



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


a_						:     Dict	     =		'bart'
a_						:     List[str]	     =		True

@st.cache(allow_output_mutation=UpperCAmelCase__ )
def     __lowercase(	):


      """simple docstring"""



      if LOAD_DENSE_INDEX:
            lowerCamelCase								=		AutoTokenizer.from_pretrained("yjernite/retribert-base-uncased" )
            lowerCamelCase								=		AutoModel.from_pretrained("yjernite/retribert-base-uncased" ).to("cuda:0" )
            lowerCamelCase								=		qar_model.eval()
      else:
            lowerCamelCase			,							lowerCamelCase								=		(None, None)
      if MODEL_TYPE == "bart":
            lowerCamelCase								=		AutoTokenizer.from_pretrained("yjernite/bart_eli5" )
            lowerCamelCase								=		AutoModelForSeqaSeqLM.from_pretrained("yjernite/bart_eli5" ).to("cuda:0" )
            lowerCamelCase								=		torch.load("seq2seq_models/eli5_bart_model_blm_2.pth" )
            sas_model.load_state_dict(save_dict["model"] )
            lowerCamelCase								=		sas_model.eval()
      else:
            lowerCamelCase			,							lowerCamelCase								=		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=UpperCAmelCase__ )
def     __lowercase(	):


      """simple docstring"""



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

@st.cache(allow_output_mutation=UpperCAmelCase__ )
def     __lowercase(	):


      """simple docstring"""



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


a_     , a_     , a_						:     Tuple	     =		load_indexes()
a_     , a_     , a_     , a_						:     Union[str, Any]	     =		load_models()
a_     , a_						:     Union[str, Any]	     =		load_train_data()

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__=10 ):


      """simple docstring"""



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

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__="wiki40b"   ,   UpperCAmelCase__="dense"   ,   UpperCAmelCase__=10 ):


      """simple docstring"""



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

@st.cache(
    hash_funcs={
        torch.Tensor: (lambda UpperCAmelCase__ : None),
        transformers.models.bart.tokenization_bart.BartTokenizer: (lambda UpperCAmelCase__ : None),
    } )
def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__=64   ,   UpperCAmelCase__=256   ,   UpperCAmelCase__=False   ,   UpperCAmelCase__=2   ,   UpperCAmelCase__=0.9_5   ,   UpperCAmelCase__=0.8 ):


      """simple docstring"""



      with torch.no_grad():
            lowerCamelCase								=		qa_sas_generate(
                UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   num_answers=1   ,   num_beams=UpperCAmelCase__   ,   min_len=UpperCAmelCase__   ,   max_len=UpperCAmelCase__   ,   do_sample=UpperCAmelCase__   ,   temp=UpperCAmelCase__   ,   top_p=UpperCAmelCase__   ,   top_k=UpperCAmelCase__   ,   max_input_length=1024   ,   device="cuda:0"   ,   )[0]
      return (answer, support_list)


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

# Start sidebar
a_						:     str	     =		'<img src=\'https://huggingface.co/front/assets/huggingface_logo.svg\'>'
a_						:     Union[str, Any]	     =		'\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
a_						:     int	     =		'\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)

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

a_						:     Any	     =		st.sidebar.checkbox('Retrieval options')
if retrieval_options:
     a_						:     Any	     =		'\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)
     a_						:     Dict	     =		st.sidebar.selectbox('Which Wikipedia format should the model use?', ['wiki40b', 'none'])
     a_						:     Union[str, Any]	     =		st.sidebar.selectbox('Which Wikipedia indexer should the model use?', ['dense', 'sparse', 'mixed'])
else:
     a_						:     int	     =		'wiki40b'
     a_						:     List[str]	     =		'dense'

a_						:     List[str]	     =		'beam'
a_						:     int	     =		2
a_						:     Optional[Any]	     =		6_4
a_						:     Optional[int]	     =		2_5_6
a_						:     List[Any]	     =		None
a_						:     Union[str, Any]	     =		None
a_						:     str	     =		st.sidebar.checkbox('Generation options')
if generate_options:
     a_						:     Dict	     =		'\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)
     a_						:     Any	     =		st.sidebar.selectbox('Would you like to use beam search or sample an answer?', ['beam', 'sampled'])
     a_						:     str	     =		st.sidebar.slider(
         'Minimum generation length', min_value=8, max_value=2_5_6, value=6_4, step=8, format=None, key=None
     )
     a_						:     int	     =		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":
          a_						:     str	     =		st.sidebar.slider('Beam size', min_value=1, max_value=8, value=2, step=None, format=None, key=None)
     else:
          a_						:     List[str]	     =		st.sidebar.slider(
              'Nucleus sampling p', min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
          )
          a_						:     List[Any]	     =		st.sidebar.slider(
              'Temperature', min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
          )
          a_						:     Tuple	     =		None

# start main text
a_						:     Union[str, Any]	     =		[
    '<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?',
]
a_						:     List[Any]	     =		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>":
     a_						:     str	     =		st.text_input('Enter your question here:', '')
else:
     a_						:     Any	     =		question_s

if st.button('Show me!'):
     if action in [0, 1, 3]:
          if index_type == "mixed":
               a_     , a_						:     List[str]	     =		make_support(question, source=wiki_source, method='dense', n_results=1_0)
               a_     , a_						:     Dict	     =		make_support(question, source=wiki_source, method='sparse', n_results=1_0)
               a_						:     Dict	     =		[]
               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)]
               a_						:     Tuple	     =		support_list[:1_0]
               a_						:     List[str]	     =		'<P> ' + ' <P> '.join([res[-1] for res in support_list])
          else:
               a_     , a_						:     Dict	     =		make_support(question, source=wiki_source, method=index_type, n_results=1_0)
     if action in [0, 3]:
          a_     , a_						:     Union[str, Any]	     =		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):
               a_						:     Dict	     =		'https://en.wikipedia.org/wiki/{}'.format(res[0].replace(' ', '_'))
               a_						:     Tuple	     =		res[1].strip()
               if sec_titles == "":
                    a_						:     List[str]	     =		'[{}]({})'.format(res[0], wiki_url)
               else:
                    a_						:     List[Any]	     =		sec_titles.split(' & ')
                    a_						:     Any	     =		' & '.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]:
          a_						:     Any	     =		find_nearest_training(question)
          a_						:     Optional[int]	     =		nn_train_list[0]
          st.markdown(
              '--- \n ### The most similar question in the ELI5 training set was: \n\n {}'.format(train_exple['title'])
          )
          a_						:     int	     =		[
              '{}. {}'.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)))


a_						:     Any	     =		'\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)




import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError

from transformers import (
    AlbertTokenizer,
    AutoTokenizer,
    BertTokenizer,
    BertTokenizerFast,
    GPTaTokenizerFast,
    is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie


sys.path.append(str(Path(__file__).parent.parent / 'utils'))

from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
     from test_module.custom_tokenization_fast import CustomTokenizerFast





class    lowerCamelCase__  (						unittest.TestCase):
     """simple docstring"""



     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		mock.Mock()
           lowerCamelCase								=		5_00
           lowerCamelCase								=		{}
           lowerCamelCase								=		HTTPError
           lowerCamelCase								=		{}

           # Download this model to make sure it's in the cache.
           lowerCamelCase								=		BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert"    )

           # Under the mock environment we get a 500 error when trying to reach the tokenizer.
           with mock.patch("requests.Session.request"				,			return_value=__a    ) as mock_head:
                 lowerCamelCase								=		BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert"    )
                 # This check we did call the fake head request
                 mock_head.assert_called()







     @require_tokenizers
     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		mock.Mock()
           lowerCamelCase								=		5_00
           lowerCamelCase								=		{}
           lowerCamelCase								=		HTTPError
           lowerCamelCase								=		{}

           # Download this model to make sure it's in the cache.
           lowerCamelCase								=		GPTaTokenizerFast.from_pretrained("gpt2"    )

           # Under the mock environment we get a 500 error when trying to reach the tokenizer.
           with mock.patch("requests.Session.request"				,			return_value=__a    ) as mock_head:
                 lowerCamelCase								=		GPTaTokenizerFast.from_pretrained("gpt2"    )
                 # This check we did call the fake head request
                 mock_head.assert_called()







     def 						_a      (self    ):






           '''simple docstring'''



           try:
                 lowerCamelCase								=		tempfile.mktemp()
                 with open(__a				,			"wb"    ) as f:
                       http_get("https://huggingface.co/albert-base-v1/resolve/main/spiece.model"				,			__a    )

                 lowerCamelCase								=		AlbertTokenizer.from_pretrained(__a    )
           finally:
                 os.remove(__a    )

           # Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
           # the current folder and have the right name.
           if os.path.isfile("tokenizer.json"    ):
                 # We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
                 return
           try:
                 with open("tokenizer.json"				,			"wb"    ) as f:
                       http_get("https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json"				,			__a    )
                 lowerCamelCase								=		AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"    )
                 # The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
                 self.assertEqual(tokenizer.vocab_size				,			10_00    )
                 # Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.

           finally:
                 os.remove("tokenizer.json"    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		AlbertTokenizer.from_pretrained("https://huggingface.co/albert-base-v1/resolve/main/spiece.model"    )





@is_staging_test
class    lowerCamelCase__  (						unittest.TestCase):
     """simple docstring"""



     _A       =			['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']







     @classmethod
     def 						_a      (cls    ):






           '''simple docstring'''



           lowerCamelCase								=		TOKEN
           HfFolder.save_token(__a    )







     @classmethod
     def 						_a      (cls    ):






           '''simple docstring'''



           try:
                 delete_repo(token=cls._token				,			repo_id="test-tokenizer"    )
           except HTTPError:
                 pass

           try:
                 delete_repo(token=cls._token				,			repo_id="valid_org/test-tokenizer-org"    )
           except HTTPError:
                 pass

           try:
                 delete_repo(token=cls._token				,			repo_id="test-dynamic-tokenizer"    )
           except HTTPError:
                 pass







     def 						_a      (self    ):






           '''simple docstring'''



           with tempfile.TemporaryDirectory() as tmp_dir:
                 lowerCamelCase								=		os.path.join(__a				,			"vocab.txt"    )
                 with open(__a				,			"w"				,			encoding="utf-8"    ) as vocab_writer:
                       vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]    )    )
                 lowerCamelCase								=		BertTokenizer(__a    )

           tokenizer.push_to_hub("test-tokenizer"				,			use_auth_token=self._token    )
           lowerCamelCase								=		BertTokenizer.from_pretrained(F"""{USER}/test-tokenizer"""    )
           self.assertDictEqual(new_tokenizer.vocab				,			tokenizer.vocab    )

           # Reset repo
           delete_repo(token=self._token				,			repo_id="test-tokenizer"    )

           # Push to hub via save_pretrained
           with tempfile.TemporaryDirectory() as tmp_dir:
                 tokenizer.save_pretrained(__a				,			repo_id="test-tokenizer"				,			push_to_hub=__a				,			use_auth_token=self._token    )

           lowerCamelCase								=		BertTokenizer.from_pretrained(F"""{USER}/test-tokenizer"""    )
           self.assertDictEqual(new_tokenizer.vocab				,			tokenizer.vocab    )







     def 						_a      (self    ):






           '''simple docstring'''



           with tempfile.TemporaryDirectory() as tmp_dir:
                 lowerCamelCase								=		os.path.join(__a				,			"vocab.txt"    )
                 with open(__a				,			"w"				,			encoding="utf-8"    ) as vocab_writer:
                       vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]    )    )
                 lowerCamelCase								=		BertTokenizer(__a    )

           tokenizer.push_to_hub("valid_org/test-tokenizer-org"				,			use_auth_token=self._token    )
           lowerCamelCase								=		BertTokenizer.from_pretrained("valid_org/test-tokenizer-org"    )
           self.assertDictEqual(new_tokenizer.vocab				,			tokenizer.vocab    )

           # Reset repo
           delete_repo(token=self._token				,			repo_id="valid_org/test-tokenizer-org"    )

           # Push to hub via save_pretrained
           with tempfile.TemporaryDirectory() as tmp_dir:
                 tokenizer.save_pretrained(
                     __a				,			repo_id="valid_org/test-tokenizer-org"				,			push_to_hub=__a				,			use_auth_token=self._token    )

           lowerCamelCase								=		BertTokenizer.from_pretrained("valid_org/test-tokenizer-org"    )
           self.assertDictEqual(new_tokenizer.vocab				,			tokenizer.vocab    )







     @require_tokenizers
     def 						_a      (self    ):






           '''simple docstring'''



           CustomTokenizer.register_for_auto_class()
           with tempfile.TemporaryDirectory() as tmp_dir:
                 lowerCamelCase								=		os.path.join(__a				,			"vocab.txt"    )
                 with open(__a				,			"w"				,			encoding="utf-8"    ) as vocab_writer:
                       vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]    )    )
                 lowerCamelCase								=		CustomTokenizer(__a    )

           # No fast custom tokenizer
           tokenizer.push_to_hub("test-dynamic-tokenizer"				,			use_auth_token=self._token    )

           lowerCamelCase								=		AutoTokenizer.from_pretrained(F"""{USER}/test-dynamic-tokenizer"""				,			trust_remote_code=__a    )
           # Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
           self.assertEqual(tokenizer.__class__.__name__				,			"CustomTokenizer"    )

           # Fast and slow custom tokenizer
           CustomTokenizerFast.register_for_auto_class()
           with tempfile.TemporaryDirectory() as tmp_dir:
                 lowerCamelCase								=		os.path.join(__a				,			"vocab.txt"    )
                 with open(__a				,			"w"				,			encoding="utf-8"    ) as vocab_writer:
                       vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]    )    )

                 lowerCamelCase								=		BertTokenizerFast.from_pretrained(__a    )
                 bert_tokenizer.save_pretrained(__a    )
                 lowerCamelCase								=		CustomTokenizerFast.from_pretrained(__a    )

           tokenizer.push_to_hub("test-dynamic-tokenizer"				,			use_auth_token=self._token    )

           lowerCamelCase								=		AutoTokenizer.from_pretrained(F"""{USER}/test-dynamic-tokenizer"""				,			trust_remote_code=__a    )
           # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
           self.assertEqual(tokenizer.__class__.__name__				,			"CustomTokenizerFast"    )
           lowerCamelCase								=		AutoTokenizer.from_pretrained(
               F"""{USER}/test-dynamic-tokenizer"""				,			use_fast=__a				,			trust_remote_code=__a    )
           # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
           self.assertEqual(tokenizer.__class__.__name__				,			"CustomTokenizer"    )





class    lowerCamelCase__  (						unittest.TestCase):
     """simple docstring"""



     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		Trie()
           trie.add("Hello 友達"    )
           self.assertEqual(trie.data				,			{"H": {"e": {"l": {"l": {"o": {" ": {"友": {"達": {"": 1}}}}}}}}}    )
           trie.add("Hello"    )
           trie.data
           self.assertEqual(trie.data				,			{"H": {"e": {"l": {"l": {"o": {"": 1, " ": {"友": {"達": {"": 1}}}}}}}}}    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		Trie()
           self.assertEqual(trie.split("[CLS] This is a extra_id_100"    )				,			["[CLS] This is a extra_id_100"]    )
           trie.add("[CLS]"    )
           trie.add("extra_id_1"    )
           trie.add("extra_id_100"    )
           self.assertEqual(trie.split("[CLS] This is a extra_id_100"    )				,			["[CLS]", " This is a ", "extra_id_100"]    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		Trie()
           trie.add("A"    )
           self.assertEqual(trie.split("ABC"    )				,			["A", "BC"]    )
           self.assertEqual(trie.split("BCA"    )				,			["BC", "A"]    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		Trie()
           trie.add("TOKEN]"    )
           trie.add("[SPECIAL_TOKEN]"    )
           self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]"    )				,			["This is something ", "[SPECIAL_TOKEN]"]    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		Trie()
           trie.add("A"    )
           trie.add("P"    )
           trie.add("[SPECIAL_TOKEN]"    )
           self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]"    )				,			["This is something ", "[SPECIAL_TOKEN]"]    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		Trie()
           trie.add("AB"    )
           trie.add("B"    )
           trie.add("C"    )
           self.assertEqual(trie.split("ABC"    )				,			["AB", "C"]    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		Trie()
           trie.add("ABC"    )
           trie.add("B"    )
           trie.add("CD"    )
           self.assertEqual(trie.split("ABCD"    )				,			["ABC", "D"]    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		Trie()
           lowerCamelCase								=		trie.cut_text("ABC"				,			[0, 0, 2, 1, 2, 3]    )
           self.assertEqual(__a				,			["AB", "C"]    )