
import functools
import operator

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


SCREAMING_SNAKE_CASE							:Tuple							 =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:str							 =				{
    'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     "sew-d"






	def __init__(       self					: List[str]							,A					: Dict=32							,A					: Optional[int]=7_68							,A					: Dict=12							,A					: Union[str, Any]=12							,A					: Any=30_72							,A					: List[str]=2							,A					: List[str]=5_12							,A					: str=2_56							,A					: int=True							,A					: Any=True							,A					: Tuple=("p2c", "c2p")							,A					: str="layer_norm"							,A					: List[str]="gelu_python"							,A					: Union[str, Any]=0.1							,A					: Optional[Any]=0.1							,A					: Dict=0.1							,A					: Optional[int]=0.0							,A					: List[str]=0.1							,A					: int=0.02							,A					: Any=1E-7							,A					: List[Any]=1E-5							,A					: str="group"							,A					: int="gelu"							,A					: List[str]=(64, 1_28, 1_28, 1_28, 1_28, 2_56, 2_56, 2_56, 2_56, 5_12, 5_12, 5_12, 5_12)							,A					: Dict=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)							,A					: Dict=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)							,A					: Union[str, Any]=False							,A					: Dict=1_28							,A					: Dict=16							,A					: Optional[Any]=True							,A					: List[str]=0.05							,A					: Dict=10							,A					: Tuple=2							,A					: List[Any]=0.0							,A					: List[str]=10							,A					: List[Any]=0							,A					: Dict="mean"							,A					: str=False							,A					: int=False							,A					: Optional[int]=2_56							,A					: Optional[int]=0							,A					: List[Any]=1							,A					: Tuple=2							,**A					: Dict							,):
					super().__init__(**A							,pad_token_id=A							,bos_token_id=A							,eos_token_id=A )
					__A   			= hidden_size
					__A   			= feat_extract_norm
					__A   			= feat_extract_activation
					__A   			= list(A )
					__A   			= list(A )
					__A   			= list(A )
					__A   			= conv_bias
					__A   			= num_conv_pos_embeddings
					__A   			= num_conv_pos_embedding_groups
					__A   			= len(self.conv_dim )
					__A   			= num_hidden_layers
					__A   			= intermediate_size
					__A   			= squeeze_factor
					__A   			= max_position_embeddings
					__A   			= position_buckets
					__A   			= share_att_key
					__A   			= relative_attention
					__A   			= norm_rel_ebd
					__A   			= list(A )
					__A   			= hidden_act
					__A   			= num_attention_heads
					__A   			= hidden_dropout
					__A   			= attention_dropout
					__A   			= activation_dropout
					__A   			= feat_proj_dropout
					__A   			= final_dropout
					__A   			= layer_norm_eps
					__A   			= feature_layer_norm_eps
					__A   			= initializer_range
					__A   			= vocab_size

					if (
					    (len(self.conv_stride ) != self.num_feat_extract_layers)
					    or (len(self.conv_kernel ) != self.num_feat_extract_layers)
					    or (len(self.conv_dim ) != self.num_feat_extract_layers)
					):
									raise ValueError(
									    "Configuration for convolutional layers is incorrect."
									    "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
									    f'''but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)'''
									    f'''= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' )

					# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
					__A   			= apply_spec_augment
					__A   			= mask_time_prob
					__A   			= mask_time_length
					__A   			= mask_time_min_masks
					__A   			= mask_feature_prob
					__A   			= mask_feature_length
					__A   			= mask_feature_min_masks

					# ctc loss
					__A   			= ctc_loss_reduction
					__A   			= ctc_zero_infinity

					# sequence classification
					__A   			= use_weighted_layer_sum
					__A   			= classifier_proj_size







	@property
	def 				UpperCamelCase_  (       self					: List[str] ):
					return functools.reduce(operator.mul							,self.conv_stride							,1 )


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

	lowercase       =    0
	lowercase       =    0
	lowercase       =    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:
			lowercase       =    class_images[count]
			count += 1
			try:
				lowercase       =    requests.get(images['url']							)
				if img.status_code == 200:
					lowercase       =    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_		(							):
	lowercase       =    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	      = parse_args()
	retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)

