f4-b431-7480817f1adc',
    'QQP': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQQP.zip?alt=media&token=700c6acf-160d-4d89-81d1-de4191d02cb5',
    'STS': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSTS-B.zip?alt=media&token=bddb94a7-8706-4e0d-a694-1109e12273b5',
    'MNLI': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FMNLI.zip?alt=media&token=50329ea1-e339-40e2-809c-10c40afff3ce',
    'SNLI': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSNLI.zip?alt=media&token=4afcfbb2-ff0c-4b2d-a09a-dbf07926f4df',
    'QNLI': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQNLIv2.zip?alt=media&token=6fdcf570-0fc5-4631-8456-9505272d1601',
    'RTE': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb',
    'WNLI': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FWNLI.zip?alt=media&token=068ad0a0-ded7-4bd7-99a5-5e00222e0faf',
    'diagnostic': 'https://storage.googleapis.com/mtl-sentence-representations.appspot.com/tsvsWithoutLabels%2FAX.tsv?GoogleAccessId=firebase-adminsdk-0khhl@mtl-sentence-representations.iam.gserviceaccount.com&Expires=2498860800&Signature=DuQ2CSPt2Yfre0C%2BiISrVYrIFaZH1Lc7hBVZDD4ZyR7fZYOMNOUGpi8QxBmTNOrNPjR3z1cggo7WXFfrgECP6FBJSsURv8Ybrue8Ypt%2FTPxbuJ0Xc2FhDi%2BarnecCBFO77RSbfuz%2Bs95hRrYhTnByqu3U%2FYZPaj3tZt5QdfpH2IUROY8LiBXoXS46LE%2FgOQc%2FKN%2BA9SoscRDYsnxHfG0IjXGwHN%2Bf88q6hOmAxeNPx6moDulUF6XMUAaXCSFU%2BnRO2RDL9CapWxj%2BDl7syNyHhB7987hZ80B%2FwFkQ3MEs8auvt5XW1%2Bd4aCU7ytgM69r8JDCwibfhZxpaa4gd50QXQ%3D%3D',
}

snake_case					  =						'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt'
snake_case					  =						'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt'

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					print(f'''Downloading and extracting {task}...'''     )
					SCREAMING_SNAKE_CASE           =      f'''{task}.zip'''
					urllib.request.urlretrieve(TASKaPATH[task],	SCREAMING_SNAKE_CASE_     )
					with zipfile.ZipFile(SCREAMING_SNAKE_CASE_     ) as zip_ref:
										zip_ref.extractall(SCREAMING_SNAKE_CASE_     )
					os.remove(SCREAMING_SNAKE_CASE_     )
					print('\tCompleted!'     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					print('Processing MRPC...'     )
					SCREAMING_SNAKE_CASE           =      os.path.join(SCREAMING_SNAKE_CASE_,	'MRPC'     )
					if not os.path.isdir(SCREAMING_SNAKE_CASE_     ):
										os.mkdir(SCREAMING_SNAKE_CASE_     )
					if path_to_data:
										SCREAMING_SNAKE_CASE           =      os.path.join(SCREAMING_SNAKE_CASE_,	'msr_paraphrase_train.txt'     )
										SCREAMING_SNAKE_CASE           =      os.path.join(SCREAMING_SNAKE_CASE_,	'msr_paraphrase_test.txt'     )
					else:
										print('Local MRPC data not specified, downloading data from %s' % MRPC_TRAIN     )
										SCREAMING_SNAKE_CASE           =      os.path.join(SCREAMING_SNAKE_CASE_,	'msr_paraphrase_train.txt'     )
										SCREAMING_SNAKE_CASE           =      os.path.join(SCREAMING_SNAKE_CASE_,	'msr_paraphrase_test.txt'     )
										urllib.request.urlretrieve(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )
										urllib.request.urlretrieve(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )
					if not os.path.isfile(SCREAMING_SNAKE_CASE_     ):
										raise ValueError(f'''Train data not found at {mrpc_train_file}'''     )
					if not os.path.isfile(SCREAMING_SNAKE_CASE_     ):
										raise ValueError(f'''Test data not found at {mrpc_test_file}'''     )
					urllib.request.urlretrieve(TASKaPATH['MRPC'],	os.path.join(SCREAMING_SNAKE_CASE_,	'dev_ids.tsv'     )     )

					SCREAMING_SNAKE_CASE           =      []
					with open(os.path.join(SCREAMING_SNAKE_CASE_,	'dev_ids.tsv'     ),	encoding='utf8'     ) as ids_fh:
										for row in ids_fh:
															dev_ids.append(row.strip().split('\t'     )     )

					with open(SCREAMING_SNAKE_CASE_,	encoding='utf8'     ) as data_fh, open(
					    os.path.join(SCREAMING_SNAKE_CASE_,	'train.tsv'     ),	'w',	encoding='utf8'     ) as train_fh, open(os.path.join(SCREAMING_SNAKE_CASE_,	'dev.tsv'     ),	'w',	encoding='utf8'     ) as dev_fh:
										SCREAMING_SNAKE_CASE           =      data_fh.readline()
										train_fh.write(SCREAMING_SNAKE_CASE_     )
										dev_fh.write(SCREAMING_SNAKE_CASE_     )
										for row in data_fh:
															SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      row.strip().split('\t'     )
															if [ida, ida] in dev_ids:
																				dev_fh.write('%s\t%s\t%s\t%s\t%s\n' % (label, ida, ida, sa, sa)     )
															else:
																				train_fh.write('%s\t%s\t%s\t%s\t%s\n' % (label, ida, ida, sa, sa)     )

					with open(SCREAMING_SNAKE_CASE_,	encoding='utf8'     ) as data_fh, open(
					    os.path.join(SCREAMING_SNAKE_CASE_,	'test.tsv'     ),	'w',	encoding='utf8'     ) as test_fh:
										SCREAMING_SNAKE_CASE           =      data_fh.readline()
										test_fh.write('index\t#1 ID\t#2 ID\t#1 String\t#2 String\n'     )
										for idx, row in enumerate(SCREAMING_SNAKE_CASE_     ):
															SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      row.strip().split('\t'     )
															test_fh.write('%d\t%s\t%s\t%s\t%s\n' % (idx, ida, ida, sa, sa)     )
					print('\tCompleted!'     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					print('Downloading and extracting diagnostic...'     )
					if not os.path.isdir(os.path.join(SCREAMING_SNAKE_CASE_,	'diagnostic'     )     ):
										os.mkdir(os.path.join(SCREAMING_SNAKE_CASE_,	'diagnostic'     )     )
					SCREAMING_SNAKE_CASE           =      os.path.join(SCREAMING_SNAKE_CASE_,	'diagnostic',	'diagnostic.tsv'     )
					urllib.request.urlretrieve(TASKaPATH['diagnostic'],	SCREAMING_SNAKE_CASE_     )
					print('\tCompleted!'     )
					return

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      task_names.split(','     )
					if "all" in task_names:
										SCREAMING_SNAKE_CASE           =      TASKS
					else:
										SCREAMING_SNAKE_CASE           =      []
										for task_name in task_names:
															if task_name not in TASKS:
																				raise ValueError(f'''Task {task_name} not found!'''     )
															tasks.append(SCREAMING_SNAKE_CASE_     )
					return tasks

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      argparse.ArgumentParser()
					parser.add_argument('--data_dir',	help='directory to save data to',	type=SCREAMING_SNAKE_CASE_,	default='glue_data'     )
					parser.add_argument(
					    '--tasks',	help='tasks to download data for as a comma separated string',	type=SCREAMING_SNAKE_CASE_,	default='all'     )
					parser.add_argument(
					    '--path_to_mrpc',	help='path to directory containing extracted MRPC data, msr_paraphrase_train.txt and msr_paraphrase_text.txt',	type=SCREAMING_SNAKE_CASE_,	default='',	)
					SCREAMING_SNAKE_CASE           =      parser.parse_args(SCREAMING_SNAKE_CASE_     )

					if not os.path.isdir(args.data_dir     ):
										os.mkdir(args.data_dir     )
					SCREAMING_SNAKE_CASE           =      get_tasks(args.tasks     )

					for task in tasks:
										if task == "MRPC":
															format_mrpc(args.data_dir,	args.path_to_mrpc     )
										elif task == "diagnostic":
															download_diagnostic(args.data_dir     )
										else:
															download_and_extract(SCREAMING_SNAKE_CASE_,	args.data_dir     )


if __name__ == "__main__":
	sys.exit(main(sys.argv[1:]))


"""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__						,      )
