



'''simple docstring'''
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


snake_case_					:      Optional[int]    			=    'bart'
snake_case_					:      str    			=    True






@st.cache(allow_output_mutation=UpperCAmelCase_				)
def A__       (	):
						if LOAD_DENSE_INDEX:
												_UpperCamelCase   :						List[str]												=  AutoTokenizer.from_pretrained('yjernite/retribert-base-uncased'				)
												_UpperCamelCase   :						Optional[Any]												=  AutoModel.from_pretrained('yjernite/retribert-base-uncased'				).to('cuda:0'				)
												_UpperCamelCase   :						Optional[int]												=  qar_model.eval()
						else:
												_UpperCamelCase							,     _UpperCamelCase   :						Optional[Any]												=  (None, None)
						if MODEL_TYPE == "bart":
												_UpperCamelCase   :						List[str]												=  AutoTokenizer.from_pretrained('yjernite/bart_eli5'				)
												_UpperCamelCase   :						List[Any]												=  AutoModelForSeqaSeqLM.from_pretrained('yjernite/bart_eli5'				).to('cuda:0'				)
												_UpperCamelCase   :						Any												=  torch.load('seq2seq_models/eli5_bart_model_blm_2.pth'				)
												sas_model.load_state_dict(save_dict['model']				)
												_UpperCamelCase   :						Any												=  sas_model.eval()
						else:
												_UpperCamelCase							,     _UpperCamelCase   :						str												=  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 A__       (	):
						if LOAD_DENSE_INDEX:
												_UpperCamelCase   :						Union[str, Any]												=  faiss.StandardGpuResources()
												_UpperCamelCase   :						Union[str, Any]												=  datasets.load_dataset(path='wiki_snippets'    ,    name='wiki40b_en_100_0'				)['train']
												_UpperCamelCase   :						Dict												=  np.memmap(
												    'wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat'    ,    dtype='float32'    ,    mode='r'    ,    shape=(wikiaab_passages.num_rows, 1_2_8)    ,    )
												_UpperCamelCase   :						Optional[Any]												=  faiss.IndexFlatIP(1_2_8				)
												_UpperCamelCase   :						Optional[Any]												=  faiss.index_cpu_to_gpu(UpperCAmelCase_    ,    1    ,    UpperCAmelCase_				)
												wikiaab_gpu_index_flat.add(UpperCAmelCase_				)  # TODO fix for larger GPU
						else:
												_UpperCamelCase							,     _UpperCamelCase   :						Union[str, Any]												=  (None, None)
						_UpperCamelCase   :						List[Any]												=  Elasticsearch([{'host': 'localhost', 'port': '9200'}]				)
						return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)






@st.cache(allow_output_mutation=UpperCAmelCase_				)
def A__       (	):
						_UpperCamelCase   :						str												=  datasets.load_dataset('eli5'    ,    name='LFQA_reddit'				)
						_UpperCamelCase   :						Tuple												=  elia['train_eli5']
						_UpperCamelCase   :						Optional[int]												=  np.memmap(
						    'eli5_questions_reps.dat'    ,    dtype='float32'    ,    mode='r'    ,    shape=(elia_train.num_rows, 1_2_8)				)
						_UpperCamelCase   :						Any												=  faiss.IndexFlatIP(1_2_8				)
						eli5_train_q_index.add(UpperCAmelCase_				)
						return (elia_train, eli5_train_q_index)


snake_case_			,				snake_case_			,				snake_case_					:      int    			=    load_indexes()
snake_case_			,				snake_case_			,				snake_case_			,				snake_case_					:      str    			=    load_models()
snake_case_			,				snake_case_					:      List[str]    			=    load_train_data()






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_=1_0				):
						_UpperCamelCase   :						Union[str, Any]												=  embed_questions_for_retrieval([question]    ,    UpperCAmelCase_    ,    UpperCAmelCase_				)
						_UpperCamelCase							,     _UpperCamelCase   :						List[str]												=  eli5_train_q_index.search(UpperCAmelCase_    ,    UpperCAmelCase_				)
						_UpperCamelCase   :						List[Any]												=  [elia_train[int(UpperCAmelCase_				)] for i in I[0]]
						return nn_examples






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_="wiki40b"    ,    UpperCAmelCase_="dense"    ,    UpperCAmelCase_=1_0				):
						if source == "none":
												_UpperCamelCase							,     _UpperCamelCase   :						List[Any]												=  (' <P> '.join(['' for _ in range(1_1				)]				).strip(), [])
						else:
												if method == "dense":
																		_UpperCamelCase							,     _UpperCamelCase   :						List[Any]												=  query_qa_dense_index(
																		    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				)
												else:
																		_UpperCamelCase							,     _UpperCamelCase   :						List[Any]												=  query_es_index(
																		    UpperCAmelCase_    ,    UpperCAmelCase_    ,    index_name='english_wiki40b_snippets_100w'    ,    n_results=UpperCAmelCase_    ,    )
						_UpperCamelCase   :						List[str]												=  [
						    (res['article_title'], res['section_title'].strip(), res['score'], res['passage_text']) for res in hit_lst
						]
						_UpperCamelCase   :						Optional[int]												=  '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 A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_=6_4    ,    UpperCAmelCase_=2_5_6    ,    UpperCAmelCase_=False    ,    UpperCAmelCase_=2    ,    UpperCAmelCase_=0.95    ,    UpperCAmelCase_=0.8				):
						with torch.no_grad():
												_UpperCamelCase   :						int												=  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=1_0_2_4    ,    device='cuda:0'    ,    )[0]
						return (answer, support_list)


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

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

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

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

snake_case_					:      int    			=    'beam'
snake_case_					:      Tuple    			=    2
snake_case_					:      Union[str, Any]    			=    64
snake_case_					:      Dict    			=    256
snake_case_					:      Optional[int]    			=    None
snake_case_					:      List[str]    			=    None
snake_case_					:      List[Any]    			=    st.sidebar.checkbox('Generation options')
if generate_options:
					snake_case_					:      Union[str, Any]    			=    '\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)
					snake_case_					:      Any    			=    st.sidebar.selectbox('Would you like to use beam search or sample an answer?', ['beam', 'sampled'])
					snake_case_					:      int    			=    st.sidebar.slider(
					    'Minimum generation length', min_value=8, max_value=256, value=64, step=8, format=None, key=None
					)
					snake_case_					:      Tuple    			=    st.sidebar.slider(
					    'Maximum generation length', min_value=64, max_value=512, value=256, step=16, format=None, key=None
					)
					if sampled == "beam":
										snake_case_					:      Union[str, Any]    			=    st.sidebar.slider('Beam size', min_value=1, max_value=8, value=2, step=None, format=None, key=None)
					else:
										snake_case_					:      Dict    			=    st.sidebar.slider(
										    'Nucleus sampling p', min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
										)
										snake_case_					:      List[str]    			=    st.sidebar.slider(
										    'Temperature', min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
										)
										snake_case_					:      Dict    			=    None

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

if st.button('Show me!'):
					if action in [0, 1, 3]:
										if index_type == "mixed":
															snake_case_			,				snake_case_					:      Union[str, Any]    			=    make_support(question, source=wiki_source, method='dense', n_results=10)
															snake_case_			,				snake_case_					:      Optional[Any]    			=    make_support(question, source=wiki_source, method='sparse', n_results=10)
															snake_case_					:      Tuple    			=    []
															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)]
															snake_case_					:      Union[str, Any]    			=    support_list[:10]
															snake_case_					:      str    			=    '<P> ' + ' <P> '.join([res[-1] for res in support_list])
										else:
															snake_case_			,				snake_case_					:      str    			=    make_support(question, source=wiki_source, method=index_type, n_results=10)
					if action in [0, 3]:
										snake_case_			,				snake_case_					:      List[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):
															snake_case_					:      str    			=    'https://en.wikipedia.org/wiki/{}'.format(res[0].replace(' ', '_'))
															snake_case_					:      Tuple    			=    res[1].strip()
															if sec_titles == "":
																				snake_case_					:      Any    			=    '[{}]({})'.format(res[0], wiki_url)
															else:
																				snake_case_					:      List[str]    			=    sec_titles.split(' & ')
																				snake_case_					:      str    			=    ' & '.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]:
										snake_case_					:      Optional[Any]    			=    find_nearest_training(question)
										snake_case_					:      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'])
										)
										snake_case_					:      List[str]    			=    [
										    '{}. {}'.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)))


snake_case_					:      Optional[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)







'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging


snake_case_					:      Union[str, Any]    			=    logging.get_logger(__name__)

snake_case_					:      Tuple    			=    '▁'

snake_case_					:      List[str]    			=    {
    'vocab_file': 'vocab.json',
    'spm_file': 'sentencepiece.bpe.model',
    'tokenizer_config_file': 'tokenizer_config.json',
}

snake_case_					:      Tuple    			=    {
    'vocab_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json',
    },
    'spm_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_config_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json',
    },
}

snake_case_					:      Union[str, Any]    			=    {
    'facebook/m2m100_418M': 1024,
}

# fmt: off
snake_case_					:      List[Any]    			=    {
    'm2m100': ['af', 'am', 'ar', 'ast', 'az', 'ba', 'be', 'bg', 'bn', 'br', 'bs', 'ca', 'ceb', 'cs', 'cy', 'da', 'de', 'el', 'en', 'es', 'et', 'fa', 'ff', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl', 'gu', 'ha', 'he', 'hi', 'hr', 'ht', 'hu', 'hy', 'id', 'ig', 'ilo', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km', 'kn', 'ko', 'lb', 'lg', 'ln', 'lo', 'lt', 'lv', 'mg', 'mk', 'ml', 'mn', 'mr', 'ms', 'my', 'ne', 'nl', 'no', 'ns', 'oc', 'or', 'pa', 'pl', 'ps', 'pt', 'ro', 'ru', 'sd', 'si', 'sk', 'sl', 'so', 'sq', 'sr', 'ss', 'su', 'sv', 'sw', 'ta', 'th', 'tl', 'tn', 'tr', 'uk', 'ur', 'uz', 'vi', 'wo', 'xh', 'yi', 'yo', 'zh', 'zu'],
    'wmt21': ['en', 'ha', 'is', 'ja', 'cs', 'ru', 'zh', 'de']
}






class 				lowercase__      (				lowercase  ):
	lowercase__    							=     VOCAB_FILES_NAMES
	lowercase__    							=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase__    							=     PRETRAINED_VOCAB_FILES_MAP
	lowercase__    							=     ["""input_ids""", """attention_mask"""]

	lowercase__    							=     []
	lowercase__    							=     []





	def __init__(					self   :							Union[str, Any]					,lowerCamelCase__   :							List[str]					,lowerCamelCase__   :							List[str]					,lowerCamelCase__   :							Dict=None					,lowerCamelCase__   :							Optional[int]=None					,lowerCamelCase__   :							Optional[int]="<s>"					,lowerCamelCase__   :							Optional[int]="</s>"					,lowerCamelCase__   :							str="</s>"					,lowerCamelCase__   :							Union[str, Any]="<pad>"					,lowerCamelCase__   :							Optional[int]="<unk>"					,lowerCamelCase__   :							str="m2m100"					,lowerCamelCase__   :							Optional[Dict[str, Any]] = None					,lowerCamelCase__   :							Optional[Any]=8					,**lowerCamelCase__   :							str					,):







							'''simple docstring'''
							_UpperCamelCase   :						int												=  {} if sp_model_kwargs is None else sp_model_kwargs

							_UpperCamelCase   :						Optional[int]												=  language_codes
							_UpperCamelCase   :						Union[str, Any]												=  FAIRSEQ_LANGUAGE_CODES[language_codes]
							_UpperCamelCase   :						str												=  {lang_code: F'__{lang_code}__' for lang_code in fairseq_language_code}

							_UpperCamelCase   :						Optional[Any]												=  kwargs.get('additional_special_tokens'					,[]					)
							kwargs["additional_special_tokens"] += [
							    self.get_lang_token(lowerCamelCase__					)
							    for lang_code in fairseq_language_code
							    if self.get_lang_token(lowerCamelCase__					) not in kwargs["additional_special_tokens"]
							]

							super().__init__(
							    src_lang=lowerCamelCase__					,tgt_lang=lowerCamelCase__					,bos_token=lowerCamelCase__					,eos_token=lowerCamelCase__					,sep_token=lowerCamelCase__					,unk_token=lowerCamelCase__					,pad_token=lowerCamelCase__					,language_codes=lowerCamelCase__					,sp_model_kwargs=self.sp_model_kwargs					,num_madeup_words=lowerCamelCase__					,**lowerCamelCase__					,)

							_UpperCamelCase   :						Any												=  vocab_file
							_UpperCamelCase   :						int												=  load_json(lowerCamelCase__					)
							_UpperCamelCase   :						int												=  {v: k for k, v in self.encoder.items()}
							_UpperCamelCase   :						Any												=  spm_file
							_UpperCamelCase   :						Optional[Any]												=  load_spm(lowerCamelCase__					,self.sp_model_kwargs					)

							_UpperCamelCase   :						Union[str, Any]												=  len(self.encoder					)

							_UpperCamelCase   :						Dict												=  {
							    self.get_lang_token(lowerCamelCase__					): self.encoder_size + i for i, lang_code in enumerate(lowerCamelCase__					)
							}
							_UpperCamelCase   :						Any												=  {lang_code: self.encoder_size + i for i, lang_code in enumerate(lowerCamelCase__					)}
							_UpperCamelCase   :						Dict												=  {v: k for k, v in self.lang_token_to_id.items()}

							_UpperCamelCase   :						Any												=  src_lang if src_lang is not None else 'en'
							_UpperCamelCase   :						int												=  tgt_lang
							_UpperCamelCase   :						Optional[int]												=  self.get_lang_id(self._src_lang					)
							self.set_src_lang_special_tokens(self._src_lang					)

							_UpperCamelCase   :						List[Any]												=  num_madeup_words





	@property
	def       UpperCamelCase_    (					self   :							int					):







							'''simple docstring'''
							return len(self.encoder					) + len(self.lang_token_to_id					)





	@property
	def       UpperCamelCase_    (					self   :							Optional[int]					):







							'''simple docstring'''
							return self._src_lang





	@src_lang.setter
	def       UpperCamelCase_    (					self   :							List[Any]					,lowerCamelCase__   :							str					):







							'''simple docstring'''
							_UpperCamelCase   :						Tuple												=  new_src_lang
							self.set_src_lang_special_tokens(self._src_lang					)





	def       UpperCamelCase_    (					self   :							Union[str, Any]					,lowerCamelCase__   :							str					):







							'''simple docstring'''
							return self.sp_model.encode(lowerCamelCase__					,out_type=lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							Union[str, Any]					,lowerCamelCase__   :							str					):







							'''simple docstring'''
							if token in self.lang_token_to_id:
													return self.lang_token_to_id[token]
							return self.encoder.get(lowerCamelCase__					,self.encoder[self.unk_token]					)





	def       UpperCamelCase_    (					self   :							List[str]					,lowerCamelCase__   :							int					):







							'''simple docstring'''
							if index in self.id_to_lang_token:
													return self.id_to_lang_token[index]
							return self.decoder.get(lowerCamelCase__					,self.unk_token					)





	def       UpperCamelCase_    (					self   :							List[str]					,lowerCamelCase__   :							Union[str, Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						List[Any]												=  []
							_UpperCamelCase   :						Union[str, Any]												=  ''
							for token in tokens:
													# make sure that special tokens are not decoded using sentencepiece model
													if token in self.all_special_tokens:
																			out_string += self.sp_model.decode(lowerCamelCase__					) + token
																			_UpperCamelCase   :						Optional[int]												=  []
													else:
																			current_sub_tokens.append(lowerCamelCase__					)
							out_string += self.sp_model.decode(lowerCamelCase__					)
							return out_string.strip()





	def       UpperCamelCase_    (					self   :							List[str]					,lowerCamelCase__   :							List[int]					,lowerCamelCase__   :							Optional[List[int]] = None					,lowerCamelCase__   :							bool = False					):







							'''simple docstring'''

							if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=lowerCamelCase__					,token_ids_a=lowerCamelCase__					,already_has_special_tokens=lowerCamelCase__					)

							_UpperCamelCase   :						Tuple												=  [1] * len(self.prefix_tokens					)
							_UpperCamelCase   :						str												=  [1] * len(self.suffix_tokens					)
							if token_ids_a is None:
													return prefix_ones + ([0] * len(lowerCamelCase__					)) + suffix_ones
							return prefix_ones + ([0] * len(lowerCamelCase__					)) + ([0] * len(lowerCamelCase__					)) + suffix_ones





	def       UpperCamelCase_    (					self   :							List[str]					,lowerCamelCase__   :							List[int]					,lowerCamelCase__   :							Optional[List[int]] = None					):







							'''simple docstring'''
							if token_ids_a is None:
													return self.prefix_tokens + token_ids_a + self.suffix_tokens
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens





	def       UpperCamelCase_    (					self   :							Dict					):







							'''simple docstring'''
							_UpperCamelCase   :						List[Any]												=  {self.convert_ids_to_tokens(lowerCamelCase__					): i for i in range(self.vocab_size					)}
							vocab.update(self.added_tokens_encoder					)
							return vocab





	def __getstate__(					self   :							Any					):







							'''simple docstring'''
							_UpperCamelCase   :						Dict												=  self.__dict__.copy()
							_UpperCamelCase   :						int												=  None
							return state





	def __setstate__(					self   :							Optional[int]					,lowerCamelCase__   :							Dict					):







							'''simple docstring'''
							_UpperCamelCase   :						Dict												=  d

							# for backward compatibility
							if not hasattr(self					,'sp_model_kwargs'					):
													_UpperCamelCase   :						int												=  {}

							_UpperCamelCase   :						Dict												=  load_spm(self.spm_file					,self.sp_model_kwargs					)





	def       UpperCamelCase_    (					self   :							str					,lowerCamelCase__   :							str					,lowerCamelCase__   :							Optional[str] = None					):







							'''simple docstring'''
							_UpperCamelCase   :						int												=  Path(lowerCamelCase__					)
							if not save_dir.is_dir():
													raise OSError(F'{save_directory} should be a directory'					)
							_UpperCamelCase   :						List[str]												=  save_dir / (
							    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
							)
							_UpperCamelCase   :						Optional[Any]												=  save_dir / (
							    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
							)

							save_json(self.encoder					,lowerCamelCase__					)

							if os.path.abspath(self.spm_file					) != os.path.abspath(lowerCamelCase__					) and os.path.isfile(self.spm_file					):
													copyfile(self.spm_file					,lowerCamelCase__					)
							elif not os.path.isfile(self.spm_file					):
													with open(lowerCamelCase__					,'wb'					) as fi:
																			_UpperCamelCase   :						Union[str, Any]												=  self.sp_model.serialized_model_proto()
																			fi.write(lowerCamelCase__					)

							return (str(lowerCamelCase__					), str(lowerCamelCase__					))





	def       UpperCamelCase_    (					self   :							Tuple					,lowerCamelCase__   :							List[str]					,lowerCamelCase__   :							str = "en"					,lowerCamelCase__   :							Optional[List[str]] = None					,lowerCamelCase__   :							str = "ro"					,**lowerCamelCase__   :							List[Any]					,):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[Any]												=  src_lang
							_UpperCamelCase   :						Any												=  tgt_lang
							self.set_src_lang_special_tokens(self.src_lang					)
							return super().prepare_seqaseq_batch(lowerCamelCase__					,lowerCamelCase__					,**lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							int					,lowerCamelCase__   :							List[str]					,lowerCamelCase__   :							Optional[str]					,lowerCamelCase__   :							Optional[str]					,**lowerCamelCase__   :							Optional[int]					):







							'''simple docstring'''
							if src_lang is None or tgt_lang is None:
													raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'					)
							_UpperCamelCase   :						int												=  src_lang
							_UpperCamelCase   :						Any												=  self(lowerCamelCase__					,add_special_tokens=lowerCamelCase__					,**lowerCamelCase__					)
							_UpperCamelCase   :						List[Any]												=  self.get_lang_id(lowerCamelCase__					)
							_UpperCamelCase   :						Any												=  tgt_lang_id
							return inputs





	def       UpperCamelCase_    (					self   :							Dict					):







							'''simple docstring'''
							self.set_src_lang_special_tokens(self.src_lang					)





	def       UpperCamelCase_    (					self   :							str					):







							'''simple docstring'''
							self.set_tgt_lang_special_tokens(self.tgt_lang					)





	def       UpperCamelCase_    (					self   :							str					,lowerCamelCase__   :							str					):







							'''simple docstring'''
							_UpperCamelCase   :						Union[str, Any]												=  self.get_lang_token(lowerCamelCase__					)
							_UpperCamelCase   :						str												=  self.lang_token_to_id[lang_token]
							_UpperCamelCase   :						Any												=  [self.cur_lang_id]
							_UpperCamelCase   :						Union[str, Any]												=  [self.eos_token_id]





	def       UpperCamelCase_    (					self   :							List[Any]					,lowerCamelCase__   :							str					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[Any]												=  self.get_lang_token(lowerCamelCase__					)
							_UpperCamelCase   :						Union[str, Any]												=  self.lang_token_to_id[lang_token]
							_UpperCamelCase   :						Union[str, Any]												=  [self.cur_lang_id]
							_UpperCamelCase   :						List[str]												=  [self.eos_token_id]





	def       UpperCamelCase_    (					self   :							Any					,lowerCamelCase__   :							str					):







							'''simple docstring'''
							return self.lang_code_to_token[lang]





	def       UpperCamelCase_    (					self   :							List[Any]					,lowerCamelCase__   :							str					):







							'''simple docstring'''
							_UpperCamelCase   :						List[str]												=  self.get_lang_token(lowerCamelCase__					)
							return self.lang_token_to_id[lang_token]






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						Union[str, Any]												=  sentencepiece.SentencePieceProcessor(**UpperCAmelCase_				)
						spm.Load(str(UpperCAmelCase_				)				)
						return spm






def A__       (	UpperCAmelCase_				):
						with open(UpperCAmelCase_    ,    'r'				) as f:
												return json.load(UpperCAmelCase_				)






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						with open(UpperCAmelCase_    ,    'w'				) as f:
												json.dump(UpperCAmelCase_    ,    UpperCAmelCase_    ,    indent=2				)


