

'''simple docstring'''

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 ViltImageProcessor







class lowerCamelCase   (    unittest.TestCase				):

				def __init__(       self,						lowercase_,						lowercase_=7,						lowercase_=3,						lowercase_=18,						lowercase_=30,						lowercase_=400,						lowercase_=True,						lowercase_=None,						lowercase_=2,						lowercase_=True,						lowercase_=[0.5, 0.5, 0.5],						lowercase_=[0.5, 0.5, 0.5],						)    ->							Optional[Any]:
					snake_case							=					size if size is not None else {'shortest_edge': 30}
					snake_case							=					parent
					snake_case							=					batch_size
					snake_case							=					num_channels
					snake_case							=					image_size
					snake_case							=					min_resolution
					snake_case							=					max_resolution
					snake_case							=					do_resize
					snake_case							=					size
					snake_case							=					size_divisor
					snake_case							=					do_normalize
					snake_case							=					image_mean
					snake_case							=					image_std

				def 					_lowerCamelCase    (       self )    ->							Any:
					return {
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_normalize": self.do_normalize,
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "size_divisor": self.size_divisor,
					}






				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_=False )    ->							Optional[int]:
					if not batched:
						snake_case							=					self.size['shortest_edge']
						snake_case							=					image_inputs[0]
						if isinstance(lowercase_,						Image.Image ):
							snake_case			,			snake_case							=					image.size
						else:
							snake_case			,			snake_case							=					image.shape[1], image.shape[2]
						snake_case							=					size / min(lowercase_,						lowercase_ )
						if h < w:
							snake_case			,			snake_case							=					size, scale * w
						else:
							snake_case			,			snake_case							=					scale * h, size

						snake_case							=					int((1333 / 800) * size )
						if max(lowercase_,						lowercase_ ) > max_size:
							snake_case							=					max_size / max(lowercase_,						lowercase_ )
							snake_case							=					newh * scale
							snake_case							=					neww * scale

						snake_case			,			snake_case							=					int(newh + 0.5 ), int(neww + 0.5 )
						snake_case			,			snake_case							=					(
						    newh // self.size_divisor * self.size_divisor,
						    neww // self.size_divisor * self.size_divisor,
						)

					else:
						snake_case							=					[]
						for image in image_inputs:
							snake_case			,			snake_case							=					self.get_expected_values([image] )
							expected_values.append((expected_height, expected_width) )
						snake_case							=					max(lowercase_,						key=lambda lowercase_ : item[0] )[0]
						snake_case							=					max(lowercase_,						key=lambda lowercase_ : item[1] )[1]

					return expected_height, expected_width







@require_torch
@require_vision
class lowerCamelCase   (    __lowerCAmelCase				,				unittest.TestCase				):
				snake_case_							     = ViltImageProcessor if is_vision_available() else None

				def 					_lowerCamelCase    (       self )    ->							int:
					snake_case							=					ViltImageProcessingTester(self )

				@property
				def 					_lowerCamelCase    (       self )    ->							int:
					return self.image_processor_tester.prepare_image_processor_dict()

				def 					_lowerCamelCase    (       self )    ->							Union[str, Any]:
					snake_case							=					self.image_processing_class(**self.image_processor_dict )
					self.assertTrue(hasattr(lowercase_,						'image_mean' ) )
					self.assertTrue(hasattr(lowercase_,						'image_std' ) )
					self.assertTrue(hasattr(lowercase_,						'do_normalize' ) )
					self.assertTrue(hasattr(lowercase_,						'do_resize' ) )
					self.assertTrue(hasattr(lowercase_,						'size' ) )
					self.assertTrue(hasattr(lowercase_,						'size_divisor' ) )

				def 					_lowerCamelCase    (       self )    ->							Tuple:
					snake_case							=					self.image_processing_class.from_dict(self.image_processor_dict )
					self.assertEqual(image_processor.size,						{'shortest_edge': 30} )

					snake_case							=					self.image_processing_class.from_dict(self.image_processor_dict,						size=42 )
					self.assertEqual(image_processor.size,						{'shortest_edge': 42} )

				def 					_lowerCamelCase    (       self )    ->							List[str]:
					pass

				def 					_lowerCamelCase    (       self )    ->							Tuple:
					# Initialize image_processing
					snake_case							=					self.image_processing_class(**self.image_processor_dict )
					# create random PIL images
					snake_case							=					prepare_image_inputs(self.image_processor_tester,						equal_resolution=lowercase_ )
					for image in image_inputs:
						self.assertIsInstance(lowercase_,						Image.Image )

					# Test not batched input
					snake_case							=					image_processing(image_inputs[0],						return_tensors='pt' ).pixel_values

					snake_case			,			snake_case							=					self.image_processor_tester.get_expected_values(lowercase_ )
					self.assertEqual(
					    encoded_images.shape,						(1, self.image_processor_tester.num_channels, expected_height, expected_width),						)

					# Test batched
					snake_case							=					image_processing(lowercase_,						return_tensors='pt' ).pixel_values

					snake_case			,			snake_case							=					self.image_processor_tester.get_expected_values(lowercase_,						batched=lowercase_ )
					self.assertEqual(
					    encoded_images.shape,						(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    ),						)

				def 					_lowerCamelCase    (       self )    ->							Optional[Any]:
					# Initialize image_processing
					snake_case							=					self.image_processing_class(**self.image_processor_dict )
					# create random numpy tensors
					snake_case							=					prepare_image_inputs(self.image_processor_tester,						equal_resolution=lowercase_,						numpify=lowercase_ )
					for image in image_inputs:
						self.assertIsInstance(lowercase_,						np.ndarray )

					# Test not batched input
					snake_case							=					image_processing(image_inputs[0],						return_tensors='pt' ).pixel_values

					snake_case			,			snake_case							=					self.image_processor_tester.get_expected_values(lowercase_ )
					self.assertEqual(
					    encoded_images.shape,						(1, self.image_processor_tester.num_channels, expected_height, expected_width),						)

					# Test batched
					snake_case							=					image_processing(lowercase_,						return_tensors='pt' ).pixel_values

					snake_case			,			snake_case							=					self.image_processor_tester.get_expected_values(lowercase_,						batched=lowercase_ )
					self.assertEqual(
					    encoded_images.shape,						(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    ),						)






				def 					_lowerCamelCase    (       self )    ->							Union[str, Any]:
					# Initialize image_processing
					snake_case							=					self.image_processing_class(**self.image_processor_dict )
					# create random PyTorch tensors
					snake_case							=					prepare_image_inputs(self.image_processor_tester,						equal_resolution=lowercase_,						torchify=lowercase_ )
					for image in image_inputs:
						self.assertIsInstance(lowercase_,						torch.Tensor )

					# Test not batched input
					snake_case							=					image_processing(image_inputs[0],						return_tensors='pt' ).pixel_values

					snake_case			,			snake_case							=					self.image_processor_tester.get_expected_values(lowercase_ )
					self.assertEqual(
					    encoded_images.shape,						(1, self.image_processor_tester.num_channels, expected_height, expected_width),						)

					# Test batched
					snake_case							=					image_processing(lowercase_,						return_tensors='pt' ).pixel_values

					snake_case			,			snake_case							=					self.image_processor_tester.get_expected_values(lowercase_,						batched=lowercase_ )
					self.assertEqual(
					    encoded_images.shape,						(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    ),						)






"""simple docstring"""






import requests

try:
						from django.contrib.auth import authenticate, login
						from django.shortcuts import redirect, render
except ImportError:
						lowerCAmelCase__    :    Union[str, Any]			=      print



def     a_		(   lowerCamelCase			):
				# Enter your recaptcha secret key here
				UpperCAmelCase__			 =				'secretKey'
				UpperCAmelCase__			 =				'https://www.google.com/recaptcha/api/siteverify'

				# when method is not POST, direct user to login page
				if request.method != "POST":
								return render(lowerCamelCase   ,     'login.html'			)

				# from the frontend, get username, password, and client_key
				UpperCAmelCase__			 =				request.POST.get('username'			)
				UpperCAmelCase__			 =				request.POST.get('password'			)
				UpperCAmelCase__			 =				request.POST.get('g-recaptcha-response'			)

				# post recaptcha response to Google's recaptcha api
				UpperCAmelCase__			 =				requests.post(lowerCamelCase   ,     data={'secret': secret_key, 'response': client_key}			)
				# if the recaptcha api verified our keys
				if response.json().get('success'   ,     lowerCamelCase			):
								# authenticate the user
								UpperCAmelCase__			 =				authenticate(lowerCamelCase   ,     username=lowerCamelCase   ,     password=lowerCamelCase			)
								if user_in_database:
												login(lowerCamelCase   ,     lowerCamelCase			)
												return redirect('/your-webpage'			)
				return render(lowerCamelCase   ,     'login.html'			)



