


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__	(      a	:   Union[str, Any]	,  a	:   List[str]	,  a	:   Optional[int]				)    ->	Optional[Any]:





			"""simple docstring"""
			a__ :Optional[int]						  =    1.5
			a__ :str						  =    int(factor * num_class_images				)
			a__ :Optional[int]						  =    ClipClient(
			    url="https://knn.laion.ai/knn-service"	,  indice_name="laion_400m"	,  num_images=a	,  aesthetic_weight=0.1				)

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

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

			a__ :int						  =    0
			a__ :Tuple						  =    0
			a__ :List[str]						  =    tqdm(desc="downloading real regularization images"	,  total=a				)

			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__ :Dict						  =    class_images[count]
									count += 1
									try:
												a__ :Optional[Any]						  =    requests.get(images["url"]				)
												if img.status_code == 200:
															a__ :Union[str, 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 				lowerCamelCase__	(      )    ->	List[str]:





			"""simple docstring"""
			a__ :Tuple						  =    argparse.ArgumentParser(""	,  add_help=a				)
			parser.add_argument("--class_prompt"	,  help="text prompt to retrieve images"	,  required=a	,  type=a				)
			parser.add_argument("--class_data_dir"	,  help="path to save images"	,  required=a	,  type=a				)
			parser.add_argument("--num_class_images"	,  help="number of images to download"	,  default=200	,  type=a				)
			return parser.parse_args()


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






from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case__			 =						logging.get_logger(__name__)

snake_case__			 =						{
    '''facebook/s2t-wav2vec2-large-en-de''': (
        '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     'speech_to_text_2'
						lowerCamelCase_         =     ['past_key_values']
						lowerCamelCase_         =     {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}




						def __init__(	self						:   Dict   ,     __A						:   Union[str, Any]=10000   ,     __A						:   Tuple=6   ,     __A						:   Tuple=2048   ,     __A						:   Any=4   ,     __A						:   str=0.0   ,     __A						:   Tuple=True   ,     __A						:   List[Any]="relu"   ,     __A						:   Union[str, Any]=256   ,     __A						:   str=0.1   ,     __A						:   Optional[Any]=0.0   ,     __A						:   Tuple=0.0   ,     __A						:   Any=0.02   ,     __A						:   int=2   ,     __A						:   Union[str, Any]=True   ,     __A						:   Tuple=1   ,     __A						:   List[str]=0   ,     __A						:   List[str]=2   ,     __A						:   Union[str, Any]=1024   ,     **__A						:   Tuple   ,     )      ->List[str]:






									"""simple docstring"""






									a__ :Tuple						  =    vocab_size
									a__ :Dict						  =    d_model
									a__ :Optional[int]						  =    decoder_ffn_dim
									a__ :Optional[int]						  =    decoder_layers
									a__ :Optional[int]						  =    decoder_attention_heads
									a__ :Dict						  =    dropout
									a__ :Optional[Any]						  =    attention_dropout
									a__ :Any						  =    activation_dropout
									a__ :int						  =    activation_function
									a__ :Optional[int]						  =    init_std
									a__ :List[Any]						  =    decoder_layerdrop
									a__ :Optional[Any]						  =    use_cache
									a__ :List[str]						  =    decoder_layers
									a__ :int						  =    scale_embedding  # scale factor will be sqrt(d_model) if True
									a__ :Optional[int]						  =    max_target_positions

									super().__init__(
									    pad_token_id=__A   ,     bos_token_id=__A   ,     eos_token_id=__A   ,     decoder_start_token_id=__A   ,     **__A   ,     )


