





from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import TFLEDForConditionalGeneration, TFLEDModel






@require_tf
class        snake_case_    :
  A_      							=   LEDConfig
  A_      							=   {}
  A_      							=   'gelu'






  def __init__(							self			:					Any       ,  _snake_case			:					List[str]       ,  _snake_case			:					Optional[int]=13       ,  _snake_case			:					int=7       ,  _snake_case			:					Dict=True       ,  _snake_case			:					Dict=False       ,  _snake_case			:					int=99       ,  _snake_case			:					int=32       ,  _snake_case			:					List[str]=2       ,  _snake_case			:					Any=4       ,  _snake_case			:					Tuple=37       ,  _snake_case			:					Optional[int]=0.1       ,  _snake_case			:					Any=0.1       ,  _snake_case			:					Any=20       ,  _snake_case			:					Any=2       ,  _snake_case			:					str=1       ,  _snake_case			:					Any=0       ,  _snake_case			:					Tuple=4       ,  )->Optional[int]:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[int]      =       parent
   __lowerCAmelCase					:	Optional[int]      =       batch_size
   __lowerCAmelCase					:	int      =       seq_length
   __lowerCAmelCase					:	Union[str, Any]      =       is_training
   __lowerCAmelCase					:	Tuple      =       use_labels
   __lowerCAmelCase					:	Optional[int]      =       vocab_size
   __lowerCAmelCase					:	Dict      =       hidden_size
   __lowerCAmelCase					:	Union[str, Any]      =       num_hidden_layers
   __lowerCAmelCase					:	int      =       num_attention_heads
   __lowerCAmelCase					:	str      =       intermediate_size

   __lowerCAmelCase					:	Union[str, Any]      =       hidden_dropout_prob
   __lowerCAmelCase					:	List[Any]      =       attention_probs_dropout_prob
   __lowerCAmelCase					:	List[str]      =       max_position_embeddings
   __lowerCAmelCase					:	List[str]      =       eos_token_id
   __lowerCAmelCase					:	Any      =       pad_token_id
   __lowerCAmelCase					:	List[Any]      =       bos_token_id
   __lowerCAmelCase					:	Union[str, Any]      =       attention_window

   # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
   # [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
   # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
   # because its local attention only attends to `self.attention_window` and one before and one after
   __lowerCAmelCase					:	Optional[int]      =       self.attention_window + 2

   # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
   # the `test_attention_outputs` and `test_hidden_states_output` tests
   __lowerCAmelCase					:	List[str]      =       (
       self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
   )






  def 				UpperCAmelCase__						(							self			:					List[str]      )->int:






   '''simple docstring'''

   __lowerCAmelCase					:	List[str]      =       ids_tensor([self.batch_size, self.seq_length - 1]       ,  self.vocab_size      )
   __lowerCAmelCase					:	Union[str, Any]      =       tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size      )       ,  1      )
   __lowerCAmelCase					:	Optional[Any]      =       tf.concat([input_ids, eos_tensor]       ,  axis=1      )

   __lowerCAmelCase					:	Optional[int]      =       ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size      )

   __lowerCAmelCase					:	Dict      =       self.config_cls(
       vocab_size=self.vocab_size       ,  d_model=self.hidden_size       ,  encoder_layers=self.num_hidden_layers       ,  decoder_layers=self.num_hidden_layers       ,  encoder_attention_heads=self.num_attention_heads       ,  decoder_attention_heads=self.num_attention_heads       ,  encoder_ffn_dim=self.intermediate_size       ,  decoder_ffn_dim=self.intermediate_size       ,  dropout=self.hidden_dropout_prob       ,  attention_dropout=self.attention_probs_dropout_prob       ,  max_position_embeddings=self.max_position_embeddings       ,  eos_token_ids=[2]       ,  bos_token_id=self.bos_token_id       ,  pad_token_id=self.pad_token_id       ,  decoder_start_token_id=self.pad_token_id       ,  attention_window=self.attention_window       ,  **self.config_updates       ,  )
   __lowerCAmelCase					:	Optional[int]      =       prepare_led_inputs_dict(_a       ,  _a       ,  _a      )
   __lowerCAmelCase					:	List[str]      =       tf.concat(
       [tf.zeros_like(_a      )[:, :-1], tf.ones_like(_a      )[:, -1:]]       ,  axis=-1       ,  )
   __lowerCAmelCase					:	str      =       global_attention_mask
   return config, inputs_dict






  def 				UpperCAmelCase__						(							self			:					List[str]       ,  _snake_case			:					Dict       ,  _snake_case			:					int      )->str:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[int]      =       TFLEDModel(config=_a      ).get_decoder()
   __lowerCAmelCase					:	Optional[Any]      =       inputs_dict["input_ids"]

   __lowerCAmelCase					:	List[Any]      =       input_ids[:1, :]
   __lowerCAmelCase					:	Tuple      =       inputs_dict["attention_mask"][:1, :]
   __lowerCAmelCase					:	Dict      =       1

   # first forward pass
   __lowerCAmelCase					:	List[Any]      =       model(_a       ,  attention_mask=_a       ,  use_cache=_a      )

   __lowerCAmelCase					:	str      =       outputs.to_tuple()

   # create hypothetical next token and extent to next_input_ids
   __lowerCAmelCase					:	Dict      =       ids_tensor((self.batch_size, 3)       ,  config.vocab_size      )
   __lowerCAmelCase					:	Union[str, Any]      =       tf.cast(ids_tensor((self.batch_size, 3)       ,  2      )       ,  tf.inta      )

   # append to next input_ids and
   __lowerCAmelCase					:	Tuple      =       tf.concat([input_ids, next_tokens]       ,  axis=-1      )
   __lowerCAmelCase					:	List[str]      =       tf.concat([attention_mask, next_attn_mask]       ,  axis=-1      )

   __lowerCAmelCase					:	Any      =       model(_a       ,  attention_mask=_a      )[0]
   __lowerCAmelCase					:	Union[str, Any]      =       model(_a       ,  attention_mask=_a       ,  past_key_values=_a      )[0]

   self.parent.assertEqual(next_tokens.shape[1]       ,  output_from_past.shape[1]      )

   # select random slice
   __lowerCAmelCase					:	Optional[Any]      =       int(ids_tensor((1,)       ,  output_from_past.shape[-1]      )      )
   __lowerCAmelCase					:	List[str]      =       output_from_no_past[:, -3:, random_slice_idx]
   __lowerCAmelCase					:	Any      =       output_from_past[:, :, random_slice_idx]

   # test that outputs are equal for slice
   tf.debugging.assert_near(_a       ,  _a       ,  rtol=1E-3      )






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int   ,   SCREAMING_SNAKE_CASE		:int   ,   SCREAMING_SNAKE_CASE		:Any   ,   SCREAMING_SNAKE_CASE		:Optional[Any]=None   ,   SCREAMING_SNAKE_CASE		:Optional[int]=None   ,   SCREAMING_SNAKE_CASE		:Optional[Any]=None   ,   SCREAMING_SNAKE_CASE		:Dict=None   ,   )      ->   Union[str, Any]:


 if attention_mask is None:
  __lowerCAmelCase					:	Dict      =       tf.cast(tf.math.not_equal(_snake_case   ,   config.pad_token_id   )   ,   tf.inta   )
 if decoder_attention_mask is None:
  __lowerCAmelCase					:	int      =       tf.concat(
      [
          tf.ones(decoder_input_ids[:, :1].shape   ,   dtype=tf.inta   ),
          tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]   ,   config.pad_token_id   )   ,   tf.inta   ),
      ]   ,   axis=-1   ,   )
 if head_mask is None:
  __lowerCAmelCase					:	str      =       tf.ones((config.encoder_layers, config.encoder_attention_heads)   )
 if decoder_head_mask is None:
  __lowerCAmelCase					:	int      =       tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
 return {
     "input_ids": input_ids,
     "attention_mask": attention_mask,
     "decoder_input_ids": decoder_input_ids,
     "decoder_attention_mask": decoder_attention_mask,
     "head_mask": head_mask,
     "decoder_head_mask": decoder_head_mask,
 }






@require_tf
class        snake_case_    (		__lowercase					,__lowercase					,unittest.TestCase						):
  A_      							=   (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
  A_      							=   (TFLEDForConditionalGeneration,) if is_tf_available() else ()
  A_      							=   (
      {
          'conversational': TFLEDForConditionalGeneration,
          'feature-extraction': TFLEDModel,
          'summarization': TFLEDForConditionalGeneration,
          'text2text-generation': TFLEDForConditionalGeneration,
          'translation': TFLEDForConditionalGeneration,
      }
      if is_tf_available()
      else {}
  )
  A_      							=   True
  A_      							=   False
  A_      							=   False
  A_      							=   False






  def 				UpperCAmelCase__						(							self			:					List[Any]      )->Dict:






   '''simple docstring'''

   __lowerCAmelCase					:	str      =       TFLEDModelTester(self      )
   __lowerCAmelCase					:	int      =       ConfigTester(self       ,  config_class=_a      )






  def 				UpperCAmelCase__						(							self			:					Any      )->List[str]:






   '''simple docstring'''

   self.config_tester.run_common_tests()






  def 				UpperCAmelCase__						(							self			:					List[str]      )->str:






   '''simple docstring'''

   __lowerCAmelCase					:	Union[str, Any]      =       self.model_tester.prepare_config_and_inputs_for_common()
   self.model_tester.check_decoder_model_past_large_inputs(*_a      )






  def 				UpperCAmelCase__						(							self			:					Union[str, Any]      )->int:






   '''simple docstring'''

   __lowerCAmelCase					:	Any      =       self.model_tester.prepare_config_and_inputs_for_common()
   __lowerCAmelCase					:	List[Any]      =       tf.zeros_like(inputs_dict["""attention_mask"""]      )
   __lowerCAmelCase					:	Optional[int]      =       2
   __lowerCAmelCase					:	Tuple      =       tf.where(
       tf.range(self.model_tester.seq_length      )[None, :] < num_global_attn_indices       ,  1       ,  inputs_dict["""global_attention_mask"""]       ,  )

   __lowerCAmelCase					:	Union[str, Any]      =       True
   __lowerCAmelCase					:	Any      =       self.model_tester.seq_length
   __lowerCAmelCase					:	str      =       self.model_tester.encoder_seq_length

   def check_decoder_attentions_output(_snake_case			:					Tuple      ):
    __lowerCAmelCase					:	List[Any]      =       outputs.decoder_attentions
    self.assertEqual(len(_a      )       ,  self.model_tester.num_hidden_layers      )
    self.assertListEqual(
        list(decoder_attentions[0].shape[-3:]      )       ,  [self.model_tester.num_attention_heads, seq_length, seq_length]       ,  )

   def check_encoder_attentions_output(_snake_case			:					Dict      ):
    __lowerCAmelCase					:	List[Any]      =       [t.numpy() for t in outputs.encoder_attentions]
    __lowerCAmelCase					:	str      =       [t.numpy() for t in outputs.encoder_global_attentions]
    self.assertEqual(len(_a      )       ,  self.model_tester.num_hidden_layers      )
    self.assertEqual(len(_a      )       ,  self.model_tester.num_hidden_layers      )
    self.assertListEqual(
        list(attentions[0].shape[-3:]      )       ,  [self.model_tester.num_attention_heads, seq_length, seq_length]       ,  )
    self.assertListEqual(
        list(global_attentions[0].shape[-3:]      )       ,  [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]       ,  )

   for model_class in self.all_model_classes:
    __lowerCAmelCase					:	str      =       True
    __lowerCAmelCase					:	List[str]      =       False
    __lowerCAmelCase					:	Any      =       False
    __lowerCAmelCase					:	Union[str, Any]      =       model_class(_a      )
    __lowerCAmelCase					:	List[Any]      =       model(self._prepare_for_class(_a       ,  _a      )      )
    __lowerCAmelCase					:	List[Any]      =       len(_a      )
    self.assertEqual(config.output_hidden_states       ,  _a      )
    check_encoder_attentions_output(_a      )

    if self.is_encoder_decoder:
     __lowerCAmelCase					:	List[Any]      =       model_class(_a      )
     __lowerCAmelCase					:	Optional[int]      =       model(self._prepare_for_class(_a       ,  _a      )      )
     self.assertEqual(config.output_hidden_states       ,  _a      )
     check_decoder_attentions_output(_a      )

    # Check that output attentions can also be changed via the config
    del inputs_dict["output_attentions"]
    __lowerCAmelCase					:	Tuple      =       True
    __lowerCAmelCase					:	Dict      =       model_class(_a      )
    __lowerCAmelCase					:	Any      =       model(self._prepare_for_class(_a       ,  _a      )      )
    self.assertEqual(config.output_hidden_states       ,  _a      )
    check_encoder_attentions_output(_a      )

    # Check attention is always last and order is fine
    __lowerCAmelCase					:	Any      =       True
    __lowerCAmelCase					:	Optional[int]      =       True
    __lowerCAmelCase					:	Union[str, Any]      =       model_class(_a      )
    __lowerCAmelCase					:	Union[str, Any]      =       model(self._prepare_for_class(_a       ,  _a      )      )

    self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)       ,  len(_a      )      )
    self.assertEqual(model.config.output_hidden_states       ,  _a      )
    check_encoder_attentions_output(_a      )






  @unittest.skip("""LED keeps using potentially symbolic tensors in conditionals and breaks tracing."""      )
  def 				UpperCAmelCase__						(							self			:					Dict      )->str:






   '''simple docstring'''

   pass






  def 				UpperCAmelCase__						(							self			:					List[str]      )->int:






   '''simple docstring'''

   pass






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Union[str, Any]   )      ->   Any:
 return tf.constant(_snake_case   ,   dtype=tf.intaa   )


_UpperCAmelCase             =							1e-4







@slow
@require_tf
class        snake_case_    (		unittest.TestCase						):






  def 				UpperCAmelCase__						(							self			:					str      )->Tuple:






   '''simple docstring'''

   __lowerCAmelCase					:	str      =       TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384"""      ).led

   # change to intended input here
   __lowerCAmelCase					:	Tuple      =       _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]      )
   __lowerCAmelCase					:	Tuple      =       _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]      )
   __lowerCAmelCase					:	Optional[Any]      =       prepare_led_inputs_dict(model.config       ,  _a       ,  _a      )
   __lowerCAmelCase					:	Union[str, Any]      =       model(**_a      )[0]
   __lowerCAmelCase					:	str      =       (1, 1024, 768)
   self.assertEqual(output.shape       ,  _a      )
   # change to expected output here
   __lowerCAmelCase					:	List[str]      =       tf.convert_to_tensor(
       [[2.3_050, 2.8_279, 0.6_531], [-1.8_457, -0.1_455, -3.5_661], [-1.0_186, 0.4_586, -2.2_043]]       ,  )
   tf.debugging.assert_near(output[:, :3, :3]       ,  _a       ,  atol=1E-3      )






  def 				UpperCAmelCase__						(							self			:					Tuple      )->List[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	List[Any]      =       TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384"""      )

   # change to intended input here
   __lowerCAmelCase					:	Optional[Any]      =       _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]      )
   __lowerCAmelCase					:	Dict      =       _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]      )
   __lowerCAmelCase					:	Any      =       prepare_led_inputs_dict(model.config       ,  _a       ,  _a      )
   __lowerCAmelCase					:	Tuple      =       model(**_a      )[0]
   __lowerCAmelCase					:	Optional[int]      =       (1, 1024, model.config.vocab_size)
   self.assertEqual(output.shape       ,  _a      )
   # change to expected output here
   __lowerCAmelCase					:	List[str]      =       tf.convert_to_tensor(
       [[33.6_507, 6.4_572, 16.8_089], [5.8_739, -2.4_238, 11.2_902], [-3.2_139, -4.3_149, 4.2_783]]       ,  )
   tf.debugging.assert_near(output[:, :3, :3]       ,  _a       ,  atol=1E-3       ,  rtol=1E-3      )






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=SCREAMING_SNAKE_CASE   )
def 	_SCREAMING_SNAKE_CASE		(      )      ->   List[Any]:
 if LOAD_DENSE_INDEX:
  __lowerCAmelCase					:	Dict      =       AutoTokenizer.from_pretrained("""yjernite/retribert-base-uncased"""   )
  __lowerCAmelCase					:	str      =       AutoModel.from_pretrained("""yjernite/retribert-base-uncased"""   ).to("""cuda:0"""   )
  __lowerCAmelCase					:	Tuple      =       qar_model.eval()
 else:
  __lowerCAmelCase ,					__lowerCAmelCase					:	str      =       (None, None)
 if MODEL_TYPE == "bart":
  __lowerCAmelCase					:	Optional[Any]      =       AutoTokenizer.from_pretrained("""yjernite/bart_eli5"""   )
  __lowerCAmelCase					:	str      =       AutoModelForSeqaSeqLM.from_pretrained("""yjernite/bart_eli5"""   ).to("""cuda:0"""   )
  __lowerCAmelCase					:	str      =       torch.load("""seq2seq_models/eli5_bart_model_blm_2.pth"""   )
  sas_model.load_state_dict(save_dict["""model"""]   )
  __lowerCAmelCase					:	List[str]      =       sas_model.eval()
 else:
  __lowerCAmelCase ,					__lowerCAmelCase					:	Optional[int]      =       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=SCREAMING_SNAKE_CASE   )
def 	_SCREAMING_SNAKE_CASE		(      )      ->   Tuple:
 if LOAD_DENSE_INDEX:
  __lowerCAmelCase					:	List[str]      =       faiss.StandardGpuResources()
  __lowerCAmelCase					:	Optional[int]      =       datasets.load_dataset(path="""wiki_snippets"""   ,   name="""wiki40b_en_100_0"""   )["""train"""]
  __lowerCAmelCase					:	Optional[int]      =       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					:	int      =       faiss.IndexFlatIP(128   )
  __lowerCAmelCase					:	Optional[int]      =       faiss.index_cpu_to_gpu(SCREAMING_SNAKE_CASE   ,   1   ,   SCREAMING_SNAKE_CASE   )
  wikiaab_gpu_index_flat.add(SCREAMING_SNAKE_CASE   )  # TODO fix for larger GPU
 else:
  __lowerCAmelCase ,					__lowerCAmelCase					:	Dict      =       (None, None)
 __lowerCAmelCase					:	List[Any]      =       Elasticsearch([{"""host""": """localhost""", """port""": """9200"""}]   )
 return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)






@st.cache(allow_output_mutation=SCREAMING_SNAKE_CASE   )
def 	_SCREAMING_SNAKE_CASE		(      )      ->   Optional[Any]:
 __lowerCAmelCase					:	Tuple      =       datasets.load_dataset("""eli5"""   ,   name="""LFQA_reddit"""   )
 __lowerCAmelCase					:	int      =       elia["""train_eli5"""]
 __lowerCAmelCase					:	int      =       np.memmap(
     """eli5_questions_reps.dat"""   ,   dtype="""float32"""   ,   mode="""r"""   ,   shape=(elia_train.num_rows, 128)   )
 __lowerCAmelCase					:	List[Any]      =       faiss.IndexFlatIP(128   )
 eli5_train_q_index.add(SCREAMING_SNAKE_CASE   )
 return (elia_train, eli5_train_q_index)


_UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase             =							load_indexes()
_UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase             =							load_models()
_UpperCAmelCase       ,     _UpperCAmelCase             =							load_train_data()






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:List[Any]   ,   SCREAMING_SNAKE_CASE		:List[str]=10   )      ->   List[str]:
 __lowerCAmelCase					:	Optional[Any]      =       embed_questions_for_retrieval([question]   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase ,					__lowerCAmelCase					:	Dict      =       eli5_train_q_index.search(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	str      =       [elia_train[int(SCREAMING_SNAKE_CASE   )] for i in I[0]]
 return nn_examples






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int   ,   SCREAMING_SNAKE_CASE		:Dict="wiki40b"   ,   SCREAMING_SNAKE_CASE		:List[Any]="dense"   ,   SCREAMING_SNAKE_CASE		:List[str]=10   )      ->   str:
 if source == "none":
  __lowerCAmelCase ,					__lowerCAmelCase					:	Any      =       (""" <P> """.join(["""""" for _ in range(11   )]   ).strip(), [])
 else:
  if method == "dense":
   __lowerCAmelCase ,					__lowerCAmelCase					:	Union[str, Any]      =       query_qa_dense_index(
       SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
  else:
   __lowerCAmelCase ,					__lowerCAmelCase					:	Optional[Any]      =       query_es_index(
       SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   index_name="""english_wiki40b_snippets_100w"""   ,   n_results=SCREAMING_SNAKE_CASE   ,   )
 __lowerCAmelCase					:	Union[str, Any]      =       [
     (res["""article_title"""], res["""section_title"""].strip(), res["""score"""], res["""passage_text"""]) for res in hit_lst
 ]
 __lowerCAmelCase					:	str      =       """question: {} context: {}""".format(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 return question_doc, support_list






@st.cache(
    hash_funcs={
        torch.Tensor: (lambda SCREAMING_SNAKE_CASE   : None),
        transformers.models.bart.tokenization_bart.BartTokenizer: (lambda SCREAMING_SNAKE_CASE   : None),
    }   )
def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Optional[int]   ,   SCREAMING_SNAKE_CASE		:int   ,   SCREAMING_SNAKE_CASE		:Dict   ,   SCREAMING_SNAKE_CASE		:int=64   ,   SCREAMING_SNAKE_CASE		:int=256   ,   SCREAMING_SNAKE_CASE		:Tuple=False   ,   SCREAMING_SNAKE_CASE		:Union[str, Any]=2   ,   SCREAMING_SNAKE_CASE		:int=0.95   ,   SCREAMING_SNAKE_CASE		:Any=0.8   )      ->   str:
 with torch.no_grad():
  __lowerCAmelCase					:	Any      =       qa_sas_generate(
      SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   num_answers=1   ,   num_beams=SCREAMING_SNAKE_CASE   ,   min_len=SCREAMING_SNAKE_CASE   ,   max_len=SCREAMING_SNAKE_CASE   ,   do_sample=SCREAMING_SNAKE_CASE   ,   temp=SCREAMING_SNAKE_CASE   ,   top_p=SCREAMING_SNAKE_CASE   ,   top_k=SCREAMING_SNAKE_CASE   ,   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             =							64
_UpperCAmelCase             =							256
_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=256, value=64, step=8, format=None, key=None
      )
      _UpperCAmelCase             =							st.sidebar.slider(
          'Maximum generation length', min_value=64, max_value=512, value=256, step=16, 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=0.95, step=0.01, format=None, key=None
            )
            _UpperCAmelCase             =							st.sidebar.slider(
                'Temperature', min_value=0.1, max_value=1.0, value=0.7, step=0.01, 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=10)
                  _UpperCAmelCase       ,     _UpperCAmelCase             =							make_support(question, source=wiki_source, method='sparse', n_results=10)
                  _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[:10]
                  _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=10)
      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)