


import functools
import operator

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


__lowercase     :						str	=     logging.get_logger(__name__)

__lowercase     :						int	=     {
    'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json',
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}





class 							__UpperCamelCase    (		lowercase__ ):
	A_           =   '''wavlm'''




	def __init__(       self						,    __a=32						,    __a=768						,    __a=12						,    __a=12						,    __a=3072						,    __a="gelu"						,    __a=0.1						,    __a=0.1						,    __a=0.1						,    __a=0.0						,    __a=0.1						,    __a=0.1						,    __a=0.02						,    __a=1E-5						,    __a="group"						,    __a="gelu"						,    __a=(512, 512, 512, 512, 512, 512, 512)						,    __a=(5, 2, 2, 2, 2, 2, 2)						,    __a=(10, 3, 3, 3, 3, 2, 2)						,    __a=False						,    __a=128						,    __a=16						,    __a=320						,    __a=800						,    __a=False						,    __a=True						,    __a=0.05						,    __a=10						,    __a=2						,    __a=0.0						,    __a=10						,    __a=320						,    __a=2						,    __a=0.1						,    __a=100						,    __a=256						,    __a=256						,    __a=0.1						,    __a="mean"						,    __a=False						,    __a=False						,    __a=256						,    __a=(512, 512, 512, 512, 1500)						,    __a=(5, 3, 3, 1, 1)						,    __a=(1, 2, 3, 1, 1)						,    __a=512						,    __a=80						,    __a=0						,    __a=1						,    __a=2						,    __a=False						,    __a=3						,    __a=2						,    __a=3						,    __a=None						,    **__a						,    ):

						'''simple docstring'''





						super().__init__(**_a						,    pad_token_id=_a						,    bos_token_id=_a						,    eos_token_id=_a							)
						__a      :	Any    =  hidden_size
						__a      :	Union[str, Any]    =  feat_extract_norm
						__a      :	List[str]    =  feat_extract_activation
						__a      :	Optional[int]    =  list(_a							)
						__a      :	str    =  list(_a							)
						__a      :	Dict    =  list(_a							)
						__a      :	int    =  conv_bias
						__a      :	Dict    =  num_buckets
						__a      :	Optional[Any]    =  max_bucket_distance
						__a      :	List[str]    =  num_conv_pos_embeddings
						__a      :	int    =  num_conv_pos_embedding_groups
						__a      :	Union[str, Any]    =  len(self.conv_dim							)
						__a      :	Optional[int]    =  num_hidden_layers
						__a      :	Any    =  intermediate_size
						__a      :	Any    =  hidden_act
						__a      :	Optional[int]    =  num_attention_heads
						__a      :	str    =  hidden_dropout
						__a      :	Tuple    =  attention_dropout
						__a      :	Union[str, Any]    =  activation_dropout
						__a      :	str    =  feat_proj_dropout
						__a      :	str    =  final_dropout
						__a      :	List[str]    =  layerdrop
						__a      :	List[str]    =  layer_norm_eps
						__a      :	str    =  initializer_range
						__a      :	str    =  num_ctc_classes
						__a      :	Union[str, Any]    =  vocab_size
						__a      :	Dict    =  do_stable_layer_norm
						__a      :	Optional[int]    =  use_weighted_layer_sum
						__a      :	List[str]    =  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
						__a      :	List[str]    =  apply_spec_augment
						__a      :	Tuple    =  mask_time_prob
						__a      :	Optional[Any]    =  mask_time_length
						__a      :	List[str]    =  mask_time_min_masks
						__a      :	Tuple    =  mask_feature_prob
						__a      :	List[Any]    =  mask_feature_length

						# parameters for pretraining with codevector quantized representations
						__a      :	List[Any]    =  num_codevectors_per_group
						__a      :	Any    =  num_codevector_groups
						__a      :	Any    =  contrastive_logits_temperature
						__a      :	Union[str, Any]    =  num_negatives
						__a      :	Tuple    =  codevector_dim
						__a      :	Tuple    =  proj_codevector_dim
						__a      :	List[Any]    =  diversity_loss_weight

						# ctc loss
						__a      :	int    =  ctc_loss_reduction
						__a      :	List[Any]    =  ctc_zero_infinity

						# adapter
						__a      :	Optional[Any]    =  add_adapter
						__a      :	int    =  adapter_kernel_size
						__a      :	List[Any]    =  adapter_stride
						__a      :	Tuple    =  num_adapter_layers
						__a      :	Dict    =  output_hidden_size or hidden_size

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

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





	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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







'''simple docstring'''



import json
import os
import re
import sys
import urllib.request

import requests
from bsa import BeautifulSoup

__lowercase     :						str	=     {
    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
    ' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582'
}






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       str = "dhaka"						,				_SCREAMING_SNAKE_CASE				:       int = 5       ):
					__a      :	Optional[Any]    =  min(_SCREAMING_SNAKE_CASE						,				50       )  # Prevent abuse!
					__a      :	Optional[Any]    =  {
					    'q': query,
					    'tbm': 'isch',
					    'hl': 'en',
					    'ijn': '0',
					}

					__a      :	Tuple    =  requests.get('https://www.google.com/search'						,				params=_SCREAMING_SNAKE_CASE						,				headers=_SCREAMING_SNAKE_CASE       )
					__a      :	Dict    =  BeautifulSoup(html.text						,				'html.parser'       )
					__a      :	List[str]    =  ''.join(
					    re.findall(r'AF_initDataCallback\(([^<]+)\);'						,				str(soup.select('script'       )       )       )       )

					__a      :	Optional[Any]    =  json.dumps(_SCREAMING_SNAKE_CASE       )
					__a      :	List[str]    =  json.loads(_SCREAMING_SNAKE_CASE       )

					__a      :	List[Any]    =  re.findall(
					    r'\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\",'						,				_SCREAMING_SNAKE_CASE						,				)
					if not matched_google_image_data:
										return 0

					__a      :	Tuple    =  re.sub(
					    r'\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]'						,				''						,				str(_SCREAMING_SNAKE_CASE       )						,				)

					__a      :	Optional[Any]    =  re.findall(
					    r'(?:\'|,),\[\"(https:|http.*?)\",\d+,\d+\]'						,				_SCREAMING_SNAKE_CASE						,				)
					for index, fixed_full_res_image in enumerate(_SCREAMING_SNAKE_CASE       ):
										if index >= max_images:
															return index
										__a      :	List[str]    =  bytes(_SCREAMING_SNAKE_CASE						,				'ascii'       ).decode(
										    'unicode-escape'       )
										__a      :	Tuple    =  bytes(_SCREAMING_SNAKE_CASE						,				'ascii'       ).decode(
										    'unicode-escape'       )
										__a      :	Dict    =  urllib.request.build_opener()
										__a      :	Union[str, Any]    =  [
										    (
										        'User-Agent',
										        'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
										        ' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582',
										    )
										]
										urllib.request.install_opener(_SCREAMING_SNAKE_CASE       )
										__a      :	List[Any]    =  F"""query_{query.replace(" "						,				"_"       )}"""
										if not os.path.exists(_SCREAMING_SNAKE_CASE       ):
															os.makedirs(_SCREAMING_SNAKE_CASE       )
										urllib.request.urlretrieve(  # noqa: S310
										    _SCREAMING_SNAKE_CASE						,				F"""{path_name}/original_size_img_{index}.jpg"""       )
					return index


if __name__ == "__main__":
	try:
		__lowercase     :						Optional[int]	=     download_images_from_google_query(sys.argv[1])
		print(f'''{image_count} images were downloaded to disk.''')
	except IndexError:
		print('Please provide a search term.')
		raise



