'''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    _SCREAMING_SNAKE_CASE		(			__snake_case       : int      ,		__snake_case       : int = 5_1_2							):
     _A								=     x.shape[1] if len(x.shape							) > 1 else 1
     _A								=     1 + np.arange(0      ,		feature_num * offset      ,		lowercase_      ,		dtype=np.intaa							)
     _A								=     x + feature_offset
     return x





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : str      ,		__snake_case       : Optional[Any]=True							):
     requires_backends(lowercase_      ,		['cython']							)

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

     _A								=     np.asarray(item['edge_index']      ,		dtype=np.intaa							)

     _A								=     convert_to_single_emb(lowercase_							) + 1
     _A								=     item['num_nodes']

     if len(edge_attr.shape							) == 1:
          _A								=     edge_attr[:, None]
     _A								=     np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]      ,		dtype=np.intaa							)
     _A								=     convert_to_single_emb(lowercase_							) + 1

     # node adj matrix [num_nodes, num_nodes] bool
     _A								=     np.zeros([num_nodes, num_nodes]      ,		dtype=lowercase_							)
     _A								=     True

     _A								=     algos_graphormer.floyd_warshall(lowercase_							)
     _A								=     np.amax(lowercase_							)

     _A								=     algos_graphormer.gen_edge_input(lowercase_      ,		lowercase_      ,		lowercase_							)
     _A								=     np.zeros([num_nodes + 1, num_nodes + 1]      ,		dtype=np.single							)  # with graph token

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

     return item







class lowercase_					:







   """simple docstring"""

   def __init__(			self			: str,   UpperCamelCase__			: Optional[int]=20,   UpperCamelCase__			: Tuple=False    )     -> str:
        if not is_cython_available():
             raise ImportError('Graphormer preprocessing needs Cython (pyximport)'    )

        _A								=     spatial_pos_max
        _A								=     on_the_fly_processing

   def __call__(			self			: Optional[int],   UpperCamelCase__			: List[dict]    )     -> Dict[str, Any]:
        if self.on_the_fly_processing:
             _A								=     [preprocess_item(UpperCamelCase__    ) for i in features]

        if not isinstance(features[0],   UpperCamelCase__    ):
             _A								=     [vars(UpperCamelCase__    ) for f in features]
        _A								=     {}

        _A								=     max(len(i['input_nodes']    ) for i in features    )
        _A								=     len(features[0]['input_nodes'][0]    )
        _A								=     len(features[0]['attn_edge_type'][0][0]    )
        _A								=     max(len(i['input_edges'][0][0]    ) for i in features    )
        _A								=     len(features[0]['input_edges'][0][0][0]    )
        _A								=     len(UpperCamelCase__    )

        _A								=     torch.zeros(UpperCamelCase__,   max_node_num + 1,   max_node_num + 1,   dtype=torch.float    )
        _A								=     torch.zeros(UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__,   dtype=torch.long    )
        _A								=     torch.zeros(UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__,   dtype=torch.long    )
        _A								=     torch.zeros(UpperCamelCase__,   UpperCamelCase__,   dtype=torch.long    )
        _A								=     torch.zeros(UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__,   dtype=torch.long    )
        _A								=     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"]:
                  _A								=     torch.tensor(f[k]    )

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

             _A								=     f['attn_bias']
             _A								=     f[
                 'attn_edge_type'
             ]
             _A								=     f['spatial_pos']
             _A								=     f['in_degree']
             _A								=     f['input_nodes']
             _A								=     f['input_edges']

        _A								=     batch['in_degree']

        _A								=     features[0]['labels']
        if len(UpperCamelCase__    ) == 1:  # one task
             if isinstance(sample[0],   UpperCamelCase__    ):  # regression
                  _A								=     torch.from_numpy(np.concatenate([i['labels'] for i in features]    )    )
             else:  # binary classification
                  _A								=     torch.from_numpy(np.concatenate([i['labels'] for i in features]    )    )
        else:  # multi task classification, left to float to keep the NaNs
             _A								=     torch.from_numpy(np.stack([i['labels'] for i in features],   axis=0    )    )

        return batch




'''simple docstring'''






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


_UpperCAmelCase						:    int     					=					['''CoLA''', '''SST''', '''MRPC''', '''QQP''', '''STS''', '''MNLI''', '''SNLI''', '''QNLI''', '''RTE''', '''WNLI''', '''diagnostic''']
_UpperCAmelCase						:    Dict     					=					{
    '''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''',
}

_UpperCAmelCase						:    Any     					=					'''https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt'''
_UpperCAmelCase						:    List[Any]     					=					'''https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt'''





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : int      ,		__snake_case       : Union[str, Any]							):
     print(F'Downloading and extracting {task}...'							)
     _A								=     F'{task}.zip'
     urllib.request.urlretrieve(TASKaPATH[task]      ,		__snake_case							)
     with zipfile.ZipFile(__snake_case							) as zip_ref:
          zip_ref.extractall(__snake_case							)
     os.remove(__snake_case							)
     print('\tCompleted!'							)





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

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

     with open(__snake_case      ,		encoding='utf8'							) as data_fh, open(
         os.path.join(__snake_case      ,		'train.tsv'							)      ,		'w'      ,		encoding='utf8'							) as train_fh, open(os.path.join(__snake_case      ,		'dev.tsv'							)      ,		'w'      ,		encoding='utf8'							) as dev_fh:
          _A								=     data_fh.readline()
          train_fh.write(__snake_case							)
          dev_fh.write(__snake_case							)
          for row in data_fh:
               _A		,					_A		,					_A		,					_A		,					_A								=     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(__snake_case      ,		encoding='utf8'							) as data_fh, open(
         os.path.join(__snake_case      ,		'test.tsv'							)      ,		'w'      ,		encoding='utf8'							) as test_fh:
          _A								=     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(__snake_case							):
               _A		,					_A		,					_A		,					_A		,					_A								=     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    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[Any]							):
     print('Downloading and extracting diagnostic...'							)
     if not os.path.isdir(os.path.join(__snake_case      ,		'diagnostic'							)							):
          os.mkdir(os.path.join(__snake_case      ,		'diagnostic'							)							)
     _A								=     os.path.join(__snake_case      ,		'diagnostic'      ,		'diagnostic.tsv'							)
     urllib.request.urlretrieve(TASKaPATH['diagnostic']      ,		__snake_case							)
     print('\tCompleted!'							)
     return





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[Any]							):
     _A								=     task_names.split(','							)
     if "all" in task_names:
          _A								=     TASKS
     else:
          _A								=     []
          for task_name in task_names:
               if task_name not in TASKS:
                    raise ValueError(F'Task {task_name} not found!'							)
               tasks.append(__snake_case							)
     return tasks





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[int]							):
     _A								=     argparse.ArgumentParser()
     parser.add_argument('--data_dir'      ,		help='directory to save data to'      ,		type=__snake_case      ,		default='glue_data'							)
     parser.add_argument(
         '--tasks'      ,		help='tasks to download data for as a comma separated string'      ,		type=__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=__snake_case      ,		default=''      ,		)
     _A								=     parser.parse_args(__snake_case							)

     if not os.path.isdir(args.data_dir							):
          os.mkdir(args.data_dir							)
     _A								=     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(__snake_case      ,		args.data_dir							)


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



