

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 LevitImageProcessor


class SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase=7 ,				__UpperCamelCase=3 ,				__UpperCamelCase=18 ,				__UpperCamelCase=30 ,				__UpperCamelCase=4_00 ,				__UpperCamelCase=True ,				__UpperCamelCase=None ,				__UpperCamelCase=True ,				__UpperCamelCase=None ,				__UpperCamelCase=True ,				__UpperCamelCase=[0.5, 0.5, 0.5] ,				__UpperCamelCase=[0.5, 0.5, 0.5] ,				):

				"""simple docstring"""


				snake_case_      		= size if size is not None else {'shortest_edge': 18}
				snake_case_      		= crop_size if crop_size is not None else {'height': 18, 'width': 18}
				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_      		= do_center_crop
				snake_case_      		= crop_size
				snake_case_      		= do_normalize
				snake_case_      		= image_mean
				snake_case_      		= image_std







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


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







@require_torch
@require_vision
class SCREAMING_SNAKE_CASE  (		__snake_case							,						unittest.TestCase							):






			"""simple docstring"""




			__A						  =  LevitImageProcessor if is_vision_available() else None


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= LevitImageProcessingTester(self							)


			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return self.image_processor_tester.prepare_image_processor_dict()


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.image_processing_class(**self.image_processor_dict							)
				self.assertTrue(hasattr(__UpperCamelCase ,				'image_mean'							)							)
				self.assertTrue(hasattr(__UpperCamelCase ,				'image_std'							)							)
				self.assertTrue(hasattr(__UpperCamelCase ,				'do_normalize'							)							)
				self.assertTrue(hasattr(__UpperCamelCase ,				'do_resize'							)							)
				self.assertTrue(hasattr(__UpperCamelCase ,				'do_center_crop'							)							)
				self.assertTrue(hasattr(__UpperCamelCase ,				'size'							)							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.image_processing_class.from_dict(self.image_processor_dict							)
				self.assertEqual(image_processor.size ,				{'shortest_edge': 18}							)
				self.assertEqual(image_processor.crop_size ,				{'height': 18, 'width': 18}							)

				snake_case_      		= 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 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				pass


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				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=__UpperCamelCase							)
				for image in image_inputs:
					self.assertIsInstance(__UpperCamelCase ,				Image.Image							)

				# Test not batched input
				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.crop_size['height'],
				        self.image_processor_tester.crop_size['width'],
				    ) ,				)

				# Test batched
				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.crop_size['height'],
				        self.image_processor_tester.crop_size['width'],
				    ) ,				)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				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=__UpperCamelCase ,				numpify=__UpperCamelCase							)
				for image in image_inputs:
					self.assertIsInstance(__UpperCamelCase ,				np.ndarray							)

				# Test not batched input
				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.crop_size['height'],
				        self.image_processor_tester.crop_size['width'],
				    ) ,				)

				# Test batched
				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.crop_size['height'],
				        self.image_processor_tester.crop_size['width'],
				    ) ,				)







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				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=__UpperCamelCase ,				torchify=__UpperCamelCase							)
				for image in image_inputs:
					self.assertIsInstance(__UpperCamelCase ,				torch.Tensor							)

				# Test not batched input
				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.crop_size['height'],
				        self.image_processor_tester.crop_size['width'],
				    ) ,				)

				# Test batched
				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.crop_size['height'],
				        self.image_processor_tester.crop_size['width'],
				    ) ,				)



import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm





def a(lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= 1.5
	snake_case_      		= int(factor * num_class_images	)
	snake_case_      		= ClipClient(
	    url='https://knn.laion.ai/knn-service'			, indice_name='laion_400m'			, num_images=lowercase__			, aesthetic_weight=0.1	)

	os.makedirs(f"""{class_data_dir}/images"""			, exist_ok=lowercase__	)
	if len(list(Path(f"""{class_data_dir}/images"""	).iterdir()	)	) >= num_class_images:
		return

	while True:
		snake_case_      		= client.query(text=lowercase__	)
		if len(lowercase__	) >= factor * num_class_images or num_images > 1e4:
			break
		else:
			snake_case_      		= int(factor * num_images	)
			snake_case_      		= ClipClient(
			    url='https://knn.laion.ai/knn-service'			, indice_name='laion_400m'			, num_images=lowercase__			, aesthetic_weight=0.1			, )

	snake_case_      		= 0
	snake_case_      		= 0
	snake_case_      		= tqdm(desc='downloading real regularization images'			, total=lowercase__	)

	with open(f"""{class_data_dir}/caption.txt"""			, 'w'	) as fa, open(f"""{class_data_dir}/urls.txt"""			, 'w'	) as fa, open(
	    f"""{class_data_dir}/images.txt"""			, 'w'	) as fa:
		while total < num_class_images:
			snake_case_      		= class_images[count]
			count += 1
			try:
				snake_case_      		= requests.get(images['url']	)
				if img.status_code == 200:
					snake_case_      		= Image.open(BytesIO(img.content	)	)
					with open(f"""{class_data_dir}/images/{total}.jpg"""			, 'wb'	) as f:
						f.write(img.content	)
					fa.write(images['caption'] + '\n'	)
					fa.write(images['url'] + '\n'	)
					fa.write(f"""{class_data_dir}/images/{total}.jpg""" + '\n'	)
					total += 1
					pbar.update(1	)
				else:
					continue
			except Exception:
				continue
	return





def a():





	'''simple docstring'''
	snake_case_      		= argparse.ArgumentParser(''			, add_help=lowercase__	)
	parser.add_argument('--class_prompt'			, help='text prompt to retrieve images'			, required=lowercase__			, type=lowercase__	)
	parser.add_argument('--class_data_dir'			, help='path to save images'			, required=lowercase__			, type=lowercase__	)
	parser.add_argument('--num_class_images'			, help='number of images to download'			, default=200			, type=lowercase__	)
	return parser.parse_args()


if __name__ == "__main__":
		A											=     parse_args()
		retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)
