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

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

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

							lowercase_      :    List[str]       		= 0
							lowercase_      :    Dict       		= 0
							lowercase_      :    Tuple       		= tqdm(desc="""downloading real regularization images"""   ,   total=UpperCAmelCase__				)

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


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






'''simple docstring'''




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


_lowercase		:				Optional[Any]	       =		logging.get_logger(__name__)

_lowercase		:				Union[str, Any]	       =		{
    "facebook/s2t-small-librispeech-asr": (
        "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}





class 					__magic_name__		(					_UpperCAmelCase):
			UpperCamelCase__       = '''speech_to_text'''
			UpperCamelCase__       = ['''past_key_values''']
			UpperCamelCase__       = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}







			def __init__(			self       :						str       ,    lowercase_       :						Optional[int]=10000       ,    lowercase_       :						int=12       ,    lowercase_       :						Any=2048       ,    lowercase_       :						Any=4       ,    lowercase_       :						Dict=6       ,    lowercase_       :						Any=2048       ,    lowercase_       :						List[str]=4       ,    lowercase_       :						str=0.0       ,    lowercase_       :						str=0.0       ,    lowercase_       :						Union[str, Any]=True       ,    lowercase_       :						List[Any]=True       ,    lowercase_       :						int="relu"       ,    lowercase_       :						str=256       ,    lowercase_       :						int=0.1       ,    lowercase_       :						int=0.0       ,    lowercase_       :						str=0.0       ,    lowercase_       :						Optional[int]=0.02       ,    lowercase_       :						str=2       ,    lowercase_       :						Union[str, Any]=True       ,    lowercase_       :						Any=1       ,    lowercase_       :						Dict=0       ,    lowercase_       :						List[str]=2       ,    lowercase_       :						List[Any]=6000       ,    lowercase_       :						Tuple=1024       ,    lowercase_       :						str=2       ,    lowercase_       :						Any=(5, 5)       ,    lowercase_       :						Union[str, Any]=1024       ,    lowercase_       :						Dict=80       ,    lowercase_       :						List[Any]=1       ,    **lowercase_       :						int       ,    ):
										lowercase_      :    List[Any]       		= vocab_size
										lowercase_      :    str       		= d_model
										lowercase_      :    List[Any]       		= encoder_ffn_dim
										lowercase_      :    str       		= encoder_layers
										lowercase_      :    Dict       		= encoder_attention_heads
										lowercase_      :    str       		= decoder_ffn_dim
										lowercase_      :    int       		= decoder_layers
										lowercase_      :    Any       		= decoder_attention_heads
										lowercase_      :    Any       		= dropout
										lowercase_      :    Dict       		= attention_dropout
										lowercase_      :    Optional[int]       		= activation_dropout
										lowercase_      :    Any       		= activation_function
										lowercase_      :    Union[str, Any]       		= init_std
										lowercase_      :    str       		= encoder_layerdrop
										lowercase_      :    Optional[int]       		= decoder_layerdrop
										lowercase_      :    Dict       		= use_cache
										lowercase_      :    Union[str, Any]       		= encoder_layers
										lowercase_      :    Tuple       		= scale_embedding  # scale factor will be sqrt(d_model) if True
										lowercase_      :    Dict       		= max_source_positions
										lowercase_      :    Optional[int]       		= max_target_positions
										lowercase_      :    Tuple       		= num_conv_layers
										lowercase_      :    Tuple       		= list(lowercase_					)
										lowercase_      :    Union[str, Any]       		= conv_channels
										lowercase_      :    str       		= input_feat_per_channel
										lowercase_      :    str       		= input_channels

										if len(self.conv_kernel_sizes					) != self.num_conv_layers:
																	raise ValueError(
																	    """Configuration for convolutional module is incorrect. """
																	    """It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` """
																	    f'''but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes					)}`, '''
																	    f'''`config.num_conv_layers = {self.num_conv_layers}`.'''					)

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





