



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

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

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

						UpperCAmelCase					=			0
						UpperCAmelCase					=			0
						UpperCAmelCase					=			tqdm(desc='downloading real regularization images'		,     total=lowercase_						)

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


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










"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_    					=	{
    """configuration_rembert""": ["""REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RemBertConfig""", """RemBertOnnxConfig"""]
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_    					=	["""RemBertTokenizer"""]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_    					=	["""RemBertTokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_    					=	[
				    """REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """RemBertForCausalLM""",
				    """RemBertForMaskedLM""",
				    """RemBertForMultipleChoice""",
				    """RemBertForQuestionAnswering""",
				    """RemBertForSequenceClassification""",
				    """RemBertForTokenClassification""",
				    """RemBertLayer""",
				    """RemBertModel""",
				    """RemBertPreTrainedModel""",
				    """load_tf_weights_in_rembert""",
				]


try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_    					=	[
				    """TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFRemBertForCausalLM""",
				    """TFRemBertForMaskedLM""",
				    """TFRemBertForMultipleChoice""",
				    """TFRemBertForQuestionAnswering""",
				    """TFRemBertForSequenceClassification""",
				    """TFRemBertForTokenClassification""",
				    """TFRemBertLayer""",
				    """TFRemBertModel""",
				    """TFRemBertPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_rembert import RemBertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_rembert_fast import RemBertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_rembert import (
								    REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    RemBertForCausalLM,
								    RemBertForMaskedLM,
								    RemBertForMultipleChoice,
								    RemBertForQuestionAnswering,
								    RemBertForSequenceClassification,
								    RemBertForTokenClassification,
								    RemBertLayer,
								    RemBertModel,
								    RemBertPreTrainedModel,
								    load_tf_weights_in_rembert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_rembert import (
								    TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFRemBertForCausalLM,
								    TFRemBertForMaskedLM,
								    TFRemBertForMultipleChoice,
								    TFRemBertForQuestionAnswering,
								    TFRemBertForSequenceClassification,
								    TFRemBertForTokenClassification,
								    TFRemBertLayer,
								    TFRemBertModel,
								    TFRemBertPreTrainedModel,
								)


else:
				import sys

				snake_case_    					=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





