
"""simple docstring"""


import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch
      from torch import nn

      from transformers import RegNetForImageClassification, RegNetModel
      from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor



class   __a  :






       def __init__(  self :							Dict       ,      UpperCAmelCase_ :							Any       ,      UpperCAmelCase_ :							Any=3       ,      UpperCAmelCase_ :							Optional[Any]=32       ,      UpperCAmelCase_ :							Dict=3       ,      UpperCAmelCase_ :							Dict=10       ,      UpperCAmelCase_ :							Tuple=[10, 20, 30, 40]       ,      UpperCAmelCase_ :							Dict=[1, 1, 2, 1]       ,      UpperCAmelCase_ :							Any=True       ,      UpperCAmelCase_ :							Tuple=True       ,      UpperCAmelCase_ :							List[Any]="relu"       ,      UpperCAmelCase_ :							Optional[Any]=3       ,      UpperCAmelCase_ :							List[Any]=None       ,      )->  Tuple:







              """simple docstring"""
              UpperCamelCase					 =		parent
              UpperCamelCase					 =		batch_size
              UpperCamelCase					 =		image_size
              UpperCamelCase					 =		num_channels
              UpperCamelCase					 =		embeddings_size
              UpperCamelCase					 =		hidden_sizes
              UpperCamelCase					 =		depths
              UpperCamelCase					 =		is_training
              UpperCamelCase					 =		use_labels
              UpperCamelCase					 =		hidden_act
              UpperCamelCase					 =		num_labels
              UpperCamelCase					 =		scope
              UpperCamelCase					 =		len(UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Union[str, Any]			)->  Dict:







              """simple docstring"""
              UpperCamelCase					 =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

              UpperCamelCase					 =		None
              if self.use_labels:
                     UpperCamelCase					 =		ids_tensor([self.batch_size]       ,      self.num_labels			)

              UpperCamelCase					 =		self.get_config()

              return config, pixel_values, labels






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]			)->  Tuple:







              """simple docstring"""
              return RegNetConfig(
                  num_channels=self.num_channels       ,      embeddings_size=self.embeddings_size       ,      hidden_sizes=self.hidden_sizes       ,      depths=self.depths       ,      hidden_act=self.hidden_act       ,      num_labels=self.num_labels       ,      )






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]       ,      UpperCAmelCase_ :							Union[str, Any]       ,      UpperCAmelCase_ :							Tuple       ,      UpperCAmelCase_ :							Any			)->  List[str]:







              """simple docstring"""
              UpperCamelCase					 =		RegNetModel(config=UpperCAmelCase_			)
              model.to(UpperCAmelCase_			)
              model.eval()
              UpperCamelCase					 =		model(UpperCAmelCase_			)
              # expected last hidden states: B, C, H // 32, W // 32
              self.parent.assertEqual(
                  result.last_hidden_state.shape       ,      (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)       ,      )






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]       ,      UpperCAmelCase_ :							Any       ,      UpperCAmelCase_ :							Optional[int]       ,      UpperCAmelCase_ :							Union[str, Any]			)->  Dict:







              """simple docstring"""
              UpperCamelCase					 =		self.num_labels
              UpperCamelCase					 =		RegNetForImageClassification(UpperCAmelCase_			)
              model.to(UpperCAmelCase_			)
              model.eval()
              UpperCamelCase					 =		model(UpperCAmelCase_       ,      labels=UpperCAmelCase_			)
              self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.num_labels)			)






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







              """simple docstring"""
              UpperCamelCase					 =		self.prepare_config_and_inputs()
              UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase					 =		config_and_inputs
              UpperCamelCase					 =		{"pixel_values": pixel_values}
              return config, inputs_dict





@require_torch
class   __a  (      _lowerCAmelCase    ,     _lowerCAmelCase    ,     unittest.TestCase	):
       UpperCamelCase_		:					Union[str, Any]   =			(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
       UpperCamelCase_		:					int   =			(
           {'''feature-extraction''': RegNetModel, '''image-classification''': RegNetForImageClassification}
           if is_torch_available()
           else {}
       )

       UpperCamelCase_		:					Tuple   =			False
       UpperCamelCase_		:					Optional[Any]   =			False
       UpperCamelCase_		:					Union[str, Any]   =			False
       UpperCamelCase_		:					Optional[Any]   =			False






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  Optional[int]:







              """simple docstring"""
              UpperCamelCase					 =		RegNetModelTester(self			)
              UpperCamelCase					 =		ConfigTester(self       ,      config_class=UpperCAmelCase_       ,      has_text_modality=UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  str:







              """simple docstring"""
              self.create_and_test_config_common_properties()
              self.config_tester.create_and_test_config_to_json_string()
              self.config_tester.create_and_test_config_to_json_file()
              self.config_tester.create_and_test_config_from_and_save_pretrained()
              self.config_tester.create_and_test_config_with_num_labels()
              self.config_tester.check_config_can_be_init_without_params()
              self.config_tester.check_config_arguments_init()






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  Dict:







              """simple docstring"""
              return






       @unittest.skip(reason="RegNet does not use inputs_embeds"			)
       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  str:







              """simple docstring"""
              pass






       @unittest.skip(reason="RegNet does not support input and output embeddings"			)
       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  Optional[Any]:







              """simple docstring"""
              pass






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







              """simple docstring"""
              UpperCamelCase ,       UpperCamelCase					 =		self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     UpperCamelCase					 =		model_class(UpperCAmelCase_			)
                     UpperCamelCase					 =		inspect.signature(model.forward			)
                     # signature.parameters is an OrderedDict => so arg_names order is deterministic
                     UpperCamelCase					 =		[*signature.parameters.keys()]

                     UpperCamelCase					 =		["pixel_values"]
                     self.assertListEqual(arg_names[:1]       ,      UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							int			)->  Any:







              """simple docstring"""
              UpperCamelCase					 =		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  Any:







              """simple docstring"""
              UpperCamelCase ,       UpperCamelCase					 =		self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     UpperCamelCase					 =		model_class(config=UpperCAmelCase_			)
                     for name, module in model.named_modules():
                            if isinstance(UpperCAmelCase_       ,      (nn.BatchNormad, nn.GroupNorm)			):
                                   self.assertTrue(
                                       torch.all(module.weight == 1			)       ,      msg=f"Parameter {name} of model {model_class} seems not properly initialized"       ,      )
                                   self.assertTrue(
                                       torch.all(module.bias == 0			)       ,      msg=f"Parameter {name} of model {model_class} seems not properly initialized"       ,      )






       def 	_SCREAMING_SNAKE_CASE       (  self :							int			)->  Optional[Any]:







              """simple docstring"""
              def check_hidden_states_output(UpperCAmelCase_ :							List[str]       ,      UpperCAmelCase_ :							Optional[Any]       ,      UpperCAmelCase_ :							Dict			):
                     UpperCamelCase					 =		model_class(UpperCAmelCase_			)
                     model.to(UpperCAmelCase_			)
                     model.eval()

                     with torch.no_grad():
                            UpperCamelCase					 =		model(**self._prepare_for_class(UpperCAmelCase_       ,      UpperCAmelCase_			)			)

                     UpperCamelCase					 =		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                     UpperCamelCase					 =		self.model_tester.num_stages
                     self.assertEqual(len(UpperCAmelCase_			)       ,      expected_num_stages + 1			)

                     # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
                     self.assertListEqual(
                         list(hidden_states[0].shape[-2:]			)       ,      [self.model_tester.image_size // 2, self.model_tester.image_size // 2]       ,      )

              UpperCamelCase ,       UpperCamelCase					 =		self.model_tester.prepare_config_and_inputs_for_common()
              UpperCamelCase					 =		["basic", "bottleneck"]
              for model_class in self.all_model_classes:
                     for layer_type in layers_type:
                            UpperCamelCase					 =		layer_type
                            UpperCamelCase					 =		True
                            check_hidden_states_output(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_			)

                            # check that output_hidden_states also work using config
                            del inputs_dict["output_hidden_states"]
                            UpperCamelCase					 =		True

                            check_hidden_states_output(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  Dict:







              """simple docstring"""
              UpperCamelCase					 =		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_			)






       @slow
       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]			)->  Tuple:







              """simple docstring"""
              for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     UpperCamelCase					 =		RegNetModel.from_pretrained(UpperCAmelCase_			)
                     self.assertIsNotNone(UpperCAmelCase_			)





def 	lowerCamelCase__			(							)->							Union[str, Any]:







       """simple docstring"""
       UpperCamelCase					 =		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
       return image



@require_torch
@require_vision
class   __a  (      unittest.TestCase	):






       @cached_property
       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  Dict:







              """simple docstring"""
              return (
                  AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)
                  if is_vision_available()
                  else None
              )






       @slow
       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]			)->  Union[str, Any]:







              """simple docstring"""
              UpperCamelCase					 =		RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			).to(UpperCAmelCase_			)

              UpperCamelCase					 =		self.default_image_processor
              UpperCamelCase					 =		prepare_img()
              UpperCamelCase					 =		image_processor(images=UpperCAmelCase_       ,      return_tensors="pt"			).to(UpperCAmelCase_			)

              # forward pass
              with torch.no_grad():
                     UpperCamelCase					 =		model(**UpperCAmelCase_			)

              # verify the logits
              UpperCamelCase					 =		torch.Size((1, 1_000)			)
              self.assertEqual(outputs.logits.shape       ,      UpperCAmelCase_			)

              UpperCamelCase					 =		torch.tensor([-0.4180, -1.5051, -3.4836]			).to(UpperCAmelCase_			)

              self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,      UpperCAmelCase_       ,      atol=1e-4			)			)







"""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


SCREAMING_SNAKE_CASE      =     """bart"""
SCREAMING_SNAKE_CASE      =     True



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







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



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







       """simple docstring"""
       if LOAD_DENSE_INDEX:
              UpperCamelCase					 =		faiss.StandardGpuResources()
              UpperCamelCase					 =		datasets.load_dataset(path="wiki_snippets"		,      name="wiki40b_en_100_0"				)["train"]
              UpperCamelCase					 =		np.memmap(
                  "wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat"		,      dtype="float32"		,      mode="r"		,      shape=(wikiaab_passages.num_rows, 1_28)		,      )
              UpperCamelCase					 =		faiss.IndexFlatIP(1_28				)
              UpperCamelCase					 =		faiss.index_cpu_to_gpu(UpperCAmelCase_		,      1		,      UpperCAmelCase_				)
              wikiaab_gpu_index_flat.add(UpperCAmelCase_				)  # TODO fix for larger GPU
       else:
              UpperCamelCase ,       UpperCamelCase					 =		(None, None)
       UpperCamelCase					 =		Elasticsearch([{"host": "localhost", "port": "9200"}]				)
       return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)



@st.cache(allow_output_mutation=UpperCAmelCase_				)
def 	lowerCamelCase__			(							)->							Optional[Any]:







       """simple docstring"""
       UpperCamelCase					 =		datasets.load_dataset("eli5"		,      name="LFQA_reddit"				)
       UpperCamelCase					 =		elia["train_eli5"]
       UpperCamelCase					 =		np.memmap(
           "eli5_questions_reps.dat"		,      dtype="float32"		,      mode="r"		,      shape=(elia_train.num_rows, 1_28)				)
       UpperCamelCase					 =		faiss.IndexFlatIP(1_28				)
       eli5_train_q_index.add(UpperCAmelCase_				)
       return (elia_train, eli5_train_q_index)


SCREAMING_SNAKE_CASE     ,      SCREAMING_SNAKE_CASE     ,      SCREAMING_SNAKE_CASE      =     load_indexes()
SCREAMING_SNAKE_CASE     ,      SCREAMING_SNAKE_CASE     ,      SCREAMING_SNAKE_CASE     ,      SCREAMING_SNAKE_CASE      =     load_models()
SCREAMING_SNAKE_CASE     ,      SCREAMING_SNAKE_CASE      =     load_train_data()



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_=10				)->							str:







       """simple docstring"""
       UpperCamelCase					 =		embed_questions_for_retrieval([question]		,      UpperCAmelCase_		,      UpperCAmelCase_				)
       UpperCamelCase ,       UpperCamelCase					 =		eli5_train_q_index.search(UpperCAmelCase_		,      UpperCAmelCase_				)
       UpperCamelCase					 =		[elia_train[int(UpperCAmelCase_				)] for i in I[0]]
       return nn_examples



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_="wiki40b"		,      UpperCAmelCase_="dense"		,      UpperCAmelCase_=10				)->							List[str]:







       """simple docstring"""
       if source == "none":
              UpperCamelCase ,       UpperCamelCase					 =		(" <P> ".join(["" for _ in range(11				)]				).strip(), [])
       else:
              if method == "dense":
                     UpperCamelCase ,       UpperCamelCase					 =		query_qa_dense_index(
                         UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_				)
              else:
                     UpperCamelCase ,       UpperCamelCase					 =		query_es_index(
                         UpperCAmelCase_		,      UpperCAmelCase_		,      index_name="english_wiki40b_snippets_100w"		,      n_results=UpperCAmelCase_		,      )
       UpperCamelCase					 =		[
           (res["article_title"], res["section_title"].strip(), res["score"], res["passage_text"]) for res in hit_lst
       ]
       UpperCamelCase					 =		"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 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_=64		,      UpperCAmelCase_=2_56		,      UpperCAmelCase_=False		,      UpperCAmelCase_=2		,      UpperCAmelCase_=0.95		,      UpperCAmelCase_=0.8				)->							int:







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

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

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

SCREAMING_SNAKE_CASE      =     st.sidebar.checkbox("""Retrieval options""")
if retrieval_options:
      SCREAMING_SNAKE_CASE      =     """
    ### Information retriever options

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

SCREAMING_SNAKE_CASE      =     """beam"""
SCREAMING_SNAKE_CASE      =     2
SCREAMING_SNAKE_CASE      =     64
SCREAMING_SNAKE_CASE      =     256
SCREAMING_SNAKE_CASE      =     None
SCREAMING_SNAKE_CASE      =     None
SCREAMING_SNAKE_CASE      =     st.sidebar.checkbox("""Generation options""")
if generate_options:
      SCREAMING_SNAKE_CASE      =     """
    ### Answer generation options

    The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)
    weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with
    **beam** search, or **sample** from the decoder's output probabilities.
    """
      st.sidebar.markdown(generate_info)
      SCREAMING_SNAKE_CASE      =     st.sidebar.selectbox("""Would you like to use beam search or sample an answer?""", ["""beam""", """sampled"""])
      SCREAMING_SNAKE_CASE      =     st.sidebar.slider(
          """Minimum generation length""", min_value=8, max_value=256, value=64, step=8, format=None, key=None
      )
      SCREAMING_SNAKE_CASE      =     st.sidebar.slider(
          """Maximum generation length""", min_value=64, max_value=512, value=256, step=16, format=None, key=None
      )
      if sampled == "beam":
            SCREAMING_SNAKE_CASE      =     st.sidebar.slider("""Beam size""", min_value=1, max_value=8, value=2, step=None, format=None, key=None)
      else:
            SCREAMING_SNAKE_CASE      =     st.sidebar.slider(
                """Nucleus sampling p""", min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
            )
            SCREAMING_SNAKE_CASE      =     st.sidebar.slider(
                """Temperature""", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
            )
            SCREAMING_SNAKE_CASE      =     None

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

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


SCREAMING_SNAKE_CASE      =     """
---

**Disclaimer**

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

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





