'''simple docstring'''




from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
						import datasets





class 					__magic_name__		(					_UpperCAmelCase):







			@slow
			@require_torch
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict					):
										lowercase_      :    int       		= EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny"""       ,    """prajjwal1/bert-tiny"""					)
										lowercase_      :    str       		= BertTokenizer.from_pretrained("""bert-base-uncased"""					)

										lowercase_      :    Optional[int]       		= bertabert.config.encoder.vocab_size
										lowercase_      :    int       		= tokenizer.sep_token_id
										lowercase_      :    int       		= tokenizer.cls_token_id
										lowercase_      :    Tuple       		= 128

										lowercase_      :    List[str]       		= datasets.load_dataset("""cnn_dailymail"""       ,    """3.0.0"""       ,    split="""train[:1%]"""					)
										lowercase_      :    Any       		= datasets.load_dataset("""cnn_dailymail"""       ,    """3.0.0"""       ,    split="""validation[:1%]"""					)

										lowercase_      :    int       		= train_dataset.select(range(32					)					)
										lowercase_      :    List[Any]       		= val_dataset.select(range(16					)					)

										lowercase_      :    Any       		= 4

										def _map_to_encoder_decoder_inputs(lowercase_       :						List[Any]					):
																	# Tokenizer will automatically set [BOS] <text> [EOS]
																	lowercase_      :    Any       		= tokenizer(batch["""article"""]       ,    padding="""max_length"""       ,    truncation=lowercase_       ,    max_length=512					)
																	lowercase_      :    Optional[Any]       		= tokenizer(batch["""highlights"""]       ,    padding="""max_length"""       ,    truncation=lowercase_       ,    max_length=128					)
																	lowercase_      :    Optional[int]       		= inputs.input_ids
																	lowercase_      :    List[Any]       		= inputs.attention_mask

																	lowercase_      :    Tuple       		= outputs.input_ids
																	lowercase_      :    List[Any]       		= outputs.input_ids.copy()
																	lowercase_      :    Union[str, Any]       		= [
																	    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
																	]
																	lowercase_      :    Union[str, Any]       		= outputs.attention_mask

																	assert all(len(lowercase_					) == 512 for x in inputs.input_ids					)
																	assert all(len(lowercase_					) == 128 for x in outputs.input_ids					)

																	return batch

										def _compute_metrics(lowercase_       :						str					):
																	lowercase_      :    Optional[int]       		= pred.label_ids
																	lowercase_      :    int       		= pred.predictions

																	# all unnecessary tokens are removed
																	lowercase_      :    int       		= tokenizer.batch_decode(lowercase_       ,    skip_special_tokens=lowercase_					)
																	lowercase_      :    str       		= tokenizer.batch_decode(lowercase_       ,    skip_special_tokens=lowercase_					)

																	lowercase_      :    Any       		= sum([int(pred_str[i] == label_str[i]					) for i in range(len(lowercase_					)					)]					) / len(lowercase_					)

																	return {"accuracy": accuracy}

										# map train dataset
										lowercase_      :    Any       		= train_dataset.map(
										    _map_to_encoder_decoder_inputs       ,    batched=lowercase_       ,    batch_size=lowercase_       ,    remove_columns=["""article""", """highlights"""]       ,    )
										train_dataset.set_format(
										    type="""torch"""       ,    columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]       ,    )

										# same for validation dataset
										lowercase_      :    Dict       		= val_dataset.map(
										    _map_to_encoder_decoder_inputs       ,    batched=lowercase_       ,    batch_size=lowercase_       ,    remove_columns=["""article""", """highlights"""]       ,    )
										val_dataset.set_format(
										    type="""torch"""       ,    columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]       ,    )

										lowercase_      :    Any       		= self.get_auto_remove_tmp_dir()

										lowercase_      :    Optional[Any]       		= SeqaSeqTrainingArguments(
										    output_dir=lowercase_       ,    per_device_train_batch_size=lowercase_       ,    per_device_eval_batch_size=lowercase_       ,    predict_with_generate=lowercase_       ,    evaluation_strategy="""steps"""       ,    do_train=lowercase_       ,    do_eval=lowercase_       ,    warmup_steps=0       ,    eval_steps=2       ,    logging_steps=2       ,    )

										# instantiate trainer
										lowercase_      :    Dict       		= SeqaSeqTrainer(
										    model=lowercase_       ,    args=lowercase_       ,    compute_metrics=_compute_metrics       ,    train_dataset=lowercase_       ,    eval_dataset=lowercase_       ,    tokenizer=lowercase_       ,    )

										# start training
										trainer.train()






'''simple docstring'''




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

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

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

							lowercase_      :    List[str]       		= 0
							lowercase_      :    Dict       		= 0
							lowercase_      :    Tuple       		= tqdm(desc="""downloading real regularization images"""   ,   total=UpperCAmelCase__				)

							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_      :    str       		= class_images[count]
																					count += 1
																					try:
																												lowercase_      :    Union[str, Any]       		= requests.get(images["""url"""]				)
																												if img.status_code == 200:
																																			lowercase_      :    List[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 							lowerCamelCase			(   )    ->   Optional[Any]:
							lowercase_      :    Any       		= argparse.ArgumentParser(""""""   ,   add_help=UpperCAmelCase__				)
							parser.add_argument("""--class_prompt"""   ,   help="""text prompt to retrieve images"""   ,   required=UpperCAmelCase__   ,   type=UpperCAmelCase__				)
							parser.add_argument("""--class_data_dir"""   ,   help="""path to save images"""   ,   required=UpperCAmelCase__   ,   type=UpperCAmelCase__				)
							parser.add_argument("""--num_class_images"""   ,   help="""number of images to download"""   ,   default=200   ,   type=UpperCAmelCase__				)
							return parser.parse_args()


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





