'''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  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Optional[Any]   ,		__snake_case							:			Any   ,		__snake_case							:			Dict				):
     _A    		=  1.5
     _A    		=  int(factor * num_class_images				)
     _A    		=  ClipClient(
         url='https://knn.laion.ai/knn-service'   ,		indice_name='laion_400m'   ,		num_images=__snake_case   ,		aesthetic_weight=0.1				)

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

     while True:
          _A    		=  client.query(text=__snake_case				)
          if len(__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=__snake_case   ,		aesthetic_weight=0.1   ,		)

     _A    		=  0
     _A    		=  0
     _A    		=  tqdm(desc='downloading real regularization images'   ,		total=__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 == 2_0_0:
                         _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  _SCREAMING_SNAKE_CASE		(      ):
     _A    		=  argparse.ArgumentParser(''   ,		add_help=__snake_case				)
     parser.add_argument('--class_prompt'   ,		help='text prompt to retrieve images'   ,		required=__snake_case   ,		type=__snake_case				)
     parser.add_argument('--class_data_dir'   ,		help='path to save images'   ,		required=__snake_case   ,		type=__snake_case				)
     parser.add_argument('--num_class_images'   ,		help='number of images to download'   ,		default=2_0_0   ,		type=__snake_case				)
     return parser.parse_args()


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







from typing import TYPE_CHECKING

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


lowerCAmelCase__    :Tuple       =				{
    '''configuration_speecht5''': [
        '''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP''',
        '''SpeechT5Config''',
        '''SpeechT5HifiGanConfig''',
    ],
    '''feature_extraction_speecht5''': ['''SpeechT5FeatureExtractor'''],
    '''processing_speecht5''': ['''SpeechT5Processor'''],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :Dict       =				['''SpeechT5Tokenizer''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :List[str]       =				[
		    '''SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''SpeechT5ForSpeechToText''',
		    '''SpeechT5ForSpeechToSpeech''',
		    '''SpeechT5ForTextToSpeech''',
		    '''SpeechT5Model''',
		    '''SpeechT5PreTrainedModel''',
		    '''SpeechT5HifiGan''',
		]

if TYPE_CHECKING:
		from .configuration_speechta import (
		    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
		    SpeechTaConfig,
		    SpeechTaHifiGanConfig,
		)
		from .feature_extraction_speechta import SpeechTaFeatureExtractor
		from .processing_speechta import SpeechTaProcessor

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_speechta import SpeechTaTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_speechta import (
				    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SpeechTaForSpeechToSpeech,
				    SpeechTaForSpeechToText,
				    SpeechTaForTextToSpeech,
				    SpeechTaHifiGan,
				    SpeechTaModel,
				    SpeechTaPreTrainedModel,
				)

else:
		import sys

		lowerCAmelCase__    :str       =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
