






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				,       __A				,       __A      )				->						List[str]:



					"""simple docstring"""
					__a					:						str      =					1.5
					__a					:						Union[str, Any]      =					int(factor * num_class_images      )
					__a					:						Any      =					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					:						List[str]      =					client.query(text=__A      )
										if len(__A      ) >= factor * num_class_images or num_images > 1e4:
															break
										else:
															__a					:						str      =					int(factor * num_images      )
															__a					:						str      =					ClipClient(
															    url="https://knn.laion.ai/knn-service"				,       indice_name="laion_400m"				,       num_images=__A				,       aesthetic_weight=0.1				,       )

					__a					:						Tuple      =					0
					__a					:						Optional[int]      =					0
					__a					:						Dict      =					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					:						Tuple      =					class_images[count]
															count += 1
															try:
																				__a					:						List[Any]      =					requests.get(images["url"]      )
																				if img.status_code == 200:
																									__a					:						str      =					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_   ()				->						int:



					"""simple docstring"""
					__a					:						List[str]      =					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 unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
			import optax
			from flax.training.common_utils import onehot

			from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
			from transformers.models.ta.modeling_flax_ta import shift_tokens_right




@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 							snake_case_       (    unittest.TestCase      ):

						"""simple docstring"""



						@slow
						def        UpperCAmelCase__					(self:					Optional[int]   )    ->     List[str]:




											'''simple docstring'''



											__a					:						Optional[Any]      =					FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"   )
											__a					:						Dict      =					AutoTokenizer.from_pretrained("google/mt5-small"   )

											__a					:						Any      =					tokenizer("Hello there" ,					return_tensors="np"   ).input_ids
											__a					:						Union[str, Any]      =					tokenizer("Hi I am" ,					return_tensors="np"   ).input_ids

											__a					:						Union[str, Any]      =					shift_tokens_right(__UpperCAmelCase ,					model.config.pad_token_id ,					model.config.decoder_start_token_id   )

											__a					:						Tuple      =					model(__UpperCAmelCase ,					decoder_input_ids=__UpperCAmelCase   ).logits
											__a					:						str      =					optax.softmax_cross_entropy(__UpperCAmelCase ,					onehot(__UpperCAmelCase ,					logits.shape[-1]   )   ).mean()

											__a					:						List[str]      =					-(labels.shape[-1] * loss.item())

											__a					:						Optional[int]      =					-84.91_27
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE   ) < 1E-4   )





