





import functools
import operator

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


_snake_case           =			logging.get_logger(__name__)

_snake_case           =			{
    "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  lowerCAmelCase							(       UpperCamelCase_ ):
  __lowerCamelCase		=      """unispeech"""







  def __init__(      self  :List[str]   ,							_lowercase  :Optional[Any]=32   ,							_lowercase  :Union[str, Any]=7_68   ,							_lowercase  :List[str]=12   ,							_lowercase  :str=12   ,							_lowercase  :Optional[int]=30_72   ,							_lowercase  :Union[str, Any]="gelu"   ,							_lowercase  :Any=0.1   ,							_lowercase  :Dict=0.1   ,							_lowercase  :Optional[int]=0.1   ,							_lowercase  :List[str]=0.0   ,							_lowercase  :Tuple=0.0   ,							_lowercase  :Union[str, Any]=0.1   ,							_lowercase  :int=0.1   ,							_lowercase  :Optional[int]=0.02   ,							_lowercase  :str=1e-5   ,							_lowercase  :List[Any]="group"   ,							_lowercase  :Optional[int]="gelu"   ,							_lowercase  :Any=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)   ,							_lowercase  :int=(5, 2, 2, 2, 2, 2, 2)   ,							_lowercase  :Optional[int]=(10, 3, 3, 3, 3, 2, 2)   ,							_lowercase  :List[Any]=False   ,							_lowercase  :Dict=1_28   ,							_lowercase  :Optional[int]=16   ,							_lowercase  :List[str]=False   ,							_lowercase  :Tuple=True   ,							_lowercase  :Union[str, Any]=0.05   ,							_lowercase  :Dict=10   ,							_lowercase  :str=2   ,							_lowercase  :Optional[Any]=0.0   ,							_lowercase  :List[Any]=10   ,							_lowercase  :Optional[int]=0   ,							_lowercase  :Optional[int]=3_20   ,							_lowercase  :Union[str, Any]=2   ,							_lowercase  :Dict=0.1   ,							_lowercase  :Any=1_00   ,							_lowercase  :str=2_56   ,							_lowercase  :List[str]=2_56   ,							_lowercase  :Optional[Any]=0.1   ,							_lowercase  :Optional[int]="mean"   ,							_lowercase  :Optional[Any]=False   ,							_lowercase  :Tuple=False   ,							_lowercase  :str=2_56   ,							_lowercase  :List[Any]=80   ,							_lowercase  :List[str]=0   ,							_lowercase  :Optional[int]=1   ,							_lowercase  :int=2   ,							_lowercase  :Union[str, Any]=0.5   ,							**_lowercase  :List[Any]   ,							):






     '''simple docstring'''





     super().__init__(**_a   ,							pad_token_id=_a   ,							bos_token_id=_a   ,							eos_token_id=_a )
     lowercase__			       =  hidden_size
     lowercase__			       =  feat_extract_norm
     lowercase__			       =  feat_extract_activation
     lowercase__			       =  list(_a )
     lowercase__			       =  list(_a )
     lowercase__			       =  list(_a )
     lowercase__			       =  conv_bias
     lowercase__			       =  num_conv_pos_embeddings
     lowercase__			       =  num_conv_pos_embedding_groups
     lowercase__			       =  len(self.conv_dim )
     lowercase__			       =  num_hidden_layers
     lowercase__			       =  intermediate_size
     lowercase__			       =  hidden_act
     lowercase__			       =  num_attention_heads
     lowercase__			       =  hidden_dropout
     lowercase__			       =  attention_dropout
     lowercase__			       =  activation_dropout
     lowercase__			       =  feat_proj_dropout
     lowercase__			       =  final_dropout
     lowercase__			       =  layerdrop
     lowercase__			       =  layer_norm_eps
     lowercase__			       =  initializer_range
     lowercase__			       =  num_ctc_classes
     lowercase__			       =  vocab_size
     lowercase__			       =  do_stable_layer_norm
     lowercase__			       =  use_weighted_layer_sum
     lowercase__			       =  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
     lowercase__			       =  apply_spec_augment
     lowercase__			       =  mask_time_prob
     lowercase__			       =  mask_time_length
     lowercase__			       =  mask_time_min_masks
     lowercase__			       =  mask_feature_prob
     lowercase__			       =  mask_feature_length
     lowercase__			       =  mask_feature_min_masks

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

     # ctc loss
     lowercase__			       =  ctc_loss_reduction
     lowercase__			       =  ctc_zero_infinity

     # pretraining loss
     lowercase__			       =  replace_prob







  @property
  def   UpperCAmelCase					(      self  :Optional[Any] ):






     '''simple docstring'''





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













from collections import namedtuple

import requests
from lxml import html  # type: ignore

_snake_case           =			namedtuple("""covid_data""", """cases deaths recovered""")


def _A       (   __magic_name__ = "https://www.worldometers.info/coronavirus/"	):
   lowercase__			       =  "//div[@class = \"maincounter-number\"]/span/text()"
   return covid_data(*html.fromstring(requests.get(__magic_name__	).content	).xpath(__magic_name__	)	)


_snake_case           =			"""Total COVID-19 cases in the world: {}
Total deaths due to COVID-19 in the world: {}
Total COVID-19 patients recovered in the world: {}"""







print(fmt.format(*covid_stats()))






