




'''simple docstring'''







import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
   import torch


if is_vision_available():
   from PIL import Image

   from transformers import MgpstrProcessor, ViTImageProcessor


@require_torch
@require_vision
class 			_a  (	unittest.TestCase ):
       __a				:     str   				=					ViTImageProcessor if is_vision_available() else None
       @property
       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           return self.image_processor_tester.prepare_image_processor_dict()
       def     A      (	self						:	int				):



           '''simple docstring'''

           UpperCAmelCase			       =		(3, 32, 128)
           UpperCAmelCase			       =		tempfile.mkdtemp()

           # fmt: off
           UpperCAmelCase			       =		['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
           # fmt: on
           UpperCAmelCase			       =		dict(zip(lowercase   ,				range(len(lowercase				)				)				)				)

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

           UpperCAmelCase			       =		{
               '''do_normalize''': False,
               '''do_resize''': True,
               '''image_processor_type''': '''ViTImageProcessor''',
               '''resample''': 3,
               '''size''': {'''height''': 32, '''width''': 128},
           }
           UpperCAmelCase			       =		os.path.join(self.tmpdirname   ,				lowercase				)
           with open(self.image_processor_file   ,				'''w'''   ,				encoding='''utf-8'''				) as fp:
               json.dump(lowercase   ,				lowercase				)
       def     A      (	self						:	str   ,				**lowercase						:	Any				):



           '''simple docstring'''

           return MgpstrTokenizer.from_pretrained(self.tmpdirname   ,				**lowercase				)
       def     A      (	self						:	Any   ,				**lowercase						:	List[str]				):



           '''simple docstring'''

           return ViTImageProcessor.from_pretrained(self.tmpdirname   ,				**lowercase				)
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           shutil.rmtree(self.tmpdirname				)
       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           UpperCAmelCase			       =		np.random.randint(255   ,				size=(3, 30, 400)   ,				dtype=np.uinta				)

           UpperCAmelCase			       =		Image.fromarray(np.moveaxis(lowercase   ,				0   ,				-1				)				)

           return image_input
       def     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_tokenizer()
           UpperCAmelCase			       =		self.get_image_processor()

           UpperCAmelCase			       =		MgpstrProcessor(tokenizer=lowercase   ,				image_processor=lowercase				)
           processor.save_pretrained(self.tmpdirname				)
           UpperCAmelCase			       =		MgpstrProcessor.from_pretrained(self.tmpdirname   ,				use_fast=lowercase				)

           self.assertEqual(processor.char_tokenizer.get_vocab()   ,				tokenizer.get_vocab()				)
           self.assertIsInstance(processor.char_tokenizer   ,				lowercase				)

           self.assertEqual(processor.image_processor.to_json_string()   ,				image_processor.to_json_string()				)
           self.assertIsInstance(processor.image_processor   ,				lowercase				)
       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_tokenizer()
           UpperCAmelCase			       =		self.get_image_processor()

           UpperCAmelCase			       =		MgpstrProcessor(tokenizer=lowercase   ,				image_processor=lowercase				)
           processor.save_pretrained(self.tmpdirname				)

           UpperCAmelCase			       =		self.get_tokenizer(bos_token='''(BOS)'''   ,				eos_token='''(EOS)'''				)
           UpperCAmelCase			       =		self.get_image_processor(do_normalize=lowercase   ,				padding_value=1.0				)

           UpperCAmelCase			       =		MgpstrProcessor.from_pretrained(
               self.tmpdirname   ,				bos_token='''(BOS)'''   ,				eos_token='''(EOS)'''   ,				do_normalize=lowercase   ,				padding_value=1.0				)

           self.assertEqual(processor.char_tokenizer.get_vocab()   ,				tokenizer_add_kwargs.get_vocab()				)
           self.assertIsInstance(processor.char_tokenizer   ,				lowercase				)

           self.assertEqual(processor.image_processor.to_json_string()   ,				image_processor_add_kwargs.to_json_string()				)
           self.assertIsInstance(processor.image_processor   ,				lowercase				)
       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_image_processor()
           UpperCAmelCase			       =		self.get_tokenizer()

           UpperCAmelCase			       =		MgpstrProcessor(tokenizer=lowercase   ,				image_processor=lowercase				)

           UpperCAmelCase			       =		self.prepare_image_inputs()

           UpperCAmelCase			       =		image_processor(lowercase   ,				return_tensors='''np'''				)
           UpperCAmelCase			       =		processor(images=lowercase   ,				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						:	Dict				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_image_processor()
           UpperCAmelCase			       =		self.get_tokenizer()

           UpperCAmelCase			       =		MgpstrProcessor(tokenizer=lowercase   ,				image_processor=lowercase				)

           UpperCAmelCase			       =		'''test'''

           UpperCAmelCase			       =		processor(text=lowercase				)

           UpperCAmelCase			       =		tokenizer(lowercase				)
           for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key]   ,				encoded_processor[key]				)
       def     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_image_processor()
           UpperCAmelCase			       =		self.get_tokenizer()

           UpperCAmelCase			       =		MgpstrProcessor(tokenizer=lowercase   ,				image_processor=lowercase				)

           UpperCAmelCase			       =		'''test'''
           UpperCAmelCase			       =		self.prepare_image_inputs()

           UpperCAmelCase			       =		processor(text=lowercase   ,				images=lowercase				)

           self.assertListEqual(list(inputs.keys()				)   ,				['''pixel_values''', '''labels''']				)

           # test if it raises when no input is passed
           with pytest.raises(lowercase				):
               processor()
       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_image_processor()
           UpperCAmelCase			       =		self.get_tokenizer()

           UpperCAmelCase			       =		MgpstrProcessor(tokenizer=lowercase   ,				image_processor=lowercase				)

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

           UpperCAmelCase			       =		processor.char_decode(lowercase				)
           UpperCAmelCase			       =		tokenizer.batch_decode(lowercase				)
           UpperCAmelCase			       =		[seq.replace(''' '''   ,				''''''				) for seq in decoded_tok]

           self.assertListEqual(lowercase   ,				lowercase				)
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_image_processor()
           UpperCAmelCase			       =		self.get_tokenizer()

           UpperCAmelCase			       =		MgpstrProcessor(tokenizer=lowercase   ,				image_processor=lowercase				)

           UpperCAmelCase			       =		None
           UpperCAmelCase			       =		self.prepare_image_inputs()

           UpperCAmelCase			       =		processor(text=lowercase   ,				images=lowercase				)

           self.assertListEqual(list(inputs.keys()				)   ,				processor.model_input_names				)







       def     A      (	self						:	Dict				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_image_processor()
           UpperCAmelCase			       =		self.get_tokenizer()

           UpperCAmelCase			       =		MgpstrProcessor(tokenizer=lowercase   ,				image_processor=lowercase				)

           UpperCAmelCase			       =		torch.randn(1   ,				27   ,				38				)
           UpperCAmelCase			       =		torch.randn(1   ,				27   ,				50_257				)
           UpperCAmelCase			       =		torch.randn(1   ,				27   ,				30_522				)

           UpperCAmelCase			       =		processor.batch_decode([char_input, bpe_input, wp_input]				)

           self.assertListEqual(list(results.keys()				)   ,				['''generated_text''', '''scores''', '''char_preds''', '''bpe_preds''', '''wp_preds''']				)






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 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    1.5
	lowercase       =    int(factor * num_class_images							)
	lowercase       =    ClipClient(
	    url='https://knn.laion.ai/knn-service' ,							indice_name='laion_400m' ,							num_images=__SCREAMING_SNAKE_CASE ,							aesthetic_weight=0.1							)

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

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

	lowercase       =    0
	lowercase       =    0
	lowercase       =    tqdm(desc='downloading real regularization images' ,							total=__SCREAMING_SNAKE_CASE							)

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


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

