

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


lowerCAmelCase_           = "bart"
lowerCAmelCase_           = True







@st.cache(allow_output_mutation=UpperCAmelCase   )
def      snake_case		(							):
				if LOAD_DENSE_INDEX:
								A	 =      AutoTokenizer.from_pretrained('yjernite/retribert-base-uncased'   )
								A	 =      AutoModel.from_pretrained('yjernite/retribert-base-uncased'   ).to('cuda:0'   )
								A	 =      qar_model.eval()
				else:
								A ,		A	 =      (None, None)
				if MODEL_TYPE == "bart":
								A	 =      AutoTokenizer.from_pretrained('yjernite/bart_eli5'   )
								A	 =      AutoModelForSeqaSeqLM.from_pretrained('yjernite/bart_eli5'   ).to('cuda:0'   )
								A	 =      torch.load('seq2seq_models/eli5_bart_model_blm_2.pth'   )
								sas_model.load_state_dict(save_dict['model']   )
								A	 =      sas_model.eval()
				else:
								A ,		A	 =      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      snake_case		(							):
				if LOAD_DENSE_INDEX:
								A	 =      faiss.StandardGpuResources()
								A	 =      datasets.load_dataset(path='wiki_snippets',						name='wiki40b_en_100_0'   )['train']
								A	 =      np.memmap(
								    'wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat',						dtype='float32',						mode='r',						shape=(wikiaab_passages.num_rows, 1_28),						)
								A	 =      faiss.IndexFlatIP(1_28   )
								A	 =      faiss.index_cpu_to_gpu(UpperCAmelCase,						1,						UpperCAmelCase   )
								wikiaab_gpu_index_flat.add(UpperCAmelCase   )  # TODO fix for larger GPU
				else:
								A ,		A	 =      (None, None)
				A	 =      Elasticsearch([{'host': 'localhost', 'port': '9200'}]   )
				return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)







@st.cache(allow_output_mutation=UpperCAmelCase   )
def      snake_case		(							):
				A	 =      datasets.load_dataset('eli5',						name='LFQA_reddit'   )
				A	 =      elia['train_eli5']
				A	 =      np.memmap(
				    'eli5_questions_reps.dat',						dtype='float32',						mode='r',						shape=(elia_train.num_rows, 1_28)   )
				A	 =      faiss.IndexFlatIP(1_28   )
				eli5_train_q_index.add(UpperCAmelCase   )
				return (elia_train, eli5_train_q_index)


lowerCAmelCase_           = load_indexes()
lowerCAmelCase_           = load_models()
lowerCAmelCase_           = load_train_data()







def      snake_case		(							UpperCAmelCase	:       Tuple,						UpperCAmelCase	:       Any=10   ):
				A	 =      embed_questions_for_retrieval([question],						UpperCAmelCase,						UpperCAmelCase   )
				A ,		A	 =      eli5_train_q_index.search(UpperCAmelCase,						UpperCAmelCase   )
				A	 =      [elia_train[int(UpperCAmelCase   )] for i in I[0]]
				return nn_examples







def      snake_case		(							UpperCAmelCase	:       str,						UpperCAmelCase	:       List[str]="wiki40b",						UpperCAmelCase	:       Union[str, Any]="dense",						UpperCAmelCase	:       Any=10   ):
				if source == "none":
								A ,		A	 =      (' <P> '.join(['' for _ in range(11   )]   ).strip(), [])
				else:
								if method == "dense":
												A ,		A	 =      query_qa_dense_index(
												    UpperCAmelCase,						UpperCAmelCase,						UpperCAmelCase,						UpperCAmelCase,						UpperCAmelCase,						UpperCAmelCase   )
								else:
												A ,		A	 =      query_es_index(
												    UpperCAmelCase,						UpperCAmelCase,						index_name='english_wiki40b_snippets_100w',						n_results=UpperCAmelCase,						)
				A	 =      [
				    (res['article_title'], res['section_title'].strip(), res['score'], res['passage_text']) for res in hit_lst
				]
				A	 =      '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      snake_case		(							UpperCAmelCase	:       int,						UpperCAmelCase	:       Any,						UpperCAmelCase	:       int,						UpperCAmelCase	:       List[str]=64,						UpperCAmelCase	:       str=2_56,						UpperCAmelCase	:       List[Any]=False,						UpperCAmelCase	:       Union[str, Any]=2,						UpperCAmelCase	:       Tuple=0.95,						UpperCAmelCase	:       Dict=0.8   ):
				with torch.no_grad():
								A	 =      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=10_24,						device='cuda:0',						)[0]
				return (answer, support_list)


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

# Start sidebar
lowerCAmelCase_           = "<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>"
lowerCAmelCase_           = "\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
lowerCAmelCase_           = "\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)

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

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

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

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

if st.button('Show me!'):
					if action in [0, 1, 3]:
										if index_type == "mixed":
															lowerCAmelCase_           = make_support(question, source=wiki_source, method='dense', n_results=10)
															lowerCAmelCase_           = make_support(question, source=wiki_source, method='sparse', n_results=10)
															lowerCAmelCase_           = []
															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)]
															lowerCAmelCase_           = support_list[:10]
															lowerCAmelCase_           = "<P> " + " <P> ".join([res[-1] for res in support_list])
										else:
															lowerCAmelCase_           = make_support(question, source=wiki_source, method=index_type, n_results=10)
					if action in [0, 3]:
										lowerCAmelCase_           = 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):
															lowerCAmelCase_           = "https://en.wikipedia.org/wiki/{}".format(res[0].replace(' ', '_'))
															lowerCAmelCase_           = res[1].strip()
															if sec_titles == "":
																				lowerCAmelCase_           = "[{}]({})".format(res[0], wiki_url)
															else:
																				lowerCAmelCase_           = sec_titles.split(' & ')
																				lowerCAmelCase_           = " & ".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]:
										lowerCAmelCase_           = find_nearest_training(question)
										lowerCAmelCase_           = nn_train_list[0]
										st.markdown(
										    '--- \n ### The most similar question in the ELI5 training set was: \n\n {}'.format(train_exple['title'])
										)
										lowerCAmelCase_           = [
										    "{}. {}".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)))


lowerCAmelCase_           = "\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 typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class      UpperCamelCase (				snake_case__			,       snake_case__			,       snake_case__ ):







		"""simple docstring"""



		snake_case    =   [R"h\.\d+\.attn\.bias", R"h\.\d+\.attn\.masked_bias"]
		@register_to_config
		def __init__(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       Optional[int] = None						,_SCREAMING_SNAKE_CASE		:       int = 5_0_2_5_7						,_SCREAMING_SNAKE_CASE		:       int = 1_0_2_4						,_SCREAMING_SNAKE_CASE		:       int = 7_6_8						,_SCREAMING_SNAKE_CASE		:       int = 1_2						,_SCREAMING_SNAKE_CASE		:       int = 1_2						,_SCREAMING_SNAKE_CASE		:       Optional[int] = None						,_SCREAMING_SNAKE_CASE		:       str = "gelu_new"						,_SCREAMING_SNAKE_CASE		:       float = 0.1						,_SCREAMING_SNAKE_CASE		:       float = 0.1						,_SCREAMING_SNAKE_CASE		:       float = 0.1						,_SCREAMING_SNAKE_CASE		:       float = 1E-5						,_SCREAMING_SNAKE_CASE		:       float = 0.02						,_SCREAMING_SNAKE_CASE		:       bool = True						,_SCREAMING_SNAKE_CASE		:       bool = True						,_SCREAMING_SNAKE_CASE		:       bool = False						,_SCREAMING_SNAKE_CASE		:       bool = False						,)    ->   Tuple:


						'''simple docstring'''
						super().__init__()

						A	 =      prefix_length

						if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
										raise ValueError(
										    f'`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'
										    f' `n_embd`: {n_embd} are not equal.'		)

						A	 =      prefix_inner_dim
						A	 =      prefix_hidden_dim

						A	 =      (
						    nn.Linear(self.prefix_inner_dim						,self.prefix_hidden_dim		)
						    if self.prefix_hidden_dim is not None
						    else nn.Identity()
						)
						A	 =      (
						    nn.Linear(self.prefix_hidden_dim						,_SCREAMING_SNAKE_CASE		) if self.prefix_hidden_dim is not None else nn.Identity()
						)

						A	 =      GPTaConfig(
						    vocab_size=_SCREAMING_SNAKE_CASE						,n_positions=_SCREAMING_SNAKE_CASE						,n_embd=_SCREAMING_SNAKE_CASE						,n_layer=_SCREAMING_SNAKE_CASE						,n_head=_SCREAMING_SNAKE_CASE						,n_inner=_SCREAMING_SNAKE_CASE						,activation_function=_SCREAMING_SNAKE_CASE						,resid_pdrop=_SCREAMING_SNAKE_CASE						,embd_pdrop=_SCREAMING_SNAKE_CASE						,attn_pdrop=_SCREAMING_SNAKE_CASE						,layer_norm_epsilon=_SCREAMING_SNAKE_CASE						,initializer_range=_SCREAMING_SNAKE_CASE						,scale_attn_weights=_SCREAMING_SNAKE_CASE						,use_cache=_SCREAMING_SNAKE_CASE						,scale_attn_by_inverse_layer_idx=_SCREAMING_SNAKE_CASE						,reorder_and_upcast_attn=_SCREAMING_SNAKE_CASE						,)
						A	 =      GPTaLMHeadModel(_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       torch.Tensor						,_SCREAMING_SNAKE_CASE		:       torch.Tensor						,_SCREAMING_SNAKE_CASE		:       Optional[torch.Tensor] = None						,_SCREAMING_SNAKE_CASE		:       Optional[torch.Tensor] = None						,)    ->   Tuple:


						'''simple docstring'''
						A	 =      self.transformer.transformer.wte(_SCREAMING_SNAKE_CASE		)
						A	 =      self.encode_prefix(_SCREAMING_SNAKE_CASE		)
						A	 =      self.decode_prefix(_SCREAMING_SNAKE_CASE		)
						A	 =      torch.cat((prefix_embeds, embedding_text)						,dim=1		)

						if labels is not None:
										A	 =      self.get_dummy_token(input_ids.shape[0]						,input_ids.device		)
										A	 =      torch.cat((dummy_token, input_ids)						,dim=1		)
						A	 =      self.transformer(inputs_embeds=_SCREAMING_SNAKE_CASE						,labels=_SCREAMING_SNAKE_CASE						,attention_mask=_SCREAMING_SNAKE_CASE		)
						if self.prefix_hidden_dim is not None:
										return out, hidden
						else:
										return out
		def       A(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       torch.device		)    ->   torch.Tensor:


						'''simple docstring'''
						return torch.zeros(_SCREAMING_SNAKE_CASE						,self.prefix_length						,dtype=torch.intaa						,device=_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       List[str]						,_SCREAMING_SNAKE_CASE		:       int		)    ->   Optional[Any]:


						'''simple docstring'''
						return self.encode_prefix(_SCREAMING_SNAKE_CASE		)
		@torch.no_grad()
		def       A(  self		:       List[str]						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]		)    ->   str:


						'''simple docstring'''
						A	 =      torch.split(_SCREAMING_SNAKE_CASE						,1						,dim=0		)
						A	 =      []
						A	 =      []
						for feature in features:
										A	 =      self.decode_prefix(feature.to(_SCREAMING_SNAKE_CASE		)		)  # back to the clip feature
										# Only support beam search for now
										A ,		A	 =      self.generate_beam(
										    input_embeds=_SCREAMING_SNAKE_CASE						,device=_SCREAMING_SNAKE_CASE						,eos_token_id=_SCREAMING_SNAKE_CASE		)
										generated_tokens.append(output_tokens[0]		)
										generated_seq_lengths.append(seq_lengths[0]		)
						A	 =      torch.stack(_SCREAMING_SNAKE_CASE		)
						A	 =      torch.stack(_SCREAMING_SNAKE_CASE		)
						return generated_tokens, generated_seq_lengths







		@torch.no_grad()
		def       A(  self		:       Dict						,_SCREAMING_SNAKE_CASE		:       List[str]=None						,_SCREAMING_SNAKE_CASE		:       str=None						,_SCREAMING_SNAKE_CASE		:       Any=None						,_SCREAMING_SNAKE_CASE		:       int = 5						,_SCREAMING_SNAKE_CASE		:       int = 6_7						,_SCREAMING_SNAKE_CASE		:       float = 1.0						,_SCREAMING_SNAKE_CASE		:       Optional[int] = None						,)    ->   str:


						'''simple docstring'''
						A	 =      eos_token_id
						A	 =      None
						A	 =      None
						A	 =      torch.ones(_SCREAMING_SNAKE_CASE						,device=_SCREAMING_SNAKE_CASE						,dtype=torch.int		)
						A	 =      torch.zeros(_SCREAMING_SNAKE_CASE						,device=_SCREAMING_SNAKE_CASE						,dtype=torch.bool		)

						if input_embeds is not None:
										A	 =      input_embeds
						else:
										A	 =      self.transformer.transformer.wte(_SCREAMING_SNAKE_CASE		)

						for i in range(_SCREAMING_SNAKE_CASE		):
										A	 =      self.transformer(inputs_embeds=_SCREAMING_SNAKE_CASE		)
										A	 =      outputs.logits
										A	 =      logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
										A	 =      logits.softmax(-1		).log()

										if scores is None:
														A ,		A	 =      logits.topk(_SCREAMING_SNAKE_CASE						,-1		)
														A	 =      generated.expand(_SCREAMING_SNAKE_CASE						,*generated.shape[1:]		)
														A ,		A	 =      next_tokens.permute(1						,0		), scores.squeeze(0		)
														if tokens is None:
																		A	 =      next_tokens
														else:
																		A	 =      tokens.expand(_SCREAMING_SNAKE_CASE						,*tokens.shape[1:]		)
																		A	 =      torch.cat((tokens, next_tokens)						,dim=1		)
										else:
														A	 =      -float(np.inf		)
														A	 =      0
														A	 =      scores[:, None] + logits
														seq_lengths[~is_stopped] += 1
														A	 =      scores_sum / seq_lengths[:, None]
														A ,		A	 =      scores_sum_average.view(-1		).topk(_SCREAMING_SNAKE_CASE						,-1		)
														A	 =      next_tokens // scores_sum.shape[1]
														A	 =      seq_lengths[next_tokens_source]
														A	 =      next_tokens % scores_sum.shape[1]
														A	 =      next_tokens.unsqueeze(1		)
														A	 =      tokens[next_tokens_source]
														A	 =      torch.cat((tokens, next_tokens)						,dim=1		)
														A	 =      generated[next_tokens_source]
														A	 =      scores_sum_average * seq_lengths
														A	 =      is_stopped[next_tokens_source]

										A	 =      self.transformer.transformer.wte(next_tokens.squeeze()		).view(generated.shape[0]						,1						,-1		)
										A	 =      torch.cat((generated, next_token_embed)						,dim=1		)
										A	 =      is_stopped + next_tokens.eq(_SCREAMING_SNAKE_CASE		).squeeze()
										if is_stopped.all():
														break

						A	 =      scores / seq_lengths
						A	 =      scores.argsort(descending=_SCREAMING_SNAKE_CASE		)
						# tokens tensors are already padded to max_seq_length
						A	 =      [tokens[i] for i in order]
						A	 =      torch.stack(_SCREAMING_SNAKE_CASE						,dim=0		)
						A	 =      torch.tensor([seq_lengths[i] for i in order]						,dtype=seq_lengths.dtype		)
						return output_texts, seq_lengths





