



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      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						Dict     =					1.5
			A_     :						Union[str, Any]     =					int(factor * num_class_images   )
			A_     :						List[Any]     =					ClipClient(
			    url="""https://knn.laion.ai/knn-service"""						,				indice_name="""laion_400m"""						,				num_images=lowerCamelCase__						,				aesthetic_weight=0.1   )

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

			while True:
						A_     :						str     =					client.query(text=lowerCamelCase__   )
						if len(lowerCamelCase__   ) >= factor * num_class_images or num_images > 1E4:
									break
						else:
									A_     :						Optional[Any]     =					int(factor * num_images   )
									A_     :						int     =					ClipClient(
									    url="""https://knn.laion.ai/knn-service"""						,				indice_name="""laion_400m"""						,				num_images=lowerCamelCase__						,				aesthetic_weight=0.1						,				)

			A_     :						Tuple     =					0
			A_     :						List[Any]     =					0
			A_     :						Dict     =					tqdm(desc="""downloading real regularization images"""						,				total=lowerCamelCase__   )

			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:
									A_     :						Union[str, Any]     =					class_images[count]
									count += 1
									try:
												A_     :						Optional[int]     =					requests.get(images["""url"""]   )
												if img.status_code == 2_00:
															A_     :						List[Any]     =					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'''





			A_     :						Union[str, Any]     =					argparse.ArgumentParser(""""""						,				add_help=lowerCamelCase__   )
			parser.add_argument("""--class_prompt"""						,				help="""text prompt to retrieve images"""						,				required=lowerCamelCase__						,				type=lowerCamelCase__   )
			parser.add_argument("""--class_data_dir"""						,				help="""path to save images"""						,				required=lowerCamelCase__						,				type=lowerCamelCase__   )
			parser.add_argument("""--num_class_images"""						,				help="""number of images to download"""						,				default=2_00						,				type=lowerCamelCase__   )
			return parser.parse_args()


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




'''simple docstring'''




import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad







class      _lowerCAmelCase							(      unittest.TestCase			):


	def    _a     (self							):
				A_     :						Dict     =					10


	def    _a     (self							):
				A_     :						str     =					[1, 2, 3, 4]
				A_     :						List[str]     =					[1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
				self.assertEqual(truncate_or_pad(lowercase	,				self.block_size	,				0							)	,				lowercase							)


	def    _a     (self							):
				A_     :						str     =					[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
				A_     :						Tuple     =					[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
				self.assertEqual(truncate_or_pad(lowercase	,				self.block_size	,				0							)	,				lowercase							)


	def    _a     (self							):
				A_     :						List[Any]     =					[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
				A_     :						str     =					[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
				self.assertEqual(truncate_or_pad(lowercase	,				self.block_size	,				0							)	,				lowercase							)


	def    _a     (self							):
				A_     :						Any     =					"""It was the year of Our Lord one thousand seven hundred and
        seventy-five.\n\nSpiritual revelations were conceded to England at that
        favoured period, as at this."""
				A_,   A_     :						Optional[int]     =					process_story(lowercase							)
				self.assertEqual(lowercase	,				[]							)


	def    _a     (self							):
				A_     :						Union[str, Any]     =					""""""
				A_,   A_     :						Union[str, Any]     =					process_story(lowercase							)
				self.assertEqual(lowercase	,				[]							)
				self.assertEqual(lowercase	,				[]							)


	def    _a     (self							):
				A_     :						List[Any]     =					(
				    """It was the year of Our Lord one thousand seven hundred and """
				    """seventy-five\n\nSpiritual revelations were conceded to England """
				    """at that favoured period, as at this.\n@highlight\n\nIt was the best of times"""
				)
				A_,   A_     :						int     =					process_story(lowercase							)

				A_     :						List[Any]     =					[
				    """It was the year of Our Lord one thousand seven hundred and seventy-five.""",
				    """Spiritual revelations were conceded to England at that favoured period, as at this.""",
				]
				self.assertEqual(lowercase	,				lowercase							)

				A_     :						Tuple     =					["""It was the best of times."""]
				self.assertEqual(lowercase	,				lowercase							)


	def    _a     (self							):
				A_     :						Dict     =					torch.tensor([1, 2, 3, 4]							)
				A_     :						Union[str, Any]     =					torch.tensor([1, 1, 1, 1]							)
				np.testing.assert_array_equal(build_mask(lowercase	,				0							).numpy()	,				expected.numpy()							)


	def    _a     (self							):
				A_     :						Union[str, Any]     =					torch.tensor([1, 2, 3, 4, 23, 23, 23]							)
				A_     :						List[str]     =					torch.tensor([1, 1, 1, 1, 0, 0, 0]							)
				np.testing.assert_array_equal(build_mask(lowercase	,				23							).numpy()	,				expected.numpy()							)


	def    _a     (self							):
				A_     :						Optional[Any]     =					torch.tensor([8, 2, 3, 4, 1, 1, 1]							)
				A_     :						Tuple     =					torch.tensor([1, 1, 1, 1, 0, 0, 0]							)
				np.testing.assert_array_equal(build_mask(lowercase	,				1							).numpy()	,				expected.numpy()							)


	def    _a     (self							):
				A_     :						List[str]     =					101
				A_     :						str     =					torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]]							)
				A_     :						int     =					torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]							)

				A_     :						Optional[Any]     =					compute_token_type_ids(lowercase	,				lowercase							)
				np.testing.assert_array_equal(lowercase	,				lowercase							)