



'''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    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Any,					SCREAMING_SNAKE_CASE__:	Union[str, Any],					SCREAMING_SNAKE_CASE__:	Tuple    )  ->			Optional[Any]:

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

   __a				     =    0
   __a				     =    0
   __a				     =    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:
         __a				     =    class_images[count]
         count += 1
         try:
            __a				     =    requests.get(images['url']    )
            if img.status_code == 200:
               __a				     =    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	(				)  ->			List[str]:

   """simple docstring"""
   __a				     =    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      :       Union[str, Any]      			=      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_speech_available,
    is_tf_available,
    is_torch_available,
)


__UpperCamelCase      :       Optional[int]      			=      {
    """configuration_speech_to_text""": ["""SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Speech2TextConfig"""],
    """processing_speech_to_text""": ["""Speech2TextProcessor"""],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Union[str, Any]      			=      ["""Speech2TextTokenizer"""]

try:
 if not is_speech_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Optional[Any]      			=      ["""Speech2TextFeatureExtractor"""]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       List[Any]      			=      [
     """TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFSpeech2TextForConditionalGeneration""",
     """TFSpeech2TextModel""",
     """TFSpeech2TextPreTrainedModel""",
 ]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Tuple      			=      [
     """SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """Speech2TextForConditionalGeneration""",
     """Speech2TextModel""",
     """Speech2TextPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
 from .processing_speech_to_text import SpeechaTextProcessor

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_speech_to_text import SpeechaTextTokenizer

 try:
  if not is_speech_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_speech_to_text import (
      TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFSpeechaTextForConditionalGeneration,
      TFSpeechaTextModel,
      TFSpeechaTextPreTrainedModel,
  )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speech_to_text import (
      SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechaTextForConditionalGeneration,
      SpeechaTextModel,
      SpeechaTextPreTrainedModel,
  )

else:
 import sys

 __UpperCamelCase      :       List[str]      			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)