





import functools
import operator

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


__lowerCAmelCase :int     =    logging.get_logger(__name__)

__lowerCAmelCase :Optional[Any]     =    {
    'microsoft/unispeech-sat-base-100h-libri-ft': (
        'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}






class        _a(    __A    ):
		lowerCamelCase__     :Any   				=		'unispeech-sat'







		def __init__(    self ,						__snake_case=3_2 ,						__snake_case=7_6_8 ,						__snake_case=1_2 ,						__snake_case=1_2 ,						__snake_case=3_0_7_2 ,						__snake_case="gelu" ,						__snake_case=0.1 ,						__snake_case=0.1 ,						__snake_case=0.1 ,						__snake_case=0.0 ,						__snake_case=0.0 ,						__snake_case=0.1 ,						__snake_case=0.1 ,						__snake_case=0.02 ,						__snake_case=1E-5 ,						__snake_case="group" ,						__snake_case="gelu" ,						__snake_case=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2) ,						__snake_case=(5, 2, 2, 2, 2, 2, 2) ,						__snake_case=(1_0, 3, 3, 3, 3, 2, 2) ,						__snake_case=False ,						__snake_case=1_2_8 ,						__snake_case=1_6 ,						__snake_case=False ,						__snake_case=True ,						__snake_case=0.05 ,						__snake_case=1_0 ,						__snake_case=2 ,						__snake_case=0.0 ,						__snake_case=1_0 ,						__snake_case=0 ,						__snake_case=3_2_0 ,						__snake_case=2 ,						__snake_case=0.1 ,						__snake_case=1_0_0 ,						__snake_case=2_5_6 ,						__snake_case=2_5_6 ,						__snake_case=0.1 ,						__snake_case="mean" ,						__snake_case=False ,						__snake_case=False ,						__snake_case=2_5_6 ,						__snake_case=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0) ,						__snake_case=(5, 3, 3, 1, 1) ,						__snake_case=(1, 2, 3, 1, 1) ,						__snake_case=5_1_2 ,						__snake_case=0 ,						__snake_case=1 ,						__snake_case=2 ,						__snake_case=5_0_4 ,						**__snake_case ,						)      ->      List[Any]:


							'''simple docstring'''

							super().__init__(**__snake_case ,						pad_token_id=__snake_case ,						bos_token_id=__snake_case ,						eos_token_id=__snake_case				)
							_snake_case       :					Tuple				       =   hidden_size
							_snake_case       :					Dict				       =   feat_extract_norm
							_snake_case       :					Optional[Any]				       =   feat_extract_activation
							_snake_case       :					Union[str, Any]				       =   list(__snake_case				)
							_snake_case       :					Tuple				       =   list(__snake_case				)
							_snake_case       :					List[str]				       =   list(__snake_case				)
							_snake_case       :					Union[str, Any]				       =   conv_bias
							_snake_case       :					int				       =   num_conv_pos_embeddings
							_snake_case       :					int				       =   num_conv_pos_embedding_groups
							_snake_case       :					Tuple				       =   len(self.conv_dim				)
							_snake_case       :					Tuple				       =   num_hidden_layers
							_snake_case       :					Union[str, Any]				       =   intermediate_size
							_snake_case       :					List[Any]				       =   hidden_act
							_snake_case       :					str				       =   num_attention_heads
							_snake_case       :					Tuple				       =   hidden_dropout
							_snake_case       :					str				       =   attention_dropout
							_snake_case       :					Optional[int]				       =   activation_dropout
							_snake_case       :					str				       =   feat_proj_dropout
							_snake_case       :					List[Any]				       =   final_dropout
							_snake_case       :					int				       =   layerdrop
							_snake_case       :					Optional[Any]				       =   layer_norm_eps
							_snake_case       :					Dict				       =   initializer_range
							_snake_case       :					List[str]				       =   vocab_size
							_snake_case       :					Union[str, Any]				       =   num_clusters
							_snake_case       :					List[str]				       =   do_stable_layer_norm
							_snake_case       :					List[str]				       =   use_weighted_layer_sum

							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
							_snake_case       :					Union[str, Any]				       =   apply_spec_augment
							_snake_case       :					int				       =   mask_time_prob
							_snake_case       :					List[str]				       =   mask_time_length
							_snake_case       :					Union[str, Any]				       =   mask_time_min_masks
							_snake_case       :					Dict				       =   mask_feature_prob
							_snake_case       :					str				       =   mask_feature_length
							_snake_case       :					Union[str, Any]				       =   mask_feature_min_masks

							# parameters for pretraining with codevector quantized representations
							_snake_case       :					List[Any]				       =   num_codevectors_per_group
							_snake_case       :					Dict				       =   num_codevector_groups
							_snake_case       :					Optional[int]				       =   contrastive_logits_temperature
							_snake_case       :					Optional[Any]				       =   feat_quantizer_dropout
							_snake_case       :					Tuple				       =   num_negatives
							_snake_case       :					Optional[int]				       =   codevector_dim
							_snake_case       :					Dict				       =   proj_codevector_dim
							_snake_case       :					List[str]				       =   diversity_loss_weight

							# ctc loss
							_snake_case       :					Tuple				       =   ctc_loss_reduction
							_snake_case       :					Union[str, Any]				       =   ctc_zero_infinity

							# SequenceClassification-specific parameter. Feel free to ignore for other classes.
							_snake_case       :					Tuple				       =   classifier_proj_size

							# XVector-specific parameters. Feel free to ignore for other classes.
							_snake_case       :					List[Any]				       =   list(__snake_case				)
							_snake_case       :					Optional[int]				       =   list(__snake_case				)
							_snake_case       :					Optional[Any]				       =   list(__snake_case				)
							_snake_case       :					List[str]				       =   xvector_output_dim







		@property
		def        lowercase       (    self				)      ->      Tuple:


							'''simple docstring'''

							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    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					List[str]				       =   1.5
					_snake_case       :					Union[str, Any]				       =   int(factor * num_class_images				)
					_snake_case       :					Dict				       =   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:
										_snake_case       :					Optional[int]				       =   client.query(text=UpperCAmelCase				)
										if len(UpperCAmelCase				) >= factor * num_class_images or num_images > 1e4:
															break
										else:
															_snake_case       :					str				       =   int(factor * num_images				)
															_snake_case       :					Union[str, Any]				       =   ClipClient(
															    url="https://knn.laion.ai/knn-service"  ,  indice_name="laion_400m"  ,  num_images=UpperCAmelCase  ,  aesthetic_weight=0.1  ,  )

					_snake_case       :					List[Any]				       =   0
					_snake_case       :					Any				       =   0
					_snake_case       :					List[Any]				       =   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:
															_snake_case       :					Optional[Any]				       =   class_images[count]
															count += 1
															try:
																				_snake_case       :					Tuple				       =   requests.get(images["url"]				)
																				if img.status_code == 200:
																									_snake_case       :					int				       =   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    A				(							):
					_snake_case       :					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__":
							__lowerCAmelCase :List[Any]     =    parse_args()
							retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)