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


__SCREAMING_SNAKE_CASE						:    str     = ["CoLA", "SST", "MRPC", "QQP", "STS", "MNLI", "SNLI", "QNLI", "RTE", "WNLI", "diagnostic"]
__SCREAMING_SNAKE_CASE						:    str     = {
    "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",
}

__SCREAMING_SNAKE_CASE						:    int     = "https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt"
__SCREAMING_SNAKE_CASE						:    str     = "https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt"
def 					snake_case						(__lowercase					,			__lowercase						)       ->       List[str]:

						'''simple docstring'''



						print(F"""Downloading and extracting {task}..."""						)
						_snake_case						:     Dict				=    F"""{task}.zip"""
						urllib.request.urlretrieve(TASKaPATH[task]					,			UpperCamelCase_						)
						with zipfile.ZipFile(UpperCamelCase_						) as zip_ref:
												zip_ref.extractall(UpperCamelCase_						)
						os.remove(UpperCamelCase_						)
						print("\tCompleted!"						)
def 					snake_case						(__lowercase					,			__lowercase						)       ->       List[str]:

						'''simple docstring'''



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

						_snake_case						:     Dict				=    []
						with open(os.path.join(UpperCamelCase_					,			"dev_ids.tsv"						)					,			encoding="utf8"						) as ids_fh:
												for row in ids_fh:
																		dev_ids.append(row.strip().split("\t"						)						)

						with open(UpperCamelCase_					,			encoding="utf8"						) as data_fh, open(
						    os.path.join(UpperCamelCase_					,			"train.tsv"						)					,			"w"					,			encoding="utf8"						) as train_fh, open(os.path.join(UpperCamelCase_					,			"dev.tsv"						)					,			"w"					,			encoding="utf8"						) as dev_fh:
												_snake_case						:     Optional[Any]				=    data_fh.readline()
												train_fh.write(UpperCamelCase_						)
												dev_fh.write(UpperCamelCase_						)
												for row in data_fh:
																		_snake_case						:     Optional[int]				=    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(UpperCamelCase_					,			encoding="utf8"						) as data_fh, open(
						    os.path.join(UpperCamelCase_					,			"test.tsv"						)					,			"w"					,			encoding="utf8"						) as test_fh:
												_snake_case						:     Any				=    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(UpperCamelCase_						):
																		_snake_case						:     int				=    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 					snake_case						(__lowercase						)       ->       List[Any]:

						'''simple docstring'''



						print("Downloading and extracting diagnostic..."						)
						if not os.path.isdir(os.path.join(UpperCamelCase_					,			"diagnostic"						)						):
												os.mkdir(os.path.join(UpperCamelCase_					,			"diagnostic"						)						)
						_snake_case						:     Optional[Any]				=    os.path.join(UpperCamelCase_					,			"diagnostic"					,			"diagnostic.tsv"						)
						urllib.request.urlretrieve(TASKaPATH["diagnostic"]					,			UpperCamelCase_						)
						print("\tCompleted!"						)
						return
def 					snake_case						(__lowercase						)       ->       Union[str, Any]:

						'''simple docstring'''



						_snake_case						:     List[Any]				=    task_names.split(","						)
						if "all" in task_names:
												_snake_case						:     int				=    TASKS
						else:
												_snake_case						:     List[str]				=    []
												for task_name in task_names:
																		if task_name not in TASKS:
																								raise ValueError(F"""Task {task_name} not found!"""						)
																		tasks.append(UpperCamelCase_						)
						return tasks
def 					snake_case						(__lowercase						)       ->       str:

						'''simple docstring'''



						_snake_case						:     str				=    argparse.ArgumentParser()
						parser.add_argument("--data_dir"					,			help="directory to save data to"					,			type=UpperCamelCase_					,			default="glue_data"						)
						parser.add_argument(
						    "--tasks"					,			help="tasks to download data for as a comma separated string"					,			type=UpperCamelCase_					,			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=UpperCamelCase_					,			default=""					,			)
						_snake_case						:     Dict				=    parser.parse_args(UpperCamelCase_						)

						if not os.path.isdir(args.data_dir						):
												os.mkdir(args.data_dir						)
						_snake_case						:     List[Any]				=    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(UpperCamelCase_					,			args.data_dir						)


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





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCamelCase					:     Optional[int]					  =  logging.get_logger(__name__)

_lowerCamelCase					:     Optional[int]					  =  {
    "squeezebert/squeezebert-uncased": (
        "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"
    ),
    "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json",
    "squeezebert/squeezebert-mnli-headless": (
        "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"
    ),
}
class 				__snake_case     (_a						):
				lowerCAmelCase__			=   SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
				lowerCAmelCase__			=   "squeezebert"



				def __init__(					self					:						Optional[int]						,		_UpperCAmelCase					:						Tuple=3_0522						,		_UpperCAmelCase					:						str=768						,		_UpperCAmelCase					:						int=12						,		_UpperCAmelCase					:						Tuple=12						,		_UpperCAmelCase					:						Tuple=3072						,		_UpperCAmelCase					:						Union[str, Any]="gelu"						,		_UpperCAmelCase					:						Union[str, Any]=0.1						,		_UpperCAmelCase					:						List[Any]=0.1						,		_UpperCAmelCase					:						List[Any]=512						,		_UpperCAmelCase					:						Tuple=2						,		_UpperCAmelCase					:						Optional[Any]=0.02						,		_UpperCAmelCase					:						Any=1E-12						,		_UpperCAmelCase					:						Optional[Any]=0						,		_UpperCAmelCase					:						Union[str, Any]=768						,		_UpperCAmelCase					:						Any=4						,		_UpperCAmelCase					:						Any=4						,		_UpperCAmelCase					:						Optional[int]=4						,		_UpperCAmelCase					:						str=1						,		_UpperCAmelCase					:						Union[str, Any]=4						,		_UpperCAmelCase					:						List[str]=4						,		**_UpperCAmelCase					:						int						,		)     ->			Any:





								'''simple docstring'''




								super().__init__(pad_token_id=_UpperCAmelCase						,		**_UpperCAmelCase					)

								_lowerCAmelCase			:			Optional[Any]        =					vocab_size
								_lowerCAmelCase			:			Optional[Any]        =					hidden_size
								_lowerCAmelCase			:			Tuple        =					num_hidden_layers
								_lowerCAmelCase			:			int        =					num_attention_heads
								_lowerCAmelCase			:			List[Any]        =					hidden_act
								_lowerCAmelCase			:			List[Any]        =					intermediate_size
								_lowerCAmelCase			:			List[str]        =					hidden_dropout_prob
								_lowerCAmelCase			:			Any        =					attention_probs_dropout_prob
								_lowerCAmelCase			:			str        =					max_position_embeddings
								_lowerCAmelCase			:			int        =					type_vocab_size
								_lowerCAmelCase			:			str        =					initializer_range
								_lowerCAmelCase			:			int        =					layer_norm_eps
								_lowerCAmelCase			:			int        =					embedding_size
								_lowerCAmelCase			:			Optional[int]        =					q_groups
								_lowerCAmelCase			:			List[Any]        =					k_groups
								_lowerCAmelCase			:			List[Any]        =					v_groups
								_lowerCAmelCase			:			Dict        =					post_attention_groups
								_lowerCAmelCase			:			Optional[Any]        =					intermediate_groups
								_lowerCAmelCase			:			str        =					output_groups






class 				__snake_case     (_a						):



				@property
				def 					SCREAMING_SNAKE_CASE				(					self					:						Union[str, Any]					)     ->			Mapping[str, Mapping[int, str]]:





								'''simple docstring'''




								if self.task == "multiple-choice":
												_lowerCAmelCase			:			Optional[int]        =					{0: """batch""", 1: """choice""", 2: """sequence"""}
								else:
												_lowerCAmelCase			:			int        =					{0: """batch""", 1: """sequence"""}
								return OrderedDict(
								    [
								        ("""input_ids""", dynamic_axis),
								        ("""attention_mask""", dynamic_axis),
								        ("""token_type_ids""", dynamic_axis),
								    ]					)

