



'''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 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





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

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

			while True:
						A_	:						Union[str, Any]	=     client.query(text=lowerCAmelCase__    )
						if len(lowerCAmelCase__    ) >= factor * num_class_images or num_images > 1E4:
									break
						else:
									A_	:						Tuple	=     int(factor * num_images    )
									A_	:						List[str]	=     ClipClient(
									    url="""https://knn.laion.ai/knn-service"""						,      indice_name="""laion_400m"""						,      num_images=lowerCAmelCase__						,      aesthetic_weight=0.1						,      )

			A_	:						str	=     0
			A_	:						str	=     0
			A_	:						int	=     tqdm(desc="""downloading real regularization images"""						,      total=lowerCAmelCase__    )

			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_	:						Any	=     class_images[count]
									count += 1
									try:
												A_	:						str	=     requests.get(images["""url"""]    )
												if img.status_code == 2_00:
															A_	:						List[Any]	=     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_	:						Any	=     argparse.ArgumentParser(""""""						,      add_help=lowerCAmelCase__    )
			parser.add_argument("""--class_prompt"""						,      help="""text prompt to retrieve images"""						,      required=lowerCAmelCase__						,      type=lowerCAmelCase__    )
			parser.add_argument("""--class_data_dir"""						,      help="""path to save images"""						,      required=lowerCAmelCase__						,      type=lowerCAmelCase__    )
			parser.add_argument("""--num_class_images"""						,      help="""number of images to download"""						,      default=2_00						,      type=lowerCAmelCase__    )
			return parser.parse_args()


if __name__ == "__main__":
						lowerCamelCase							:List[str]				    =					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,
)


UpperCamelCase__    					=      {
    '''configuration_rembert''': ['''REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RemBertConfig''', '''RemBertOnnxConfig''']
}

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

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

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      [
      '''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:
  UpperCamelCase__    					=      [
      '''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

  UpperCamelCase__    					=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
