




import functools
import operator

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


_lowerCamelCase			:	Dict							 =     logging.get_logger(__name__)

_lowerCamelCase			:	Optional[int]							 =     {
    """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        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    UpperCAmelCase__													=   '''unispeech'''


    def __init__(    self   :			Union[str, Any]      ,				UpperCAmelCase__   :			Any=32      ,				UpperCAmelCase__   :			Any=768      ,				UpperCAmelCase__   :			Optional[Any]=12      ,				UpperCAmelCase__   :			Dict=12      ,				UpperCAmelCase__   :			List[str]=3_072      ,				UpperCAmelCase__   :			str="gelu"      ,				UpperCAmelCase__   :			Any=0.1      ,				UpperCAmelCase__   :			Any=0.1      ,				UpperCAmelCase__   :			int=0.1      ,				UpperCAmelCase__   :			Optional[int]=0.0      ,				UpperCAmelCase__   :			List[str]=0.0      ,				UpperCAmelCase__   :			int=0.1      ,				UpperCAmelCase__   :			Union[str, Any]=0.1      ,				UpperCAmelCase__   :			Optional[int]=0.02      ,				UpperCAmelCase__   :			str=1e-5      ,				UpperCAmelCase__   :			Optional[Any]="group"      ,				UpperCAmelCase__   :			Tuple="gelu"      ,				UpperCAmelCase__   :			List[Any]=(512, 512, 512, 512, 512, 512, 512)      ,				UpperCAmelCase__   :			str=(5, 2, 2, 2, 2, 2, 2)      ,				UpperCAmelCase__   :			Dict=(10, 3, 3, 3, 3, 2, 2)      ,				UpperCAmelCase__   :			Tuple=False      ,				UpperCAmelCase__   :			Dict=128      ,				UpperCAmelCase__   :			int=16      ,				UpperCAmelCase__   :			int=False      ,				UpperCAmelCase__   :			int=True      ,				UpperCAmelCase__   :			Union[str, Any]=0.05      ,				UpperCAmelCase__   :			Tuple=10      ,				UpperCAmelCase__   :			Any=2      ,				UpperCAmelCase__   :			str=0.0      ,				UpperCAmelCase__   :			Any=10      ,				UpperCAmelCase__   :			int=0      ,				UpperCAmelCase__   :			Tuple=320      ,				UpperCAmelCase__   :			Optional[Any]=2      ,				UpperCAmelCase__   :			Optional[int]=0.1      ,				UpperCAmelCase__   :			Any=100      ,				UpperCAmelCase__   :			List[str]=256      ,				UpperCAmelCase__   :			List[Any]=256      ,				UpperCAmelCase__   :			Tuple=0.1      ,				UpperCAmelCase__   :			Tuple="mean"      ,				UpperCAmelCase__   :			List[str]=False      ,				UpperCAmelCase__   :			int=False      ,				UpperCAmelCase__   :			Optional[Any]=256      ,				UpperCAmelCase__   :			Tuple=80      ,				UpperCAmelCase__   :			List[Any]=0      ,				UpperCAmelCase__   :			int=1      ,				UpperCAmelCase__   :			Optional[int]=2      ,				UpperCAmelCase__   :			Any=0.5      ,				**UpperCAmelCase__   :			str      ,				)		->Union[str, Any]:





           '''simple docstring'''

           super().__init__(**UpperCAmelCase__      ,				pad_token_id=UpperCAmelCase__      ,				bos_token_id=UpperCAmelCase__      ,				eos_token_id=UpperCAmelCase__)
           A__   				=					hidden_size
           A__   				=					feat_extract_norm
           A__   				=					feat_extract_activation
           A__   				=					list(UpperCAmelCase__)
           A__   				=					list(UpperCAmelCase__)
           A__   				=					list(UpperCAmelCase__)
           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__   				=					hidden_act
           A__   				=					num_attention_heads
           A__   				=					hidden_dropout
           A__   				=					attention_dropout
           A__   				=					activation_dropout
           A__   				=					feat_proj_dropout
           A__   				=					final_dropout
           A__   				=					layerdrop
           A__   				=					layer_norm_eps
           A__   				=					initializer_range
           A__   				=					num_ctc_classes
           A__   				=					vocab_size
           A__   				=					do_stable_layer_norm
           A__   				=					use_weighted_layer_sum
           A__   				=					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__   				=					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

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

           # ctc loss
           A__   				=					ctc_loss_reduction
           A__   				=					ctc_zero_infinity

           # pretraining loss
           A__   				=					replace_prob





    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			List[str])		->Dict:





           '''simple docstring'''

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








from collections import namedtuple

import requests
from lxml import html  # type: ignore

_lowerCamelCase			:	Optional[int]							 =     namedtuple("""covid_data""", """cases deaths recovered""")







def SCREAMING_SNAKE_CASE       ( lowercase_ = "https://www.worldometers.info/coronavirus/"		)     -> covid_data:



       """simple docstring"""

       A__   				=					'''//div[@class = "maincounter-number"]/span/text()'''
       return covid_data(*html.fromstring(requests.get(lowercase_		).content		).xpath(lowercase_		)		)


_lowerCamelCase			:	Optional[int]							 =     """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()))


