



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_      (	__a     :				Optional[Any] ,		__a     :				Any ,		__a     :				Dict		):

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

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

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

				a__				      =		0
				a__				      =		0
				a__				      =		tqdm(desc="""downloading real regularization images""" ,		total=__a		)

				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__				      =		class_images[count]
												count += 1
												try:
																a__				      =		requests.get(images["""url"""]		)
																if img.status_code == 200:
																				a__				      =		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__				      =		argparse.ArgumentParser("""""" ,		add_help=__a		)
				parser.add_argument("""--class_prompt""" ,		help="""text prompt to retrieve images""" ,		required=__a ,		type=__a		)
				parser.add_argument("""--class_data_dir""" ,		help="""path to save images""" ,		required=__a ,		type=__a		)
				parser.add_argument("""--num_class_images""" ,		help="""number of images to download""" ,		default=200 ,		type=__a		)
				return parser.parse_args()


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






import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import OwlViTImageProcessor, OwlViTProcessor

@require_vision
class  __snake_case      (   unittest.TestCase):







							'''simple docstring'''






							def _a      (							self		):
											a__				      =		tempfile.mkdtemp()

											# fmt: off
											a__				      =		["""""", """l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
											# fmt: on
											a__				      =		dict(zip(a_  ,  range(len(a_		)		)		)		)
											a__				      =		["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
											a__				      =		{"""unk_token""": """<unk>"""}

											a__				      =		os.path.join(self.tmpdirname  ,  VOCAB_FILES_NAMES["""vocab_file"""]		)
											a__				      =		os.path.join(self.tmpdirname  ,  VOCAB_FILES_NAMES["""merges_file"""]		)
											with open(self.vocab_file  ,  """w"""  ,  encoding="""utf-8"""		) as fp:
															fp.write(json.dumps(a_		) + """\n"""		)
											with open(self.merges_file  ,  """w"""  ,  encoding="""utf-8"""		) as fp:
															fp.write("""\n""".join(a_		)		)

											a__				      =		{
											    """do_resize""": True,
											    """size""": 20,
											    """do_center_crop""": True,
											    """crop_size""": 18,
											    """do_normalize""": True,
											    """image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
											    """image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
											}
											a__				      =		os.path.join(self.tmpdirname  ,  a_		)
											with open(self.image_processor_file  ,  """w"""  ,  encoding="""utf-8"""		) as fp:
															json.dump(a_  ,  a_		)


							def _a      (							self  ,  **a_		):
											return CLIPTokenizer.from_pretrained(self.tmpdirname  ,  pad_token="""!"""  ,  **a_		)


							def _a      (							self  ,  **a_		):
											return CLIPTokenizerFast.from_pretrained(self.tmpdirname  ,  pad_token="""!"""  ,  **a_		)


							def _a      (							self  ,  **a_		):
											return OwlViTImageProcessor.from_pretrained(self.tmpdirname  ,  **a_		)


							def _a      (							self		):
											shutil.rmtree(self.tmpdirname		)


							def _a      (							self		):
											a__				      =		[np.random.randint(255  ,  size=(3, 30, 400)  ,  dtype=np.uinta		)]

											a__				      =		[Image.fromarray(np.moveaxis(a_  ,  0  ,  -1		)		) for x in image_inputs]

											return image_inputs


							def _a      (							self		):
											a__				      =		self.get_tokenizer()
											a__				      =		self.get_rust_tokenizer()
											a__				      =		self.get_image_processor()

											a__				      =		OwlViTProcessor(tokenizer=a_  ,  image_processor=a_		)
											processor_slow.save_pretrained(self.tmpdirname		)
											a__				      =		OwlViTProcessor.from_pretrained(self.tmpdirname  ,  use_fast=a_		)

											a__				      =		OwlViTProcessor(tokenizer=a_  ,  image_processor=a_		)
											processor_fast.save_pretrained(self.tmpdirname		)
											a__				      =		OwlViTProcessor.from_pretrained(self.tmpdirname		)

											self.assertEqual(processor_slow.tokenizer.get_vocab()  ,  tokenizer_slow.get_vocab()		)
											self.assertEqual(processor_fast.tokenizer.get_vocab()  ,  tokenizer_fast.get_vocab()		)
											self.assertEqual(tokenizer_slow.get_vocab()  ,  tokenizer_fast.get_vocab()		)
											self.assertIsInstance(processor_slow.tokenizer  ,  a_		)
											self.assertIsInstance(processor_fast.tokenizer  ,  a_		)

											self.assertEqual(processor_slow.image_processor.to_json_string()  ,  image_processor.to_json_string()		)
											self.assertEqual(processor_fast.image_processor.to_json_string()  ,  image_processor.to_json_string()		)
											self.assertIsInstance(processor_slow.image_processor  ,  a_		)
											self.assertIsInstance(processor_fast.image_processor  ,  a_		)


							def _a      (							self		):
											a__				      =		OwlViTProcessor(tokenizer=self.get_tokenizer()  ,  image_processor=self.get_image_processor()		)
											processor.save_pretrained(self.tmpdirname		)

											a__				      =		self.get_tokenizer(bos_token="""(BOS)"""  ,  eos_token="""(EOS)"""		)
											a__				      =		self.get_image_processor(do_normalize=a_		)

											a__				      =		OwlViTProcessor.from_pretrained(
											    self.tmpdirname  ,  bos_token="""(BOS)"""  ,  eos_token="""(EOS)"""  ,  do_normalize=a_		)

											self.assertEqual(processor.tokenizer.get_vocab()  ,  tokenizer_add_kwargs.get_vocab()		)
											self.assertIsInstance(processor.tokenizer  ,  a_		)

											self.assertEqual(processor.image_processor.to_json_string()  ,  image_processor_add_kwargs.to_json_string()		)
											self.assertIsInstance(processor.image_processor  ,  a_		)


							def _a      (							self		):
											a__				      =		self.get_image_processor()
											a__				      =		self.get_tokenizer()

											a__				      =		OwlViTProcessor(tokenizer=a_  ,  image_processor=a_		)

											a__				      =		self.prepare_image_inputs()

											a__				      =		image_processor(a_  ,  return_tensors="""np"""		)
											a__				      =		processor(images=a_  ,  return_tensors="""np"""		)

											for key in input_image_proc.keys():
															self.assertAlmostEqual(input_image_proc[key].sum()  ,  input_processor[key].sum()  ,  delta=1E-2		)


							def _a      (							self		):
											a__				      =		self.get_image_processor()
											a__				      =		self.get_tokenizer()

											a__				      =		OwlViTProcessor(tokenizer=a_  ,  image_processor=a_		)

											a__				      =		"""lower newer"""

											a__				      =		processor(text=a_  ,  return_tensors="""np"""		)

											a__				      =		tokenizer(a_  ,  return_tensors="""np"""		)

											for key in encoded_tok.keys():
															self.assertListEqual(encoded_tok[key][0].tolist()  ,  encoded_processor[key][0].tolist()		)


							def _a      (							self		):
											a__				      =		self.get_image_processor()
											a__				      =		self.get_tokenizer()

											a__				      =		OwlViTProcessor(tokenizer=a_  ,  image_processor=a_		)

											a__				      =		"""lower newer"""
											a__				      =		self.prepare_image_inputs()

											a__				      =		processor(text=a_  ,  images=a_		)

											self.assertListEqual(list(inputs.keys()		)  ,  ["""input_ids""", """attention_mask""", """pixel_values"""]		)

											# test if it raises when no input is passed
											with pytest.raises(a_		):
															processor()


							def _a      (							self		):
											a__				      =		"""google/owlvit-base-patch32"""
											a__				      =		OwlViTProcessor.from_pretrained(a_		)

											a__				      =		["""cat""", """nasa badge"""]
											a__				      =		processor(text=a_		)

											a__				      =		16
											self.assertListEqual(list(inputs.keys()		)  ,  ["""input_ids""", """attention_mask"""]		)
											self.assertEqual(inputs["""input_ids"""].shape  ,  (2, seq_length)		)

											# test if it raises when no input is passed
											with pytest.raises(a_		):
															processor()


							def _a      (							self		):
											a__				      =		"""google/owlvit-base-patch32"""
											a__				      =		OwlViTProcessor.from_pretrained(a_		)

											a__				      =		[["""cat""", """nasa badge"""], ["""person"""]]
											a__				      =		processor(text=a_		)

											a__				      =		16
											a__				      =		len(a_		)
											a__				      =		max([len(a_		) for texts in input_texts]		)

											self.assertListEqual(list(inputs.keys()		)  ,  ["""input_ids""", """attention_mask"""]		)
											self.assertEqual(inputs["""input_ids"""].shape  ,  (batch_size * num_max_text_queries, seq_length)		)

											# test if it raises when no input is passed
											with pytest.raises(a_		):
															processor()


							def _a      (							self		):
											a__				      =		"""google/owlvit-base-patch32"""
											a__				      =		OwlViTProcessor.from_pretrained(a_		)

											a__				      =		["""cat""", """nasa badge"""]
											a__				      =		processor(text=a_		)

											a__				      =		16
											a__				      =		inputs["""input_ids"""]
											a__				      =		[
											    [49_406, 2_368, 49_407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
											    [49_406, 6_841, 11_301, 49_407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
											]

											self.assertListEqual(list(inputs.keys()		)  ,  ["""input_ids""", """attention_mask"""]		)
											self.assertEqual(inputs["""input_ids"""].shape  ,  (2, seq_length)		)
											self.assertListEqual(list(input_ids[0]		)  ,  predicted_ids[0]		)
											self.assertListEqual(list(input_ids[1]		)  ,  predicted_ids[1]		)


							def _a      (							self		):
											a__				      =		self.get_image_processor()
											a__				      =		self.get_tokenizer()

											a__				      =		OwlViTProcessor(tokenizer=a_  ,  image_processor=a_		)

											a__				      =		self.prepare_image_inputs()
											a__				      =		self.prepare_image_inputs()

											a__				      =		processor(images=a_  ,  query_images=a_		)

											self.assertListEqual(list(inputs.keys()		)  ,  ["""query_pixel_values""", """pixel_values"""]		)

											# test if it raises when no input is passed
											with pytest.raises(a_		):
															processor()







							def _a      (							self		):
											a__				      =		self.get_image_processor()
											a__				      =		self.get_tokenizer()

											a__				      =		OwlViTProcessor(tokenizer=a_  ,  image_processor=a_		)

											a__				      =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											a__				      =		processor.batch_decode(a_		)
											a__				      =		tokenizer.batch_decode(a_		)

											self.assertListEqual(a_  ,  a_		)

