I have this code for LDA when I run it i keep getting an error which is difficult to track

below is the code i am trying to run but I keep getting an error which is below the code but I am having a hard time understanding where exactly the float variable is being accessed is it with the variables in the code or somewhere in the data? If someone understands the issue please help me out.

import numpy as np
import tqdm
grid = {}
grid['Validation_Set'] = {}
# Topics range
min_topics = 10
max_topics = 20
step_size = 5
topics_range = range(min_topics, max_topics, step_size)
# Alpha parameter
alpha = list(np.arange(0.01, 1, 0.3))
alpha.append('symmetric')
alpha.append('asymmetric')
# Beta parameter
beta = list(np.arange(0.01, 1, 0.3))
beta.append('symmetric')
# Validation sets
num_of_doc = len(corpus)
num_of_docs = int(num_of_doc)
corpus_sets = [# gensim.utils.ClippedCorpus(corpus, num_of_docs*0.25), 
               # gensim.utils.ClippedCorpus(corpus, num_of_docs*0.5), 
               gensim.utils.ClippedCorpus(corpus, num_of_docs*0.75), 
               corpus]
corpus_title = ['75% Corpus', '100% Corpus']
model_results = {'Validation_Set': [],
                 'Topics': [],
                 'Alpha': [],
                 'Beta': [],
                 'Coherence': []
                }

if 1 == 1:
    pbar = tqdm.tqdm(total=540)
    
    # iterate through validation corpuses
    for i in range(len(corpus_sets)):
        # iterate through number of topics
        for k in topics_range:
            # iterate through alpha values
            for a in alpha:
                # iterare through beta values
                for b in beta:
                    # get the coherence score for the given parameters
                    cv = compute_coherence_values(corpus=corpus_sets[i], dictionary=id2word, 
                                                  k=k, a=a, b=b)
                    # Save the model results
                    model_results['Validation_Set'].append(corpus_title[i])
                    model_results['Topics'].append(k)
                    model_results['Alpha'].append(a)
                    model_results['Beta'].append(b)
                    model_results['Coherence'].append(cv)
                    
                    pbar.update(1)
    pd.DataFrame(model_results).to_csv('lda_tuning_results.csv', index=False)
    pbar.close()

the following is the error that I keep getting stuck into is as follows:

>     >  0%|          | 0/540 [00:00<?, ?it/s]
>     ---------------------------------------------------------------------------
> 
>     
> 
> > TypeError                                 Traceback (most recent call
> > last)
> >     /usr/local/lib/python3.7/dist-packages/gensim/models/ldamulticore.py
> > in update(self, corpus, chunks_as_numpy)
> >         212         try:
> >     --> 213             lencorpus = len(corpus)
> >         214         except TypeError:
> > 
> > 
> > TypeError: 'float' object cannot be interpreted as an integer
> >     
> >     During handling of the above exception, another exception occurred:
> >     
> >     ValueError                                Traceback (most recent call last)
> >     5 frames
> >     /usr/local/lib/python3.7/dist-packages/gensim/utils.py in __iter__(self)
> >         992 
> >         993     def __iter__(self):
> >     --> 994         return itertools.islice(self.corpus, self.max_docs)
> >         995 
> >         996     def __len__(self):
> >     
> >     ValueError: Stop argument for islice() must be None or an integer: 0 <= x <= sys.maxsize.
> >     
> >