


'''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 __snake_case      (   UpperCAmelCase_		:		Union[str, Any]      ,       UpperCAmelCase_		:		Union[str, Any]      ,       UpperCAmelCase_		:		int	):
 lowerCamelCase_  				=						1.5
 lowerCamelCase_  				=						int(factor * num_class_images	)
 lowerCamelCase_  				=						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:
  lowerCamelCase_  				=						client.query(text=UpperCAmelCase_	)
  if len(UpperCAmelCase_	) >= factor * num_class_images or num_images > 1E4:
   break
  else:
   lowerCamelCase_  				=						int(factor * num_images	)
   lowerCamelCase_  				=						ClipClient(
       url="https://knn.laion.ai/knn-service"      ,       indice_name="laion_400m"      ,       num_images=UpperCAmelCase_      ,       aesthetic_weight=0.1      ,       )

 lowerCamelCase_  				=						0
 lowerCamelCase_  				=						0
 lowerCamelCase_  				=						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:
   lowerCamelCase_  				=						class_images[count]
   count += 1
   try:
    lowerCamelCase_  				=						requests.get(images["url"]	)
    if img.status_code == 200:
     lowerCamelCase_  				=						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 __snake_case      (   ):
 lowerCamelCase_  				=						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__":
     a_						:       Any      			=				parse_args()
     retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)







'''simple docstring'''

import functools
import operator

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


a_						:       List[str]      			=				logging.get_logger(__name__)

a_						:       Dict      			=				{
    """microsoft/unispeech-large-1500h-cv""": (
        """https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json"""
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}





class   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						"unispeech"






  def __init__(					self    ,						UpperCamelCase=32    ,						UpperCamelCase=768    ,						UpperCamelCase=12    ,						UpperCamelCase=12    ,						UpperCamelCase=3072    ,						UpperCamelCase="gelu"    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.0    ,						UpperCamelCase=0.0    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.02    ,						UpperCamelCase=1e-5    ,						UpperCamelCase="group"    ,						UpperCamelCase="gelu"    ,						UpperCamelCase=(512, 512, 512, 512, 512, 512, 512)    ,						UpperCamelCase=(5, 2, 2, 2, 2, 2, 2)    ,						UpperCamelCase=(10, 3, 3, 3, 3, 2, 2)    ,						UpperCamelCase=False    ,						UpperCamelCase=128    ,						UpperCamelCase=16    ,						UpperCamelCase=False    ,						UpperCamelCase=True    ,						UpperCamelCase=0.05    ,						UpperCamelCase=10    ,						UpperCamelCase=2    ,						UpperCamelCase=0.0    ,						UpperCamelCase=10    ,						UpperCamelCase=0    ,						UpperCamelCase=320    ,						UpperCamelCase=2    ,						UpperCamelCase=0.1    ,						UpperCamelCase=100    ,						UpperCamelCase=256    ,						UpperCamelCase=256    ,						UpperCamelCase=0.1    ,						UpperCamelCase="mean"    ,						UpperCamelCase=False    ,						UpperCamelCase=False    ,						UpperCamelCase=256    ,						UpperCamelCase=80    ,						UpperCamelCase=0    ,						UpperCamelCase=1    ,						UpperCamelCase=2    ,						UpperCamelCase=0.5    ,						**UpperCamelCase    ,						):







   """simple docstring"""
   super().__init__(**UpperCamelCase    ,						pad_token_id=UpperCamelCase    ,						bos_token_id=UpperCamelCase    ,						eos_token_id=UpperCamelCase  )
   lowerCamelCase_  				=						hidden_size
   lowerCamelCase_  				=						feat_extract_norm
   lowerCamelCase_  				=						feat_extract_activation
   lowerCamelCase_  				=						list(UpperCamelCase  )
   lowerCamelCase_  				=						list(UpperCamelCase  )
   lowerCamelCase_  				=						list(UpperCamelCase  )
   lowerCamelCase_  				=						conv_bias
   lowerCamelCase_  				=						num_conv_pos_embeddings
   lowerCamelCase_  				=						num_conv_pos_embedding_groups
   lowerCamelCase_  				=						len(self.conv_dim  )
   lowerCamelCase_  				=						num_hidden_layers
   lowerCamelCase_  				=						intermediate_size
   lowerCamelCase_  				=						hidden_act
   lowerCamelCase_  				=						num_attention_heads
   lowerCamelCase_  				=						hidden_dropout
   lowerCamelCase_  				=						attention_dropout
   lowerCamelCase_  				=						activation_dropout
   lowerCamelCase_  				=						feat_proj_dropout
   lowerCamelCase_  				=						final_dropout
   lowerCamelCase_  				=						layerdrop
   lowerCamelCase_  				=						layer_norm_eps
   lowerCamelCase_  				=						initializer_range
   lowerCamelCase_  				=						num_ctc_classes
   lowerCamelCase_  				=						vocab_size
   lowerCamelCase_  				=						do_stable_layer_norm
   lowerCamelCase_  				=						use_weighted_layer_sum
   lowerCamelCase_  				=						classifier_proj_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)`, but is `len(config.conv_dim) ="
        f''' {len(self.conv_dim  )}`, `len(config.conv_stride) = {len(self.conv_stride  )}`,'''
        f''' `len(config.conv_kernel) = {len(self.conv_kernel  )}`.'''  )

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

   # parameters for pretraining with codevector quantized representations
   lowerCamelCase_  				=						num_codevectors_per_group
   lowerCamelCase_  				=						num_codevector_groups
   lowerCamelCase_  				=						contrastive_logits_temperature
   lowerCamelCase_  				=						feat_quantizer_dropout
   lowerCamelCase_  				=						num_negatives
   lowerCamelCase_  				=						codevector_dim
   lowerCamelCase_  				=						proj_codevector_dim
   lowerCamelCase_  				=						diversity_loss_weight

   # ctc loss
   lowerCamelCase_  				=						ctc_loss_reduction
   lowerCamelCase_  				=						ctc_zero_infinity

   # pretraining loss
   lowerCamelCase_  				=						replace_prob







  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   return functools.reduce(operator.mul    ,						self.conv_stride    ,						1  )



