import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
    embed_questions_for_retrieval,
    make_qa_sas_model,
    qa_sas_generate,
    query_es_index,
    query_qa_dense_index,
)

import transformers
from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer


UpperCamelCase__											=					'bart'
UpperCamelCase__											=					True




@st.cache(allow_output_mutation=__A							)
def    lowerCAmelCase_ (     )     ->				Optional[int]:







      '''simple docstring'''


      if LOAD_DENSE_INDEX:
            UpperCAmelCase__								=  AutoTokenizer.from_pretrained("yjernite/retribert-base-uncased"							)
            UpperCAmelCase__								=  AutoModel.from_pretrained("yjernite/retribert-base-uncased"							).to("cuda:0"							)
            UpperCAmelCase__								=  qar_model.eval()
      else:
            UpperCAmelCase__		,      UpperCAmelCase__								=  (None, None)
      if MODEL_TYPE == "bart":
            UpperCAmelCase__								=  AutoTokenizer.from_pretrained("yjernite/bart_eli5"							)
            UpperCAmelCase__								=  AutoModelForSeqaSeqLM.from_pretrained("yjernite/bart_eli5"							).to("cuda:0"							)
            UpperCAmelCase__								=  torch.load("seq2seq_models/eli5_bart_model_blm_2.pth"							)
            sas_model.load_state_dict(save_dict["model"]							)
            UpperCAmelCase__								=  sas_model.eval()
      else:
            UpperCAmelCase__		,      UpperCAmelCase__								=  make_qa_sas_model(
                model_name="t5-small",  from_file="seq2seq_models/eli5_t5_model_1024_4.pth",  device="cuda:0"							)
      return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)




@st.cache(allow_output_mutation=__A							)
def    lowerCAmelCase_ (     )     ->				Dict:







      '''simple docstring'''


      if LOAD_DENSE_INDEX:
            UpperCAmelCase__								=  faiss.StandardGpuResources()
            UpperCAmelCase__								=  datasets.load_dataset(path="wiki_snippets",  name="wiki40b_en_100_0"							)["train"]
            UpperCAmelCase__								=  np.memmap(
                "wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat",  dtype="float32",  mode="r",  shape=(wikiaab_passages.num_rows, 128),  )
            UpperCAmelCase__								=  faiss.IndexFlatIP(128							)
            UpperCAmelCase__								=  faiss.index_cpu_to_gpu(__A,  1,  __A							)
            wikiaab_gpu_index_flat.add(__A							)  # TODO fix for larger GPU
      else:
            UpperCAmelCase__		,      UpperCAmelCase__								=  (None, None)
      UpperCAmelCase__								=  Elasticsearch([{"host": "localhost", "port": "9200"}]							)
      return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)




@st.cache(allow_output_mutation=__A							)
def    lowerCAmelCase_ (     )     ->				Union[str, Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  datasets.load_dataset("eli5",  name="LFQA_reddit"							)
      UpperCAmelCase__								=  elia["train_eli5"]
      UpperCAmelCase__								=  np.memmap(
          "eli5_questions_reps.dat",  dtype="float32",  mode="r",  shape=(elia_train.num_rows, 128)							)
      UpperCAmelCase__								=  faiss.IndexFlatIP(128							)
      eli5_train_q_index.add(__A							)
      return (elia_train, eli5_train_q_index)


UpperCamelCase__							,							UpperCamelCase__							,							UpperCamelCase__											=					load_indexes()
UpperCamelCase__							,							UpperCamelCase__							,							UpperCamelCase__							,							UpperCamelCase__											=					load_models()
UpperCamelCase__							,							UpperCamelCase__											=					load_train_data()




def    lowerCAmelCase_ (     __A,  __A=10							)     ->				Optional[int]:







      '''simple docstring'''


      UpperCAmelCase__								=  embed_questions_for_retrieval([question],  __A,  __A							)
      UpperCAmelCase__		,      UpperCAmelCase__								=  eli5_train_q_index.search(__A,  __A							)
      UpperCAmelCase__								=  [elia_train[int(__A							)] for i in I[0]]
      return nn_examples




def    lowerCAmelCase_ (     __A,  __A="wiki40b",  __A="dense",  __A=10							)     ->				Optional[Any]:







      '''simple docstring'''


      if source == "none":
            UpperCAmelCase__		,      UpperCAmelCase__								=  (" <P> ".join(["" for _ in range(11							)]							).strip(), [])
      else:
            if method == "dense":
                  UpperCAmelCase__		,      UpperCAmelCase__								=  query_qa_dense_index(
                      __A,  __A,  __A,  __A,  __A,  __A							)
            else:
                  UpperCAmelCase__		,      UpperCAmelCase__								=  query_es_index(
                      __A,  __A,  index_name="english_wiki40b_snippets_100w",  n_results=__A,  )
      UpperCAmelCase__								=  [
          (res["article_title"], res["section_title"].strip(), res["score"], res["passage_text"]) for res in hit_lst
      ]
      UpperCAmelCase__								=  "question: {} context: {}".format(__A,  __A							)
      return question_doc, support_list




@st.cache(
    hash_funcs={
        torch.Tensor: (lambda __A							: None),
        transformers.models.bart.tokenization_bart.BartTokenizer: (lambda __A							: None),
    }							)
def    lowerCAmelCase_ (     __A,  __A,  __A,  __A=64,  __A=256,  __A=False,  __A=2,  __A=0.95,  __A=0.8							)     ->				Optional[Any]:







      '''simple docstring'''


      with torch.no_grad():
            UpperCAmelCase__								=  qa_sas_generate(
                __A,  __A,  __A,  num_answers=1,  num_beams=__A,  min_len=__A,  max_len=__A,  do_sample=__A,  temp=__A,  top_p=__A,  top_k=__A,  max_input_length=1_024,  device="cuda:0",  )[0]
      return (answer, support_list)


st.title('Long Form Question Answering with ELI5')

# Start sidebar
UpperCamelCase__											=					'<img src=\'https://huggingface.co/front/assets/huggingface_logo.svg\'>'
UpperCamelCase__											=					'\n<html>\n  <head>\n    <style>\n      .img-container {\n        padding-left: 90px;\n        padding-right: 90px;\n        padding-top: 50px;\n        padding-bottom: 50px;\n        background-color: #f0f3f9;\n      }\n    </style>\n  </head>\n  <body>\n    <span class="img-container"> <!-- Inline parent element -->\n      %s\n    </span>\n  </body>\n</html>\n' % (
    header_html,
)
st.sidebar.markdown(
    header_full,
    unsafe_allow_html=True,
)

# Long Form QA with ELI5 and Wikipedia
UpperCamelCase__											=					'\nThis demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).\nFirst, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,\na pre-processed fixed snapshot of Wikipedia.\n'
st.sidebar.markdown(description, unsafe_allow_html=True)

UpperCamelCase__											=					[
    'Answer the question',
    'View the retrieved document only',
    'View the most similar ELI5 question and answer',
    'Show me everything, please!',
]
UpperCamelCase__											=					st.sidebar.checkbox('Demo options')
if demo_options:
      UpperCamelCase__											=					st.sidebar.selectbox(
          '',
          action_list,
          index=3,
      )
      UpperCamelCase__											=					action_list.index(action_st)
      UpperCamelCase__											=					st.sidebar.selectbox(
          '',
          ['Show full text of passages', 'Show passage section titles'],
          index=0,
      )
      UpperCamelCase__											=					show_type == 'Show full text of passages'
else:
      UpperCamelCase__											=					3
      UpperCamelCase__											=					True

UpperCamelCase__											=					st.sidebar.checkbox('Retrieval options')
if retrieval_options:
      UpperCamelCase__											=					'\n    ### Information retriever options\n\n    The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding\n    trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.\n    The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.\n    '
      st.sidebar.markdown(retriever_info)
      UpperCamelCase__											=					st.sidebar.selectbox('Which Wikipedia format should the model use?', ['wiki40b', 'none'])
      UpperCamelCase__											=					st.sidebar.selectbox('Which Wikipedia indexer should the model use?', ['dense', 'sparse', 'mixed'])
else:
      UpperCamelCase__											=					'wiki40b'
      UpperCamelCase__											=					'dense'

UpperCamelCase__											=					'beam'
UpperCamelCase__											=					2
UpperCamelCase__											=					6_4
UpperCamelCase__											=					2_5_6
UpperCamelCase__											=					None
UpperCamelCase__											=					None
UpperCamelCase__											=					st.sidebar.checkbox('Generation options')
if generate_options:
      UpperCamelCase__											=					'\n    ### Answer generation options\n\n    The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)\n    weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with\n    **beam** search, or **sample** from the decoder\'s output probabilities.\n    '
      st.sidebar.markdown(generate_info)
      UpperCamelCase__											=					st.sidebar.selectbox('Would you like to use beam search or sample an answer?', ['beam', 'sampled'])
      UpperCamelCase__											=					st.sidebar.slider(
          'Minimum generation length', min_value=8, max_value=2_5_6, value=6_4, step=8, format=None, key=None
      )
      UpperCamelCase__											=					st.sidebar.slider(
          'Maximum generation length', min_value=6_4, max_value=5_1_2, value=2_5_6, step=1_6, format=None, key=None
      )
      if sampled == "beam":
            UpperCamelCase__											=					st.sidebar.slider('Beam size', min_value=1, max_value=8, value=2, step=None, format=None, key=None)
      else:
            UpperCamelCase__											=					st.sidebar.slider(
                'Nucleus sampling p', min_value=0.1, max_value=1.0, value=0.9_5, step=0.0_1, format=None, key=None
            )
            UpperCamelCase__											=					st.sidebar.slider(
                'Temperature', min_value=0.1, max_value=1.0, value=0.7, step=0.0_1, format=None, key=None
            )
            UpperCamelCase__											=					None

# start main text
UpperCamelCase__											=					[
    '<MY QUESTION>',
    'How do people make chocolate?',
    'Why do we get a fever when we are sick?',
    'How can different animals perceive different colors?',
    'What is natural language processing?',
    'What\'s the best way to treat a sunburn?',
    'What exactly are vitamins ?',
    'How does nuclear energy provide electricity?',
    'What\'s the difference between viruses and bacteria?',
    'Why are flutes classified as woodwinds when most of them are made out of metal ?',
    'Why do people like drinking coffee even though it tastes so bad?',
    'What happens when wine ages? How does it make the wine taste better?',
    'If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?',
    'How can we set a date to the beginning or end of an artistic period? Doesn\'t the change happen gradually?',
    'How does New Zealand have so many large bird predators?',
]
UpperCamelCase__											=					st.selectbox(
    'What would you like to ask? ---- select <MY QUESTION> to enter a new query',
    questions_list,
    index=1,
)
if question_s == "<MY QUESTION>":
      UpperCamelCase__											=					st.text_input('Enter your question here:', '')
else:
      UpperCamelCase__											=					question_s

if st.button('Show me!'):
      if action in [0, 1, 3]:
            if index_type == "mixed":
                  UpperCamelCase__							,							UpperCamelCase__											=					make_support(question, source=wiki_source, method='dense', n_results=1_0)
                  UpperCamelCase__							,							UpperCamelCase__											=					make_support(question, source=wiki_source, method='sparse', n_results=1_0)
                  UpperCamelCase__											=					[]
                  for res_d, res_s in zip(support_list_dense, support_list_sparse):
                        if tuple(res_d) not in support_list:
                              support_list += [tuple(res_d)]
                        if tuple(res_s) not in support_list:
                              support_list += [tuple(res_s)]
                  UpperCamelCase__											=					support_list[:1_0]
                  UpperCamelCase__											=					'<P> ' + ' <P> '.join([res[-1] for res in support_list])
            else:
                  UpperCamelCase__							,							UpperCamelCase__											=					make_support(question, source=wiki_source, method=index_type, n_results=1_0)
      if action in [0, 3]:
            UpperCamelCase__							,							UpperCamelCase__											=					answer_question(
                question_doc,
                sas_model,
                sas_tokenizer,
                min_len=min_len,
                max_len=int(max_len),
                sampling=(sampled == 'sampled'),
                n_beams=n_beams,
                top_p=top_p,
                temp=temp,
            )
            st.markdown('### The model generated answer is:')
            st.write(answer)
      if action in [0, 1, 3] and wiki_source != "none":
            st.markdown('--- \n ### The model is drawing information from the following Wikipedia passages:')
            for i, res in enumerate(support_list):
                  UpperCamelCase__											=					'https://en.wikipedia.org/wiki/{}'.format(res[0].replace(' ', '_'))
                  UpperCamelCase__											=					res[1].strip()
                  if sec_titles == "":
                        UpperCamelCase__											=					'[{}]({})'.format(res[0], wiki_url)
                  else:
                        UpperCamelCase__											=					sec_titles.split(' & ')
                        UpperCamelCase__											=					' & '.join(
                            ['[{}]({}#{})'.format(sec.strip(), wiki_url, sec.strip().replace(' ', '_')) for sec in sec_list]
                        )
                  st.markdown(
                      '{0:02d} - **Article**: {1:<18} <br>  _Section_: {2}'.format(i + 1, res[0], sections),
                      unsafe_allow_html=True,
                  )
                  if show_passages:
                        st.write(
                            '> <span style="font-family:arial; font-size:10pt;">' + res[-1] + '</span>', unsafe_allow_html=True
                        )
      if action in [2, 3]:
            UpperCamelCase__											=					find_nearest_training(question)
            UpperCamelCase__											=					nn_train_list[0]
            st.markdown(
                '--- \n ### The most similar question in the ELI5 training set was: \n\n {}'.format(train_exple['title'])
            )
            UpperCamelCase__											=					[
                '{}. {}'.format(i + 1, '  \n'.join([line.strip() for line in ans.split('\n') if line.strip() != '']))
                for i, (ans, sc) in enumerate(zip(train_exple['answers']['text'], train_exple['answers']['score']))
                if i == 0 or sc > 2
            ]
            st.markdown('##### Its answers were: \n\n {}'.format('\n'.join(answers_st)))


UpperCamelCase__											=					'\n---\n\n**Disclaimer**\n\n*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.\nEvaluating biases of such a model and ensuring factual generations are still very much open research problems.\nTherefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*\n'







st.sidebar.markdown(disclaimer, unsafe_allow_html=True)




from __future__ import annotations





class      A							:


   def __init__(self     :						Union[str, Any]       ,  __UpperCAmelCase     :						list[list[int]]		)						->				List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  TypeError(
             "Matrices must be formed from a list of zero or more lists containing at "
             "least one and the same number of values, each of which must be of type "
             "int or float."		)
         if len(__UpperCAmelCase		) != 0:
               UpperCAmelCase__								=  len(rows[0]		)
               if cols == 0:
                     raise error
               for row in rows:
                     if len(__UpperCAmelCase		) != cols:
                           raise error
                     for value in row:
                           if not isinstance(__UpperCAmelCase       ,  (int, float)		):
                                 raise error
               UpperCAmelCase__								=  rows
         else:
               UpperCAmelCase__								=  []


   def 			lowercase_      (self     :						Any		)						->				list[list[int]]:
         """simple docstring"""



         return [[row[i] for row in self.rows] for i in range(len(self.rows[0]		)		)]


   @property
   def 			lowercase_      (self     :						Any		)						->				int:
         """simple docstring"""



         return len(self.rows		)


   @property
   def 			lowercase_      (self     :						Union[str, Any]		)						->				int:
         """simple docstring"""



         return len(self.rows[0]		)


   @property
   def 			lowercase_      (self     :						List[Any]		)						->				tuple[int, int]:
         """simple docstring"""



         return (self.num_rows, self.num_columns)


   @property
   def 			lowercase_      (self     :						Tuple		)						->				bool:
         """simple docstring"""



         return self.order[0] == self.order[1]


   def 			lowercase_      (self     :						Any		)						->				Matrix:
         """simple docstring"""



         UpperCAmelCase__								=  [
             [0 if column_num != row_num else 1 for column_num in range(self.num_rows		)]
             for row_num in range(self.num_rows		)
         ]
         return Matrix(__UpperCAmelCase		)


   def 			lowercase_      (self     :						int		)						->				int:
         """simple docstring"""



         if not self.is_square:
               return 0
         if self.order == (0, 0):
               return 1
         if self.order == (1, 1):
               return int(self.rows[0][0]		)
         if self.order == (2, 2):
               return int(
                   (self.rows[0][0] * self.rows[1][1])
                   - (self.rows[0][1] * self.rows[1][0])		)
         else:
               return sum(
                   self.rows[0][column] * self.cofactors().rows[0][column]
                   for column in range(self.num_columns		)		)


   def 			lowercase_      (self     :						Tuple		)						->				bool:
         """simple docstring"""



         return bool(self.determinant()		)


   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						int		)						->				int:
         """simple docstring"""



         UpperCAmelCase__								=  [
             [
                 self.rows[other_row][other_column]
                 for other_column in range(self.num_columns		)
                 if other_column != column
             ]
             for other_row in range(self.num_rows		)
             if other_row != row
         ]
         return Matrix(__UpperCAmelCase		).determinant()


   def 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						int		)						->				int:
         """simple docstring"""



         if (row + column) % 2 == 0:
               return self.get_minor(__UpperCAmelCase       ,  __UpperCAmelCase		)
         return -1 * self.get_minor(__UpperCAmelCase       ,  __UpperCAmelCase		)


   def 			lowercase_      (self     :						Union[str, Any]		)						->				Matrix:
         """simple docstring"""



         return Matrix(
             [
                 [self.get_minor(__UpperCAmelCase       ,  __UpperCAmelCase		) for column in range(self.num_columns		)]
                 for row in range(self.num_rows		)
             ]		)


   def 			lowercase_      (self     :						List[str]		)						->				Matrix:
         """simple docstring"""



         return Matrix(
             [
                 [
                     self.minors().rows[row][column]
                     if (row + column) % 2 == 0
                     else self.minors().rows[row][column] * -1
                     for column in range(self.minors().num_columns		)
                 ]
                 for row in range(self.minors().num_rows		)
             ]		)


   def 			lowercase_      (self     :						Optional[Any]		)						->				Matrix:
         """simple docstring"""



         UpperCAmelCase__								=  [
             [self.cofactors().rows[column][row] for column in range(self.num_columns		)]
             for row in range(self.num_rows		)
         ]
         return Matrix(__UpperCAmelCase		)


   def 			lowercase_      (self     :						List[Any]		)						->				Matrix:
         """simple docstring"""



         UpperCAmelCase__								=  self.determinant()
         if not determinant:
               raise TypeError("Only matrices with a non-zero determinant have an inverse"		)
         return self.adjugate() * (1 / determinant)


   def __repr__(self     :						Dict		)						->				str:
         """simple docstring"""



         return str(self.rows		)


   def __str__(self     :						Optional[Any]		)						->				str:
         """simple docstring"""



         if self.num_rows == 0:
               return "[]"
         if self.num_rows == 1:
               return "[[" + ". ".join(str(self.rows[0]		)		) + "]]"
         return (
             "["
             + "\n ".join(
                 [
                     "[" + ". ".join([str(__UpperCAmelCase		) for value in row]		) + ".]"
                     for row in self.rows
                 ]		)
             + "]"
         )


   def 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						list[int]       ,  __UpperCAmelCase     :						int | None = None		)						->				None:
         """simple docstring"""



         UpperCAmelCase__								=  TypeError("Row must be a list containing all ints and/or floats"		)
         if not isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		):
               raise type_error
         for value in row:
               if not isinstance(__UpperCAmelCase       ,  (int, float)		):
                     raise type_error
         if len(__UpperCAmelCase		) != self.num_columns:
               raise ValueError(
                   "Row must be equal in length to the other rows in the matrix"		)
         if position is None:
               self.rows.append(__UpperCAmelCase		)
         else:
               UpperCAmelCase__								=  self.rows[0:position] + [row] + self.rows[position:]


   def 			lowercase_      (self     :						Union[str, Any]       ,  __UpperCAmelCase     :						list[int]       ,  __UpperCAmelCase     :						int | None = None		)						->				None:
         """simple docstring"""



         UpperCAmelCase__								=  TypeError(
             "Column must be a list containing all ints and/or floats"		)
         if not isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		):
               raise type_error
         for value in column:
               if not isinstance(__UpperCAmelCase       ,  (int, float)		):
                     raise type_error
         if len(__UpperCAmelCase		) != self.num_rows:
               raise ValueError(
                   "Column must be equal in length to the other columns in the matrix"		)
         if position is None:
               UpperCAmelCase__								=  [self.rows[i] + [column[i]] for i in range(self.num_rows		)]
         else:
               UpperCAmelCase__								=  [
                   self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
                   for i in range(self.num_rows		)
               ]


   def __eq__(self     :						Any       ,  __UpperCAmelCase     :						object		)						->				bool:
         """simple docstring"""



         if not isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		):
               return NotImplemented
         return self.rows == other.rows


   def __ne__(self     :						int       ,  __UpperCAmelCase     :						object		)						->				bool:
         """simple docstring"""



         return not self == other


   def __neg__(self     :						Dict		)						->				Matrix:
         """simple docstring"""



         return self * -1


   def __add__(self     :						Dict       ,  __UpperCAmelCase     :						Matrix		)						->				Matrix:
         """simple docstring"""



         if self.order != other.order:
               raise ValueError("Addition requires matrices of the same order"		)
         return Matrix(
             [
                 [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns		)]
                 for i in range(self.num_rows		)
             ]		)


   def __sub__(self     :						Optional[Any]       ,  __UpperCAmelCase     :						Matrix		)						->				Matrix:
         """simple docstring"""



         if self.order != other.order:
               raise ValueError("Subtraction requires matrices of the same order"		)
         return Matrix(
             [
                 [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns		)]
                 for i in range(self.num_rows		)
             ]		)


   def __mul__(self     :						Tuple       ,  __UpperCAmelCase     :						Matrix | int | float		)						->				Matrix:
         """simple docstring"""



         if isinstance(__UpperCAmelCase       ,  (int, float)		):
               return Matrix(
                   [[int(element * other		) for element in row] for row in self.rows]		)
         elif isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		):
               if self.num_columns != other.num_rows:
                     raise ValueError(
                         "The number of columns in the first matrix must "
                         "be equal to the number of rows in the second"		)
               return Matrix(
                   [
                       [Matrix.dot_product(__UpperCAmelCase       ,  __UpperCAmelCase		) for column in other.columns()]
                       for row in self.rows
                   ]		)
         else:
               raise TypeError(
                   "A Matrix can only be multiplied by an int, float, or another matrix"		)


   def __pow__(self     :						List[Any]       ,  __UpperCAmelCase     :						int		)						->				Matrix:
         """simple docstring"""



         if not isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		):
               raise TypeError("A Matrix can only be raised to the power of an int"		)
         if not self.is_square:
               raise ValueError("Only square matrices can be raised to a power"		)
         if other == 0:
               return self.identity()
         if other < 0:
               if self.is_invertable():
                     return self.inverse() ** (-other)
               raise ValueError(
                   "Only invertable matrices can be raised to a negative power"		)
         UpperCAmelCase__								=  self
         for _ in range(other - 1		):
               result *= self
         return result





   @classmethod
   def 			lowercase_      (cls     :						Dict       ,  __UpperCAmelCase     :						list[int]       ,  __UpperCAmelCase     :						list[int]		)						->				int:
         """simple docstring"""



         return sum(row[i] * column[i] for i in range(len(__UpperCAmelCase		)		)		)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



