



'''simple docstring'''


from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase







def 							__lowerCamelCase							(  A__       )			->      None:






   """simple docstring"""
   UpperCamelCase							, UpperCamelCase      							=    analyze_text(snake_case__       )
   UpperCamelCase      							=    list(' ' + ascii_lowercase       )
   # what is our total sum of probabilities.
   UpperCamelCase      							=    sum(single_char_strings.values()       )

   # one length string
   UpperCamelCase      							=    0
   # for each alpha we go in our dict and if it is in it we calculate entropy
   for ch in my_alphas:
      if ch in single_char_strings:
         UpperCamelCase      							=    single_char_strings[ch]
         UpperCamelCase      							=    my_str / all_sum
         my_fir_sum += prob * math.loga(snake_case__       )  # entropy formula.

    # print entropy
   print(F"""{round(-1 * my_fir_sum       ):.1f}"""       )

   # two len string
   UpperCamelCase      							=    sum(two_char_strings.values()       )
   UpperCamelCase      							=    0
   # for each alpha (two in size) calculate entropy.
   for cha in my_alphas:
      for cha in my_alphas:
         UpperCamelCase      							=    cha + cha
         if sequence in two_char_strings:
            UpperCamelCase      							=    two_char_strings[sequence]
            UpperCamelCase      							=    int(snake_case__       ) / all_sum
            my_sec_sum += prob * math.loga(snake_case__       )

    # print second entropy
   print(F"""{round(-1 * my_sec_sum       ):.1f}"""       )

   # print the difference between them
   print(F"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum)       ):.1f}"""       )







def 							__lowerCamelCase							(  A__       )			->      tuple[dict, dict]:






   """simple docstring"""
   UpperCamelCase      							=    Counter()  # type: ignore
   UpperCamelCase      							=    Counter()  # type: ignore
   single_char_strings[text[-1]] += 1

   # first case when we have space at start.
   two_char_strings[" " + text[0]] += 1
   for i in range(0       ,		len(snake_case__       ) - 1       ):
      single_char_strings[text[i]] += 1
      two_char_strings[text[i : i + 2]] += 1
   return single_char_strings, two_char_strings







def 							__lowerCamelCase							(  )			->      Any:






   """simple docstring"""
   import doctest

   doctest.testmod()
   # text = (
   #     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
   #     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
   #     "jointure saw horrible. He private he on be imagine suppose. Fertile "
   #     "beloved evident through no service elderly is. Blind there if every no so "
   #     "at. Own neglected you preferred way sincerity delivered his attempted. To "
   #     "of message cottage windows do besides against uncivil.  Delightful "
   #     "unreserved impossible few estimating men favourable see entreaties. She "
   #     "propriety immediate was improving. He or entrance humoured likewise "
   #     "moderate. Much nor game son say feel. Fat make met can must form into "
   #     "gate. Me we offending prevailed discovery. "
   # )

   # calculate_prob(text)


if __name__ == "__main__":
   main()






import functools
import operator

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


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

_lowerCAmelCase	:   Tuple    =   {
    "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 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		SCREAMING_SNAKE_CASE__								=	'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_						)
						lowerCAmelCase__		  =  hidden_size
						lowerCAmelCase__		  =  feat_extract_norm
						lowerCAmelCase__		  =  feat_extract_activation
						lowerCAmelCase__		  =  list(a_						)
						lowerCAmelCase__		  =  list(a_						)
						lowerCAmelCase__		  =  list(a_						)
						lowerCAmelCase__		  =  conv_bias
						lowerCAmelCase__		  =  num_buckets
						lowerCAmelCase__		  =  max_bucket_distance
						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

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

						# ctc loss
						lowerCAmelCase__		  =  ctc_loss_reduction
						lowerCAmelCase__		  =  ctc_zero_infinity

						# adapter
						lowerCAmelCase__		  =  add_adapter
						lowerCAmelCase__		  =  adapter_kernel_size
						lowerCAmelCase__		  =  adapter_stride
						lowerCAmelCase__		  =  num_adapter_layers
						lowerCAmelCase__		  =  output_hidden_size or hidden_size

						# SequenceClassification-specific parameter. Feel free to ignore for other classes.
						lowerCAmelCase__		  =  classifier_proj_size

						# XVector-specific parameters. Feel free to ignore for other classes.
						lowerCAmelCase__		  =  list(a_						)
						lowerCAmelCase__		  =  list(a_						)
						lowerCAmelCase__		  =  list(a_						)
						lowerCAmelCase__		  =  xvector_output_dim



		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







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