




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


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

lowerCamelCase_  						=   "https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt"
lowerCamelCase_  						=   "https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt"



def 	UpperCAmelCase_    (	__UpperCamelCase,				__UpperCamelCase	):
    print(f"""Downloading and extracting {task}..."""	)
    SCREAMING_SNAKE_CASE__								=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 	UpperCAmelCase_    (	__UpperCamelCase,				__UpperCamelCase	):
    print("""Processing MRPC..."""	)
    SCREAMING_SNAKE_CASE__								=os.path.join(__UpperCamelCase,				"""MRPC"""	)
    if not os.path.isdir(__UpperCamelCase	):
        os.mkdir(__UpperCamelCase	)
    if path_to_data:
        SCREAMING_SNAKE_CASE__								=os.path.join(__UpperCamelCase,				"""msr_paraphrase_train.txt"""	)
        SCREAMING_SNAKE_CASE__								=os.path.join(__UpperCamelCase,				"""msr_paraphrase_test.txt"""	)
    else:
        print("""Local MRPC data not specified, downloading data from %s""" % MRPC_TRAIN	)
        SCREAMING_SNAKE_CASE__								=os.path.join(__UpperCamelCase,				"""msr_paraphrase_train.txt"""	)
        SCREAMING_SNAKE_CASE__								=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"""	)	)

    SCREAMING_SNAKE_CASE__								=[]
    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:
        SCREAMING_SNAKE_CASE__								=data_fh.readline()
        train_fh.write(__UpperCamelCase	)
        dev_fh.write(__UpperCamelCase	)
        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(__UpperCamelCase,				encoding="""utf8"""	) as data_fh, open(
        os.path.join(__UpperCamelCase,				"""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(__UpperCamelCase	):
            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_    (	__UpperCamelCase	):
    print("""Downloading and extracting diagnostic..."""	)
    if not os.path.isdir(os.path.join(__UpperCamelCase,				"""diagnostic"""	)	):
        os.mkdir(os.path.join(__UpperCamelCase,				"""diagnostic"""	)	)
    SCREAMING_SNAKE_CASE__								=os.path.join(__UpperCamelCase,				"""diagnostic""",				"""diagnostic.tsv"""	)
    urllib.request.urlretrieve(TASKaPATH["""diagnostic"""],				__UpperCamelCase	)
    print("""\tCompleted!"""	)
    return



def 	UpperCAmelCase_    (	__UpperCamelCase	):
    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(__UpperCamelCase	)
    return tasks



def 	UpperCAmelCase_    (	__UpperCamelCase	):
    SCREAMING_SNAKE_CASE__								=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="""""",				)
    SCREAMING_SNAKE_CASE__								=parser.parse_args(__UpperCamelCase	)

    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(__UpperCamelCase,				args.data_dir	)


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












import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ConvNextImageProcessor
class       __a					( unittest.TestCase     ):


     """simple docstring"""


     def __init__(	self			: List[str]				,_UpperCamelCase			: int				,_UpperCamelCase			: int=7				,_UpperCamelCase			: int=3				,_UpperCamelCase			: List[str]=1_8				,_UpperCamelCase			: Dict=3_0				,_UpperCamelCase			: Tuple=4_0_0				,_UpperCamelCase			: Optional[int]=True				,_UpperCamelCase			: int=None				,_UpperCamelCase			: str=0.875				,_UpperCamelCase			: Tuple=True				,_UpperCamelCase			: Dict=[0.5, 0.5, 0.5]				,_UpperCamelCase			: str=[0.5, 0.5, 0.5]				,)		->   int:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=size if size is not None else {"""shortest_edge""": 2_0}
         SCREAMING_SNAKE_CASE__								=parent
         SCREAMING_SNAKE_CASE__								=batch_size
         SCREAMING_SNAKE_CASE__								=num_channels
         SCREAMING_SNAKE_CASE__								=image_size
         SCREAMING_SNAKE_CASE__								=min_resolution
         SCREAMING_SNAKE_CASE__								=max_resolution
         SCREAMING_SNAKE_CASE__								=do_resize
         SCREAMING_SNAKE_CASE__								=size
         SCREAMING_SNAKE_CASE__								=crop_pct
         SCREAMING_SNAKE_CASE__								=do_normalize
         SCREAMING_SNAKE_CASE__								=image_mean
         SCREAMING_SNAKE_CASE__								=image_std




     def   __A				(	self			: Dict						)		->   str:


         '''simple docstring'''





         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "crop_pct": self.crop_pct,
         }






@require_torch
@require_vision
class       __a					( __lowerCamelCase							,     unittest.TestCase     ):


     """simple docstring"""


     _A				:    Dict									=  ConvNextImageProcessor if is_vision_available() else None




     def   __A				(	self			: List[str]						)		->   Dict:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=ConvNextImageProcessingTester(self						)




     @property
     def   __A				(	self			: str						)		->   Optional[int]:


         '''simple docstring'''





         return self.image_processor_tester.prepare_image_processor_dict()




     def   __A				(	self			: Optional[int]						)		->   Any:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.image_processing_class(**self.image_processor_dict						)
         self.assertTrue(hasattr(_UpperCamelCase				,"""do_resize"""						)						)
         self.assertTrue(hasattr(_UpperCamelCase				,"""size"""						)						)
         self.assertTrue(hasattr(_UpperCamelCase				,"""crop_pct"""						)						)
         self.assertTrue(hasattr(_UpperCamelCase				,"""do_normalize"""						)						)
         self.assertTrue(hasattr(_UpperCamelCase				,"""image_mean"""						)						)
         self.assertTrue(hasattr(_UpperCamelCase				,"""image_std"""						)						)




     def   __A				(	self			: Tuple						)		->   Optional[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.image_processing_class.from_dict(self.image_processor_dict						)
         self.assertEqual(image_processor.size				,{"""shortest_edge""": 2_0}						)

         SCREAMING_SNAKE_CASE__								=self.image_processing_class.from_dict(self.image_processor_dict				,size=4_2						)
         self.assertEqual(image_processor.size				,{"""shortest_edge""": 4_2}						)




     def   __A				(	self			: Any						)		->   List[str]:


         '''simple docstring'''





         pass




     def   __A				(	self			: Optional[int]						)		->   int:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.image_processing_class(**self.image_processor_dict						)
         # create random PIL images
         SCREAMING_SNAKE_CASE__								=prepare_image_inputs(self.image_processor_tester				,equal_resolution=_UpperCamelCase						)
         for image in image_inputs:
             self.assertIsInstance(_UpperCamelCase				,Image.Image						)

         # Test not batched input
         SCREAMING_SNAKE_CASE__								=image_processing(image_inputs[0]				,return_tensors="""pt"""						).pixel_values
         self.assertEqual(
             encoded_images.shape				,(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,)

         # Test batched
         SCREAMING_SNAKE_CASE__								=image_processing(_UpperCamelCase				,return_tensors="""pt"""						).pixel_values
         self.assertEqual(
             encoded_images.shape				,(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,)




     def   __A				(	self			: str						)		->   str:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.image_processing_class(**self.image_processor_dict						)
         # create random numpy tensors
         SCREAMING_SNAKE_CASE__								=prepare_image_inputs(self.image_processor_tester				,equal_resolution=_UpperCamelCase				,numpify=_UpperCamelCase						)
         for image in image_inputs:
             self.assertIsInstance(_UpperCamelCase				,np.ndarray						)

         # Test not batched input
         SCREAMING_SNAKE_CASE__								=image_processing(image_inputs[0]				,return_tensors="""pt"""						).pixel_values
         self.assertEqual(
             encoded_images.shape				,(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,)

         # Test batched
         SCREAMING_SNAKE_CASE__								=image_processing(_UpperCamelCase				,return_tensors="""pt"""						).pixel_values
         self.assertEqual(
             encoded_images.shape				,(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,)




     def   __A				(	self			: List[Any]						)		->   Union[str, Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.image_processing_class(**self.image_processor_dict						)
         # create random PyTorch tensors
         SCREAMING_SNAKE_CASE__								=prepare_image_inputs(self.image_processor_tester				,equal_resolution=_UpperCamelCase				,torchify=_UpperCamelCase						)
         for image in image_inputs:
             self.assertIsInstance(_UpperCamelCase				,torch.Tensor						)

         # Test not batched input
         SCREAMING_SNAKE_CASE__								=image_processing(image_inputs[0]				,return_tensors="""pt"""						).pixel_values
         self.assertEqual(
             encoded_images.shape				,(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,)

         # Test batched
         SCREAMING_SNAKE_CASE__								=image_processing(_UpperCamelCase				,return_tensors="""pt"""						).pixel_values
         self.assertEqual(
             encoded_images.shape				,(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,)






