

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


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import CLIPImageProcessor






class 	__magic_name__				(       unittest.TestCase							):






     def __init__(							self		,	_lowercase		,	_lowercase=7		,	_lowercase=3		,	_lowercase=18		,	_lowercase=30		,	_lowercase=400		,	_lowercase=True		,	_lowercase=None		,	_lowercase=True		,	_lowercase=None		,	_lowercase=True		,	_lowercase=[0.48_145_466, 0.4_578_275, 0.40_821_073]		,	_lowercase=[0.26_862_954, 0.26_130_258, 0.27_577_711]		,	_lowercase=True		,	)->    Any:
         UpperCamelCase_      =	size if size is not None else {'''shortest_edge''': 20}
         UpperCamelCase_      =	crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
         UpperCamelCase_      =	parent
         UpperCamelCase_      =	batch_size
         UpperCamelCase_      =	num_channels
         UpperCamelCase_      =	image_size
         UpperCamelCase_      =	min_resolution
         UpperCamelCase_      =	max_resolution
         UpperCamelCase_      =	do_resize
         UpperCamelCase_      =	size
         UpperCamelCase_      =	do_center_crop
         UpperCamelCase_      =	crop_size
         UpperCamelCase_      =	do_normalize
         UpperCamelCase_      =	image_mean
         UpperCamelCase_      =	image_std
         UpperCamelCase_      =	do_convert_rgb






     def   UpperCAmelCase_							(							self  )->    int:
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_center_crop": self.do_center_crop,
             "crop_size": self.crop_size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_convert_rgb": self.do_convert_rgb,
         }






     def   UpperCAmelCase_							(							self		,	_lowercase=False		,	_lowercase=False		,	_lowercase=False  )->    Optional[int]:
         assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

         if equal_resolution:
             UpperCamelCase_      =	[]
             for i in range(self.batch_size  ):
                 image_inputs.append(
                     np.random.randint(
                         255		,	size=(self.num_channels, self.max_resolution, self.max_resolution)		,	dtype=np.uinta  )  )
         else:
             UpperCamelCase_      =	[]
             for i in range(self.batch_size  ):
                 UpperCamelCase_      =	np.random.choice(np.arange(self.min_resolution		,	self.max_resolution  )		,	2  )
                 image_inputs.append(np.random.randint(255		,	size=(self.num_channels, width, height)		,	dtype=np.uinta  )  )

         if not numpify and not torchify:
             # PIL expects the channel dimension as last dimension
             UpperCamelCase_      =	[Image.fromarray(np.moveaxis(lowercase_		,	0		,	-1  )  ) for x in image_inputs]

         if torchify:
             UpperCamelCase_      =	[torch.from_numpy(lowercase_  ) for x in image_inputs]

         return image_inputs






@require_torch
@require_vision
class 	__magic_name__				(       __UpperCAmelCase ,			unittest.TestCase							):
     UpperCamelCase_  :Optional[Any]								=			CLIPImageProcessor if is_vision_available() else None






     def   UpperCAmelCase_							(							self  )->    int:
         UpperCamelCase_      =	CLIPImageProcessingTester(self  )






     @property
     def   UpperCAmelCase_							(							self  )->    List[Any]:
         return self.image_processor_tester.prepare_image_processor_dict()






     def   UpperCAmelCase_							(							self  )->    Tuple:
         UpperCamelCase_      =	self.image_processing_class(**self.image_processor_dict  )
         self.assertTrue(hasattr(lowercase_		,	"do_resize"  )  )
         self.assertTrue(hasattr(lowercase_		,	"size"  )  )
         self.assertTrue(hasattr(lowercase_		,	"do_center_crop"  )  )
         self.assertTrue(hasattr(lowercase_		,	"center_crop"  )  )
         self.assertTrue(hasattr(lowercase_		,	"do_normalize"  )  )
         self.assertTrue(hasattr(lowercase_		,	"image_mean"  )  )
         self.assertTrue(hasattr(lowercase_		,	"image_std"  )  )
         self.assertTrue(hasattr(lowercase_		,	"do_convert_rgb"  )  )






     def   UpperCAmelCase_							(							self  )->    Any:
         UpperCamelCase_      =	self.image_processing_class.from_dict(self.image_processor_dict  )
         self.assertEqual(image_processor.size		,	{"shortest_edge": 20}  )
         self.assertEqual(image_processor.crop_size		,	{"height": 18, "width": 18}  )

         UpperCamelCase_      =	self.image_processing_class.from_dict(self.image_processor_dict		,	size=42		,	crop_size=84  )
         self.assertEqual(image_processor.size		,	{"shortest_edge": 42}  )
         self.assertEqual(image_processor.crop_size		,	{"height": 84, "width": 84}  )






     def   UpperCAmelCase_							(							self  )->    Optional[int]:
         pass






     def   UpperCAmelCase_							(							self  )->    str:
         UpperCamelCase_      =	self.image_processing_class(**self.image_processor_dict  )
         # create random PIL images
         UpperCamelCase_      =	self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_  )
         for image in image_inputs:
             self.assertIsInstance(lowercase_		,	Image.Image  )

         # Test not batched input
         UpperCamelCase_      =	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.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )		,	)

         # Test batched
         UpperCamelCase_      =	image_processing(lowercase_		,	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.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )		,	)






     def   UpperCAmelCase_							(							self  )->    Tuple:
         UpperCamelCase_      =	self.image_processing_class(**self.image_processor_dict  )
         # create random numpy tensors
         UpperCamelCase_      =	self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_		,	numpify=lowercase_  )
         for image in image_inputs:
             self.assertIsInstance(lowercase_		,	np.ndarray  )

         # Test not batched input
         UpperCamelCase_      =	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.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )		,	)

         # Test batched
         UpperCamelCase_      =	image_processing(lowercase_		,	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.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )		,	)






     def   UpperCAmelCase_							(							self  )->    List[str]:
         UpperCamelCase_      =	self.image_processing_class(**self.image_processor_dict  )
         # create random PyTorch tensors
         UpperCamelCase_      =	self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_		,	torchify=lowercase_  )
         for image in image_inputs:
             self.assertIsInstance(lowercase_		,	torch.Tensor  )

         # Test not batched input
         UpperCamelCase_      =	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.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )		,	)

         # Test batched
         UpperCamelCase_      =	image_processing(lowercase_		,	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.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )		,	)






@require_torch
@require_vision
class 	__magic_name__				(       __UpperCAmelCase ,			unittest.TestCase							):
     UpperCamelCase_  :List[Any]								=			CLIPImageProcessor if is_vision_available() else None






     def   UpperCAmelCase_							(							self  )->    Dict:
         UpperCamelCase_      =	CLIPImageProcessingTester(self		,	num_channels=4  )
         UpperCamelCase_      =	3






     @property
     def   UpperCAmelCase_							(							self  )->    Tuple:
         return self.image_processor_tester.prepare_image_processor_dict()






     def   UpperCAmelCase_							(							self  )->    Optional[Any]:
         UpperCamelCase_      =	self.image_processing_class(**self.image_processor_dict  )
         self.assertTrue(hasattr(lowercase_		,	"do_resize"  )  )
         self.assertTrue(hasattr(lowercase_		,	"size"  )  )
         self.assertTrue(hasattr(lowercase_		,	"do_center_crop"  )  )
         self.assertTrue(hasattr(lowercase_		,	"center_crop"  )  )
         self.assertTrue(hasattr(lowercase_		,	"do_normalize"  )  )
         self.assertTrue(hasattr(lowercase_		,	"image_mean"  )  )
         self.assertTrue(hasattr(lowercase_		,	"image_std"  )  )
         self.assertTrue(hasattr(lowercase_		,	"do_convert_rgb"  )  )






     def   UpperCAmelCase_							(							self  )->    Dict:
         pass






     def   UpperCAmelCase_							(							self  )->    List[Any]:
         UpperCamelCase_      =	self.image_processing_class(**self.image_processor_dict  )
         # create random PIL images
         UpperCamelCase_      =	self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_  )
         for image in image_inputs:
             self.assertIsInstance(lowercase_		,	Image.Image  )

         # Test not batched input
         UpperCamelCase_      =	image_processing(image_inputs[0]		,	return_tensors="pt"  ).pixel_values
         self.assertEqual(
             encoded_images.shape		,	(
                 1,
                 self.expected_encoded_image_num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )		,	)

         # Test batched
         UpperCamelCase_      =	image_processing(lowercase_		,	return_tensors="pt"  ).pixel_values
         self.assertEqual(
             encoded_images.shape		,	(
                 self.image_processor_tester.batch_size,
                 self.expected_encoded_image_num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )		,	)



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


SCREAMING_SNAKE_CASE		:List[Any]				  =    ["""CoLA""", """SST""", """MRPC""", """QQP""", """STS""", """MNLI""", """SNLI""", """QNLI""", """RTE""", """WNLI""", """diagnostic"""]
SCREAMING_SNAKE_CASE		:List[Any]				  =    {
    """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		:str				  =    """https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt"""
SCREAMING_SNAKE_CASE		:int				  =    """https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt"""




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )->	Tuple:



    """simple docstring"""






    print(f"Downloading and extracting {task}..." )
    UpperCamelCase_      =	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  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )->	Optional[Any]:



    """simple docstring"""






    print("Processing MRPC..." )
    UpperCamelCase_      =	os.path.join(SCREAMING_SNAKE_CASE_ ,	"MRPC" )
    if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
        os.mkdir(SCREAMING_SNAKE_CASE_ )
    if path_to_data:
        UpperCamelCase_      =	os.path.join(SCREAMING_SNAKE_CASE_ ,	"msr_paraphrase_train.txt" )
        UpperCamelCase_      =	os.path.join(SCREAMING_SNAKE_CASE_ ,	"msr_paraphrase_test.txt" )
    else:
        print("Local MRPC data not specified, downloading data from %s" % MRPC_TRAIN )
        UpperCamelCase_      =	os.path.join(SCREAMING_SNAKE_CASE_ ,	"msr_paraphrase_train.txt" )
        UpperCamelCase_      =	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" ) )

    UpperCamelCase_      =	[]
    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:
        UpperCamelCase_      =	data_fh.readline()
        train_fh.write(SCREAMING_SNAKE_CASE_ )
        dev_fh.write(SCREAMING_SNAKE_CASE_ )
        for row in data_fh:
            UpperCamelCase_     ,					UpperCamelCase_     ,					UpperCamelCase_     ,					UpperCamelCase_     ,					UpperCamelCase_      =	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:
        UpperCamelCase_      =	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_ ):
            UpperCamelCase_     ,					UpperCamelCase_     ,					UpperCamelCase_     ,					UpperCamelCase_     ,					UpperCamelCase_      =	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  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ )->	Tuple:



    """simple docstring"""






    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" ) )
    UpperCamelCase_      =	os.path.join(SCREAMING_SNAKE_CASE_ ,	"diagnostic" ,	"diagnostic.tsv" )
    urllib.request.urlretrieve(TASKaPATH["diagnostic"] ,	SCREAMING_SNAKE_CASE_ )
    print("\tCompleted!" )
    return




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ )->	Any:



    """simple docstring"""






    UpperCamelCase_      =	task_names.split("," )
    if "all" in task_names:
        UpperCamelCase_      =	TASKS
    else:
        UpperCamelCase_      =	[]
        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  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ )->	int:



    """simple docstring"""






    UpperCamelCase_      =	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="" ,	)
    UpperCamelCase_      =	parser.parse_args(SCREAMING_SNAKE_CASE_ )

    if not os.path.isdir(args.data_dir ):
        os.mkdir(args.data_dir )
    UpperCamelCase_      =	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:]))
